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fitsoilwater3 <-
function (theta, x, xlab = NULL, ylab = NULL, ...)
{
if (!requireNamespace("rpanel", quietly = TRUE))
stop("package rpanel is required")
if (!inherits(c(theta, x), "numeric"))
stop("non-numeric arguments!")
if (length(theta) != length(x))
stop("incompatible dimensions!")
dat <- data.frame(theta, x)
if (is.null(xlab))
xlab = "Matric potential"
if (is.null(ylab))
ylab = "Soil water content"
f.graph <- function() {
plot(theta ~ x, data = dat, las = 1, xlab = xlab, ylab = ylab,
main = "Soil Water Retention Curve", ...)
}
f.graph()
theta_R <- a1 <- p1 <- a2 <- p2 <- NULL
f.panel <- function(pan) {
f.graph()
with(pan, curve(soilwater3(x, theta_R, a1, p1, a2, p2),
add = TRUE, col = "red"))
return(pan)
}
f.fit <- function(pan) {
start <- with(pan, pan[c("theta_R", "a1", "p1", "a2",
"p2")])
fit <- try(with(pan, nls(theta ~ soilwater3(x, theta_R,
a1, p1, a2, p2), data = dat, start = start)))
if (inherits(fit, "try-error")) {
rpanel::rp.messagebox("No convergence... try other initial values.",
title = "Warning!")
}
else {
f.graph()
est <- coef(fit)
curve(soilwater3(x, est[1], est[2], est[3], est[4],
est[5]), add = TRUE, col = "blue")
print(summary(fit))
print(Rsq(fit))
}
return(pan)
}
panel <- rpanel::rp.control("Interactive fit")
ran.t <- 2 * range(theta)
rpanel::rp.slider(panel, variable = theta_R, from = 0, to = max(theta),
resolution = 0.01, initval = 0.8 * min(theta), title = "theta_R",
action = f.panel)
rpanel::rp.doublebutton(panel, variable = theta_R, step = 0.01, title = "",
action = f.panel, showvalue = TRUE, foreground = "blue")
rpanel::rp.slider(panel, variable = a1, from = -0.5, to = 10, resolution = 0.01,
initval = 0.07, title = "a1", action = f.panel)
rpanel::rp.doublebutton(panel, variable = a1, step = 0.01, title = "",
action = f.panel, showvalue = TRUE, foreground = "blue")
rpanel::rp.slider(panel, variable = p1, from = 0, to = 15000, resolution = 5,
initval = 3670, title = "p1", action = f.panel)
rpanel::rp.doublebutton(panel, variable = p1, step = 1, title = "",
action = f.panel, showvalue = TRUE, foreground = "blue")
rpanel::rp.slider(panel, variable = a2, from = 0, to = 10, resolution = 0.01,
initval = 0.32, title = "a2", action = f.panel)
rpanel::rp.doublebutton(panel, variable = a2, step = 0.01, title = "",
action = f.panel, showvalue = TRUE, foreground = "blue")
rpanel::rp.slider(panel, variable = p2, from = 0, to = 1500, resolution = 5,
initval = 70, title = "p2", action = f.panel)
rpanel::rp.doublebutton(panel, variable = p2, step = 1, title = "",
action = f.panel, showvalue = TRUE, foreground = "blue")
rpanel::rp.button(panel, title = "NLS estimates", action = f.fit,
foreground = "white", background = "navy")
rpanel::rp.button(panel, title = "__________________ Quit __________________",
action = function(pan) return(pan), quitbutton = TRUE,
foreground = "red")
} |
summary.marqLevAlg <- function(object,digits=8,loglik=FALSE,...){
x <- object
if (!inherits(x, "marqLevAlg")) stop("use only with \"marqLevAlg\" objects")
cat(" \n")
cat(" Robust marqLevAlg algorithm ", "\n")
cat(" \n")
cl <- x$cl
minimize <- TRUE
if(length(cl$minimize)){
if(cl$minimize==FALSE) minimize <- FALSE
}
dput(cl)
cat(" \n")
cat("Iteration process:", "\n")
cat(" Number of parameters:", length(x$b)," \n")
cat(" Number of iterations:", x$ni, "\n")
cat(" Optimized objective function:", round(x$fn.value,digits)," \n")
if(x$istop==1) cat(" Convergence criteria satisfied","\n")
if(x$istop==2) cat(" Maximum number of iteration reached without convergence","\n")
if(x$istop==4|x$istop==5) {
cat(" The program stopped abnormally. No results can be displayed.\n")
}
cat(" \n")
cat("Convergence criteria: parameters stability=", round(x$ca[1],digits), "\n")
cat(" : objective function stability=", round(x$cb,digits), "\n")
if (x$ier == -1){
cat(" : Matrix inversion for RDM failed \n")
}else{
cat(" : Matrix inversion for RDM successful \n")
}
if(minimize==TRUE){
cat(" : relative distance to minimum(RDM)=", round(x$rdm,digits), "\n")
}else{
cat(" : relative distance to maximum(RDM)=", round(x$rdm,digits), "\n")
}
if(x$istop!=4&x$istop!=5) {
cat(" \n")
cat("Final parameter values:", "\n")
id <- 1:length(x$b)
indice <- rep(id*(id+1)/2)
se <-sqrt(x$v[indice])
wald <- (x$b/se)**2
z <- abs(qnorm((1 + .95)/2))
binf <- x$b-1.96*se
bsup <- x$b+1.96*se
if(loglik==FALSE){
tmp <- data.frame("coef"=format(round(x$b,3)))
cat(format(round(x$b,3)),"\n")
}else{
tmp <- data.frame("coef"=format(round(x$b,3)),"SE coef"=format(round(se,3)),"Wald"=format(wald,4),"P-value"=round(1 - pchisq(wald, 1),5),"binf"=round(binf,3),"bsup"=round(bsup,3))
print(tmp,row.names=F)
}
cat(" \n")
return(invisible(tmp))
}
} |
.create_diagonal <- function(params) {
diagonal <- data.frame(
"Parameter1" = unique(params$Parameter1),
"Parameter2" = unique(params$Parameter1)
)
if ("Group" %in% names(params)) diagonal$Group <- unique(params$Group)[1]
if ("r" %in% names(params)) diagonal$r <- 1
if ("rho" %in% names(params)) diagonal$rho <- 1
if ("tau" %in% names(params)) diagonal$tau <- 1
if ("p" %in% names(params)) diagonal$p <- 0
if ("t" %in% names(params)) diagonal$t <- Inf
if ("S" %in% names(params)) diagonal$S <- Inf
if ("z" %in% names(params)) diagonal$z <- Inf
if ("df" %in% names(params)) diagonal$df <- unique(params$df)[1]
if ("df_error" %in% names(params)) diagonal$df_error <- unique(params$df_error)[1]
if ("CI" %in% names(params)) diagonal$CI <- unique(params$CI)[1]
if ("CI_low" %in% names(params)) diagonal$CI_low <- 1
if ("CI_high" %in% names(params)) diagonal$CI_high <- 1
if ("Method" %in% names(params)) diagonal$Method <- unique(params$Method)[1]
if ("n_Obs" %in% names(params)) diagonal$n_Obs <- unique(params$n_Obs)[1]
if ("Median" %in% names(params)) diagonal$Median <- 1
if ("Mean" %in% names(params)) diagonal$Mean <- 1
if ("MAP" %in% names(params)) diagonal$MAP <- 1
if ("SD" %in% names(params)) diagonal$SD <- 0
if ("MAD" %in% names(params)) diagonal$MAD <- 0
if ("pd" %in% names(params)) diagonal$pd <- 1
if ("ROPE_Percentage" %in% names(params)) diagonal$ROPE_Percentage <- 0
if ("BF" %in% names(params)) diagonal$BF <- Inf
if ("log_BF" %in% names(params)) diagonal$log_BF <- Inf
if ("Prior_Distribution" %in% names(params)) diagonal$Prior_Distribution <- unique(params$Prior_Distribution)[1]
if ("Prior_Location" %in% names(params)) diagonal$Prior_Location <- unique(params$Prior_Location)[1]
if ("Prior_Scale" %in% names(params)) diagonal$Prior_Scale <- unique(params$Prior_Scale)[1]
for (var in names(params)[!names(params) %in% names(diagonal)]) {
if (length(unique(params[[var]])) > 1) {
stop("Something's unexpected happened when creating the diagonal data. Please open an issue at https://github.com/easystats/correlation/issues")
}
diagonal[[var]] <- unique(params[[var]])[1]
}
diagonal
} |
knitr::opts_chunk$set(
collapse = TRUE,
comment = "
)
library(dplyr)
library(purrr)
library(tidyr)
library(tsibble)
library(loadflux)
data(djanturb)
df <- hydro_events(dataframe = djanturb,
q = discharge,
datetime = time,
window = 21)
df %>%
event_plot(q = discharge,
datetime = time,
he = he,
ssc = ntu,
y2label = "Turbidity")
TI_index <- df %>%
group_by(he) %>%
nest() %>%
mutate(TI = map_dbl(data, ~TI(.x, ntu, time))) %>%
select(-data) %>%
ungroup()
TI_index
library(tsibble)
df_ts <- df %>%
as_tsibble(key = he,
index = time)
df_ts
library(feasts)
df_ts %>%
features(time,
feat_event)
library(brolgar)
library(feasts)
df_ts %>%
features(ntu, feat_five_num)
df_ts %>%
features(ntu, c(feat_spectral,
feat_acf)) |
summary.fmem <-
function(object, ...){
chains <- object$chains
p <- object$p
q <- object$q
ks <- object$ks
nu0 <- object$nu0
homo <- object$homo
quant005 <- function(x){quantile(x,prob=0.025)}
quant095 <- function(x){quantile(x,prob=0.975)}
reem <- function(aa,b){
ag <- aa
ag <- sub("(", "", ag,fixed=TRUE)
ag <- sub(")", "", ag,fixed=TRUE)
ag <- sub(b, "", ag,fixed=TRUE)
ag <- strsplit(ag, ",")
ag <- ag[[1]][1]
ag
}
if(object$family=="Normal" || object$family=="Laplace" || attr(object$eta,"know")==0){
cat("\n Error distribution:", object$family)
}else{
cat("\n Error distribution:", object$family,"(",object$eta,")")
}
cat("\n Sample size:", length(object$y))
cat("\n Size of posterior sample:", object$post.sam.s, "\n")
cat("\n =============== Parametric part =============== ")
if(p >0){
cat("\n ======= Covariates measured without error \n")
a <- round(apply(as.matrix(chains[,1:p]), 2, mean), digits=4)
a2 <- round(apply(as.matrix(chains[,1:p]), 2, median), digits=4)
b <- round(apply(as.matrix(chains[,1:p]), 2, sd), digits=4)
c <- round(apply(as.matrix(chains[,1:p]), 2, quant005), digits=4)
d <- round(apply(as.matrix(chains[,1:p]), 2, quant095), digits=4)
e <- cbind(a,a2,b,c,d)
colnames(e) <- c("Mean ","Median","SD "," C.I.","95% ")
rownames(e) <- colnames(object$X)
printCoefmat(e)
}
cat("\n ======= Covariates measured with error \n")
a <- round(apply(as.matrix(chains[,(p+1):(p+q)]), 2, mean), digits=4)
a2 <- round(apply(as.matrix(chains[,(p+1):(p+q)]), 2, median), digits=4)
b <- round(apply(as.matrix(chains[,(p+1):(p+q)]), 2, sd), digits=4)
c <- round(apply(as.matrix(chains[,(p+1):(p+q)]), 2, quant005), digits=4)
d <- round(apply(as.matrix(chains[,(p+1):(p+q)]), 2, quant095), digits=4)
e <- cbind(a,a2,b,c,d)
colnames(e) <- c("Mean ","Median","SD "," C.I.","95% ")
rownames(e) <- colnames(object$M)
printCoefmat(e)
if(sum(ks) >0){
cat("\n =============== Nonparametric part =============== \n")
nvar <- matrix(0,length(ks),1)
for(i in 1:length(ks)){
nvar[i] <- reem(colnames(object$nps)[i],"bsp")}
cat(" Effects internal knots ")
b <- (object$ks - 3)
rownames(b) <- nvar
colnames(b) <- " "
printCoefmat(b)
cat("\n Graphs of the nonparametric effects are provided by \n")
cat(" using the function 'bsp.graph.fmem' \n")
}
if(object$family!="Normal" && object$family!="Laplace" && attr(object$eta,"know")==0){
cat("\n ======= Eta parameter \n")
if(homo==1){
if(sum(ks)==0) length(ks) <- 0
a <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+1+length(ks)+1):(p+3*q+sum(ks)+1+length(ks)+length(nu0))]), 2, mean), digits=4)
a2 <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+1+length(ks)+1):(p+3*q+sum(ks)+1+length(ks)+length(nu0))]), 2, median), digits=4)
b <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+1+length(ks)+1):(p+3*q+sum(ks)+1+length(ks)+length(nu0))]), 2, sd), digits=4)
c <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+1+length(ks)+1):(p+3*q+sum(ks)+1+length(ks)+length(nu0))]), 2, quant005), digits=4)
d <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+1+length(ks)+1):(p+3*q+sum(ks)+1+length(ks)+length(nu0))]), 2, quant095), digits=4)
e <- cbind(a,a2,b,c,d)
colnames(e) <- c("Mean ","Median","SD "," C.I.","95% ")
rownames(e) <- colnames(object$nu0)
printCoefmat(e)
}else{
if(sum(ks)==0) length(ks) <- 0
a <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+length(ks)+1):(p+3*q+sum(ks)+length(ks)+length(nu0))]), 2, mean), digits=4)
a2 <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+length(ks)+1):(p+3*q+sum(ks)+length(ks)+length(nu0))]), 2, median), digits=4)
b <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+length(ks)+1):(p+3*q+sum(ks)+length(ks)+length(nu0))]), 2, sd), digits=4)
c <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+length(ks)+1):(p+3*q+sum(ks)+length(ks)+length(nu0))]), 2, quant005), digits=4)
d <- round(apply(as.matrix(chains[,(p+3*q+sum(ks)+length(ks)+1):(p+3*q+sum(ks)+length(ks)+length(nu0))]), 2, quant095), digits=4)
e <- cbind(a,a2,b,c,d)
colnames(e) <- c("Mean ","Median","SD "," C.I.","95% ")
rownames(e) <- colnames(object$nu0)
printCoefmat(e)
}
}
if(homo ==1){
cat("\n ======= Dispersion parameter \n")
a <- round(apply(as.matrix(chains[,(p+3*q+1)]), 2, mean), digits=4)
a2 <- round(apply(as.matrix(chains[,(p+3*q+1)]), 2, median), digits=4)
b <- round(apply(as.matrix(chains[,(p+3*q+1)]), 2, sd), digits=4)
c <- round(apply(as.matrix(chains[,(p+3*q+1)]), 2, quant005), digits=4)
d <- round(apply(as.matrix(chains[,(p+3*q+1)]), 2, quant095), digits=4)
e <- cbind(a,a2,b,c,d)
colnames(e) <- c("Mean ","Median","SD "," C.I.","95% ")
rownames(e) <- "Sigma2_y "
printCoefmat(e)
cat("\n Ratio of the error variances: " , (object$omeg))
}
cat("\n\n ========== Model Selection Criteria ==========")
cat("\n DIC=" , object$DIC, " LMPL=" , object$LMPL, "\n")
} |
epim <- function(
rt,
inf,
obs,
data,
algorithm = c("sampling", "meanfield", "fullrank"),
group_subset = NULL,
prior_PD = FALSE,
...
) {
call <- match.call(expand.dots = TRUE)
op <- options("warn")
on.exit(options(op))
options(warn=1)
check_rt(rt)
check_inf(inf)
check_obs(obs)
if (inherits(obs, "epiobs")) obs <- list(obs)
check_group_subset(group_subset)
check_data(data, rt, inf, obs, group_subset)
check_logical(prior_PD)
check_scalar(prior_PD)
algorithm <- match.arg(algorithm)
sampling_args <- list(...)
data <- parse_data(data, rt, inf, obs, group_subset)
rt_orig <- rt
obs_orig <- obs
rt <- epirt_(rt, data)
obs <- lapply(obs_orig, epiobs_, data)
args <- nlist(rt, inf, obs, data, prior_PD)
sdat <- do.call(standata_all, args)
if (algorithm == "sampling") {
chains <- sampling_args$chains
if (!is.null(chains) && chains == 0) {
message("Returning standata as chains = 0")
return(do.call(standata_all, args))
}
}
if (is.null(sampling_args$init_r))
sampling_args$init_r <- 1e-6
args <- c(
sampling_args,
list(
object = stanmodels$epidemia_base,
pars = pars(sdat),
data = sdat
)
)
fit <-
if (algorithm == "sampling") {
do.call(rstan::sampling, args)
} else {
args$algorithm <- algorithm
do.call(rstan::vb, args)
}
orig_names <- fit@sim$fnames_oi
fit@sim$fnames_oi <- new_names(sdat, rt, obs, fit, data)
out <- nlist(
rt_orig,
obs_orig,
call,
stanfit = fit,
rt,
inf,
obs,
data,
algorithm,
standata = sdat,
orig_names
)
return(epimodel(out))
}
pars <- function(sdat) {
out <- c(
if (sdat$has_intercept) "alpha",
if (sdat$K > 0) "beta",
if (sdat$q > 0) "b",
if (length(sdat$ac_nterms)) "ac_noise",
if (sdat$num_ointercepts > 0) "ogamma",
if (sdat$K_all > 0) "obeta",
if (length(sdat$obs_ac_nterms)) "obs_ac_noise",
if (sdat$len_theta_L) "theta_L",
"seeds",
if (sdat$hseeds) "seeds_aux",
if (length(sdat$ac_nterms)) "ac_scale",
if (length(sdat$obs_ac_nterms)) "obs_ac_scale",
if (sdat$num_oaux > 0) "oaux",
if (sdat$latent) "infections_raw",
if (sdat$latent) "inf_aux",
if (!sdat$S0_fixed) "S0",
if (!sdat$veps_fixed) "veps"
)
return(out)
}
make_Sigma_nms <- function(rt, sdat, fit) {
if (sdat$len_theta_L) {
cnms <- rt$group$cnms
fit <- transformTheta_L(fit, cnms)
Sigma_nms <- lapply(cnms, FUN = function(grp) {
nm <- outer(grp, grp, FUN = paste, sep = ",")
nm[lower.tri(nm, diag = TRUE)]
})
nms <- names(cnms)
for (j in seq_along(Sigma_nms)) {
Sigma_nms[[j]] <- paste0(nms[j], ":", Sigma_nms[[j]])
}
Sigma_nms <- unlist(Sigma_nms)
return(Sigma_nms)
}
}
new_names <- function(sdat, rt, obs, fit, data) {
out <- c(
if (sdat$has_intercept) {
"R|(Intercept)"
},
if (sdat$K > 0) {
paste0("R|", colnames(sdat$X))
},
if (length(rt$group) && length(rt$group$flist)) {
c(paste0("R|", colnames(rt$group$Z)))
},
if (sdat$ac_nterms > 0) {
paste0("R|", grep("NA", colnames(rt$autocor$Z), invert=TRUE, value=TRUE))
},
if (sdat$num_ointercepts > 0) {
make_ointercept_nms(obs, sdat)
},
if (sdat$K_all > 0) {
make_obeta_nms(obs, sdat)
},
if (sdat$obs_ac_nterms > 0) {
make_obs_ac_nms(obs)
},
if (sdat$len_theta_L) {
paste0("R|Sigma[", make_Sigma_nms(rt, sdat, fit), "]")
},
c(paste0("seeds[", sdat$groups, "]")),
if (sdat$hseeds) "seeds_aux",
if (sdat$ac_nterms > 0) {
make_rw_sigma_nms(rt, data)
},
if (sdat$obs_ac_nterms > 0) {
do.call("c", as.list(sapply(obs, function(x) make_rw_sigma_nms(x, data))))
},
if (sdat$num_oaux > 0) {
make_oaux_nms(obs)
},
if (sdat$latent) {
make_inf_nms(sdat$begin, sdat$starts, sdat$N0, sdat$NC, sdat$groups)
},
if (sdat$latent) {
"inf|dispersion"
},
if (!sdat$S0_fixed) {
paste0("S0[",sdat$groups, "]")
},
if (!sdat$veps_fixed) {
paste0("rm_noise[", sdat$groups, "]")
},
"log-posterior"
)
return(out)
}
transformTheta_L <- function(stanfit, cnms) {
thetas <- rstan::extract(stanfit,
pars = "theta_L", inc_warmup = TRUE,
permuted = FALSE
)
nc <- sapply(cnms, FUN = length)
nms <- names(cnms)
Sigma <- apply(thetas, 1:2, FUN = function(theta) {
Sigma <- lme4::mkVarCorr(sc = 1, cnms, nc, theta, nms)
unlist(sapply(Sigma,
simplify = FALSE,
FUN = function(x) x[lower.tri(x, TRUE)]
))
})
l <- length(dim(Sigma))
end <- tail(dim(Sigma), 1L)
shift <- grep("^theta_L", names(stanfit@sim$samples[[1]]))[1] - 1L
if (l == 3) {
for (chain in 1:end) {
for (param in 1:nrow(Sigma)) {
stanfit@sim$samples[[chain]][[shift + param]] <- Sigma[param, , chain]
}
}
} else {
for (chain in 1:end) {
stanfit@sim$samples[[chain]][[shift + 1]] <- Sigma[, chain]
}
}
return(stanfit)
}
make_obs_ac_nms <- function(obs) {
nms <- c()
for (o in obs) {
x <- grep("NA", colnames(o$autocor$Z), invert=T, value=T)
if (length(x) > 0) {
x <- paste0(.get_obs(o$formula), "|", x)
nms <- c(nms, x)
}
}
return(nms)
}
make_rw_nms <- function(formula, data) {
trms <- terms_rw(formula)
nms <- character()
for (trm in trms) {
trm <- eval(parse(text = trm))
time <- if (is.null(trm$time)) data$date else data[[trm$time]]
group <- if (is.null(trm$gr)) "all" else droplevels(as.factor(data[[trm$gr]]))
f <- unique(paste0(trm$label, "[", time, ",", group, "]"))
nms <- c(nms, f)
}
return(c(
grep("NA", nms, invert=TRUE, value=TRUE),
grep("NA", nms, value=TRUE)
))
}
make_rw_sigma_nms <- function(obj, data) {
trms <- terms_rw(formula(obj))
nme <- ifelse(class(obj) == "epirt_", "R", .get_obs(formula(obj)))
nms <- character()
for (trm in trms) {
trm <- eval(parse(text = trm))
group <- if (is.null(trm$gr)) "all" else droplevels(as.factor(data[[trm$gr]]))
nms <- c(nms, unique(paste0(nme, "|sigma:", trm$label, "[", group, "]")))
}
return(nms)
}
make_oaux_nms <- function(obs) {
nms <- list()
for (o in obs) {
if (!is.null(o$prior_aux)) {
if (o$family == "neg_binom") {
x <- "|reciprocal dispersion"
}
else if (o$family == "quasi_poisson") {
x <- "|dispersion"
}
else if (o$family == "normal"){
x <- "|standard deviation"
} else {
x <- "|sigma"
}
nms <- c(nms,
paste0(.get_obs(formula(o)), x))
}
}
return(unlist(nms))
}
make_ointercept_nms <- function(obs, sdat) {
nms <- character()
for (i in 1:length(obs)) {
if (sdat$has_ointercept[i]) {
nms <- c(nms,
paste0(.get_obs(formula(obs[[i]])), "|(Intercept)"))
}
}
return(nms)
}
make_obeta_nms <- function(obs, sdat) {
if (sdat$K_all == 0) {
return(character(0))
}
obs_nms <- sapply(
obs,
function(x) .get_obs(formula(x))
)
repnms <- unlist(Map(
rep,
obs_nms,
utils::head(sdat$oK, length(obs_nms))
))
obs_beta_nms <- unlist(lapply(
obs,
function(a) colnames(get_x(a))
))
obs_beta_nms <- grep(
pattern = "^\\(Intercept\\)|^rw\\(",
x = obs_beta_nms,
invert = T,
value = T
)
return(paste0(repnms, "|", obs_beta_nms))
}
make_inf_nms <- function(begin, starts, N0, NC, groups) {
nms <- c()
for (m in 1:length(groups))
nms <- c(nms, paste0("infections_raw[", begin -1 + N0 + starts[m] + seq(0, NC[m]-N0 - 1), ",", groups[m],"]"))
return(nms)
} |
Exhaustive_double_deletion<-function(fba_object,thread_no=0,core_number=1){
message("Generating Reaction Combinations...")
reacn_combos<-combn(1:dim(fba_object$mat)[2],2)
message("Done!")
ko_1x=vector()
ko_2x=vector()
ko_stat=vector()
message("Starting Double Knockout Simulation...")
if(core_number==1)
{
for(i in 1:dim(reacn_combos)[2])
{
print(paste(i,dim(reacn_combos)[2],sep=" "))
fba_mutant<-CHANGE_RXN_BOUNDS(reaction_number=reacn_combos[,i],fba_object,lb=0,ub=0)
FBA_MUTANT<-FBA_solve(fba_mutant)
if(FBA_MUTANT$objective==0)
{
ko_1x=c(ko_1x,reacn_combos[,i][1])
ko_2x=c(ko_2x,reacn_combos[,i][2])
ko_stat=c(ko_stat,FBA_MUTANT$status)
}
}
message("End of simulation.")
flux_pairs<-cbind(ko_1x,ko_2x,ko_stat)
message("Writing output to file...")
write.table(flux_pairs,file=paste("results",thread_no+1,sep=""),sep="\t",row.names=TRUE, col.names=FALSE,quote=FALSE)
message("Complete!")
}
if(core_number>1)
{
OP_size<-round(dim(reacn_combos)[2]/core_number)
I_1=1+(OP_size*thread_no)
I_2=I_1+OP_size-1
if(I_2>dim(reacn_combos)[2]){I_2=dim(reacn_combos)[2]}
for(i in I_1:I_2)
{
print(paste(i,I_2,sep=" "))
fba_mutant<-CHANGE_RXN_BOUNDS(reaction_number=reacn_combos[,i],fba_object,lb=0,ub=0)
FBA_MUTANT<-FBA_solve(fba_mutant)
if(FBA_MUTANT$objective==0)
{
ko_1x=c(ko_1x,reacn_combos[,i][1])
ko_2x=c(ko_2x,reacn_combos[,i][2])
ko_stat=c(ko_stat,FBA_MUTANT$status)
}
}
message("End of Simulation")
flux_pairs<-cbind(ko_1x,ko_2x,ko_stat)
message("Writing output to file...")
write.table(flux_pairs,file=paste("results",thread_no+1,sep=""),sep="\t",row.names=FALSE, col.names=FALSE)
message("Complete!")
}
} |
test_that("table has 3 rows on grouped iris (species)", {
rowcount_tbl <- nrow(make_group_table(dplyr::group_by(iris, Species)))
rowcount_dplyr <- dplyr::n_groups(dplyr::group_by(iris, Species))
expect_identical(rowcount_tbl, rowcount_dplyr)
}) |
data <- httr::RETRY(
"GET",
"https://randomuser.me/api/?seed=123&results=10&format=csv",
times = 5)
text <- httr::content(data, as = "text")
data <- readr::read_csv(I(text))
names(data) <- gsub(".", "_", names(data), fixed = TRUE)
final <- data
test_that("testing equal results", {
testthat::expect_equal(final,get_data(n = 10, seed = "123"))
testthat::expect_s3_class(final, "data.frame")
testthat::expect_equal(ncol(final), ncol(get_data(n = 10)))
}) |
knitr::opts_chunk$set(collapse = TRUE,
comment = "
fig.width = 7, fig.height = 7, fig.align = "center",
eval = FALSE)
suppressPackageStartupMessages(library(pathfindR))
knitr::kable(head(myeloma_input))
enrichment_chart(myeloma_output)
knitr::kable(myeloma_output) |
predict.fast = function(object, data= NULL, quantiles= c(0.1,0.5,0.9),obs=1,...) {
origObs = object$origObs
nobs = length(origObs)
origNodes = object$origNodes
ntree = object$num.trees
thres = 5*.Machine$double.eps
filterednodes = rep(0, nobs*ntree)
z = matrix(nrow=nobs, ncol=ntree)
newnodes = matrix(nrow = nobs, ncol = ntree)
newindex = matrix(0, nrow = nobs, ncol = ntree)
z = apply(origNodes, 2, function(x) order(x, stats::rnorm(length(x))))
newnodes = sapply(seq(ncol(z)), function(x) origNodes[z[, x], x])
if(is.null(data)){
weightvec = rep(0, nobs*nobs)
quant = matrix(nrow=nobs,ncol=length(quantiles))
result = Findweightsinbagfast(as.double(as.vector(origNodes)),
as.double(as.vector(newnodes)),
as.double(filterednodes),
as.integer(as.vector(z)),
as.integer(as.vector(newindex)),
as.integer(as.vector(unlist(t(as.data.frame(object$inbag))))),
as.double(weightvec),
as.integer(nobs),
as.integer(ntree),
as.double(thres),
as.integer(obs))
} else {
nnew = nrow(data)
weightvec = rep(0, nobs*nnew)
quant = matrix(nrow = nrow(data), ncol = length(quantiles))
nodes = getnodes(object, data)
result = Findweightsfast(as.double(as.vector(newnodes)),
as.double(as.vector(nodes)),
as.double(filterednodes),
as.integer(as.vector(z)),
as.integer(as.vector(newindex)),
as.double(weightvec),
as.integer(nobs),
as.integer(nnew),
as.integer(ntree),
as.double(thres),
as.integer(obs))
}
weights = matrix(result, nrow = nobs)
ord = order(origObs)
origObs = origObs[ord]
weights = weights[ord, , drop = FALSE]
cumweights = apply(weights, 2, cumsum)
cumweights = sweep(cumweights, 2, as.numeric(cumweights[nobs,]), FUN = "/")
for (qc in 1:length(quantiles)){
larg = cumweights<quantiles[qc]
wc = apply(larg, 2, sum)+1
ind1 = which(wc<1.1)
indn1 = which(wc>1.1)
quant[ind1,qc] = rep(origObs[1], length(ind1))
quantmax = origObs[wc[indn1]]
quantmin = origObs[wc[indn1]-1]
weightmax = cumweights[cbind(wc[indn1], indn1)]
weightmin = cumweights[cbind(wc[indn1]-1, indn1)]
factor = numeric(length(indn1))
indz = weightmax-weightmin<10^(-10)
factor[indz] = 0.5
factor[!indz] = (quantiles[qc]-weightmin[!indz])/(weightmax[!indz]-weightmin[!indz])
quant[indn1,qc] = quantmin + factor* (quantmax-quantmin)
}
colnames(quant) = paste("quantile=", quantiles)
return(quant)
} |
gen_events <- function(.data, ease, start, end = NULL, range = NULL, enter = NULL, exit = NULL, enter_length = 0, exit_length = 0) {
start <- enquo(start)
if (quo_is_missing(start)) stop('start must be provided', call. = FALSE)
start <- eval_tidy(start, .data)
end <- enquo(end)
end <- eval_tidy(end, .data)
enter_length <- enquo(enter_length)
enter_length <- eval_tidy(enter_length, .data)
exit_length <- enquo(exit_length)
exit_length <- eval_tidy(exit_length, .data)
if (is.null(enter_length)) enter_length <- 0
if (is.null(exit_length)) exit_length <- 0
.data$.phase <- NULL
if (length(ease) == 1) ease <- rep(ease, ncol(.data))
if (length(ease) == ncol(.data)) {
ease <- c(ease, 'linear')
} else {
stop('Ease must be either a single string or one for each column', call. = FALSE)
}
.data$.phase <- rep_len(factor("raw", levels = PHASE_LEVELS), nrow(.data))
class(.data) <- c(c("component_generator", "frame_generator"), class(.data))
gen_data <- .complete_events(.data, start, end, enter, exit, enter_length, exit_length)
time <- gen_data$.time
id <- gen_data$.id
gen_data$.time <- NULL
gen_data$.id <- NULL
d_order <- order(id, time)
if (is.null(range)) range <- range(time)
if (diff(range) == 0) stop('range cannot be 0', call. = FALSE)
generator_settings(.data) <- list(
data = gen_data[d_order, ],
id = id[d_order],
time = time[d_order],
range = range,
ease_type = ease,
col_types = col_classes(.data)
)
.data
} |
test_that("lst handles named and unnamed NULL arguments", {
expect_equal(lst(NULL), list("NULL" = NULL))
expect_identical(lst(a = NULL), list(a = NULL))
expect_identical(
lst(NULL, b = NULL, 1:3),
list("NULL" = NULL, b = NULL, "1:3" = 1:3)
)
})
test_that("lst handles internal references", {
expect_identical(lst(a = 1, b = a), list(a = 1, b = 1))
expect_identical(lst(a = NULL, b = a), list(a = NULL, b = NULL))
})
test_that("lst supports duplicate names (
expect_identical(lst(a = 1, a = a + 1, b = a), list(a = 1, a = 2, b = 2))
expect_identical(lst(b = 1, a = b, a = b + 1, b = a), list(b = 1, a = 1, a = 2, b = 2))
}) |
mod_Rectangular_Lattice_ui <- function(id){
ns <- NS(id)
tagList(
h4("Rectangular Lattice Design"),
sidebarLayout(
sidebarPanel(width = 4,
radioButtons(ns("owndata_rectangular"), label = "Import entries' list?", choices = c("Yes", "No"), selected = "No",
inline = TRUE, width = NULL, choiceNames = NULL, choiceValues = NULL),
conditionalPanel("input.owndata_rectangular != 'Yes'", ns = ns,
numericInput(ns("t.rectangular"), label = "Input
value = NULL, min = 2)
),
conditionalPanel("input.owndata_rectangular == 'Yes'", ns = ns,
fluidRow(
column(8, style=list("padding-right: 28px;"),
fileInput(inputId = ns("file.rectangular"), label = "Upload a CSV File:", multiple = FALSE)),
column(4, style=list("padding-left: 5px;"),
radioButtons(inputId = ns("sep.rectangular"), "Separator",
choices = c(Comma = ",",
Semicolon = ";",
Tab = "\t"),
selected = ","))
)
),
numericInput(inputId = ns("r.rectangular"), label = "Input
selectInput(inputId = ns("k.rectangular"), label = "Input
numericInput(inputId = ns("l.rectangular"), label = "Input
fluidRow(
column(6, style=list("padding-right: 28px;"),
textInput(inputId = ns("plot_start.rectangular"), "Starting Plot Number:", value = 101)
),
column(6,style=list("padding-left: 5px;"),
textInput(inputId = ns("Location.rectangular"), "Input Location:", value = "FARGO")
)
),
numericInput(inputId = ns("myseed.rectangular"), label = "Seed Number:",
value = 007, min = 1),
fluidRow(
column(6,
actionButton(inputId = ns("RUN.rectangular"), "Run!", icon = icon("cocktail"), width = '100%'),
),
column(6,
actionButton(inputId = ns("Simulate.rectangular"), "Simulate!", icon = icon("cocktail"), width = '100%')
)
),
br(),
downloadButton(ns("downloadData.rectangular"), "Save My Experiment", style = "width:100%")
),
mainPanel(
width = 8,
tabsetPanel(
tabPanel("Rectangular Lattice Field Book", shinycssloaders::withSpinner(DT::DTOutput(ns("RECTANGULAR.output")), type = 5))
)
)
)
)
}
mod_Rectangular_Lattice_server <- function(id) {
moduleServer(id, function(input, output, session) {
ns <- session$ns
getData.rectangular <- reactive({
req(input$file.rectangular)
inFile <- input$file.rectangular
dataUp.rectangular<- load_file(name = inFile$name, path = inFile$datapat, sep = input$sep.rectangular)
return(list(dataUp.rectangular= dataUp.rectangular))
})
get_tRECT <- reactive({
if(is.null(input$file.rectangular)) {
req(input$t.rectangular)
t.rectangular <- input$t.rectangular
}else {
req(input$file.rectangular)
t.rectangular <- nrow(getData.rectangular()$dataUp.rectangular)
}
return(list(t.rectangular = t.rectangular))
})
observeEvent(get_tRECT()$t.rectangular, {
req(get_tRECT()$t.rectangular)
t <- as.numeric(get_tRECT()$t.rectangular)
D <- numbers::divisors(t)
D <- D[2:(length(D)-1)]
pk <- numeric()
z <- 1
for (i in D) {
s <- t / i
if (i == s - 1) {
pk[z] <- i
z <- z + 1
}else z <- z
}
if (length(pk) == 0) {
k <- "No Options Available"
}else {
k <- pk
}
updateSelectInput(session = session, inputId = 'k.rectangular', label = "Input
choices = k, selected = k[1])
})
entryListFormat_RECT <- data.frame(ENTRY = 1:9,
NAME = c(paste("Genotype", LETTERS[1:9], sep = "")))
entriesInfoModal_RECT <- function() {
modalDialog(
title = div(tags$h3("Important message", style = "color: red;")),
h4("Please, follow the format shown in the following example. Make sure to upload a CSV file!"),
renderTable(entryListFormat_RECT,
bordered = TRUE,
align = 'c',
striped = TRUE),
h4("Entry numbers can be any set of consecutive positive numbers."),
easyClose = FALSE
)
}
toListen <- reactive({
list(input$owndata_rectangular)
})
observeEvent(toListen(), {
if (input$owndata_rectangular == "Yes") {
showModal(
shinyjqui::jqui_draggable(
entriesInfoModal_RECT()
)
)
}
})
RECTANGULAR_reactive <- eventReactive(input$RUN.rectangular,{
req(input$k.rectangular)
req(input$owndata_rectangular)
req(input$myseed.rectangular)
req(input$plot_start.rectangular)
req(input$Location.rectangular)
req(input$l.rectangular)
req(input$r.rectangular)
r.rectangular<- as.numeric(input$r.rectangular)
k.rectangular<- as.numeric(input$k.rectangular)
plot_start.rectangular<- as.vector(unlist(strsplit(input$plot_start.rectangular, ",")))
plot_start.rectangular<- as.numeric(plot_start.rectangular)
loc <- as.vector(unlist(strsplit(input$Location.rectangular, ",")))
seed.rcbd <- as.numeric(input$myseed.rectangular)
if (input$owndata_rectangular == "Yes") {
t.rectangular <- as.numeric(get_tRECT()$t.rectangular)
data.rectangular <- getData.rectangular()$dataUp.rectangular
}else {
req(input$t.rectangular)
t.rectangular <- as.numeric(input$t.rectangular)
data.rectangular <- NULL
}
seed.rectangular <- as.numeric(input$myseed.rectangular)
l.rectangular <- as.numeric(input$l.rectangular)
rectangular_lattice(t = t.rectangular, k = k.rectangular, r = r.rectangular,
l = l.rectangular,
plotNumber = plot_start.rectangular,
seed = seed.rectangular,
locationNames = loc,
data = data.rectangular)
})
valsRECT <- reactiveValues(maxV.rectangular= NULL, minV.rectangular= NULL, trail.rectangular= NULL)
simuModal.rectangular<- function(failed = FALSE) {
modalDialog(
selectInput(inputId = ns("trailsRECT"), label = "Select One:", choices = c("YIELD", "MOISTURE", "HEIGHT", "Other")),
conditionalPanel("input.trailsRECT == 'Other'", ns = ns,
textInput(inputId = ns("OtherRECT"), label = "Input Trial Name:", value = NULL)
),
fluidRow(
column(6,
numericInput(inputId = ns("min.rectangular"), "Input the min value", value = NULL)
),
column(6,
numericInput(inputId = ns("max.rectangular"), "Input the max value", value = NULL)
)
),
if (failed)
div(tags$b("Invalid input of data max and min", style = "color: red;")),
footer = tagList(
modalButton("Cancel"),
actionButton(inputId = ns("ok.rectangular"), "GO")
)
)
}
observeEvent(input$Simulate.rectangular, {
req(input$k.rectangular)
req(input$r.rectangular)
req(RECTANGULAR_reactive()$fieldBook)
showModal(
shinyjqui::jqui_draggable(
simuModal.rectangular()
)
)
})
observeEvent(input$ok.rectangular, {
req(input$max.rectangular, input$min.rectangular)
if (input$max.rectangular> input$min.rectangular&& input$min.rectangular!= input$max.rectangular) {
valsRECT$maxV.rectangular<- input$max.rectangular
valsRECT$minV.rectangular<- input$min.rectangular
if(input$trailsRECT == "Other") {
req(input$OtherRECT)
if(!is.null(input$OtherRECT)) {
valsRECT$trail.rectangular<- as.character(input$OtherRECT)
}else showModal(simuModal.rectangular(failed = TRUE))
}else {
valsRECT$trail.rectangular<- as.character(input$trailsRECT)
}
removeModal()
}else {
showModal(
shinyjqui::jqui_draggable(
simuModal.rectangular(failed = TRUE)
)
)
}
})
simuDataRECT <- reactive({
req(RECTANGULAR_reactive()$fieldBook)
if(!is.null(valsRECT$maxV.rectangular) && !is.null(valsRECT$minV.rectangular) && !is.null(valsRECT$trail.rectangular)) {
max <- as.numeric(valsRECT$maxV.rectangular)
min <- as.numeric(valsRECT$minV.rectangular)
df.rectangular<- RECTANGULAR_reactive()$fieldBook
cnamesdf.rectangular<- colnames(df.rectangular)
df.rectangular<- norm_trunc(a = min, b = max, data = df.rectangular)
colnames(df.rectangular) <- c(cnamesdf.rectangular[1:(ncol(df.rectangular) - 1)], valsRECT$trail.rectangular)
a <- ncol(df.rectangular)
}else {
df.rectangular<- RECTANGULAR_reactive()$fieldBook
a <- ncol(df.rectangular)
}
return(list(df = df.rectangular, a = a))
})
output$RECTANGULAR.output <- DT::renderDataTable({
req(input$k.rectangular)
k.rect <- input$k.rectangular
if (k.rect == "No Options Available") {
validate("A Rectangular Lattice requires t = s*(s-1), where s is the number of iBlock per replicate.")
}
df <- simuDataRECT()$df
a <- as.numeric(simuDataRECT()$a)
options(DT.options = list(pageLength = nrow(df), autoWidth = FALSE,
scrollX = TRUE, scrollY = "500px"))
DT::datatable(df, rownames = FALSE, options = list(
columnDefs = list(list(className = 'dt-center', targets = "_all"))))
})
output$downloadData.rectangular <- downloadHandler(
filename = function() {
loc <- paste("Rectangular_Lattice_", sep = "")
paste(loc, Sys.Date(), ".csv", sep = "")
},
content = function(file) {
df <- as.data.frame(simuDataRECT()$df)
write.csv(df, file, row.names = FALSE)
}
)
})
} |
"thiesclima_sensors" |
getRuleFromIonSymbol <- function(ions="[M+H]+") {
checkSymbol <- function(ion) {
regexpr("\\[[0-9]{0,2}M.*\\][0-9]{0,2}[\\+\\-]{1,2}", ion) != -1
}
shortCuts <- cbind(c("M+H","M+Na","M+K","M+NH4", "M+", "M",
"M-H","M+Cl-", "M-"),
c("[M+H]+", "[M+Na]+", "[M+K]+", "[M+NH4]+", "[M]+", "[M]+",
"[M-H]-","[M+Cl]-", "[M]-"))
em <- 0.0005485799
chemical_elements <- NULL
utils::data(chemical_elements, envir=environment(), package="InterpretMSSpectrum")
on.exit(rm(chemical_elements))
out <- lapply(ions, function(ion) {
if(ion %in% shortCuts[,1]) ion <- shortCuts[,2][ which(shortCuts[,1] == ion) ]
if(!checkSymbol(ion)) stop("invalid ion")
nmol <- sub(".*[^0-9M]([0-9]?M).*", "\\1", ion)
nmol <- sub("M", "", nmol)
nmol <- as.numeric(ifelse(nmol=="", 1, nmol))
ch <- sub(".*[^0-9]([0-9]{0,2}[\\+\\-])$", "\\1", ion)
sgn <- sub("[^\\+\\-]", "", ch)
sgn <- ifelse(sgn=="+", 1, -1)
ch <- sub("[\\+\\-]", "", ch)
ch[ch==""] <- "1"
ch <- as.numeric(ch)
ch <- ch * sgn
x <- ion
x <- sub("^.*\\[", "", x)
x <- sub("\\].*", "", x)
x <- sub("[0-9]?M", "", x)
starts <- gregexpr("[\\+\\-]", x)[[1]]
ends <- c(starts[-1]-1, nchar(x))
n <- length(starts)
spl <- lapply(1:n, function(i) substr(x, starts[i], ends[i]))
massdiff <- lapply(spl, function(y) {
sgn <- sub("^([\\+\\-]).*", "\\1", y)
sgn <- ifelse(sgn=="+", 1, -1)
el <- sub("^[\\+\\-]", "", y)
if (regexpr("^[0-9]+[A-Za-z]+", el) != -1) el <- gsub("([0-9]+)([A-Za-z]+)", "\\2\\1", el)
el <- CountChemicalElements(x=el)
masses <- sapply(names(el), function(a) {
chemical_elements[,2][ which(chemical_elements[,1] == a)[1] ]
})
return(sum(masses * el) * sgn)
})
massdiff <- sum(unlist(massdiff), na.rm=TRUE) + ch * -em
return(data.frame(name=ion, nmol=nmol, charge=ch, massdiff=massdiff, stringsAsFactors = FALSE))
})
return(do.call("rbind", out))
} |
context("head-to-head")
cr_data <- data.frame(
game = c(1, 1, 1, 2, 2, 3, 3, 4),
player = c(1, NA, NA, 1, 2, 2, 1, 2),
score = as.numeric(1:8),
scoreSP = -(1:8)
)
output_long <- tibble::tibble(
player1 = rep(c(1, 2, NA), each = 3),
player2 = rep(c(1, 2, NA), times = 3),
mean_score1 = c(4, 5.5, 1, 5.5, 19 / 3, NA, 2.5, NA, 2.5),
sum_score = c(24, 22, 7, 22, 38, NA, 7, NA, 20)
)
class(output_long) <- c("h2h_long", class(tibble::tibble()))
output_mat <- matrix(
c( 4, 5.5, 1,
5.5, 19/3, NA,
2.5, NA, 2.5),
nrow = 3, dimnames = list(c("1", "2", NA), c("1", "2", NA)),
byrow = TRUE
)
matrix_class <- class(matrix(1:2, nrow = 1))
test_that("h2h_long works", {
output_1 <- h2h_long(
cr_data,
mean_score1 = mean(score1), sum_score = sum(score1 + score2)
)
expect_is(output_1, "h2h_long")
expect_equal(output_1, output_long)
output_2 <- h2h_long(cr_data)
output_ref_2 <- output_long[, 1:2]
expect_is(output_2, "h2h_long")
expect_equal(output_2, output_ref_2)
})
test_that("h2h_long handles `player` as factor", {
input <- cr_data
input$player <- factor(input$player, levels = c(1, 2, 3))
output <- h2h_long(input, sum_score = sum(score1 + score2))
output_ref <- tibble::tibble(
player1 = factor(rep(c(1, 2, 3), each = 3), levels = c(1, 2, 3)),
player2 = factor(rep(c(1, 2, 3), times = 3), levels = c(1, 2, 3)),
sum_score = c(24, 22, NA, 22, 38, rep(NA, 4))
)
class(output_ref) <- c("h2h_long", class(tibble::tibble()))
expect_is(output, "h2h_long")
expect_equal(output, output_ref)
})
test_that("h2h_long handles unnamed Head-to-Head functions", {
output <- h2h_long(cr_data, sum(score1))
output_ref <- h2h_long(cr_data, sum_score1 = sum(score1))
colnames(output_ref) <- c("player1", "player2", "sum(score1)")
expect_equal(output, output_ref)
})
test_that("h2h_long handles not NULL `fill`", {
output <- h2h_long(
cr_data,
mean_score1 = mean(score1), sum_score = sum(score1 + score2),
fill = list(mean_score1 = 0, sum_score = -1)
)
output_ref <- output_long
output_ref$mean_score1[c(6, 8)] <- 0
output_ref$sum_score[c(6, 8)] <- -1
expect_equal(output, output_ref)
})
test_that("to_h2h_long works", {
output_1 <- output_mat %>% to_h2h_long(value = "mean_score1")
output_ref_1 <- output_long[, 1:3]
output_ref_1$player1 <- as.character(output_ref_1$player1)
output_ref_1$player2 <- as.character(output_ref_1$player2)
expect_is(output_1, "h2h_long")
expect_equal(output_1, output_ref_1)
expect_equal(
output_mat %>% to_h2h_long(value = "new_val") %>% colnames(),
c("player1", "player2", "new_val")
)
output_2 <- output_mat %>% to_h2h_long(value = "mean_score1", drop = TRUE)
output_ref_2 <- output_ref_1[-c(6, 8), ]
expect_equal(output_2, output_ref_2)
})
test_that("as_tibble.h2h_long removes `h2h_long` class", {
input <- output_long
output_ref <- input
class(output_ref) <- class(tibble::tibble())
expect_identical(tibble::as_tibble(input), output_ref)
})
test_that("h2h_mat works", {
expect_equal(
cr_data %>% h2h_mat(mean_score1 = mean(score1)),
cr_data %>% h2h_mat(mean(score1))
)
output_1 <- cr_data %>% h2h_mat(mean_score1 = mean(score1))
output_ref_1 <- output_mat
class(output_ref_1) <- c("h2h_mat", matrix_class)
expect_equal(output_1, output_ref_1)
output_2 <- cr_data %>% h2h_mat()
output_ref_2 <- matrix(
rep(NA, 9),
nrow = 3,
dimnames = list(c("1", "2", NA), c("1", "2", NA)),
byrow = TRUE
)
class(output_ref_2) <- c("h2h_mat", matrix_class)
expect_is(output_2, "h2h_mat")
expect_equal(output_2, output_ref_2)
})
test_that("h2h_mat handles `player` as factor", {
input <- cr_data
input$player <- factor(input$player, levels = c(1, 2, 3))
output <- h2h_mat(input, sum(score1 + score2))
output_ref <- matrix(
c(24, 22, NA,
22, 38, NA,
NA, NA, NA),
nrow = 3, dimnames = list(c("1", "2", "3"), c("1", "2", "3")),
byrow = TRUE
)
class(output_ref) <- c("h2h_mat", matrix_class)
expect_equal(output, output_ref)
})
test_that("h2h_mat allows multiple Head-to-Head functions", {
expect_silent(h2h_mat(cr_data))
expect_message(
h2h_mat(
cr_data,
mean_score1 = mean(score1),
sum_score = sum(score1 + score2)
),
"mean_score1"
)
capt_output <- capture_error(
h2h_mat(cr_data, mean_score1 = mean(score1), error = stop())
)
expect_identical(capt_output, NULL)
})
test_that("h2h_mat handles not NULL `fill`", {
output <- cr_data %>% h2h_mat(mean_score1 = mean(score1), fill = 0)
output_ref <- output_mat
output_ref[cbind(c(3, 2), c(2, 3))] <- 0
class(output_ref) <- c("h2h_mat", matrix_class)
expect_equal(output, output_ref)
})
test_that("to_h2h_mat works", {
output <- to_h2h_mat(output_long, value = "mean_score1")
output_ref <- output_mat
class(output_ref) <- c("h2h_mat", matrix_class)
expect_is(output, "h2h_mat")
expect_equal(output, output_ref)
})
test_that("to_h2h_mat gives messages", {
expect_message(to_h2h_mat(output_long), "mean_score1")
expect_message(to_h2h_mat(output_long[, 1:2]), "dummy")
})
test_that("to_h2h_mat handles not NULL `fill`", {
output <- output_long[-1, ] %>% to_h2h_mat(fill = 0)
output_ref <- output_mat
output_ref[1, 1] <- 0
class(output_ref) <- c("h2h_mat", matrix_class)
expect_equal(output, output_ref)
})
test_that("h2h_funs can be used with !!!", {
output <- h2h_long(cr_data, !!!h2h_funs[c("num_wins", "num_wins2")])
expect_true(tibble::is_tibble(output))
expect_equal(
colnames(output),
c("player1", "player2", "num_wins", "num_wins2")
)
})
test_that("num_wins works", {
score_1 <- c(1, NA, 2, 1, 0)
score_2 <- c(2, 1, 1, 1, 10^(-17))
expect_equal(num_wins(score_1, score_2), 1)
expect_equal(num_wins(score_1, score_2, half_for_draw = TRUE), 2)
expect_equal(num_wins(score_1, score_2, na.rm = FALSE), NA_real_)
}) |
to_miniature <- function(filename, row = NULL, width = NULL,
border_color = "
fileout = NULL, use_docx2pdf = FALSE, timeout = 120) {
if (!file.exists(filename)) {
stop("filename does not exist")
}
if(grepl("\\.(ppt|pptx)$", filename)){
if(is.null(width)) width <- 750
pptx_to_miniature(
filename, row = row, width = width,
border_color = border_color, border_geometry = border_geometry,
fileout = fileout,
timeout = timeout)
} else if(grepl("\\.(doc|docx)$", filename)){
if(is.null(width)) width <- 650
docx_to_miniature(
filename, row = row, width = width,
border_color = border_color, border_geometry = border_geometry,
fileout = fileout, use_docx2pdf = use_docx2pdf,
timeout = timeout)
} else if(grepl("\\.pdf$", filename)){
if(is.null(width)) width <- 650
pdf_to_miniature(
filename, row = row, width = width,
border_color = border_color, border_geometry = border_geometry,
fileout = fileout)
} else {
stop("function to_miniature do support this type of file:", basename(filename))
}
}
pdf_to_miniature <- function(filename, row = NULL, width = 650,
border_color = "
fileout = NULL) {
img_list <- pdf_to_images(filename)
x <- images_to_miniature(
img_list = img_list,
row = row, width = width,
border_color = border_color, border_geometry = border_geometry
)
if(!is.null(fileout))
image_write(x, path = fileout, format = "png")
x
}
docx_to_miniature <- function(filename, row = NULL, width = 650,
border_color = "
fileout = NULL, use_docx2pdf = FALSE,
timeout = 120) {
pdf_filename <- tempfile(fileext = ".pdf")
if(use_docx2pdf && exec_available("word") && docx2pdf_available())
docx2pdf(input = filename, output = pdf_filename)
else to_pdf(input = filename, output = pdf_filename, timeout = timeout)
x <- pdf_to_miniature(pdf_filename,
row = row, width = width,
border_color = border_color, border_geometry = border_geometry
)
if(!is.null(fileout))
image_write(x, path = fileout, format = "png")
x
}
pptx_to_miniature <- function(filename, row = NULL, width = 750,
border_color = "
fileout = NULL,
timeout = 120) {
pdf_filename <- tempfile(fileext = ".pdf")
to_pdf(input = filename, output = pdf_filename, timeout = timeout)
x <- pdf_to_miniature(pdf_filename,
row = row, width = width,
border_color = border_color, border_geometry = border_geometry
)
if(!is.null(fileout))
image_write(x, path = fileout, format = "png")
x
} |
df.residual.lme <-
function(object, ...){max(object$fixDF$terms)} |
is_integer <- function(x) {
UseMethod("is_integer", x)
}
is_integer.default <- function(x) {
if (mode(x) != "numeric") FALSE
}
is_integer.factor <- function(x) {
FALSE
}
is_integer.numeric <- function(x) {
(x %% 1) == 0
}
is_not_integer <- function(x) {
!is_integer(x)
}
is_positive_integer <- function(x) {
(is_positive(x) & is_integer(x))
}
is_negative_integer <- function(x) {
(is_negative(x) & is_integer(x))
} |
SardiniaHotels <-
structure(list(municipality = structure(c(55L, 55L, 55L, 55L,
7L, 7L, 7L, 7L, 48L, 48L, 48L, 48L, 48L, 49L, 48L, 48L, 55L,
55L, 55L, 7L, 7L, 48L, 49L, 48L, 48L, 48L, 48L, 48L, 48L, 48L,
48L, 48L, 55L, 7L, 48L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L,
55L, 55L, 7L, 7L, 48L, 48L, 74L, 74L, 47L, 74L, 47L, 74L, 74L,
74L, 74L, 13L, 44L, 74L, 74L, 74L, 74L, 74L, 74L, 74L, 74L, 74L,
74L, 74L, 74L, 74L, 47L, 13L, 57L, 57L, 13L, 13L, 13L, 13L, 74L,
74L, 74L, 13L, 74L, 44L, 74L, 74L, 74L, 74L, 13L, 13L, 26L, 26L,
26L, 26L, 26L, 79L, 26L, 26L, 76L, 79L, 79L, 79L, 79L, 79L, 79L,
79L, 79L, 79L, 79L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L,
76L, 79L, 79L, 26L, 76L, 79L, 79L, 76L, 76L, 76L, 78L, 76L, 25L,
25L, 25L, 25L, 72L, 22L, 61L, 36L, 61L, 36L, 36L, 36L, 36L, 61L,
61L, 61L, 25L, 25L, 25L, 25L, 25L, 72L, 25L, 21L, 72L, 72L, 72L,
72L, 72L, 72L, 17L, 72L, 72L, 72L, 72L, 71L, 71L, 21L, 21L, 17L,
25L, 25L, 72L, 72L, 72L, 72L, 66L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
70L, 23L, 84L, 3L, 3L, 3L, 3L, 3L, 3L, 41L, 70L, 70L, 9L, 8L,
34L, 82L, 39L, 5L, 77L, 51L, 10L, 54L, 3L, 3L, 3L, 3L, 3L, 16L,
23L, 16L, 3L, 70L, 23L, 45L, 3L, 8L, 3L, 53L, 53L, 53L, 53L,
53L, 16L, 16L, 16L, 50L, 16L, 33L, 16L, 53L, 16L, 16L, 53L, 53L,
53L, 53L, 53L, 53L, 53L, 53L, 53L, 53L, 53L, 53L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 33L, 33L, 53L, 16L, 33L, 53L, 53L,
53L, 16L, 53L, 43L, 60L, 40L, 40L, 35L, 35L, 35L, 35L, 35L, 49L,
48L, 48L, 48L, 48L, 48L, 49L, 48L, 49L, 49L, 40L, 40L, 40L, 40L,
40L, 40L, 40L, 40L, 40L, 35L, 35L, 35L, 35L, 49L, 48L, 48L, 49L,
49L, 49L, 48L, 48L, 48L, 35L, 86L, 86L, 86L, 86L, 86L, 86L, 86L,
86L, 86L, 32L, 86L, 87L, 15L, 28L, 75L, 32L, 15L, 15L, 86L, 86L,
87L, 87L, 87L, 87L, 32L, 32L, 87L, 87L, 87L, 86L, 86L, 86L, 86L,
86L, 62L, 62L, 62L, 62L, 62L, 15L, 15L, 15L, 62L, 86L, 19L, 64L,
62L, 28L, 69L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 7L, 7L, 59L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 18L,
56L, 56L, 56L, 56L, 7L, 18L, 56L, 56L, 56L, 56L, 56L, 56L, 56L,
7L, 18L, 7L, 7L, 7L, 18L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 80L, 4L, 11L, 14L, 52L, 52L, 52L, 14L,
4L, 65L, 63L, 11L, 12L, 11L, 11L, 11L, 52L, 52L, 52L, 14L, 14L,
11L, 68L, 73L, 73L, 73L, 37L, 37L, 20L, 73L, 73L, 73L, 73L, 67L,
58L, 67L, 73L, 73L, 73L, 29L, 37L, 73L, 6L, 1L, 73L, 1L, 37L,
38L, 83L, 30L, 1L, 27L, 31L, 31L, 31L, 31L, 27L, 27L, 27L, 27L,
27L, 27L, 8L, 8L, 24L, 24L, 8L, 42L, 24L, 24L, 24L, 24L, 24L,
8L, 8L, 46L, 46L, 31L, 31L, 31L, 81L, 81L, 85L, 85L), .Label = c("Aglientu",
"Alghero", "Arbatax/Tortol_", "Arborea", "Aritzo", "Badesi",
"Baja Sardinia", "Bari Sardo", "Baunei", "Belvi", "Bosa", "Bosa ",
"Budoni", "Cabras", "Cagliari", "Cala Gonone", "Calasetta ",
"Cannigione", "Capitana", "Capo Testa", "Carbonia", "Carbonia ",
"Cardedu", "Cardedu ", "Carloforte", "Castelsardo", "Castiadas",
"Chia ", "Conca Verde", "Costa Paradiso", "Costa Rei (Muravera)",
"Domus De Maria ", "Dorgali", "Girasole", "Golfo Aranci ", "Iglesias",
"Isola Rossa", "Isola Rossa. Trinit_ D'agultu E Vignola", "Jerzu",
"La Maddalena", "Lanusei", "Loceri", "Loc Nido D'aquila - Maddalena",
"Loiri Porto San Paolo", "Lotzorai", "Muravera", "Murta Maria. Olbia",
"Olbia", "Olbia ", "Oliena", "Orgosolo", "Oristano", "Orosei",
"Ottana", "Palau", "Porto Cervo", "Porto Ottiolu. Budoni", "Porto Pozzo",
"Porto Rotondo", "Porto Rotondo ", "Portoscuso", "Pula", "Putzu Idu",
"Quartu", "Riola Sardo", "San Giovanni Suergiu", "San Pasquale",
"Santa Caterina", "Santa Margherita Di Pula ", "Santa Maria Navarrese",
"Sant'anna Arresi", "Sant'antioco", "Santa Teresa Di Gallura",
"San Teodoro", "Sarroch", "Sassari", "Seulo", "Sorso", "Stintino",
"Terralba", "Tertenia", "Tortoli", "Trinit_ D'agulta E Vignola",
"Villagrande Strisaili", "Villaputzu", "Villasimius", "Villasimius "
), class = "factor"), stars = structure(c(2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 4L, 3L,
2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 4L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 4L, 4L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L,
4L, 5L, 5L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L,
2L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 3L, 5L, 5L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 5L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 3L, 5L, 2L, 4L, 4L, 5L, 5L, 2L,
5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L,
1L), .Label = c("1OR2stars", "3stars", "residence", "4stars",
"5starsORresort"), class = c("ordered", "factor")), area = structure(c(5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 5L,
5L, 5L, 5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 5L,
5L, 8L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 8L, 8L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 1L, 6L, 6L, 6L, 1L, 6L, 6L, 1L,
1L, 1L, 1L, 1L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 4L, 7L, 7L, 7L, 7L,
7L, 4L, 7L, 4L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 3L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 5L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 10L, 2L, 2L,
10L, 10L, 10L, 10L, 10L, 10L, 2L, 2L, 2L, 2L, 2L, 2L, 10L, 10L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 2L, 10L, 10L, 10L, 10L, 10L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 5L, 3L, 3L, 3L, 3L, 5L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L,
5L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 4L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 11L, 11L, 11L, 11L, 11L, 7L, 7L, 11L, 11L), .Label = c("AlgheroSassari",
"CagliariVillasimius", "CostaSmeralda", "DorgaliOrosei", "Gallura",
"NurraAnglona", "Ogliastra", "Olbia", "OristanoBosa", "PulaChia",
"Sarrabus", "Sulcis"), class = "factor"), seaLocation = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L), .Label = c("no", "yes"), class = "factor"),
excellent = c(33L, 74L, 20L, 29L, 19L, 12L, 32L, 10L, 2L,
6L, 50L, 39L, 29L, 32L, 3L, 47L, 19L, 68L, 216L, 38L, 33L,
13L, 3L, 87L, 119L, 63L, 15L, 74L, 10L, 232L, 20L, 84L, 83L,
90L, 11L, 12L, 13L, 58L, 3L, 1L, 2L, 23L, 0L, 6L, 2L, 36L,
36L, 1L, 4L, 37L, 130L, 223L, 201L, 104L, 40L, 81L, 141L,
135L, 370L, 50L, 106L, 62L, 209L, 75L, 89L, 17L, 88L, 67L,
27L, 127L, 107L, 23L, 14L, 57L, 1L, 75L, 59L, 55L, 37L, 39L,
19L, 64L, 226L, 78L, 61L, 27L, 186L, 61L, 1L, 6L, 1L, 10L,
13L, 86L, 12L, 69L, 8L, 3L, 82L, 86L, 5L, 73L, 78L, 370L,
11L, 92L, 25L, 22L, 142L, 60L, 194L, 35L, 113L, 2L, 0L, 78L,
52L, 97L, 25L, 2L, 41L, 10L, 127L, 60L, 3L, 8L, 10L, 7L,
111L, 3L, 40L, 7L, 68L, 49L, 45L, 13L, 32L, 49L, 183L, 31L,
17L, 11L, 8L, 18L, 5L, 15L, 16L, 2L, 4L, 33L, 37L, 112L,
18L, 54L, 11L, 51L, 4L, 14L, 23L, 3L, 14L, 63L, 96L, 56L,
33L, 62L, 15L, 20L, 26L, 32L, 1L, 3L, 12L, 11L, 19L, 0L,
4L, 6L, 9L, 13L, 55L, 366L, 0L, 264L, 82L, 10L, 94L, 69L,
53L, 228L, 94L, 87L, 115L, 32L, 22L, 23L, 62L, 94L, 1L, 71L,
109L, 39L, 15L, 9L, 1L, 20L, 14L, 8L, 0L, 317L, 85L, 59L,
71L, 105L, 388L, 120L, 102L, 315L, 123L, 136L, 80L, 15L,
7L, 23L, 55L, 35L, 385L, 55L, 33L, 55L, 34L, 15L, 255L, 97L,
35L, 16L, 203L, 260L, 96L, 286L, 206L, 72L, 69L, 84L, 86L,
16L, 25L, 22L, 22L, 0L, 30L, 7L, 4L, 48L, 32L, 24L, 24L,
31L, 26L, 9L, 12L, 47L, 68L, 1L, 16L, 18L, 7L, 9L, 68L, 33L,
86L, 26L, 100L, 26L, 173L, 118L, 18L, 2L, 130L, 309L, 141L,
438L, 149L, 13L, 108L, 97L, 11L, 27L, 109L, 166L, 39L, 45L,
19L, 18L, 27L, 12L, 8L, 0L, 92L, 10L, 45L, 153L, 82L, 5L,
22L, 7L, 51L, 68L, 11L, 71L, 55L, 1L, 49L, 38L, 24L, 59L,
29L, 7L, 27L, 6L, 48L, 23L, 83L, 43L, 3L, 6L, 6L, 6L, 55L,
6L, 14L, 150L, 92L, 83L, 154L, 287L, 48L, 116L, 20L, 225L,
81L, 304L, 116L, 93L, 61L, 105L, 24L, 124L, 107L, 163L, 135L,
878L, 33L, 195L, 74L, 539L, 155L, 30L, 237L, 157L, 51L, 36L,
85L, 74L, 82L, 118L, 41L, 116L, 171L, 77L, 52L, 25L, 97L,
142L, 76L, 150L, 128L, 3L, 59L, 32L, 75L, 100L, 27L, 158L,
25L, 11L, 14L, 4L, 35L, 34L, 54L, 32L, 0L, 3L, 1L, 3L, 39L,
46L, 66L, 170L, 81L, 71L, 56L, 296L, 367L, 40L, 84L, 213L,
25L, 327L, 163L, 122L, 105L, 302L, 173L, 25L, 216L, 193L,
13L, 15L, 31L, 15L, 20L, 26L, 10L, 91L, 3L, 14L, 0L, 1L,
2L, 10L, 26L, 12L, 19L, 2L, 3L, 25L, 18L, 5L, 22L, 18L, 0L,
16L, 32L, 21L, 87L, 28L, 14L, 23L, 18L, 17L, 17L, 20L, 19L,
28L, 22L, 45L, 82L, 35L, 2L, 21L, 14L, 37L, 65L, 3L, 29L,
41L, 13L, 30L, 34L, 33L, 59L, 25L, 14L, 71L, 4L, 6L, 336L,
49L, 147L, 372L, 9L, 12L, 36L, 62L, 21L, 175L, 301L, 66L,
125L, 220L, 145L, 47L, 98L, 124L, 142L, 49L, 12L, 4L, 26L,
0L, 17L, 3L, 39L, 37L, 35L, 26L, 16L, 2L, 236L, 33L, 40L,
9L, 14L, 8L, 47L, 24L), good = c(49L, 66L, 29L, 19L, 10L,
12L, 33L, 15L, 13L, 13L, 19L, 56L, 31L, 23L, 8L, 33L, 25L,
27L, 149L, 19L, 17L, 17L, 7L, 65L, 32L, 58L, 22L, 33L, 4L,
104L, 19L, 85L, 16L, 20L, 11L, 23L, 6L, 117L, 2L, 12L, 2L,
12L, 5L, 4L, 3L, 21L, 17L, 0L, 3L, 46L, 176L, 165L, 334L,
43L, 21L, 67L, 96L, 121L, 182L, 45L, 161L, 72L, 189L, 47L,
51L, 23L, 97L, 63L, 28L, 65L, 34L, 12L, 11L, 44L, 0L, 27L,
16L, 10L, 47L, 28L, 16L, 28L, 147L, 132L, 19L, 52L, 45L,
88L, 0L, 1L, 0L, 10L, 2L, 70L, 12L, 18L, 16L, 6L, 94L, 70L,
12L, 90L, 69L, 272L, 8L, 113L, 8L, 9L, 61L, 83L, 136L, 48L,
128L, 15L, 1L, 112L, 84L, 33L, 33L, 0L, 48L, 23L, 149L, 114L,
14L, 14L, 15L, 4L, 137L, 45L, 32L, 6L, 232L, 72L, 39L, 9L,
20L, 31L, 78L, 46L, 12L, 5L, 16L, 8L, 7L, 23L, 29L, 3L, 5L,
31L, 32L, 21L, 24L, 23L, 6L, 49L, 5L, 24L, 22L, 1L, 26L,
27L, 63L, 79L, 33L, 17L, 27L, 21L, 46L, 35L, 3L, 1L, 28L,
1L, 2L, 1L, 2L, 0L, 0L, 23L, 68L, 146L, 1L, 93L, 57L, 14L,
26L, 21L, 51L, 65L, 59L, 50L, 81L, 18L, 6L, 29L, 12L, 53L,
2L, 23L, 55L, 39L, 3L, 17L, 9L, 12L, 7L, 1L, 0L, 276L, 97L,
66L, 51L, 71L, 328L, 120L, 175L, 117L, 46L, 103L, 55L, 3L,
7L, 11L, 60L, 49L, 369L, 64L, 48L, 27L, 16L, 7L, 58L, 44L,
38L, 13L, 190L, 101L, 168L, 214L, 193L, 38L, 17L, 56L, 48L,
9L, 15L, 15L, 13L, 11L, 16L, 14L, 9L, 19L, 20L, 23L, 49L,
13L, 23L, 20L, 25L, 21L, 49L, 1L, 6L, 33L, 2L, 8L, 8L, 49L,
22L, 10L, 86L, 46L, 75L, 29L, 11L, 1L, 58L, 265L, 69L, 98L,
55L, 17L, 71L, 44L, 18L, 31L, 90L, 44L, 27L, 39L, 32L, 32L,
26L, 22L, 28L, 1L, 67L, 3L, 19L, 171L, 43L, 32L, 42L, 21L,
40L, 9L, 8L, 30L, 76L, 8L, 70L, 50L, 18L, 79L, 28L, 7L, 18L,
19L, 18L, 30L, 50L, 62L, 6L, 13L, 11L, 12L, 80L, 11L, 15L,
87L, 8L, 42L, 49L, 55L, 3L, 150L, 29L, 49L, 79L, 13L, 95L,
122L, 58L, 88L, 64L, 58L, 87L, 247L, 79L, 495L, 75L, 283L,
11L, 235L, 190L, 58L, 38L, 51L, 33L, 20L, 24L, 44L, 27L,
45L, 9L, 106L, 77L, 20L, 23L, 35L, 91L, 107L, 31L, 67L, 84L,
6L, 59L, 28L, 63L, 39L, 29L, 79L, 24L, 14L, 18L, 10L, 12L,
47L, 40L, 9L, 2L, 5L, 2L, 9L, 14L, 33L, 34L, 74L, 110L, 40L,
44L, 90L, 270L, 114L, 187L, 67L, 43L, 232L, 110L, 198L, 212L,
243L, 149L, 86L, 169L, 217L, 17L, 13L, 32L, 23L, 81L, 41L,
50L, 158L, 15L, 53L, 1L, 5L, 8L, 21L, 37L, 26L, 6L, 1L, 3L,
27L, 21L, 18L, 32L, 13L, 0L, 5L, 18L, 26L, 44L, 26L, 3L,
31L, 7L, 37L, 28L, 10L, 65L, 19L, 20L, 58L, 42L, 22L, 1L,
27L, 42L, 17L, 23L, 5L, 33L, 63L, 11L, 21L, 27L, 37L, 27L,
15L, 15L, 44L, 8L, 10L, 52L, 25L, 63L, 117L, 22L, 9L, 19L,
10L, 16L, 110L, 378L, 73L, 46L, 183L, 121L, 6L, 132L, 136L,
28L, 44L, 8L, 7L, 30L, 7L, 9L, 5L, 13L, 29L, 12L, 30L, 14L,
3L, 119L, 37L, 32L, 2L, 9L, 6L, 12L, 17L), average = c(17L,
31L, 4L, 11L, 2L, 1L, 8L, 7L, 15L, 9L, 0L, 9L, 12L, 6L, 12L,
4L, 7L, 4L, 53L, 7L, 1L, 14L, 8L, 9L, 2L, 11L, 12L, 6L, 0L,
11L, 14L, 21L, 3L, 6L, 10L, 7L, 1L, 48L, 13L, 7L, 3L, 11L,
1L, 5L, 0L, 1L, 4L, 0L, 1L, 23L, 63L, 41L, 158L, 12L, 4L,
13L, 46L, 64L, 44L, 12L, 44L, 33L, 43L, 5L, 14L, 14L, 27L,
20L, 8L, 11L, 4L, 1L, 14L, 5L, 0L, 14L, 7L, 0L, 9L, 6L, 10L,
8L, 49L, 46L, 31L, 13L, 2L, 28L, 0L, 0L, 0L, 0L, 1L, 15L,
1L, 3L, 7L, 0L, 31L, 15L, 16L, 30L, 13L, 82L, 4L, 22L, 2L,
6L, 10L, 20L, 33L, 20L, 34L, 1L, 0L, 59L, 31L, 7L, 19L, 0L,
19L, 41L, 105L, 53L, 6L, 11L, 11L, 0L, 47L, 65L, 22L, 0L,
64L, 29L, 8L, 1L, 9L, 19L, 15L, 15L, 5L, 2L, 9L, 9L, 11L,
10L, 22L, 3L, 5L, 12L, 7L, 2L, 2L, 5L, 10L, 29L, 3L, 17L,
8L, 3L, 10L, 3L, 13L, 26L, 7L, 3L, 12L, 4L, 16L, 22L, 0L,
1L, 7L, 2L, 0L, 0L, 2L, 0L, 0L, 5L, 17L, 38L, 2L, 24L, 13L,
8L, 0L, 1L, 24L, 21L, 6L, 6L, 11L, 2L, 2L, 11L, 2L, 6L, 1L,
2L, 10L, 11L, 0L, 3L, 4L, 1L, 2L, 0L, 0L, 154L, 62L, 16L,
18L, 40L, 129L, 59L, 114L, 23L, 8L, 44L, 5L, 0L, 2L, 1L,
18L, 21L, 125L, 16L, 31L, 10L, 1L, 18L, 19L, 11L, 17L, 6L,
55L, 19L, 100L, 62L, 70L, 2L, 1L, 7L, 6L, 6L, 3L, 7L, 1L,
4L, 6L, 4L, 1L, 4L, 16L, 16L, 14L, 1L, 5L, 11L, 6L, 2L, 4L,
0L, 0L, 9L, 1L, 1L, 0L, 31L, 9L, 3L, 18L, 36L, 23L, 7L, 5L,
0L, 12L, 92L, 20L, 7L, 12L, 14L, 28L, 10L, 11L, 28L, 23L,
13L, 1L, 12L, 7L, 20L, 11L, 3L, 18L, 1L, 9L, 2L, 10L, 62L,
8L, 25L, 25L, 22L, 9L, 2L, 3L, 3L, 12L, 4L, 8L, 12L, 4L,
15L, 13L, 0L, 6L, 5L, 1L, 8L, 8L, 19L, 0L, 11L, 3L, 3L, 15L,
6L, 14L, 18L, 1L, 2L, 4L, 6L, 10L, 53L, 8L, 2L, 17L, 19L,
28L, 58L, 19L, 32L, 38L, 24L, 22L, 123L, 26L, 86L, 69L, 57L,
10L, 78L, 103L, 40L, 17L, 31L, 25L, 9L, 15L, 16L, 13L, 21L,
3L, 57L, 18L, 7L, 12L, 17L, 48L, 36L, 6L, 28L, 24L, 3L, 32L,
11L, 23L, 6L, 9L, 9L, 6L, 8L, 9L, 2L, 2L, 26L, 19L, 6L, 0L,
5L, 1L, 3L, 7L, 4L, 3L, 22L, 37L, 5L, 14L, 26L, 89L, 69L,
118L, 11L, 52L, 50L, 48L, 81L, 112L, 64L, 59L, 66L, 53L,
69L, 7L, 1L, 4L, 4L, 63L, 29L, 33L, 67L, 18L, 40L, 1L, 10L,
7L, 5L, 20L, 10L, 0L, 0L, 3L, 7L, 11L, 20L, 18L, 7L, 0L,
1L, 10L, 10L, 9L, 5L, 1L, 7L, 2L, 46L, 27L, 2L, 14L, 10L,
8L, 19L, 14L, 8L, 0L, 4L, 27L, 2L, 4L, 3L, 6L, 12L, 2L, 19L,
6L, 7L, 18L, 3L, 1L, 30L, 3L, 3L, 31L, 4L, 18L, 15L, 12L,
5L, 5L, 0L, 5L, 29L, 169L, 32L, 18L, 55L, 7L, 0L, 37L, 52L,
2L, 24L, 3L, 5L, 4L, 0L, 4L, 1L, 3L, 7L, 0L, 4L, 5L, 3L,
14L, 13L, 8L, 1L, 1L, 2L, 4L, 5L), bad = c(2L, 15L, 0L, 6L,
1L, 0L, 3L, 8L, 13L, 3L, 1L, 2L, 14L, 1L, 5L, 2L, 6L, 1L,
29L, 4L, 2L, 7L, 4L, 3L, 0L, 1L, 4L, 5L, 0L, 3L, 2L, 3L,
5L, 3L, 6L, 5L, 0L, 17L, 5L, 5L, 2L, 2L, 2L, 3L, 0L, 1L,
1L, 0L, 0L, 8L, 25L, 14L, 47L, 7L, 5L, 4L, 15L, 59L, 9L,
15L, 30L, 12L, 18L, 2L, 8L, 5L, 27L, 11L, 3L, 1L, 0L, 0L,
6L, 4L, 1L, 6L, 2L, 1L, 6L, 3L, 3L, 12L, 42L, 25L, 1L, 6L,
0L, 5L, 0L, 0L, 0L, 0L, 1L, 4L, 0L, 1L, 0L, 0L, 18L, 4L,
4L, 4L, 6L, 24L, 1L, 13L, 0L, 7L, 0L, 11L, 16L, 11L, 11L,
0L, 0L, 31L, 9L, 7L, 24L, 0L, 5L, 24L, 55L, 30L, 2L, 3L,
10L, 0L, 7L, 20L, 10L, 0L, 39L, 4L, 2L, 3L, 0L, 7L, 1L, 5L,
0L, 0L, 3L, 2L, 3L, 3L, 23L, 0L, 2L, 4L, 4L, 1L, 2L, 1L,
0L, 8L, 1L, 4L, 2L, 0L, 7L, 0L, 2L, 10L, 6L, 5L, 7L, 2L,
3L, 6L, 0L, 0L, 3L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 13L,
1L, 5L, 8L, 3L, 0L, 3L, 20L, 19L, 0L, 3L, 1L, 5L, 0L, 10L,
0L, 2L, 0L, 0L, 0L, 6L, 1L, 3L, 1L, 1L, 1L, 0L, 0L, 124L,
41L, 17L, 16L, 27L, 52L, 49L, 88L, 10L, 2L, 29L, 5L, 1L,
5L, 1L, 15L, 7L, 40L, 8L, 18L, 3L, 0L, 12L, 11L, 5L, 4L,
4L, 25L, 8L, 79L, 19L, 21L, 2L, 0L, 0L, 0L, 2L, 2L, 2L, 1L,
1L, 9L, 2L, 1L, 0L, 5L, 1L, 14L, 2L, 0L, 4L, 1L, 2L, 6L,
0L, 0L, 1L, 0L, 1L, 0L, 18L, 3L, 2L, 2L, 26L, 8L, 3L, 6L,
0L, 4L, 62L, 13L, 1L, 3L, 7L, 10L, 0L, 10L, 21L, 11L, 2L,
1L, 3L, 3L, 6L, 2L, 4L, 6L, 3L, 5L, 1L, 5L, 28L, 1L, 21L,
2L, 9L, 2L, 0L, 0L, 2L, 2L, 5L, 2L, 2L, 2L, 5L, 6L, 0L, 1L,
0L, 0L, 7L, 3L, 5L, 1L, 4L, 1L, 2L, 7L, 1L, 12L, 13L, 2L,
2L, 2L, 1L, 8L, 36L, 1L, 1L, 5L, 4L, 12L, 4L, 15L, 7L, 15L,
1L, 13L, 91L, 10L, 29L, 30L, 11L, 2L, 38L, 65L, 44L, 5L,
10L, 25L, 4L, 10L, 9L, 9L, 6L, 4L, 23L, 9L, 8L, 5L, 6L, 8L,
19L, 5L, 8L, 10L, 1L, 24L, 4L, 4L, 1L, 7L, 7L, 3L, 3L, 3L,
1L, 2L, 11L, 8L, 0L, 2L, 1L, 0L, 0L, 0L, 2L, 2L, 8L, 18L,
4L, 15L, 6L, 31L, 33L, 42L, 2L, 46L, 32L, 7L, 24L, 48L, 28L,
31L, 18L, 29L, 22L, 1L, 0L, 0L, 5L, 39L, 14L, 19L, 19L, 7L,
12L, 0L, 14L, 8L, 2L, 3L, 2L, 0L, 1L, 4L, 0L, 4L, 10L, 4L,
4L, 0L, 0L, 5L, 4L, 2L, 2L, 0L, 1L, 0L, 12L, 7L, 3L, 0L,
7L, 3L, 2L, 1L, 1L, 0L, 0L, 16L, 0L, 1L, 1L, 3L, 5L, 1L,
14L, 4L, 6L, 13L, 5L, 2L, 20L, 0L, 1L, 23L, 0L, 4L, 4L, 7L,
4L, 4L, 1L, 3L, 14L, 73L, 12L, 11L, 25L, 23L, 0L, 11L, 23L,
1L, 20L, 0L, 5L, 4L, 0L, 0L, 0L, 1L, 0L, 2L, 4L, 0L, 3L,
7L, 3L, 6L, 0L, 0L, 1L, 1L, 3L), poor = c(2L, 11L, 0L, 3L,
0L, 0L, 0L, 4L, 8L, 3L, 4L, 1L, 10L, 0L, 1L, 0L, 0L, 0L,
13L, 0L, 1L, 4L, 6L, 1L, 1L, 1L, 3L, 6L, 1L, 1L, 1L, 0L,
2L, 0L, 2L, 2L, 0L, 6L, 15L, 3L, 6L, 3L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 4L, 15L, 11L, 46L, 8L, 1L, 4L, 8L, 31L, 12L,
9L, 10L, 1L, 6L, 1L, 3L, 1L, 15L, 6L, 2L, 1L, 0L, 0L, 3L,
8L, 3L, 3L, 2L, 0L, 7L, 1L, 6L, 22L, 16L, 24L, 2L, 5L, 0L,
9L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 3L, 0L, 0L, 11L, 1L, 3L,
2L, 6L, 25L, 0L, 7L, 0L, 7L, 1L, 5L, 7L, 5L, 3L, 0L, 0L,
18L, 5L, 5L, 12L, 0L, 4L, 20L, 36L, 21L, 2L, 0L, 7L, 2L,
3L, 2L, 12L, 0L, 24L, 7L, 0L, 5L, 2L, 5L, 1L, 4L, 0L, 1L,
4L, 2L, 0L, 3L, 10L, 0L, 3L, 1L, 1L, 2L, 1L, 1L, 2L, 6L,
5L, 1L, 1L, 5L, 2L, 0L, 0L, 4L, 3L, 2L, 3L, 4L, 3L, 3L, 0L,
0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 3L, 1L, 7L, 1L, 3L, 6L, 0L,
0L, 1L, 21L, 9L, 1L, 0L, 4L, 1L, 0L, 2L, 0L, 1L, 5L, 0L,
0L, 4L, 0L, 4L, 0L, 2L, 1L, 2L, 2L, 173L, 43L, 13L, 4L, 26L,
34L, 36L, 63L, 5L, 0L, 12L, 0L, 0L, 2L, 0L, 21L, 2L, 24L,
4L, 16L, 1L, 1L, 17L, 7L, 5L, 2L, 3L, 11L, 7L, 59L, 12L,
10L, 0L, 0L, 1L, 2L, 5L, 2L, 0L, 1L, 0L, 8L, 0L, 3L, 0L,
3L, 4L, 1L, 0L, 2L, 2L, 3L, 1L, 2L, 1L, 0L, 1L, 0L, 2L, 1L,
16L, 6L, 1L, 4L, 25L, 1L, 2L, 3L, 0L, 2L, 51L, 8L, 1L, 0L,
5L, 3L, 1L, 18L, 20L, 6L, 2L, 0L, 2L, 2L, 3L, 5L, 2L, 4L,
4L, 6L, 0L, 0L, 5L, 0L, 15L, 3L, 2L, 1L, 1L, 1L, 5L, 1L,
1L, 3L, 3L, 1L, 4L, 5L, 0L, 1L, 0L, 0L, 4L, 2L, 6L, 1L, 1L,
2L, 0L, 7L, 1L, 19L, 4L, 0L, 0L, 1L, 1L, 1L, 25L, 0L, 1L,
1L, 7L, 5L, 24L, 8L, 3L, 14L, 2L, 4L, 72L, 6L, 15L, 11L,
5L, 2L, 20L, 79L, 35L, 6L, 14L, 23L, 2L, 6L, 6L, 4L, 11L,
6L, 20L, 3L, 4L, 3L, 5L, 8L, 24L, 4L, 10L, 7L, 2L, 8L, 0L,
2L, 1L, 9L, 3L, 2L, 1L, 0L, 0L, 0L, 8L, 6L, 0L, 1L, 3L, 0L,
1L, 0L, 2L, 0L, 6L, 3L, 4L, 14L, 10L, 7L, 9L, 17L, 1L, 20L,
18L, 3L, 16L, 17L, 6L, 10L, 8L, 14L, 12L, 1L, 1L, 2L, 3L,
44L, 11L, 9L, 5L, 3L, 4L, 2L, 12L, 3L, 1L, 3L, 6L, 0L, 2L,
3L, 0L, 10L, 7L, 3L, 16L, 3L, 0L, 7L, 3L, 0L, 3L, 0L, 5L,
0L, 5L, 3L, 0L, 0L, 3L, 1L, 2L, 0L, 1L, 1L, 0L, 20L, 0L,
0L, 3L, 1L, 0L, 1L, 8L, 5L, 0L, 14L, 1L, 0L, 12L, 1L, 1L,
8L, 0L, 3L, 0L, 4L, 2L, 1L, 1L, 1L, 8L, 21L, 3L, 2L, 9L,
15L, 0L, 4L, 11L, 1L, 21L, 0L, 6L, 0L, 0L, 0L, 0L, 1L, 1L,
1L, 0L, 1L, 8L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 2L), family = c(15L,
36L, 6L, 18L, 12L, 1L, 14L, 7L, 10L, 13L, 17L, 23L, 12L,
13L, 6L, 6L, 10L, 14L, 144L, 20L, 14L, 5L, 2L, 15L, 19L,
7L, 16L, 31L, 0L, 35L, 1L, 16L, 20L, 35L, 18L, 9L, 7L, 146L,
9L, 13L, 3L, 21L, 2L, 6L, 4L, 29L, 15L, 0L, 1L, 21L, 134L,
132L, 321L, 45L, 23L, 33L, 109L, 199L, 295L, 30L, 134L, 31L,
289L, 24L, 39L, 13L, 98L, 30L, 5L, 63L, 23L, 11L, 9L, 26L,
1L, 19L, 31L, 9L, 47L, 16L, 9L, 24L, 266L, 104L, 36L, 66L,
118L, 63L, 1L, 5L, 0L, 7L, 11L, 36L, 6L, 18L, 31L, 1L, 160L,
36L, 11L, 16L, 42L, 370L, 4L, 61L, 6L, 10L, 54L, 79L, 179L,
19L, 91L, 0L, 0L, 100L, 16L, 23L, 15L, 1L, 10L, 13L, 310L,
157L, 6L, 11L, 11L, 6L, 34L, 5L, 13L, 1L, 291L, 22L, 13L,
5L, 16L, 20L, 34L, 16L, 6L, 2L, 6L, 11L, 7L, 14L, 9L, 1L,
4L, 10L, 23L, 18L, 10L, 18L, 4L, 41L, 8L, 9L, 7L, 2L, 5L,
24L, 26L, 28L, 24L, 17L, 10L, 18L, 25L, 17L, 1L, 1L, 6L,
7L, 4L, 1L, 4L, 2L, 4L, 10L, 34L, 368L, 0L, 61L, 29L, 7L,
32L, 20L, 82L, 73L, 35L, 30L, 38L, 13L, 6L, 16L, 16L, 21L,
1L, 15L, 63L, 22L, 5L, 10L, 5L, 7L, 6L, 2L, 0L, 460L, 146L,
35L, 53L, 39L, 572L, 215L, 250L, 319L, 23L, 194L, 69L, 5L,
7L, 23L, 45L, 62L, 586L, 74L, 77L, 24L, 8L, 12L, 63L, 36L,
25L, 11L, 295L, 246L, 237L, 353L, 323L, 23L, 22L, 21L, 29L,
13L, 9L, 20L, 12L, 14L, 29L, 7L, 2L, 11L, 17L, 14L, 6L, 5L,
9L, 10L, 8L, 17L, 52L, 0L, 8L, 16L, 2L, 7L, 25L, 77L, 18L,
6L, 24L, 80L, 74L, 41L, 9L, 2L, 112L, 143L, 55L, 92L, 24L,
5L, 24L, 14L, 5L, 19L, 67L, 46L, 14L, 36L, 12L, 12L, 12L,
5L, 11L, 0L, 33L, 2L, 26L, 253L, 19L, 19L, 16L, 8L, 8L, 16L,
3L, 26L, 25L, 5L, 68L, 49L, 28L, 52L, 27L, 4L, 27L, 7L, 28L,
30L, 22L, 22L, 1L, 4L, 0L, 2L, 19L, 1L, 13L, 44L, 6L, 20L,
30L, 103L, 24L, 214L, 6L, 24L, 42L, 72L, 54L, 159L, 25L,
143L, 28L, 24L, 63L, 270L, 49L, 113L, 16L, 54L, 39L, 287L,
213L, 43L, 81L, 123L, 26L, 16L, 28L, 31L, 25L, 38L, 9L, 77L,
83L, 25L, 14L, 9L, 40L, 135L, 18L, 36L, 43L, 2L, 58L, 10L,
35L, 20L, 20L, 40L, 10L, 4L, 7L, 4L, 9L, 17L, 26L, 5L, 2L,
6L, 3L, 2L, 20L, 26L, 52L, 103L, 42L, 25L, 24L, 50L, 97L,
74L, 66L, 72L, 40L, 108L, 73L, 146L, 57L, 95L, 69L, 48L,
121L, 163L, 9L, 8L, 8L, 7L, 22L, 19L, 18L, 47L, 8L, 13L,
0L, 5L, 13L, 7L, 22L, 26L, 1L, 1L, 4L, 10L, 12L, 4L, 10L,
5L, 0L, 2L, 17L, 17L, 23L, 9L, 0L, 6L, 2L, 11L, 7L, 11L,
8L, 6L, 8L, 23L, 28L, 6L, 0L, 12L, 23L, 8L, 12L, 7L, 7L,
49L, 3L, 28L, 76L, 13L, 26L, 4L, 11L, 69L, 2L, 9L, 178L,
16L, 29L, 164L, 35L, 13L, 13L, 5L, 16L, 186L, 580L, 125L,
89L, 332L, 238L, 14L, 161L, 190L, 44L, 18L, 6L, 15L, 14L,
0L, 7L, 3L, 10L, 28L, 10L, 14L, 14L, 1L, 276L, 30L, 21L,
1L, 8L, 8L, 13L, 28L), couple = c(43L, 76L, 26L, 17L, 11L,
15L, 36L, 20L, 16L, 10L, 24L, 43L, 33L, 31L, 12L, 30L, 30L,
53L, 187L, 33L, 28L, 15L, 3L, 40L, 39L, 60L, 16L, 45L, 12L,
103L, 19L, 62L, 62L, 53L, 14L, 20L, 8L, 52L, 17L, 5L, 11L,
15L, 4L, 3L, 0L, 21L, 16L, 0L, 4L, 52L, 173L, 218L, 288L,
91L, 33L, 86L, 128L, 108L, 196L, 71L, 131L, 79L, 85L, 56L,
88L, 31L, 82L, 84L, 39L, 102L, 100L, 13L, 17L, 51L, 2L, 79L,
40L, 37L, 44L, 41L, 30L, 41L, 64L, 135L, 26L, 24L, 80L, 104L,
0L, 0L, 0L, 7L, 4L, 98L, 13L, 46L, 11L, 7L, 24L, 98L, 14L,
46L, 90L, 215L, 12L, 123L, 18L, 24L, 100L, 69L, 131L, 57L,
123L, 14L, 0L, 126L, 113L, 90L, 75L, 0L, 71L, 15L, 59L, 46L,
14L, 16L, 23L, 4L, 71L, 15L, 14L, 6L, 54L, 94L, 53L, 31L,
31L, 70L, 147L, 43L, 8L, 14L, 27L, 11L, 2L, 16L, 71L, 4L,
6L, 36L, 30L, 88L, 20L, 39L, 31L, 55L, 3L, 25L, 20L, 4L,
32L, 45L, 93L, 100L, 31L, 33L, 33L, 21L, 23L, 47L, 1L, 1L,
30L, 1L, 12L, 1L, 2L, 3L, 5L, 18L, 56L, 97L, 4L, 230L, 84L,
21L, 55L, 60L, 38L, 147L, 79L, 76L, 106L, 31L, 8L, 30L, 16L,
91L, 4L, 54L, 73L, 46L, 5L, 13L, 6L, 8L, 7L, 5L, 0L, 284L,
114L, 88L, 73L, 176L, 122L, 63L, 122L, 66L, 120L, 171L, 55L,
5L, 11L, 8L, 75L, 25L, 114L, 41L, 33L, 45L, 33L, 29L, 141L,
86L, 40L, 23L, 86L, 70L, 107L, 94L, 62L, 63L, 38L, 88L, 89L,
12L, 25L, 23L, 14L, 2L, 22L, 12L, 8L, 45L, 32L, 32L, 64L,
30L, 29L, 23L, 14L, 34L, 54L, 2L, 8L, 29L, 4L, 9L, 30L, 38L,
77L, 15L, 134L, 46L, 148L, 86L, 29L, 1L, 56L, 302L, 120L,
249L, 131L, 15L, 135L, 56L, 20L, 61L, 110L, 127L, 40L, 39L,
30L, 42L, 31L, 15L, 35L, 6L, 90L, 9L, 36L, 75L, 86L, 29L,
42L, 24L, 35L, 36L, 10L, 41L, 73L, 9L, 38L, 31L, 11L, 68L,
29L, 6L, 16L, 17L, 30L, 23L, 86L, 67L, 5L, 20L, 14L, 13L,
57L, 7L, 42L, 174L, 80L, 87L, 124L, 199L, 56L, 83L, 34L,
18L, 96L, 330L, 172L, 76L, 96L, 49L, 58L, 128L, 107L, 229L,
156L, 586L, 57L, 180L, 25L, 397L, 202L, 79L, 84L, 75L, 73L,
25L, 56L, 66L, 77L, 92L, 29L, 216L, 136L, 61L, 51L, 44L,
103L, 100L, 77L, 130L, 137L, 6L, 78L, 43L, 93L, 96L, 35L,
168L, 35L, 25L, 19L, 6L, 34L, 67L, 53L, 31L, 2L, 5L, 1L,
7L, 27L, 29L, 43L, 105L, 142L, 70L, 57L, 256L, 404L, 97L,
215L, 171L, 95L, 360L, 164L, 157L, 211L, 320L, 223L, 94L,
220L, 219L, 22L, 9L, 41L, 24L, 93L, 55L, 54L, 204L, 19L,
66L, 0L, 16L, 5L, 23L, 26L, 10L, 6L, 2L, 2L, 33L, 20L, 23L,
28L, 23L, 2L, 15L, 34L, 28L, 78L, 25L, 11L, 31L, 9L, 32L,
22L, 10L, 43L, 39L, 27L, 61L, 78L, 35L, 2L, 33L, 65L, 33L,
49L, 5L, 37L, 31L, 17L, 42L, 98L, 52L, 51L, 27L, 16L, 73L,
9L, 4L, 169L, 47L, 164L, 238L, 8L, 5L, 31L, 54L, 21L, 72L,
124L, 21L, 51L, 62L, 36L, 23L, 46L, 71L, 90L, 72L, 9L, 2L,
34L, 7L, 13L, 4L, 32L, 33L, 25L, 34L, 13L, 2L, 15L, 33L,
35L, 4L, 5L, 6L, 18L, 9L), single = c(3L, 4L, 2L, 1L, 1L,
0L, 2L, 3L, 0L, 1L, 4L, 3L, 11L, 3L, 2L, 6L, 0L, 3L, 4L,
0L, 0L, 1L, 3L, 5L, 2L, 8L, 0L, 4L, 0L, 11L, 5L, 13L, 4L,
1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 2L, 6L, 7L, 6L, 2L, 1L, 5L, 3L, 3L, 10L, 1L, 6L, 5L,
2L, 1L, 3L, 2L, 7L, 7L, 1L, 4L, 3L, 1L, 2L, 3L, 0L, 2L, 1L,
0L, 2L, 1L, 3L, 3L, 3L, 4L, 0L, 1L, 6L, 0L, 0L, 0L, 0L, 0L,
0L, 4L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 13L, 3L, 15L, 0L, 2L,
0L, 1L, 4L, 6L, 4L, 6L, 5L, 0L, 0L, 2L, 2L, 1L, 1L, 0L, 1L,
5L, 8L, 3L, 0L, 0L, 0L, 0L, 18L, 8L, 6L, 0L, 5L, 2L, 1L,
2L, 0L, 0L, 5L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 2L, 0L, 3L,
0L, 2L, 0L, 3L, 3L, 1L, 1L, 3L, 4L, 0L, 1L, 3L, 5L, 8L, 3L,
3L, 2L, 0L, 0L, 1L, 0L, 0L, 3L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
8L, 4L, 0L, 7L, 3L, 0L, 4L, 2L, 0L, 9L, 8L, 5L, 6L, 1L, 0L,
0L, 5L, 3L, 0L, 3L, 3L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 20L,
4L, 3L, 4L, 5L, 7L, 3L, 9L, 1L, 3L, 3L, 0L, 2L, 1L, 0L, 0L,
3L, 7L, 0L, 1L, 2L, 0L, 1L, 2L, 0L, 1L, 1L, 3L, 4L, 9L, 1L,
2L, 0L, 1L, 2L, 1L, 2L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 2L,
2L, 1L, 0L, 4L, 2L, 1L, 2L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L,
0L, 1L, 4L, 3L, 4L, 5L, 0L, 0L, 2L, 13L, 11L, 17L, 5L, 2L,
4L, 13L, 7L, 1L, 5L, 1L, 2L, 2L, 1L, 1L, 2L, 0L, 2L, 0L,
4L, 1L, 3L, 3L, 62L, 6L, 6L, 9L, 8L, 0L, 1L, 8L, 4L, 1L,
0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 5L, 5L, 0L, 2L, 2L,
1L, 13L, 7L, 2L, 6L, 1L, 0L, 4L, 3L, 1L, 3L, 1L, 8L, 2L,
2L, 3L, 5L, 3L, 1L, 2L, 3L, 4L, 14L, 4L, 64L, 16L, 29L, 1L,
21L, 24L, 5L, 6L, 4L, 5L, 1L, 2L, 3L, 1L, 5L, 1L, 8L, 3L,
4L, 2L, 1L, 28L, 3L, 3L, 8L, 3L, 0L, 1L, 4L, 1L, 1L, 5L,
8L, 0L, 1L, 2L, 0L, 0L, 3L, 6L, 1L, 0L, 0L, 0L, 0L, 2L, 2L,
0L, 5L, 7L, 2L, 2L, 13L, 12L, 8L, 21L, 0L, 5L, 20L, 8L, 33L,
21L, 21L, 12L, 6L, 11L, 16L, 0L, 1L, 5L, 0L, 17L, 1L, 4L,
7L, 1L, 11L, 0L, 2L, 0L, 0L, 6L, 0L, 2L, 2L, 1L, 2L, 2L,
4L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 6L, 3L, 0L,
5L, 0L, 2L, 1L, 1L, 4L, 0L, 0L, 4L, 3L, 0L, 1L, 2L, 0L, 0L,
1L, 2L, 1L, 8L, 1L, 2L, 0L, 0L, 1L, 5L, 1L, 2L, 5L, 0L, 0L,
1L, 2L, 2L, 1L, 4L, 1L, 2L, 3L, 4L, 0L, 5L, 2L, 5L, 1L, 2L,
0L, 0L, 0L, 3L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 2L, 5L, 1L, 3L,
0L, 0L, 0L, 0L), business = c(5L, 1L, 2L, 1L, 0L, 1L, 1L,
2L, 9L, 4L, 5L, 11L, 13L, 1L, 3L, 31L, 1L, 1L, 12L, 4L, 0L,
20L, 13L, 74L, 63L, 34L, 13L, 7L, 0L, 148L, 25L, 50L, 5L,
2L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L,
2L, 3L, 3L, 6L, 2L, 2L, 3L, 3L, 6L, 9L, 5L, 1L, 4L, 3L, 0L,
2L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L,
1L, 0L, 0L, 3L, 2L, 5L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 93L, 2L, 8L, 1L, 4L, 1L,
0L, 4L, 2L, 2L, 2L, 3L, 1L, 0L, 1L, 10L, 3L, 1L, 1L, 8L,
62L, 0L, 0L, 0L, 0L, 0L, 0L, 126L, 87L, 48L, 2L, 2L, 6L,
0L, 3L, 0L, 2L, 33L, 17L, 6L, 0L, 3L, 6L, 6L, 6L, 4L, 0L,
2L, 0L, 0L, 3L, 1L, 1L, 1L, 1L, 0L, 1L, 3L, 0L, 4L, 1L, 0L,
1L, 1L, 1L, 0L, 1L, 6L, 3L, 1L, 3L, 1L, 3L, 0L, 0L, 0L, 0L,
0L, 1L, 13L, 6L, 0L, 19L, 12L, 1L, 3L, 2L, 0L, 12L, 15L,
4L, 15L, 1L, 8L, 1L, 15L, 3L, 0L, 2L, 2L, 3L, 0L, 1L, 0L,
0L, 1L, 1L, 0L, 11L, 3L, 1L, 0L, 0L, 3L, 3L, 2L, 5L, 1L,
3L, 0L, 0L, 0L, 0L, 1L, 4L, 25L, 0L, 0L, 0L, 0L, 0L, 21L,
5L, 1L, 3L, 3L, 1L, 2L, 6L, 5L, 2L, 0L, 5L, 4L, 2L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 2L, 1L, 2L, 0L, 0L, 2L, 2L, 1L, 1L,
0L, 0L, 2L, 0L, 0L, 0L, 0L, 3L, 5L, 9L, 2L, 4L, 3L, 1L, 0L,
1L, 67L, 10L, 74L, 27L, 20L, 15L, 58L, 17L, 11L, 15L, 6L,
0L, 2L, 1L, 3L, 1L, 2L, 1L, 0L, 4L, 0L, 0L, 0L, 1L, 14L,
12L, 3L, 33L, 8L, 5L, 5L, 11L, 0L, 0L, 1L, 0L, 159L, 1L,
0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 5L, 42L, 1L, 1L,
3L, 0L, 1L, 2L, 0L, 1L, 18L, 0L, 278L, 189L, 3L, 2L, 1L,
0L, 6L, 14L, 20L, 7L, 30L, 3L, 509L, 79L, 193L, 12L, 43L,
16L, 17L, 67L, 15L, 8L, 10L, 18L, 16L, 10L, 16L, 2L, 35L,
3L, 20L, 10L, 7L, 6L, 5L, 7L, 8L, 12L, 2L, 4L, 4L, 6L, 5L,
0L, 3L, 1L, 2L, 4L, 5L, 1L, 4L, 5L, 1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 6L, 1L, 2L, 2L, 30L, 110L, 10L, 41L, 2L, 6L, 33L,
21L, 7L, 78L, 48L, 28L, 4L, 16L, 22L, 2L, 0L, 3L, 3L, 17L,
3L, 5L, 7L, 9L, 2L, 0L, 0L, 0L, 1L, 4L, 0L, 10L, 0L, 0L,
2L, 13L, 14L, 13L, 2L, 0L, 1L, 0L, 4L, 10L, 3L, 0L, 2L, 6L,
44L, 41L, 4L, 31L, 2L, 6L, 3L, 1L, 2L, 0L, 1L, 1L, 0L, 0L,
0L, 0L, 5L, 0L, 0L, 0L, 0L, 4L, 3L, 0L, 5L, 0L, 0L, 6L, 0L,
2L, 5L, 0L, 1L, 2L, 1L, 0L, 5L, 4L, 1L, 5L, 2L, 11L, 0L,
1L, 2L, 3L, 2L, 1L, 1L, 2L, 0L, 1L, 1L, 3L, 1L, 1L, 2L, 0L,
4L, 0L, 0L, 5L, 1L, 0L, 0L, 14L, 0L), MarMay = c(5L, 4L,
5L, 8L, 3L, 3L, 6L, 3L, 6L, 3L, 8L, 12L, 11L, 0L, 5L, 13L,
4L, 10L, 29L, 2L, 2L, 11L, 6L, 46L, 37L, 27L, 9L, 11L, 11L,
78L, 13L, 44L, 8L, 10L, 10L, 0L, 6L, 12L, 5L, 3L, 1L, 4L,
2L, 0L, 0L, 2L, 0L, 0L, 0L, 18L, 9L, 80L, 36L, 15L, 2L, 8L,
5L, 31L, 67L, 5L, 27L, 6L, 15L, 13L, 19L, 5L, 3L, 17L, 1L,
12L, 13L, 4L, 4L, 20L, 0L, 10L, 6L, 9L, 3L, 1L, 3L, 11L,
23L, 39L, 6L, 10L, 24L, 14L, 0L, 2L, 0L, 0L, 1L, 13L, 0L,
44L, 1L, 0L, 5L, 13L, 3L, 54L, NA, NA, NA, 25L, 1L, 14L,
13L, 16L, 6L, 9L, 15L, 1L, 0L, 5L, 28L, 10L, NA, 3L, 28L,
26L, 9L, 6L, 4L, 6L, 3L, 3L, 72L, 35L, 24L, 4L, 22L, 33L,
14L, 11L, 8L, 71L, 10L, 8L, 9L, 8L, 5L, 9L, 8L, 12L, 15L,
1L, 2L, 16L, 1L, 28L, 0L, 8L, 5L, 7L, 9L, 9L, 16L, 4L, 12L,
8L, 15L, 21L, 2L, 8L, 0L, 2L, 13L, 7L, 0L, 0L, 6L, 3L, 1L,
1L, 0L, 0L, 8L, 4L, 24L, 24L, 1L, 79L, 29L, 5L, 20L, 17L,
2L, 40L, 25L, 18L, 29L, 13L, 7L, 5L, 14L, 17L, 1L, 11L, 19L,
18L, 6L, 5L, 4L, 11L, 2L, 0L, 0L, 66L, 14L, 6L, 8L, 35L,
35L, 2L, 15L, 24L, 34L, 39L, 11L, 6L, 0L, 3L, 25L, 10L, 55L,
6L, 13L, 6L, 7L, 10L, 85L, 45L, 18L, 8L, 17L, 5L, 14L, 19L,
19L, 15L, 12L, 16L, 10L, 8L, 2L, 3L, 5L, 3L, 2L, 5L, 3L,
0L, 13L, 7L, 18L, 3L, 7L, 7L, 1L, 6L, 7L, 0L, 1L, 2L, 5L,
3L, 7L, 14L, 24L, 6L, 24L, 9L, 26L, 16L, 0L, 1L, 20L, 193L,
14L, 38L, 39L, 12L, 26L, 22L, 17L, 11L, 25L, 35L, 6L, 4L,
2L, 11L, 7L, 2L, 0L, 0L, 26L, 0L, 7L, 9L, 89L, 14L, 13L,
12L, 15L, 11L, 2L, 13L, 21L, 3L, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L,
4L, 2L, 6L, 9L, 5L, 3L, 2L, 2L, 4L, 16L, 15L, 6L, 20L, 12L,
2L, 5L, 4L, 9L, 3L, 3L, 4L, 2L, 6L, 3L, 1L, 3L, 8L, 13L,
1L, 0L, 2L, 1L, 1L, 2L, 3L, 2L, 20L, 4L, 2L, 5L, 85L, 116L,
56L, 119L, 89L, 17L, 81L, 75L, 44L, 110L, 144L, 55L, 30L,
71L, 87L, 12L, 17L, 35L, 17L, 46L, 2L, 28L, 53L, 7L, 16L,
0L, 6L, NA, 15L, NA, 8L, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
11L, 5L, 3L, 4L, 7L, 10L, 4L, 3L, 6L, 3L, 9L, 14L, 5L, 2L,
4L, 2L, 12L, 8L, 17L, 1L, 13L, 7L, NA, 5L, 9L, NA, 13L, 6L,
2L, 3L, 21L, 32L, 6L, 7L, 9L, 22L, 0L, 24L, 10L, 5L, 9L,
3L, 0L, 2L, 0L, 3L, 0L, 12L, 1L, 2L, 2L, 1L, 4L, 3L, 6L,
8L, 2L, 1L, 2L, 15L, 6L), JunAug = c(57L, 108L, 36L, 19L,
21L, 14L, 50L, 28L, 25L, 11L, 43L, 62L, 42L, 40L, 20L, 38L,
38L, 62L, 280L, 50L, 32L, 16L, 16L, 59L, 34L, 50L, 21L, 74L,
0L, 83L, 24L, 65L, 65L, 74L, 31L, 31L, 11L, 131L, 24L, 17L,
9L, 26L, 5L, 14L, 4L, 30L, 30L, 0L, 4L, 64L, 278L, 212L,
515L, 98L, 46L, 112L, 191L, 276L, 335L, 88L, 193L, 130L,
332L, 87L, 106L, 36L, 189L, 107L, 46L, 112L, 78L, 16L, 31L,
63L, 4L, 73L, 41L, 40L, 84L, 55L, 33L, 79L, 322L, 139L, 56L,
50L, 141L, 108L, 1L, 2L, 0L, 15L, 12L, 68L, 16L, 122L, 24L,
3L, 162L, 68L, 28L, 48L, NA, NA, NA, 139L, 19L, 28L, 131L,
94L, 203L, 56L, 190L, 10L, 0L, 119L, 35L, 31L, NA, 0L, 35L,
27L, 344L, 188L, 37L, 22L, 27L, 8L, 80L, 31L, 32L, 5L, 275L,
60L, 62L, 29L, 33L, 84L, 59L, 28L, 11L, 38L, 5L, 17L, 8L,
19L, 27L, 4L, 8L, 38L, 49L, 66L, 37L, 55L, 12L, 79L, 10L,
32L, 19L, 3L, 21L, 51L, 78L, 74L, 38L, 39L, 38L, 36L, 52L,
61L, 1L, 3L, 28L, 5L, 12L, 1L, 5L, 4L, 6L, 22L, 68L, 342L,
1L, 164L, 77L, 23L, 63L, 45L, 131L, 146L, 74L, 81L, 119L,
31L, 11L, 45L, 33L, 93L, 6L, 48L, 81L, 39L, 6L, 16L, 2L,
15L, 8L, 1L, 2L, 640L, 198L, 106L, 102L, 150L, 669L, 280L,
404L, 342L, 98L, 186L, 92L, 6L, 18L, 23L, 93L, 60L, 529L,
85L, 81L, 55L, 31L, 50L, 135L, 83L, 47L, 21L, 317L, 285L,
381L, 397L, 331L, 66L, 40L, 95L, 85L, 18L, 32L, 27L, 18L,
6L, 39L, 12L, 11L, 46L, 43L, 41L, 53L, 34L, 38L, 27L, 24L,
36L, 85L, 1L, 9L, 35L, 3L, 11L, 45L, 81L, 69L, 22L, 55L,
111L, 151L, 79L, 28L, 1L, 109L, 246L, 74L, 34L, 83L, 16L,
136L, 66L, 38L, 85L, 131L, 126L, 44L, 66L, 48L, 37L, 44L,
25L, 46L, 5L, 83L, 13L, 47L, 306L, 51L, 49L, 47L, 32L, 46L,
45L, 16L, 63L, 82L, 9L, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 56L, 45L, 92L, 60L,
32L, 21L, 17L, 36L, 28L, 14L, 21L, 31L, 123L, 26L, 66L, 22L,
5L, 79L, 18L, 20L, 29L, 38L, 26L, 33L, 13L, 17L, 6L, 14L,
69L, 34L, 7L, 3L, 3L, 1L, 4L, 20L, 39L, 30L, 114L, 38L, 32L,
87L, 141L, 335L, 114L, 158L, 112L, 98L, 328L, 144L, 253L,
193L, 228L, 202L, 100L, 212L, 247L, 50L, 99L, 77L, 48L, 99L,
70L, 60L, 173L, 17L, 67L, 2L, 23L, NA, 36L, NA, 27L, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 34L, 16L, 12L, 15L, 22L, 31L,
11L, 18L, 22L, 10L, 18L, 30L, 41L, 19L, 11L, 13L, 20L, 14L,
24L, 14L, 27L, 14L, NA, 15L, 24L, NA, 22L, 12L, 44L, 30L,
289L, 623L, 97L, 144L, 333L, 234L, 26L, 208L, 220L, 69L,
43L, 15L, 20L, 28L, 5L, 2L, 4L, 22L, 32L, 19L, 28L, 17L,
5L, 291L, 55L, 46L, 7L, 16L, 9L, 22L, 27L), SepNov = c(38L,
49L, 12L, 21L, 7L, 7L, 18L, 11L, 16L, 18L, 21L, 26L, 27L,
20L, 3L, 26L, 13L, 23L, 140L, 9L, 16L, 24L, 4L, 53L, 34L,
29L, 18L, 36L, 1L, 109L, 14L, 53L, 28L, 34L, 15L, 18L, 3L,
90L, 8L, 5L, 5L, 19L, 1L, 5L, 0L, 23L, 27L, 0L, 4L, 26L,
113L, 123L, 226L, 50L, 21L, 47L, 105L, 98L, 195L, 31L, 120L,
40L, 105L, 29L, 36L, 17L, 58L, 35L, 20L, 72L, 39L, 10L, 10L,
29L, 1L, 36L, 37L, 15L, 17L, 18L, 13L, 34L, 121L, 107L, 21L,
37L, 66L, 58L, 0L, 3L, 0L, 3L, 4L, 26L, 9L, 41L, 3L, 6L,
67L, 26L, 8L, 56L, NA, NA, NA, 81L, 14L, 7L, 66L, 56L, 100L,
22L, 77L, 5L, 0L, 39L, 35L, 13L, NA, 0L, 35L, 40L, 111L,
74L, 4L, 8L, 21L, 0L, 88L, 35L, 30L, 3L, 119L, 62L, 15L,
11L, 20L, 70L, 32L, 27L, 3L, 14L, 8L, 5L, 6L, 19L, 24L, 2L,
7L, 20L, 28L, 29L, 10L, 15L, 7L, 53L, 2L, 18L, 11L, 5L, 20L,
28L, 46L, 37L, 25L, 27L, 15L, 10L, 22L, 27L, 1L, 0L, 7L,
6L, 6L, 0L, 3L, 1L, 2L, 12L, 34L, 184L, 2L, 126L, 49L, 6L,
31L, 30L, 33L, 127L, 51L, 46L, 58L, 13L, 6L, 20L, 16L, 43L,
2L, 32L, 56L, 33L, 5L, 11L, 7L, 9L, 13L, 4L, 0L, 317L, 112L,
56L, 46L, 77L, 206L, 94L, 114L, 184L, 44L, 94L, 36L, 5L,
5L, 9L, 44L, 40L, 338L, 54L, 46L, 31L, 12L, 8L, 110L, 32L,
20L, 12L, 114L, 102L, 102L, 166L, 121L, 30L, 31L, 32L, 43L,
8L, 9L, 13L, 13L, 7L, 26L, 9L, 4L, 2L, 19L, 17L, 28L, 9L,
11L, 8L, 8L, 27L, 35L, 2L, 12L, 24L, 2L, 7L, 21L, 46L, 30L,
10L, 34L, 32L, 88L, 55L, 12L, 1L, 69L, 184L, 36L, 34L, 58L,
24L, 53L, 47L, 9L, 27L, 69L, 64L, 18L, 28L, 11L, 28L, 18L,
16L, 18L, 3L, 61L, 3L, 22L, 102L, 3L, 29L, 28L, 12L, 32L,
19L, 2L, 33L, 34L, 7L, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 25L, 31L, 18L, 14L,
11L, 9L, 20L, 13L, 12L, 10L, 8L, 10L, 52L, 10L, 56L, 8L,
2L, 30L, 4L, 11L, 13L, 17L, 14L, 8L, 5L, 7L, 5L, 7L, 28L,
9L, 4L, 1L, 2L, 2L, 4L, 12L, 21L, 23L, 36L, 13L, 16L, 26L,
120L, 231L, 94L, 95L, 94L, 67L, 250L, 104L, 138L, 141L, 188L,
139L, 71L, 185L, 152L, 30L, 48L, 49L, 29L, 72L, 43L, 29L,
114L, 13L, 33L, 1L, 9L, NA, 12L, NA, 20L, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 8L, 15L, 6L, 14L, 11L, 9L, 12L, 6L, 3L, 8L,
11L, 16L, 5L, 8L, 7L, 7L, 8L, 7L, 15L, 13L, 11L, 13L, NA,
4L, 10L, NA, 13L, 10L, 25L, 13L, 81L, 257L, 79L, 46L, 131L,
87L, 21L, 56L, 90L, 30L, 26L, 4L, 6L, 21L, 1L, 6L, 2L, 11L,
18L, 13L, 21L, 4L, 6L, 69L, 24L, 15L, 3L, 2L, 5L, 6L, 10L
), DecFeb = c(3L, 2L, 0L, 1L, 1L, 1L, 2L, 2L, 4L, 2L, 2L,
7L, 16L, 2L, 1L, 9L, 2L, 5L, 11L, 7L, 4L, 4L, 2L, 7L, 49L,
28L, 8L, 3L, 3L, 81L, 5L, 31L, 8L, 1L, 3L, 0L, 0L, 13L, 1L,
3L, 0L, 2L, 1L, 0L, 1L, 4L, 1L, 1L, 0L, 10L, 9L, 39L, 9L,
11L, 2L, 2L, 5L, 5L, 20L, 7L, 11L, 4L, 13L, 1L, 4L, 2L, 4L,
8L, 1L, 9L, 15L, 6L, 3L, 6L, 0L, 6L, 2L, 2L, 2L, 3L, 5L,
10L, 14L, 20L, 3L, 6L, 2L, 11L, 0L, 0L, 1L, 2L, 0L, 6L, 1L,
22L, 3L, 0L, 0L, 6L, 1L, 43L, NA, NA, NA, 4L, 1L, 2L, 4L,
13L, 10L, 1L, 7L, 2L, 0L, 16L, 19L, 2L, NA, 0L, 19L, 15L,
8L, 10L, 1L, 0L, 2L, 2L, 67L, 36L, 31L, 1L, 11L, 6L, 3L,
7L, 2L, 58L, 4L, 12L, 5L, 9L, 7L, 8L, 4L, 4L, 3L, 0L, 0L,
7L, 4L, 15L, 0L, 7L, 6L, 4L, 0L, 1L, 7L, 0L, 6L, 6L, 1L,
2L, 3L, 8L, 0L, 2L, 7L, 4L, 1L, 2L, 6L, 0L, 2L, 0L, 1L, 2L,
1L, 6L, 17L, 20L, 1L, 20L, 11L, 1L, 6L, 3L, 3L, 28L, 10L,
4L, 7L, 1L, 7L, 5L, 13L, 3L, 0L, 5L, 18L, 9L, 2L, 4L, 2L,
1L, 2L, 6L, 0L, 21L, 5L, 3L, 4L, 7L, 21L, 8L, 9L, 20L, 5L,
7L, 6L, 2L, 0L, 4L, 7L, 4L, 21L, 2L, 6L, 4L, 2L, 1L, 20L,
2L, 11L, 1L, 36L, 3L, 5L, 11L, 29L, 3L, 4L, 5L, 4L, 4L, 4L,
3L, 2L, 0L, 2L, 1L, 0L, 3L, 1L, 3L, 3L, 1L, 0L, 4L, 4L, 4L,
2L, 0L, 0L, 1L, 0L, 0L, 4L, 6L, 3L, 4L, 3L, 7L, 15L, 9L,
3L, 0L, 8L, 156L, 3L, 49L, 39L, 4L, 5L, 17L, 4L, 4L, 14L,
2L, 0L, 3L, 2L, 3L, 2L, 0L, 0L, 1L, 9L, 0L, 3L, 2L, 2L, 6L,
6L, 5L, 10L, 5L, 3L, 2L, 9L, 0L, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2L, 0L,
1L, 0L, 3L, 2L, 0L, 4L, 1L, 2L, 4L, 6L, 10L, 4L, 16L, 4L,
3L, 2L, 2L, 3L, 1L, 4L, 0L, 2L, 0L, 2L, 1L, 1L, 1L, 3L, 0L,
1L, 1L, 0L, 3L, 3L, 3L, 4L, 7L, 2L, 4L, 7L, 89L, 93L, 5L,
83L, 6L, 6L, 17L, 14L, 12L, 62L, 89L, 30L, 5L, 20L, 31L,
3L, 6L, 11L, 8L, 32L, 6L, 6L, 7L, 9L, 7L, 1L, 4L, NA, 6L,
NA, 1L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 3L, 5L, 2L, 8L,
5L, 5L, 0L, 2L, 7L, 9L, 0L, 3L, 6L, 4L, 9L, 9L, 6L, 10L,
10L, 6L, 9L, NA, 6L, 2L, NA, 11L, 8L, 3L, 0L, 4L, 31L, 4L,
7L, 7L, 20L, 0L, 7L, 7L, 8L, 3L, 1L, 0L, 1L, 0L, 0L, 1L,
1L, 4L, 1L, 1L, 2L, 2L, 7L, 1L, 4L, 0L, 1L, 0L, 13L, 0L),
location = c(5, 5, 4, 4, 5, 4, 4, 4, 4, 4, 4, 5, 3, 4, 4,
5, 5, 4, 4, 4, 4, 5, 5, 4, 5, 4, 4, 5, 5, 4, 4, 3, 5, 4,
5, 4, 4, 4, 4, 4, 3, 5, 5, 4, 4, 4, 4, 5, 5, 4.5, 4.5, 4.5,
4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 5, 4, 4.5, 5, 4.5, 4.5, 4.5, NA, 4.5, 5, 4.5,
4.5, 4.5, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4, 5, NA,
4.5, 4.5, 4, 4.5, 4.5, 0, 4.5, 4, 4, NA, 4.5, NA, NA, NA,
4, 4.5, 4.5, 4.5, 5, 4.5, 4, 5, NA, 0, 4.5, 4.5, 4.5, 4,
NA, 4.5, 3.5, 4, 4.5, NA, 4.5, 4, 5, 4, 4, 3.5, NA, 4, 4.5,
5, 4.5, 4.5, 4.5, 4, 4.5, 4.5, 5, 4.5, 3.5, 4.5, 3.5, 4,
4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4, 3, 4.5, 4, 4, 4, 4.5,
4, 4.5, 4.5, 4, 4, 4.5, NA, 4, NA, 4, 4.5, 5, 5, 4, 4.5,
5, 5, 3.5, 4, 4.5, 2, 4.5, 4, 4.5, 4.5, 4.5, 4, 4.5, 4, 4,
3.5, 4.5, 5, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4, 4.5, 4, 5, 5,
4.5, 5, 1, 4, 4, 4, 4.5, 4.5, 4, 4, 4, 4.5, 4.5, 4, 4.5,
4.5, 3.5, 4, 4, 4, 4, 4, 4, 4.5, 4.5, NA, 4.5, 4.5, 4.5,
4.5, 4, 4.5, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4, 4, 4.5,
4.5, 4.5, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4,
4.5, 4.5, 5, 5, 4, 4.5, 4.5, 4.5, 4, 4, 5, 4.5, 5, 4.5, 4.5,
4.5, 4, 4.5, 4, 4.5, 5, 4.5, 4.5, 4.5, 4.5, 3.5, 4, 4.5,
4.5, 4.5, 4.5, 4.5, 4.5, 4, 4, 4.5, 3, 5, 4.5, 5, 4.5, 4.5,
4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 3.5, 4, 4.5, 4.5, 4,
4, 5, NA, 4.5, 5, NA, 4.5, 4.5, NA, 4.5, 5, NA, 5, 4.5, NA,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, NA, 4.5, 4.5, 4.5, 5,
4.5, 4.5, 4.5, 4, 4.5, 4.5, 4, NA, 4.5, 3.5, 4.5, 5, 4.5,
3.5, 3.5, 4.5, 4.5, 4.5, 4.5, 5, 5, 4.5, 4.5, 5, 4.5, 4.5,
4.5, 5, 4.5, 4, 4.5, 4.5, 5, 4.5, 4.5, 4, 4.5, 4, 4.5, 4.5,
4.5, 5, 4.5, 4.5, 4, 4.5, 4, 4, 4.5, 4, 3.5, 4.5, 5, 5, 4.5,
4.5, 4, 4.5, 4.5, 4, 5, 4.5, 3.5, 4, 4.5, 4, 4.5, 4, 4, 4.5,
4.5, 4, 4.5, 4.5, 4.5, NA, 4.5, 3, 4, 4.5, 4, 4.5, 4.5, 4,
3, 5, 3, 3.5, 3.5, 4, 3, 5, NA, 5, 5, 3, 4, 5, 4, 1.5, 4,
5, 4, 5, 4.5, 5, 5, 5, 5, 3, 5, 4, 5, 4, 4.4, 4, 5, 5, 5,
NA, 5, 5, 5, 4, 5, 4, 4, 5, 4, 4, 5, 4, 4, 4, 5, 5, 5, 5,
5, 5, 4, 5, 5, 4, 4.5, 4.5, 5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 4, 4.5, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4, 3,
4.5, 4.5, 4, 4.5, 4, 4.5, 4, 4), sleepQuality = c(4, 4, 4,
4, 4, 4, 4, 3, 4, 4, 4, 5, 4, 4, 3, 5, 4, 4, 4, 4, 4, 4,
4, 5, 4, 4, 1, 5, 5, 4, 4, 4, 5, 4, 5, 3, 4, 4, 2, 3, 1,
5, 4, 5, 4, 5, 4, 5, 5, 4, 3.5, 4, 4, 4.5, 4.5, 4, 4, 4,
4, 4, 4, 4, 4, 4.5, 4.5, 4, 4, 4, 4, 4.5, 4.5, 4.5, 4, 4,
2, 4.5, 5, 4.5, 4, 4.5, 3.5, 3.5, 4.5, 4, 4.5, 4, 4.5, 4,
4, 4, 5, 4.5, 4, 4.5, 4.5, 4.5, 4, 4, 4, 4.5, NA, 4, NA,
NA, NA, 4, 4.5, 4, 4.5, 5, 4, 4, NA, NA, 0, 4, 4, 4.5, 4,
NA, 4, 3, 4, 4, NA, 3.5, 3.5, 4.5, 4.5, 3.5, 3, NA, 4, 4.5,
4, 4, 5, 4, 4.5, 4.5, 4, 5, 3, 4.5, 3.5, 4, 3, 3.5, 4, 4.5,
4.5, 4.5, 4.5, 4.5, 4, 4.5, 4.5, 4, 4, 3, 4, 4.5, 4.5, 4,
4.5, 4.5, 4, 4.5, NA, 4, NA, 5, 4, 4.5, 5, 4, 3, 5, 5, 4.5,
4.5, 4.5, 3, 4.5, 4.5, 4, 4.5, 4.5, 3.5, 4.5, 4.5, 4.5, 4.5,
4.5, 4.5, 4, 4.5, 4.5, 1, 4.5, 4.5, 3.5, 4.5, 4, 3.5, 4.5,
3.5, 4.5, 1, 3.5, 3.5, 4, 4.5, 3.5, 4, 4.5, 3.5, 4.5, 4.5,
4, 4, 4.5, 3, 3.5, 4, 4, 4, 3.5, 3.5, 4, 4.5, NA, 4.5, 3.5,
4, 4, 4, 4, 3.5, 4, 4, 4.5, 4.5, 4, 4.5, 4, 4, 4.5, 4.5,
4, 4, 4, 4, 4.5, 4, 4, 4, 4.5, 4, 4, 4, 4.5, 4.5, 4.5, 5,
4, 4, 3.5, 4.5, 3.5, 4.5, 4.5, 4.5, 4, 4.5, 4.5, 4, 5, 4.5,
3.5, 4.5, 4.5, 4.5, 4, 4.5, 4.5, 3.5, 3.5, 4, 4.5, 4.5, 4,
4, 4, 4, 4, 3.5, 2, 4.5, 4, 4, 4, 4.5, 3, 4, 4, 4, 4.5, 3.5,
4.5, 4, 3.5, 4, 4, 4.5, 4, 4, 3.5, NA, 3.5, 4.5, NA, 4.5,
4, NA, 4.5, 3.5, NA, 4, 4, NA, 4, 4.5, 4, 4.5, 4.5, 4, 4,
NA, 4.5, 4, 4, 4.5, 4, 4, 4, 3.5, 4.5, 4.5, 4, NA, 4.5, 3.5,
4.5, 4.5, 4, 3.5, 3, 4.5, 4.5, 4, 4.5, 4.5, 4.5, 4.5, 4.5,
4.5, 4, 4, 4, 4.5, 4, 4, 4, 4.5, 4.5, 4.5, 5, 4, 4, 4.5,
4.5, 4, 4.5, 4.5, 3.5, 4, 4.5, 4.5, 4, 4.5, 4.5, 2.5, 3,
3, 4, 4.5, 4.5, 4.5, 4.5, 4, 4.5, 4, 4.5, 4.5, 3.5, 3.5,
4, 3, 4.5, 4.5, 3.5, 4, 4, 4, 3.5, 4, 4, NA, 4.5, 4.5, 4,
3.5, 3.5, 3.5, 4, 3, 3, 0, 2.5, 3, 3.5, 3.5, 4.5, 5, NA,
NA, 4.5, 3.5, 4, 5, 4.5, 2.5, 4.5, 3.5, 4, 4.5, 3, 5, 5,
4, 4, 4, 4, 4, 4, 4, 4.2, 5, 4.5, 5, 3, 3, 3, 5, 4, 4, 4,
5, 5, 5, 4, 5, 5, 4, 4, 3, 4, 5, NA, 4, 5, 3, 4, 4, 5, 4.5,
4, 4, 4, 4.5, 4.5, 4, 5, 4.5, 4.5, 4, 4, 4, 3.5, 4.5, 5,
4.5, 5, 4.5, 4, 4.5, 4.5, 4, 3, 4.5, 4, 4, 4.5, 4, 3.5, 4.5,
4), room = c(3, 5, 4, 3, 4, 4, 4, 3, 2, 4, 4, 4, 3, 4, 4,
4, 5, 4, 3, 4, 4, 4, 3, 5, 5, 4, 5, 5, 5, 4, 4, 4, 5, 4,
3, 3, 4, 3, 1, 3, 3, 4, 3, 2, 3, 5, 4, 5, 5, 4, 4, 4.5, 4,
4, 3.5, 4.5, 4, 4.5, 4.5, 3.5, 4, 4, 3.5, 4.5, 4, 3.5, 4,
4, 4, 4.5, 4.5, 4.5, 3.5, 4.5, NA, 4, 4.5, 5, 4, 4.5, 4,
3, 4, 3.5, 4.5, 3.5, 4.5, 3.5, 4, NA, NA, 4.5, 5, 4.5, 4.5,
4.5, 3.5, 4, 3.5, 4.5, NA, 4, NA, NA, NA, 4, 4.5, 4.5, 4.5,
3.5, 4, 4, NA, NA, 0, 3.5, 4, 4.5, 3.5, NA, 4, 2.5, 3.5,
3, NA, 4, 3, 4, 4, 3, 3.5, NA, 3.5, 4, 4.5, 4, 4.5, 4, 4.5,
4, 4.5, 4.5, 4, 4.5, 3, 4, 2.5, 4, 3, 4, 4, 5, 3.5, 4.5,
3.5, 3.5, 3.5, 4, 4, 3, 4, 4.5, 4.5, 4, 4, 4.5, 3, 4, NA,
4, NA, 5, 3.5, 4.5, 5, 4, 2.5, 5, 5, 3.5, 4, 4.5, 2.5, 4.5,
4.5, 4, 4.5, 4.5, 3.5, 4.5, 4.5, 4.5, 4.5, 4, 5, 4, 4.5,
4.5, 2, 4.5, 4.5, 4, 4.5, 3, 3.5, 4, 3.5, 4.5, 1, 3, 3.5,
3.5, 4, 3.5, 3.5, 4, 4, 4.5, 4.5, 4, 4, 4.5, 3, 4, 4, 4,
4, 4, 3.5, 4.5, 4.5, NA, 4.5, 4, 4.5, 4, 4, 4, 4, 4, 4, 4.5,
5, 4.5, 4, 4, 4, 4, 4.5, 4, 3.5, 4, 3.5, 4.5, 4, 3.5, 3.5,
4, 4, 3.5, 4, 4, 4.5, 4.5, 5, 4.5, 4.5, 4, 4.5, 3.5, 4.5,
4.5, 4.5, 3.2, 4, 4.5, 4.5, 4.5, 4.5, 4, 4, 5, 4.5, 4, 4,
4.5, 3, 3.5, 4.5, 4.5, 4.5, 4, 3.5, 3.5, 4, 3.5, 3, 2.5,
4, 4.5, 4, 4, 4, 2.5, 3.5, 3, 4, 5, 4.5, 4, 4, 2.5, 4, 4,
4, 4, 4, 4.5, NA, 3.5, 4.5, NA, 4.5, 3.5, NA, 3.5, 3, NA,
4, 4, NA, 4.5, 5, 4.5, 4.5, 4.5, 4, 3.5, NA, 4.5, 4.5, 4.5,
4, 3.5, 3.5, 4, 3.5, 4, 4, 3.5, NA, 4, 3, 4, 4.5, 4, 3.5,
3, 4.5, 4.5, 3.5, 4, 4, 4, 4, 4, 4, 4, 4.5, 4.5, 4.5, 3.5,
4, 4, 4, 4, 4, 3, 3.5, 3.5, 4, 4.5, 3.5, 4.5, 4, 3.5, 3.5,
4, 4, 3, 4, 4.5, 3, 3, 4.5, 3, 4, 4.5, 4.5, 4, 3.5, 4, 3.5,
4.5, 4.5, 3.5, 3.5, 4.5, 3, 4, 4.5, 3.5, 3.5, 4, 4, 3.5,
4, 3.5, NA, 4.5, 4.5, 3.5, 3, 3, 3, 3.5, 3, 3.5, 2.5, 2,
3, 3.5, 3.5, 4, 5, NA, NA, 4, NA, 1.5, 4, 4, 1, 4, 3.5, 4.5,
4, 4, 4, 5, 4, 5, 3, 4, 4, 3, 5, 4, 5, 4.5, 3, 4, 3, 4, 4,
4, 4, 3, 4, 4, 5, 4, 3, 4, 4, 3, 4, 3, 4, NA, 5, 4, 3, 5,
5, 4.5, 4.5, 3.5, 3.5, 4.5, 4.5, 4, 3.5, 4.5, 3.5, 3.5, 4.5,
4, 4.5, 3.5, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4, 3.5, 2.5,
4, 4.5, 4, 4.5, 4, 4, 4.5, 4), services = c(5, 5, 4, 4, 4,
4, 4, 3, 3, 4, 4, 5, 3, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4,
5, 5, 4, 5, 5, 4, 4, 4, 5, 4, 5, 3, 4, 1, 1, 3, 2, 4, 2,
5, 2, 4, 4, 5, 5, 4, 4, 4.5, 4, 4.5, 4, 4.5, 4, 3.5, 4.5,
4, 4, 4, 4, 4.5, 4, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 3.5, 4,
1.5, 4, 4.5, 5, 3.5, 4.5, 3.5, 3.5, 4, 3.5, 4.5, 3.5, 4.5,
4, 4, 5, NA, 4.5, 4.5, 4.5, 4.5, 4.5, 4, 4, 4, 4.5, NA, 4,
NA, NA, NA, 4, 4.5, 4.5, 4.5, 3.5, 4.5, 4, 4, 3, 4, 3.5,
4, 4.5, 3.5, NA, 4, 3, 3.5, 3.5, NA, 3.5, 2.5, 4.5, 4, 3.5,
3.5, NA, 3.5, 4, 4, 4, 4, 4, 4.5, 4, 4.5, 4.5, 3.5, 4, 3.5,
4, 3.5, 4, 3.5, 4, 4, 4.5, 4, 4.5, 4, 4, 3.5, 3.5, 4, 3,
3.5, 4.5, 4.5, 4, 4, 4.5, 3.5, 4, NA, 4, 4, 4.5, 4, 4.5,
5, 1.5, 3.5, 5, 5, 4, 4.5, 4.5, 3, 4.5, 4, 4, 5, 4.5, 3.5,
4.5, 4.5, 4.5, 4.5, 4, 5, 3.5, 5, 4.5, 2.5, 4.5, 4.5, 4,
4.5, 3.5, 4, 4.5, 4, 4.5, 0, 3.5, 3.5, 4, 4, 3.5, 4, 3.5,
3, 4.5, 4.5, 4, 4.5, 4.5, 3, 4, 3.5, 4, 4, 4, 3.5, 4.5, 4.5,
NA, 4.5, 4.5, 4, 4.5, 4, 4.5, 3, 4.5, 4, 4.5, 4.5, 4.5, 4.5,
4, 4, 4, 4.5, 4.5, 3.5, 4, 3.5, 4.5, 4, 4, 4, 4.5, 4, 4,
4, 4.5, 4, 3.5, 5, 4, 4.5, 4, 5, 3.5, 4.5, 4, 4.5, 3, 4.5,
4.5, 4, 4.5, 4.5, 3.5, 4.5, 5, 4.5, 4, 4.5, 4.5, 3.5, 3,
4, 4.5, 4.5, 4, 4, 3.5, 4, 4, 3.5, 2, 4.5, 4.5, 4, 4, 4.5,
3, 4, 3.5, 4.5, 4.5, 4, 4.5, 4, 3, 4, 4, 4, 4, 4, 4.5, NA,
4, 4.5, NA, 4.5, 4, NA, 3, 3.5, NA, 4, 4, NA, 4.5, 5, 4.5,
4.5, 5, 4, 3.5, NA, 5, 4, 4.5, 4.5, 3.5, 4, 4, 3.5, 4.5,
4, 3.5, NA, 4.5, 3.5, 4, 4.5, 4.5, 3.5, 3.5, 4.5, 4, 3.5,
4.5, 4.5, 4.5, 4.5, 4.5, 4, 4, 4.5, 4.5, 4, 4, 4, 4, 4.5,
4.5, 4.5, 3, 4, 4.5, 4.5, 4.5, 4, 4.5, 4, 3.5, 4, 3.5, 4.5,
3.5, 4, 5, 2.5, 3.5, 4, 3.5, 4.5, 4.5, 4.5, 4.5, 4, 4.5,
3.5, 4.5, 4, 3.5, 3.5, 4.5, 3, 4, 4.5, 4, 4, 4.5, 4.5, 3.5,
4, 4, NA, 4.5, 4.5, 4, 3, 3.5, 3.5, 4, 3.5, 3.5, 3.5, 2.5,
3, 3.5, 3.5, 3, 5, NA, 3.5, 4, 3, 3.5, 4, 3, 2, 4.5, 3.5,
5, 4.5, 3.5, 4, 5, 5, 4, 4, 5, 4, 4, 4, 4, 5, 5, 2, 3, 2,
4, 5, 4, 4, 3, 3, 4, 5, 4, 3, 4, 4, 5, 4, 5, 4, NA, 5, 4,
4, 3, 3, 5, 4.5, 4, 4, 4, 4, 4, 4, 5, 4, 3.5, 4.5, 4, 4.5,
2.5, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4, 4.5, 3, 4, 4, 4, 4.5,
4.5, 4.5, 4.5, 4), priceQualityRate = c(4, 5, 4, 4, 4, 4,
4, 3, 3, 4, 4, 5, 3, 4, 5, 5, 4, 4, 4, 4, 4, 3, 4, 5, 5,
5, 5, 5, 4, 4, 3, 4, 5, 4, 1, 3, 4, 1, 2, 3, 2, 4, 1, 5,
3, 3, 4, 5, 5, 4, 4, 4.5, 3.5, 3.5, 3, 4, 4, 3.5, 4, 3, 4,
4, 4, 4.5, 4, 3.5, 4, 4, 4, 4, 4.5, 4.5, 3.5, 4.5, 2, 4.5,
4.5, 5, 4, 4.5, 3.5, 3, 4, 3.5, 4.5, 3.5, 4.5, 4.5, 5, 4.5,
5, 4.5, 5, 4.5, 4.5, 4.5, 3.5, 4, 4, 4.5, NA, 4.5, NA, NA,
NA, 4, 4.5, 4.5, 4.5, 3.5, 4, 4, NA, 4, 4, 3.5, 4, 4, 3.5,
NA, 4, 3, 3.5, 3, NA, 3.5, 3.5, 4.5, 4.5, 3.5, 3.5, NA, 4,
3.5, 4, 3.5, 4.5, 4, 4.5, 4, 4.5, 4.5, 4, 4, 3.5, 4.5, 3.5,
4.5, 4, 4, 4.5, 4.5, 4.5, 4.5, 4, 4, 3.5, 4, 4, 3, 4, 4,
4.5, 4, 4, 4.5, 3.5, 4, NA, 4, 4.5, 4, 4.5, 4.5, 4.5, 2,
3, 5, 5, 4.5, 4, 4, 2.5, 4, 4, 4.5, 4.5, 4, 3.5, 4.5, 4.5,
4.5, 4.5, 4, 4.5, 3.5, 5, 4.5, 2.5, 4.5, 4.5, 4, 3.5, 4,
4.5, 4.5, 4.5, 4, 0, 3, 3.5, 3.5, 4, 3.5, 4, 3.5, 3, 4, 3,
4, 4.5, 3.5, 3, 4, 4, 4, 3.5, 4, 3.5, 4, 4, NA, 4, 4, 4,
4, 4, 4.5, 3, 3.5, 3.5, 4.5, 4.5, 4.5, 4.5, 4, 4, 4.5, 4.5,
4, 3.5, 4, 3, 4.5, 4, 4, 4, 4.5, 4, 3.5, 4, 4.5, 4, 4, 4.5,
4, 4.5, 4, 4.5, 3.5, 4, 4, 4, 3.5, 4, 4.5, 4, NA, 4, 4, 4,
4.5, 4.5, 4, 4, 4.5, 3, 3.5, 4, 4.5, 4.5, 4, 4, 4, 4, 4,
3.5, 1.5, 4.5, 3.5, 4, 4, 4.5, 3, 3.5, 3, 4.5, 4.5, 4.5,
4.5, 4.5, 2.5, 4, 3.5, 4, 4, 4, 3.5, NA, 4, 4.5, NA, 4.5,
4, NA, 3, 4, NA, 4, 4.5, NA, 4, 4.5, 4.5, 4.5, 4.5, 3.5,
3, NA, 4.5, 4, 4, 4, 3.5, 3, 3.5, 3.5, 4, 4, 4, NA, 4, 3.5,
4, 4, 4, 3.5, 3.5, 4, 3.5, 3, 3, 3.5, 3.5, 4, 4, 3, 4, 4,
3.5, 4, 3.5, 3.5, 4, 4, 4, 4, 3, 3.5, 4, 4, 4.5, 4, 4, 4.5,
3.5, 4, 4, 4, 3.5, 4, 4.5, 2.5, 3, 4, 3, 4.5, 4.5, 4.5, 4,
3.5, 4, 4, 4, 4, 3.5, 4, 4.5, 3, 4, 4, 4, 3.5, 4, 4, 3.5,
3.5, 3.5, NA, 4.5, 4.5, 3.5, 3, 3.5, 3, 4, 3, 3.5, 3.5, 2.5,
3.5, 4, 4, 3.5, 5, NA, 5, 4, 2.5, 3.3, 5, 2.5, 1.5, 4.5,
4, 2, 4.5, 5, 4, 4, 3, 3, 4, 4, 5, 3, 5, 4.7, 4, 5, 2, 4,
3, 4, 5, 5, 5, 5, 4, 5, 5, 3, 4, 4, 3, 3, 5, 4, 5, NA, 3,
4, 3, 3, 4, 4.5, 4, 4, 3.5, 3.5, 4, 3.5, 3, 5, 3.5, 3.5,
4.5, 4, 4, 3, 4.5, 4, 4, 5, 4.5, 4.5, 4.5, 4.5, 4, 3, 4,
3.5, 4, 5, 4.5, 4, 4.5, 4.5), cleaning = c(5, 4, 4, 4, 4,
4, 4, 4, 4, 3, 4, 4, 3, 4, 5, 5, 5, 4, 4, 4, 4, 5, 3, 5,
4, 5, 3, 5, 5, 5, 4, 4, 5, 5, 5, 3, 4, 5, 3, 3, 1, 5, 3,
4, 4, 5, 4, 5, 5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4, 4,
4.5, 4, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 5,
4.5, 4, 4.5, 1.5, 4.5, 4.5, 5, 4, 4.5, 4, 3.5, 4, 3.5, 4.5,
3.5, 5, 4, 4, 5, NA, 4.5, 4.5, 5, 4.5, 4.5, 4.5, 4.5, 4,
5, NA, 4.5, NA, NA, NA, 4.5, 5, 4, 5, 4, 4.5, 4.5, 5, 4,
5, 4, 4.5, 4.5, 4, NA, 4.5, 3, 3.5, 3.5, NA, 3.5, 2.5, 4.5,
4.5, 3.5, 3.5, NA, 4, 4, 4.5, 4, 5, 4.5, 5, 4.5, 4.5, 4.5,
4, 4, 4, 4.5, 3.5, 4.5, 4, 4.5, 4.5, 5, 4.5, 4.5, 4, 4, 3.5,
4, 4.5, 3, 4, 4.5, 4.5, 4.5, 4, 4.5, 4, 4, NA, 4.5, 5, 5,
4.5, 5, 5, 3, 3.5, 5, 4.5, 4.5, 4.5, 4.5, 2.5, 4.5, 4.5,
4.5, 5, 5, 4, 4.5, 4.5, 4.5, 4.5, 4, 5, 4, 5, 4.5, 3, 5,
4.5, 4.5, 4.5, 4, 4, 4.5, 4.5, 4.5, 0, 3, 3.5, 4, 4.5, 4,
4.5, 4, 4, 4.5, 5, 4.5, 4.5, 4.5, 4, 4, 4, 4.5, 4.5, 4.5,
3.5, 4.5, 5, NA, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4, 4.5, 4.5,
4.5, 5, 4.5, 4.5, 4, 4.5, 4.5, 4.5, 4.5, 4, 4.5, 4, 5, 4.5,
4.5, 4, 4.5, 4.5, 4, 4.5, 4.5, 4.5, 4.5, 5, 4.5, 4.5, 4.5,
5, 3.5, 4.5, 4.5, 4.5, 3.5, 5, 5, 4, 5, 4.5, 4, 4.5, 5, 5,
4, 4.5, 4.5, 3, 3, 4.5, 5, 4.5, 4.5, 4.5, 4, 4, 4, 3.5, 3,
4.5, 4.5, 4, 4.5, 4.5, 3.5, 4, 3.5, 4.5, 5, 4.5, 4.5, 4.5,
3, 4.5, 4, 4, 4, 4, 4.5, NA, 4, 4.5, NA, 5, 4.5, NA, 4.5,
4, NA, 4.5, 4.5, NA, 4.5, 4, 5, 5, 5, 3, 4, NA, 5, 4.5, 4.5,
4.5, 4, 4, 4, 4, 4.5, 4.5, 4, NA, 4.5, 4, 4.5, 4.5, 4.5,
4, 3.5, 4.5, 3.5, 4, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 5,
4.5, 4.5, 4, 4, 4, 4.5, 4.5, 4.5, 4.5, 4, 4.5, 4.5, 5, 4,
4.5, 4, 4.5, 4, 4.5, 4, 3.5, 4.5, 4.5, 2.5, 3.5, 4.5, 4,
4.5, 4.5, 4.5, 4.5, 4.5, 4.5, 3.5, 4.5, 4.5, 4, 4, 5, 3.5,
4.5, 4.5, 4, 4, 4.5, 4.5, 4, 4.5, 4.5, NA, 4.5, 4.5, 4, 3.5,
4, 3.5, 4.5, 3.5, 4, 3.5, 3, 3.5, 3.5, 4, 3.5, 5, NA, 3,
5, 2, 4, 4.2, 3.5, 1, 5, 5, 3.5, 4.5, 3.5, 5, 5, 4, 5, 4,
3, 4, 4, 4, 5, 5, 5, 2, 5, 2, 5, 5, 4, 4, 4, 5, 4, 4, 5,
3, 5, 4, 4, 4, 3, 4, NA, 5, 5, 3, 4, 4, 5, 4.5, 4.5, 4.5,
4, 4.5, 4.5, 4, 5, 4, 4, 5, 4.5, 4.5, 3.5, 4.5, 4.5, 4.5,
4.5, 4.5, 4.5, 5, 4.5, 4.5, 2.5, 4, 4.5, 4.5, 4.5, 4, 4,
5, 4), bt1 = structure(c(7L, 3L, 1L, 2L, 3L, 9L, 3L, 4L,
4L, 4L, 9L, 1L, 10L, 9L, 10L, 9L, 6L, 1L, 9L, 4L, 4L, 4L,
3L, 9L, 3L, 3L, 7L, 3L, 7L, 4L, 3L, 4L, 3L, 3L, 8L, 4L, 4L,
3L, 3L, 3L, 3L, 8L, 4L, 2L, 10L, 9L, 9L, NA, NA, 9L, 7L,
6L, 6L, 9L, 6L, 10L, 2L, 7L, 10L, 6L, 9L, 9L, 6L, 3L, 6L,
3L, 8L, 3L, 2L, 9L, 8L, 2L, 3L, 2L, 5L, 7L, 6L, 1L, 3L, 2L,
6L, 3L, 7L, 3L, 8L, 9L, 9L, 8L, 7L, 7L, 3L, 4L, 4L, 4L, 9L,
4L, 4L, 4L, 4L, 4L, NA, 2L, NA, NA, NA, 4L, 10L, 4L, 4L,
4L, 4L, 4L, 9L, 3L, 7L, 8L, 8L, 4L, 9L, 9L, 3L, 9L, 8L, 3L,
2L, 2L, 2L, 3L, 1L, 3L, 1L, 3L, 8L, 1L, 9L, 1L, 3L, 6L, 8L,
3L, 4L, 7L, 7L, 6L, 3L, 2L, 3L, 2L, 9L, 9L, 3L, 1L, 1L, 9L,
1L, 3L, 3L, 3L, 2L, 6L, 9L, 4L, 2L, 9L, 9L, 1L, 9L, 3L, 9L,
3L, 6L, 6L, 3L, 3L, 9L, 3L, 3L, 7L, 3L, 6L, 10L, 6L, 9L,
10L, 9L, 1L, 4L, 3L, 3L, 3L, 10L, 9L, 9L, 4L, 3L, 9L, 1L,
1L, 1L, 1L, 6L, 9L, 4L, 4L, 4L, 9L, 4L, 9L, 4L, 9L, 4L, 9L,
3L, 3L, 6L, 9L, 4L, 6L, 4L, 9L, 10L, 4L, 5L, 9L, 4L, 4L,
9L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 9L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 9L, 4L, 3L, 3L,
2L, 4L, 3L, 3L, 4L, 6L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L,
9L, 9L, 8L, 1L, 3L, 7L, 4L, 4L, 4L, 6L, 9L, 4L, 3L, 9L, 3L,
3L, 3L, 3L, 9L, 3L, 8L, 11L, 11L, 9L, 3L, 4L, 2L, 3L, 9L,
3L, 8L, 9L, 10L, 3L, 3L, 6L, 9L, 7L, 4L, 3L, 3L, 4L, 8L,
10L, 3L, 2L, 2L, 10L, 8L, 9L, 3L, 7L, 6L, 3L, 4L, 10L, 10L,
9L, 7L, 2L, 8L, 2L, 9L, 8L, 8L, 8L, 10L, 9L, 4L, 1L, 1L,
8L, 4L, 6L, 2L, 10L, 2L, 4L, 2L, 3L, 2L, 10L, 4L, 9L, 1L,
2L, 9L, 4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 9L, 10L, 4L, 9L, 4L,
2L, 3L, 5L, 9L, 3L, 4L, 10L, 4L, 4L, 8L, 9L, 9L, 4L, 4L,
4L, 4L, 4L, 4L, 3L, 1L, 4L, 7L, 7L, 5L, 8L, 8L, 6L, 10L,
8L, 2L, 7L, 7L, 3L, NA, NA, NA, NA, 3L, 1L, 9L, 9L, 7L, 7L,
NA, 7L, 3L, 10L, 3L, 8L, 9L, 9L, 3L, 4L, 7L, 9L, 2L, 10L,
3L, 4L, 4L, 10L, 1L, 6L, 6L, 1L, 1L, 3L, 3L, 9L, 1L, 9L,
3L, 9L, 3L, 3L, 9L, 3L, 7L, 4L, 9L, 9L, 3L, 4L, 10L, 9L,
4L, 9L, 4L, 4L, 3L, 7L, 4L, 3L, 10L, NA, 3L, 10L, 2L, 4L,
6L, 9L, 9L, 9L, 3L, 3L, 6L, 8L, 10L, 9L, 6L, 10L, 9L, 3L,
9L, 6L, 4L, 10L, NA, 9L, 3L, 7L, 9L, 9L, 9L, 4L, 8L, 3L,
6L, 9L, 9L, 9L, 6L, 2L), .Label = c("breakfast", "cleaning",
"location", "overall", "price", "restaurant", "room", "services",
"staff", "structure", "WiFi"), class = "factor"), ratebt1 = c(-1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
-1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, NA,
NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, -1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, -1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA,
NA, 1L, 1L, 1L, 1L, 1L, 1L, NA, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, NA, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, NA, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), bt2 = structure(c(1L,
10L, 9L, 4L, 4L, 4L, 9L, 3L, 3L, 1L, 2L, 5L, 2L, 1L, 3L,
6L, 3L, 7L, 3L, 1L, 9L, 7L, 7L, 7L, 8L, 3L, 7L, 3L, 3L, 10L,
7L, 9L, 6L, 2L, 3L, 9L, 9L, 3L, 3L, 8L, 10L, 3L, 3L, 3L,
3L, 7L, 3L, NA, NA, 3L, 9L, 9L, 3L, 9L, 4L, 9L, 9L, 2L, 6L,
3L, 7L, 3L, 9L, 1L, 2L, 2L, 6L, 9L, 9L, 4L, 2L, 9L, 9L, 3L,
1L, 6L, 3L, 3L, 4L, 3L, 6L, 6L, 8L, 9L, 2L, 6L, 2L, 9L, 3L,
10L, 2L, 9L, 3L, 7L, 4L, 1L, 3L, 4L, 4L, 7L, NA, 9L, NA,
NA, NA, 4L, 5L, 3L, 4L, 3L, 4L, 4L, 6L, 9L, 7L, 6L, 9L, 3L,
9L, 9L, 9L, 2L, 8L, 10L, 3L, 3L, 3L, 4L, 9L, 1L, 8L, 2L,
8L, 2L, 3L, 3L, 6L, 3L, 9L, 7L, 4L, 1L, 8L, 2L, 7L, 6L, 2L,
3L, 7L, 1L, 6L, 10L, 9L, 2L, 3L, 2L, 9L, 9L, 9L, 9L, 1L,
9L, 1L, 9L, 6L, 3L, 3L, 2L, 6L, 8L, 2L, 10L, 9L, 2L, 4L,
3L, 9L, 10L, 9L, 4L, 2L, 3L, 8L, 2L, 9L, 2L, 9L, 9L, 9L,
6L, 1L, 1L, 10L, 4L, 9L, 3L, 9L, 9L, 10L, 9L, 8L, 8L, 4L,
4L, 3L, 9L, 4L, 9L, 7L, 6L, 9L, 3L, 9L, 6L, 8L, 7L, 4L, 3L,
4L, 4L, 3L, 4L, 9L, 9L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
10L, 4L, 4L, 4L, 4L, 4L, 4L, 10L, 4L, 4L, 4L, 3L, 3L, 4L,
4L, 4L, 10L, 6L, 3L, 4L, 4L, 3L, 9L, 3L, 3L, 4L, 4L, 5L,
9L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 9L, 10L, 1L, 7L,
3L, 6L, 7L, 9L, 3L, 9L, 3L, 9L, 9L, 9L, 10L, 7L, 8L, 4L,
7L, 10L, 8L, 3L, 5L, 9L, 5L, 9L, 3L, 3L, 9L, 7L, 2L, 9L,
2L, 7L, 3L, 11L, 5L, 2L, 3L, 3L, 4L, 2L, 9L, 4L, 3L, 8L,
3L, 1L, 10L, 9L, 10L, 2L, 7L, 3L, 7L, 2L, 9L, 6L, 9L, 4L,
8L, 9L, 1L, 9L, 9L, 6L, 9L, 8L, 9L, 9L, 9L, 2L, 9L, 3L, 8L,
3L, 8L, 9L, 4L, 10L, 3L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L,
9L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 9L, 6L, 4L, 4L,
10L, 4L, 4L, 4L, 4L, 6L, 2L, 10L, 1L, 3L, 3L, 3L, 5L, 1L,
4L, 4L, 2L, 4L, 4L, 4L, 4L, 4L, 10L, 10L, 6L, 4L, 4L, 4L,
6L, 3L, 2L, 9L, 9L, 9L, 3L, 4L, 9L, 9L, 4L, 9L, 2L, 9L, 7L,
NA, NA, NA, NA, 10L, 7L, 7L, 7L, 3L, 2L, NA, 7L, 2L, 9L,
1L, 8L, 9L, 3L, 6L, 4L, 1L, 3L, 3L, 7L, 7L, 7L, 4L, 3L, 3L,
9L, 3L, 9L, 9L, 9L, 9L, 7L, 7L, 3L, 3L, 3L, 2L, 9L, 3L, 2L,
3L, 4L, 9L, 6L, 9L, 9L, 4L, 7L, 4L, 6L, 9L, 2L, 6L, 8L, 2L,
3L, 2L, NA, 9L, 6L, 8L, 3L, 9L, 3L, 6L, 8L, 8L, 3L, 9L, 9L,
9L, 10L, 3L, 6L, 6L, 6L, 3L, 9L, 9L, 9L, NA, 10L, 1L, 6L,
3L, 3L, 6L, 9L, 8L, 7L, 2L, 6L, 2L, 6L, 9L, 9L), .Label = c("breakfast",
"cleaning", "location", "overall", "price", "restaurant",
"room", "services", "staff", "structure", "Wi-Fi"), class = "factor"),
ratebt2 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, NA, 1L, NA, NA, NA, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, NA, NA, NA, NA, -1L, 1L, 1L, 1L,
1L, -1L, NA, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, NA, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), bt3 = structure(c(8L, 11L,
5L, 6L, 4L, 8L, 10L, 10L, 7L, 7L, 8L, 9L, 8L, 4L, 4L, 7L,
9L, 3L, 1L, 9L, 8L, 1L, 1L, 4L, 10L, 11L, 1L, 6L, 9L, 9L,
4L, 3L, 7L, 9L, 7L, 3L, 10L, 9L, 2L, 9L, 9L, 4L, 10L, 10L,
8L, 3L, 4L, NA, NA, 10L, 2L, 2L, 7L, 7L, 3L, 11L, 6L, 3L,
8L, 3L, 8L, 2L, 8L, 9L, 9L, 9L, 8L, 9L, 8L, 3L, 1L, 3L, 6L,
9L, 9L, 3L, 7L, 9L, 2L, 6L, 9L, 2L, 9L, 7L, 9L, 3L, 4L, 4L,
10L, 9L, 5L, 8L, 8L, 4L, 6L, 3L, 2L, 7L, 4L, 4L, NA, 9L,
NA, NA, NA, 8L, 9L, 4L, 2L, 4L, 9L, 9L, 7L, 2L, 6L, 3L, 3L,
4L, 6L, 6L, 6L, 9L, 4L, 6L, 4L, 10L, 4L, 3L, 9L, 9L, 9L,
6L, 5L, 3L, 2L, 2L, 2L, 10L, 9L, 6L, 9L, 6L, 8L, 9L, 8L,
9L, 4L, 1L, 2L, 2L, 2L, 2L, 4L, 3L, 7L, 9L, 3L, 2L, 7L, 7L,
3L, 6L, 9L, 3L, 7L, 4L, 7L, 7L, 4L, 9L, 10L, 9L, 7L, 10L,
4L, 3L, 9L, 4L, 7L, 2L, 8L, 3L, 1L, 8L, 6L, 3L, 3L, 9L, 2L,
9L, 7L, 2L, 2L, 9L, 7L, 2L, 7L, 3L, 4L, 2L, 4L, 6L, 9L, 4L,
3L, 4L, 4L, 2L, 4L, 4L, 2L, 4L, 6L, 3L, 9L, 6L, 4L, 8L, 6L,
9L, 8L, 9L, 7L, 4L, 4L, 5L, 10L, 5L, 4L, 4L, 4L, 4L, 4L,
9L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 4L, 9L,
4L, 4L, 4L, 4L, 4L, 4L, 10L, 4L, 9L, 4L, 4L, 10L, 9L, 3L,
4L, 8L, 9L, 4L, 10L, 4L, 4L, 4L, 4L, 3L, 3L, 6L, 10L, 9L,
3L, 8L, 9L, 9L, 9L, 3L, 9L, 8L, 10L, 7L, 8L, 10L, 2L, 1L,
3L, 11L, 7L, 9L, 9L, 11L, 10L, 7L, 7L, 2L, 9L, 8L, 9L, 9L,
8L, 3L, 2L, 8L, 9L, 9L, 3L, 7L, 7L, 10L, 9L, 10L, 3L, 7L,
3L, 9L, 4L, 3L, 10L, 9L, 9L, 9L, 3L, 4L, 9L, 10L, 3L, 1L,
3L, 8L, 9L, 2L, 1L, 4L, 4L, 9L, 4L, 4L, 7L, 8L, 7L, 6L, 9L,
9L, 3L, 9L, 9L, 8L, 2L, 2L, 8L, 8L, 1L, 10L, 9L, 3L, 4L,
4L, 8L, 4L, 4L, 4L, 4L, 9L, 10L, 4L, 4L, 9L, 4L, 4L, 4L,
9L, 4L, 9L, 9L, 4L, 4L, 4L, 9L, 4L, 4L, 4L, 9L, 9L, 3L, 4L,
4L, 4L, 4L, 2L, 4L, 4L, 4L, 9L, 4L, 6L, 4L, 4L, 4L, 9L, 7L,
3L, 2L, 9L, 9L, 4L, 3L, 8L, 9L, 10L, 1L, 3L, 6L, 3L, NA,
NA, NA, NA, 4L, 9L, 2L, 6L, 3L, 1L, NA, 9L, 7L, 3L, 2L, 2L,
3L, 7L, 3L, 8L, 3L, 7L, 9L, 2L, 11L, 6L, 3L, 7L, 9L, 9L,
4L, 7L, 3L, 9L, 9L, 6L, 9L, 6L, 9L, 1L, 8L, 2L, 1L, 7L, 10L,
10L, 9L, 8L, 7L, 9L, 2L, 9L, 9L, 4L, 9L, 9L, 11L, 9L, 4L,
4L, 9L, NA, 6L, 8L, 9L, 8L, 6L, 10L, 2L, 8L, 6L, 4L, 3L,
6L, 2L, 9L, 9L, 8L, 4L, 3L, 3L, 8L, 3L, 3L, NA, 2L, 3L, 9L,
10L, 4L, 2L, 2L, 9L, 1L, 9L, 2L, 7L, 10L, 3L, 3L), .Label = c("breakfast",
"cleaning", "location", "overall", "price", "restaurant",
"room", "services", "staff", "structure", "Wi-Fi"), class = "factor"),
ratebt3 = c(1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, -1L, -1L, -1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L,
1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L,
-1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, NA, 1L, NA, NA, NA, 1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, -1L, 1L, 1L, 1L, -1L,
-1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, NA,
NA, NA, NA, -1L, 1L, 1L, 1L, 1L, -1L, NA, 1L, -1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
-1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, NA,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L), bt4 = structure(c(7L, 3L, 7L, 3L, 9L, 3L, 7L, 2L, 8L,
9L, 1L, 2L, 7L, 4L, 4L, 9L, 8L, 4L, 8L, 7L, 4L, 7L, 4L, 7L,
3L, 1L, 11L, 4L, 1L, 3L, 4L, 8L, 9L, 6L, 2L, 9L, 4L, 7L,
10L, 5L, 7L, 3L, 9L, 3L, 11L, 5L, 8L, NA, NA, 2L, 9L, 9L,
2L, 3L, 9L, 8L, 9L, 3L, 9L, 3L, 9L, 1L, 8L, 2L, 1L, 1L, 3L,
7L, 3L, 6L, 3L, 9L, 9L, 1L, 4L, 3L, 2L, 3L, 9L, 3L, 3L, 9L,
10L, 9L, 7L, 9L, 9L, 6L, 3L, 2L, NA, 9L, 10L, 2L, 6L, 10L,
4L, 6L, 8L, 2L, NA, 3L, NA, NA, NA, 8L, 9L, 9L, 7L, 4L, 8L,
9L, 9L, 3L, 3L, 2L, 3L, 2L, 3L, 6L, 5L, 9L, 4L, 9L, 3L, 3L,
9L, 3L, 2L, 9L, 9L, 3L, 3L, 9L, 1L, 7L, 7L, 10L, 10L, 9L,
2L, 6L, 10L, 3L, 9L, 7L, 3L, 9L, 1L, 3L, 1L, 3L, 3L, 4L,
2L, 6L, 6L, 7L, 3L, 9L, 2L, 9L, 10L, 2L, 10L, 9L, 3L, 6L,
6L, 2L, 9L, NA, 10L, 3L, 4L, 4L, 6L, 8L, 8L, 9L, 1L, 9L,
6L, 6L, 4L, 8L, 9L, 2L, 9L, 8L, 7L, 3L, 3L, 3L, 2L, 7L, 2L,
2L, 8L, 9L, 3L, 10L, 4L, 4L, 4L, 5L, 9L, 4L, 9L, 8L, 8L,
2L, 4L, 9L, 7L, 3L, 6L, 10L, 4L, 9L, 9L, 4L, 9L, 4L, 10L,
4L, 4L, 4L, 4L, 10L, 9L, 5L, 4L, 4L, 4L, 9L, 4L, 4L, 4L,
4L, 5L, 4L, 4L, 3L, 4L, 6L, 4L, 4L, 4L, 4L, 4L, 9L, 6L, 3L,
4L, 1L, 9L, 2L, 2L, 4L, 4L, 4L, 9L, 4L, 3L, 3L, 4L, 4L, 4L,
4L, 9L, 9L, 1L, 8L, 11L, 2L, 8L, 10L, 8L, 2L, 10L, 9L, 10L,
1L, 10L, 6L, 9L, 4L, 9L, 3L, 4L, 10L, 7L, 1L, 10L, 3L, 9L,
7L, 9L, 3L, 9L, 9L, 4L, 9L, 8L, 3L, 7L, 3L, 9L, 7L, 1L, 9L,
4L, 8L, 9L, 8L, 11L, 3L, 4L, 8L, 4L, 8L, 10L, 10L, 10L, 4L,
9L, 3L, 10L, 10L, 2L, 3L, 4L, 3L, 10L, 9L, 4L, 4L, 8L, 9L,
9L, 4L, 9L, 8L, 4L, 8L, 8L, 9L, 10L, 10L, 9L, 9L, 10L, 9L,
2L, 3L, 4L, 10L, 6L, 3L, 3L, 4L, 4L, 4L, 10L, 4L, 9L, 4L,
9L, 4L, 4L, 10L, 4L, 4L, 4L, 9L, 9L, 3L, 9L, 9L, 4L, 9L,
4L, 4L, 4L, 9L, 5L, 4L, 3L, 6L, 4L, 9L, 2L, 10L, 2L, 2L,
10L, 4L, 4L, 4L, 4L, 4L, 3L, 9L, 6L, 1L, 3L, 2L, 9L, 7L,
9L, 3L, 3L, 7L, 10L, 5L, 8L, 8L, NA, NA, NA, NA, 8L, 2L,
1L, 2L, 6L, 9L, NA, 1L, 8L, 4L, 9L, 2L, 7L, 3L, 4L, 4L, 11L,
8L, 6L, 9L, 2L, 9L, 6L, 10L, 7L, 4L, 4L, 4L, 2L, 1L, 1L,
6L, 9L, 9L, 9L, 7L, 7L, 1L, NA, 1L, 9L, 1L, 1L, 4L, 2L, 4L,
4L, 9L, 10L, 8L, 3L, 4L, 8L, 3L, 3L, 5L, 3L, NA, 8L, 2L,
3L, 9L, 4L, 6L, 4L, 8L, 10L, 4L, 4L, 3L, 6L, 4L, 8L, 3L,
4L, 2L, 3L, 9L, 3L, 2L, NA, 7L, 2L, 3L, 2L, 3L, 8L, 7L, 4L,
3L, 3L, 9L, 6L, 4L, 9L, 4L), .Label = c("breakfast", "cleaning",
"location", "overall", "price", "restaurant", "room", "services",
"staff", "structure", "Wi-Fi"), class = "factor"), ratebt4 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, -1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA,
1L, NA, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, NA,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, NA, NA, NA, NA, 1L, -1L, 1L, 1L, 1L, 1L, NA,
1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L,
1L, -1L, NA, -1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), bt5 = structure(c(6L, 7L, 10L, 4L, 4L, 8L,
9L, 10L, 1L, 9L, 4L, 8L, 6L, 3L, 3L, 11L, 8L, 8L, 4L, 11L,
4L, 3L, 7L, 5L, 8L, 8L, 10L, 8L, 5L, 8L, 1L, 4L, 4L, 8L,
10L, 10L, 3L, 4L, 4L, 4L, 9L, 5L, 3L, 10L, 10L, 7L, 11L,
NA, NA, 1L, 4L, 10L, 10L, 5L, 10L, 6L, 11L, 12L, 10L, 8L,
9L, 9L, 11L, 11L, 10L, 8L, 8L, 1L, 5L, 8L, 9L, 1L, 11L, 10L,
4L, 3L, 6L, 3L, 9L, 1L, 4L, 10L, 7L, 11L, 4L, 9L, 11L, 4L,
NA, 4L, NA, 9L, 5L, 5L, 5L, 5L, 5L, 4L, 7L, 5L, NA, 5L, NA,
NA, NA, 10L, 4L, 11L, 10L, 10L, 10L, 7L, 3L, 10L, 10L, 5L,
11L, 10L, 8L, 7L, 5L, 7L, 4L, 6L, NA, 5L, 6L, 4L, 11L, 3L,
7L, 5L, 9L, 8L, 8L, 11L, 10L, 9L, 3L, 7L, 1L, 3L, 1L, 10L,
7L, 8L, 10L, 10L, 4L, 4L, 10L, 5L, 3L, 10L, 10L, 9L, 3L,
1L, 7L, 8L, 8L, 3L, 8L, 1L, 9L, 7L, 8L, 10L, 3L, 7L, NA,
NA, 9L, 11L, 5L, NA, 9L, 8L, 9L, 6L, 10L, 3L, 5L, 9L, 3L,
10L, 3L, 7L, 11L, 10L, 11L, 10L, 5L, 4L, 5L, 10L, 10L, 10L,
8L, 4L, 10L, 4L, 5L, 5L, 10L, 10L, 5L, 5L, 5L, 9L, 4L, 7L,
4L, 4L, 9L, 3L, 9L, 8L, 4L, 10L, 10L, 5L, 4L, 5L, 9L, 5L,
5L, 5L, 5L, 10L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
4L, 5L, 5L, 11L, 10L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L, 10L,
10L, 5L, 5L, 10L, 1L, 5L, 5L, 5L, 11L, 3L, 5L, 3L, 4L, 5L,
3L, 5L, 5L, 4L, 8L, 10L, 3L, 11L, 10L, 11L, 8L, 5L, 9L, 9L,
5L, 8L, 8L, 10L, 10L, 11L, 10L, 8L, 1L, 9L, 11L, 4L, 8L,
11L, 8L, 8L, 3L, 1L, 4L, 7L, 9L, 8L, 7L, 3L, 10L, 7L, 1L,
8L, 7L, 10L, 10L, 11L, 9L, 5L, 10L, 2L, 7L, 4L, 7L, 8L, 10L,
8L, 10L, 10L, 4L, 11L, 11L, 10L, 10L, 4L, 11L, 4L, 10L, 5L,
10L, 4L, 11L, 5L, 11L, 10L, 11L, 9L, 10L, 1L, 4L, 11L, 3L,
3L, 4L, 9L, 4L, 10L, 9L, 4L, 9L, 10L, 4L, 3L, 9L, 6L, 6L,
5L, 5L, 5L, 5L, 4L, 10L, 1L, 10L, 5L, 5L, 5L, 9L, 5L, 3L,
11L, 5L, 11L, 5L, 10L, 5L, 3L, 5L, 5L, 5L, 5L, 3L, 10L, 3L,
10L, 7L, 10L, 5L, 5L, 9L, 5L, 11L, 5L, 5L, 5L, 10L, 5L, 9L,
10L, 1L, 1L, 3L, 4L, 11L, 5L, 5L, 10L, 10L, 4L, 7L, 10L,
NA, NA, NA, NA, 5L, 4L, 4L, 9L, 3L, 4L, NA, 7L, 9L, 3L, 10L,
4L, 3L, 11L, 7L, 1L, 10L, 1L, 10L, 9L, 9L, 10L, 10L, 10L,
5L, 5L, 10L, 10L, 5L, 7L, 10L, 4L, 10L, 11L, 5L, 10L, 4L,
4L, NA, 10L, 6L, 4L, 10L, 11L, 6L, 8L, 8L, 5L, 7L, 1L, 8L,
6L, 3L, 8L, 10L, 10L, 4L, NA, 11L, 1L, NA, 11L, 5L, 8L, 4L,
3L, 8L, 3L, 5L, 5L, 4L, 9L, 4L, 10L, 4L, 8L, 5L, 8L, 7L,
12L, NA, 10L, 10L, 3L, 8L, 1L, 4L, 10L, 7L, 7L, 10L, 5L,
5L, 8L, 11L, 9L), .Label = c("breakfast", "camere", "cleaning",
"location", "overall", "price", "restaurant", "room", "services",
"staff", "structure", "Wi-Fi"), class = "factor"), ratebt5 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, NA, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L,
NA, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, NA, 1L, -1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, -1L,
1L, 1L, NA, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, -1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, -1L, 1L, 1L,
1L, 1L, 1L, NA, 1L, 1L, -1L, 1L, -1L, -1L, 1L, -1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, NA, 1L, 1L,
NA, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, -1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), bt6 = structure(c(8L, 7L,
9L, 9L, 1L, 5L, 1L, 4L, 9L, 7L, 4L, 8L, 3L, 3L, 1L, 4L, 8L,
4L, 6L, 8L, 2L, 3L, 11L, 4L, 1L, 5L, 10L, 9L, 8L, 4L, 3L,
1L, 3L, 8L, 3L, 9L, 3L, 2L, 9L, 10L, 2L, 9L, 3L, 4L, 7L,
7L, 7L, NA, NA, 1L, 6L, 3L, 8L, 2L, 4L, 2L, 9L, 6L, 3L, 9L,
6L, 9L, 2L, 9L, 3L, 4L, 2L, 2L, 9L, 9L, 9L, 4L, 7L, 9L, 9L,
9L, 9L, 9L, 4L, 9L, 10L, 4L, 3L, 3L, 9L, 3L, 9L, 2L, NA,
4L, NA, 10L, 9L, 1L, 3L, 9L, 9L, 9L, 3L, 1L, NA, 1L, NA,
NA, NA, 10L, 9L, 4L, 9L, 2L, 2L, 3L, 5L, 5L, 9L, 9L, 2L,
10L, 9L, 9L, 10L, 9L, 3L, 9L, NA, 3L, 10L, 5L, 9L, 9L, 9L,
9L, 4L, 3L, 4L, 9L, 1L, 4L, 7L, 8L, 4L, 5L, 6L, 9L, 9L, 8L,
1L, 5L, 10L, 4L, 4L, 9L, 4L, 3L, 7L, 3L, 8L, 4L, 1L, 3L,
8L, 7L, 3L, 1L, 3L, 3L, 1L, 11L, 7L, 7L, NA, NA, 10L, 9L,
3L, NA, 7L, 4L, 7L, 4L, 3L, 8L, 9L, 3L, 1L, 9L, 1L, 4L, 11L,
9L, 4L, 7L, 7L, 8L, 3L, 9L, 9L, 4L, 3L, 10L, 3L, 9L, 4L,
8L, 10L, 9L, 4L, 9L, 8L, 4L, 4L, 3L, 7L, 4L, NA, 4L, 8L,
9L, 3L, 9L, 4L, 4L, 3L, 9L, 9L, 3L, 4L, 2L, 4L, 9L, 4L, 6L,
4L, 4L, 9L, 4L, 9L, 4L, 4L, 4L, 4L, 4L, 9L, 4L, 4L, 4L, 4L,
4L, 2L, 4L, 9L, 9L, 6L, 2L, 6L, 4L, 4L, 4L, 4L, 2L, 6L, 2L,
4L, 5L, 4L, 10L, 4L, 4L, 10L, 4L, 9L, 8L, 7L, 8L, 6L, 8L,
10L, 9L, 4L, 7L, 7L, 7L, 2L, 2L, 3L, 2L, 1L, 9L, 8L, 10L,
7L, 8L, 1L, 11L, 3L, 3L, 10L, 9L, 7L, 7L, 1L, 4L, 1L, 1L,
9L, 7L, 9L, 6L, 3L, 3L, 8L, 3L, 8L, 11L, 3L, 8L, 10L, 10L,
4L, 10L, 9L, 7L, 4L, 9L, 2L, 3L, 10L, 4L, 10L, 10L, 4L, 4L,
9L, 10L, 4L, 3L, 3L, 2L, 3L, 3L, 4L, 9L, 4L, 3L, 9L, 4L,
3L, 9L, 8L, 8L, 1L, 3L, 9L, 4L, 9L, 2L, 8L, 9L, 10L, 8L,
4L, 4L, 3L, 9L, 4L, 4L, 4L, 4L, 4L, 3L, 5L, 4L, 4L, 4L, 4L,
9L, 4L, 2L, 4L, 3L, 4L, 3L, 4L, 4L, 4L, 9L, 4L, NA, 4L, 3L,
4L, 5L, NA, 4L, 4L, 4L, 7L, 4L, 10L, 4L, 4L, 4L, 10L, 10L,
8L, 9L, 8L, 6L, 9L, 9L, 7L, 7L, 2L, 1L, 8L, 8L, 3L, 8L, NA,
NA, NA, NA, 1L, 10L, 3L, 3L, 9L, 3L, NA, 7L, 7L, 3L, 8L,
7L, 1L, 2L, 9L, 9L, 4L, 8L, 7L, 1L, 6L, 8L, 9L, 3L, 4L, 3L,
6L, 2L, 11L, 9L, 4L, 9L, 3L, 3L, 10L, 2L, 9L, 5L, NA, 7L,
4L, 2L, 8L, 8L, 4L, 3L, 9L, 3L, 3L, 7L, 6L, 7L, 7L, 11L,
4L, 10L, 1L, NA, 7L, 3L, NA, 1L, 7L, 11L, 7L, 4L, 8L, 9L,
2L, 9L, 8L, 1L, 9L, 2L, 7L, 9L, 6L, 9L, 3L, 4L, NA, 3L, 4L,
4L, 3L, 8L, 9L, 9L, 3L, 5L, 4L, 4L, 8L, 5L, 7L, 7L), .Label = c("breakfast",
"cleaning", "location", "overall", "price", "restaurant",
"room", "services", "staff", "structure", "Wi-Fi"), class = "factor"),
ratebt6 = c(1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, -1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, 1L, -1L, 1L, -1L, -1L,
1L, -1L, 1L, -1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, -1L, -1L,
-1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, NA, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, NA, 1L, NA, NA, NA, 1L, 1L, -1L, 1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L,
1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
-1L, -1L, 1L, 1L, NA, NA, -1L, 1L, 1L, NA, -1L, 1L, -1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, -1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, -1L, 1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L,
1L, NA, 1L, 1L, -1L, -1L, 1L, 1L, 1L, -1L, 1L, -1L, -1L,
1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
NA, NA, NA, NA, -1L, -1L, 1L, 1L, 1L, 1L, NA, -1L, -1L, 1L,
1L, -1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
NA, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, -1L, 1L, 1L, 1L, NA, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L,
1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), bt7 = structure(c(7L, 7L, 8L, 9L, 2L, 1L, 10L, 7L, 7L,
5L, 4L, 3L, 7L, 4L, 9L, 9L, 10L, 3L, 3L, 10L, 10L, 4L, 7L,
5L, 8L, 7L, 5L, 8L, 8L, 4L, 6L, 2L, 5L, 4L, 6L, 3L, 4L, 3L,
10L, 10L, 4L, 8L, 3L, 7L, 3L, 7L, 3L, NA, NA, 9L, 8L, 7L,
9L, 6L, 7L, 1L, 7L, 8L, 4L, 8L, 3L, 9L, 3L, 9L, 4L, 9L, 9L,
8L, 6L, 8L, 8L, 9L, 2L, 4L, 7L, 9L, 9L, 8L, 3L, 9L, 2L, 4L,
6L, 2L, 10L, 2L, 3L, 10L, NA, 8L, NA, 8L, 2L, 5L, 5L, 4L,
6L, 4L, 8L, 5L, NA, 2L, NA, NA, NA, 7L, 3L, 3L, 5L, 3L, 6L,
4L, 10L, 1L, 8L, 7L, 7L, 7L, 4L, 8L, 4L, 3L, 8L, 9L, NA,
9L, NA, 9L, NA, 5L, 10L, NA, 6L, 3L, 7L, 3L, 4L, 4L, 7L,
7L, 7L, NA, 9L, 10L, 9L, 9L, 9L, 9L, 7L, 10L, 9L, 3L, 9L,
1L, 9L, 9L, 1L, 8L, 9L, 6L, 7L, 3L, 3L, 9L, 2L, 8L, 7L, 10L,
4L, 9L, NA, NA, NA, 4L, 3L, NA, 10L, 4L, 8L, 7L, 9L, 4L,
2L, 9L, 8L, 7L, 7L, 1L, 7L, 7L, 8L, 9L, 9L, 9L, 10L, 1L,
3L, 10L, 7L, 4L, 9L, 1L, 9L, 6L, 4L, 9L, 4L, 4L, 9L, 8L,
6L, 4L, 7L, 4L, 2L, NA, 2L, 2L, 6L, 7L, 9L, 9L, 3L, 10L,
4L, 4L, 4L, 9L, 9L, 4L, 4L, 4L, 4L, 3L, 4L, 10L, 4L, 4L,
9L, 6L, 4L, 4L, 9L, 9L, 4L, 9L, 4L, 9L, 4L, 8L, 4L, 4L, 9L,
4L, 2L, 4L, 6L, 6L, 4L, 9L, 2L, 4L, 4L, 7L, 10L, 4L, 4L,
4L, 4L, 4L, 10L, 8L, 3L, 7L, 3L, 1L, 2L, 6L, 4L, 7L, 2L,
10L, 1L, 4L, 6L, 1L, 5L, 7L, 8L, 8L, 10L, 8L, 9L, 7L, 3L,
9L, 1L, 4L, 9L, 3L, 8L, 11L, 9L, 3L, 5L, 7L, 9L, 8L, 3L,
3L, 7L, 7L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 3L, 4L, 9L, 3L, 4L, 9L, 9L, 5L,
4L, 4L, 4L, 2L, 9L, 4L, 4L, 4L, 9L, 4L, 4L, 9L, 4L, 3L, 4L,
2L, 4L, 9L, NA, 4L, 4L, 4L, 4L, NA, 4L, 4L, 4L, 8L, 9L, 8L,
4L, 9L, 4L, 4L, 4L, 3L, 7L, 7L, 9L, 1L, 9L, 4L, 4L, 10L,
8L, 10L, 8L, 3L, 1L, NA, NA, NA, NA, 2L, 7L, 3L, 8L, 10L,
7L, NA, 5L, 8L, 5L, 10L, 7L, 10L, 6L, 7L, 8L, 4L, 6L, 10L,
9L, 4L, 3L, 4L, 9L, 4L, 4L, 4L, 9L, 4L, 9L, 10L, 9L, 4L,
9L, 9L, 10L, 10L, 7L, NA, 10L, 5L, 4L, 2L, 1L, 5L, 10L, 4L,
8L, 1L, 7L, 8L, 1L, 1L, 9L, 3L, 8L, 3L, NA, 9L, 8L, NA, 8L,
2L, 8L, 10L, 6L, 4L, 4L, 8L, 2L, 10L, 3L, 4L, 7L, 4L, 10L,
4L, 3L, 2L, 3L, NA, 9L, 3L, 8L, 1L, 6L, 7L, 9L, 5L, 6L, 3L,
7L, 6L, 2L, 9L, 8L), .Label = c("breakfast", "cleaning",
"location", "overall", "price", "restaurant", "room", "services",
"staff", "structure", "Wi-Fi"), class = "factor"), ratebt7 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L,
-1L, -1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, -1L, -1L,
1L, -1L, 1L, 1L, NA, 1L, NA, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, NA, 1L, NA, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, -1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, NA, 1L, NA,
1L, NA, -1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
-1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L,
NA, NA, NA, 1L, 1L, NA, -1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L, 1L,
1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L,
-1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, NA, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, NA, NA, NA, NA, -1L, 1L, 1L, 1L, 1L, -1L, NA, -1L,
1L, 1L, 1L, -1L, 1L, -1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, NA, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, -1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, NA, 1L, 1L, NA, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), bt8 = structure(c(10L, 8L, 4L, 3L, 10L, 9L, 1L,
7L, 2L, 10L, 7L, 10L, 10L, 4L, 7L, 4L, 7L, 9L, 9L, 3L, 4L,
9L, 4L, 8L, 9L, 4L, 3L, 8L, 5L, 7L, 9L, 7L, 3L, 4L, 7L, 7L,
3L, 10L, 3L, 4L, 7L, 3L, 7L, 5L, 4L, 6L, 5L, NA, NA, 5L,
8L, 6L, 8L, 5L, 9L, 3L, 11L, 9L, 8L, 9L, 10L, 9L, 9L, 9L,
9L, 8L, 9L, 10L, 11L, 1L, 10L, 4L, 1L, 4L, 7L, 9L, 4L, 8L,
9L, 7L, 4L, 10L, 5L, 4L, 6L, 4L, 4L, 9L, NA, 10L, NA, 9L,
9L, 9L, 9L, 4L, 10L, 9L, 4L, 9L, NA, 6L, NA, NA, NA, 1L,
4L, 6L, 1L, 1L, 7L, 3L, 8L, 7L, NA, 9L, 9L, 4L, 2L, 9L, NA,
NA, 6L, 8L, NA, 10L, NA, 2L, NA, 7L, NA, NA, NA, 9L, 7L,
10L, 10L, 9L, 8L, 3L, 9L, NA, 3L, 8L, 2L, 3L, 1L, 7L, 3L,
7L, 9L, 7L, 3L, 7L, 7L, 7L, 5L, 1L, 7L, 4L, 7L, 10L, 7L,
10L, 8L, 7L, 10L, 1L, 3L, 4L, NA, NA, NA, 7L, 3L, NA, 7L,
NA, NA, 7L, 10L, 1L, 3L, 10L, 3L, 10L, 8L, 9L, 6L, 8L, 4L,
9L, 4L, 6L, 9L, 3L, 10L, 7L, 10L, 4L, 7L, 2L, 9L, 6L, 4L,
4L, 6L, 4L, 6L, 3L, 7L, 4L, 2L, 10L, 10L, 8L, 4L, 6L, 9L,
2L, 7L, 9L, 1L, 2L, 6L, 9L, 4L, 6L, 4L, 3L, 9L, 4L, 4L, 4L,
4L, 4L, 4L, 3L, 8L, 10L, 4L, 4L, 4L, 4L, 4L, 9L, 6L, 4L,
4L, 4L, 4L, 2L, 3L, 9L, 4L, 4L, 10L, 9L, 9L, 6L, 7L, 4L,
4L, NA, 6L, 4L, 4L, 6L, 4L, 4L, 10L, 3L, 3L, 3L, 3L, 3L,
6L, 3L, 4L, 4L, 9L, 2L, 6L, 11L, 8L, 5L, 3L, 8L, 10L, 5L,
4L, 7L, 5L, 8L, 6L, 8L, 8L, 1L, 3L, 4L, 10L, 7L, 3L, 3L,
4L, 9L, 3L, 9L, 3L, 3L, 10L, 7L, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3L, 4L,
4L, 4L, 3L, 4L, 2L, 3L, 5L, 9L, 10L, 9L, 2L, 4L, 4L, 6L,
4L, 4L, 4L, 4L, 4L, 9L, 4L, 4L, 4L, 4L, NA, 4L, 9L, 9L, 4L,
NA, NA, 4L, NA, 4L, 4L, 4L, 4L, 4L, 4L, 9L, 3L, 9L, 7L, 10L,
10L, 3L, 9L, 4L, 7L, 2L, 3L, 2L, 7L, 2L, 9L, NA, NA, NA,
NA, 7L, 7L, 10L, 3L, 10L, 9L, NA, 9L, 10L, 8L, 8L, 3L, 10L,
9L, 10L, 9L, 1L, 10L, 11L, 3L, 9L, 4L, 4L, 9L, 10L, 10L,
2L, 9L, 4L, 4L, 11L, 9L, 9L, 9L, 1L, 7L, 1L, 9L, NA, 4L,
4L, 10L, 10L, 9L, 9L, 9L, 4L, 4L, 4L, 10L, 4L, 8L, 9L, 1L,
8L, 2L, 5L, NA, 11L, 9L, NA, 4L, 4L, 8L, 4L, 8L, 3L, 9L,
7L, 4L, 8L, 9L, 3L, 4L, 3L, 8L, 4L, 7L, 8L, 7L, NA, 7L, 2L,
4L, 7L, 8L, 8L, 7L, 9L, 2L, 7L, 3L, 5L, 9L, 3L, 10L), .Label = c("breakfast",
"cleaning", "location", "overall", "price", "restaurant",
"room", "services", "staff", "structure", "Wi-FI"), class = "factor"),
ratebt8 = c(-1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, -1L, 1L,
-1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, 1L, 1L, 1L, -1L,
-1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, NA, -1L, NA, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, NA, 1L, NA, NA, NA, 1L, 1L, -1L, 1L,
-1L, 1L, 1L, -1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, NA, NA, 1L,
1L, NA, 1L, NA, 1L, NA, -1L, NA, NA, NA, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L,
-1L, -1L, 1L, 1L, NA, NA, NA, 1L, 1L, NA, -1L, NA, NA, 1L,
1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L,
-1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, NA,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, NA, NA, NA, NA,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, -1L, -1L, -1L,
1L, 1L, 1L, NA, 1L, -1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, NA, 1L, -1L, -1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, -1L,
1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, -1L, 1L, 1L, 1L, NA, 1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), bt9 = structure(c(3L,
9L, 3L, 9L, 9L, 3L, 3L, 10L, 7L, 3L, 4L, 7L, 2L, 3L, 7L,
3L, 2L, 10L, 7L, 6L, 3L, 7L, 5L, 10L, 7L, 10L, 8L, 3L, 8L,
6L, 10L, 11L, 8L, 3L, 4L, 8L, 5L, 7L, 3L, 10L, 7L, 2L, 3L,
9L, 8L, 8L, 3L, NA, NA, 2L, 4L, 11L, 9L, 11L, 2L, 4L, 10L,
6L, 1L, 1L, 5L, 10L, 8L, 8L, 7L, 5L, 9L, 10L, 1L, 4L, 7L,
8L, 10L, 5L, 9L, 4L, 3L, 4L, 9L, 10L, 8L, 5L, 9L, 3L, 3L,
8L, 1L, 9L, NA, 5L, NA, 2L, 9L, 9L, 4L, 2L, 3L, 7L, 4L, 9L,
NA, 4L, NA, NA, NA, 9L, 2L, 4L, 4L, 7L, 3L, 4L, NA, NA, NA,
NA, NA, NA, 9L, NA, NA, NA, NA, NA, NA, NA, NA, 10L, NA,
NA, NA, NA, NA, 8L, 4L, 9L, 5L, 7L, 4L, 11L, 11L, NA, 4L,
1L, 7L, 3L, 5L, NA, 4L, 9L, 6L, 9L, 10L, 9L, 8L, 4L, 2L,
7L, 7L, 3L, 7L, 1L, 3L, 7L, 7L, 10L, 10L, 6L, 1L, 1L, NA,
NA, NA, 4L, 4L, NA, 3L, NA, NA, 1L, 6L, 10L, 3L, 3L, 4L,
4L, 9L, 4L, 8L, 11L, 2L, 6L, 9L, 3L, 7L, 9L, 9L, 9L, 10L,
10L, 1L, 9L, 2L, 6L, 9L, 6L, 4L, 2L, 9L, 7L, 6L, 8L, 10L,
3L, 3L, 8L, 10L, 1L, 8L, 8L, 8L, 2L, 10L, 4L, 3L, 4L, 4L,
10L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 4L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 9L, 4L, 3L, 4L, 5L, 1L, 7L, 4L, 4L, 2L,
4L, 4L, 5L, 8L, 4L, 3L, 4L, NA, 2L, 9L, 6L, 4L, 10L, 4L,
7L, 8L, 3L, 6L, 1L, 4L, 9L, 3L, 3L, 8L, 9L, 11L, 6L, 7L,
7L, 3L, 10L, 6L, 8L, 9L, 9L, 10L, 3L, 10L, 1L, 7L, 5L, 10L,
1L, 4L, 7L, 6L, 4L, 7L, 11L, 9L, 8L, 9L, 3L, 1L, 9L, 4L,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 9L, 3L, 5L, 6L, 4L, 6L, 3L, 8L, 9L, 2L, 10L,
9L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 9L, 2L, 4L, 4L, NA, NA,
NA, 9L, 9L, 5L, 4L, NA, NA, 7L, NA, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 8L, 2L, 6L, 11L, 8L, 6L, 3L, 4L, 8L, 7L, 3L, 3L, 1L,
10L, 6L, NA, NA, NA, NA, 7L, 3L, 6L, 8L, 7L, 8L, NA, 4L,
8L, 8L, 3L, 9L, 10L, 6L, 9L, 3L, 9L, 11L, 7L, 6L, 10L, 3L,
4L, 1L, 3L, 10L, 9L, 3L, 9L, 9L, 10L, 9L, 4L, 4L, 9L, 9L,
6L, 9L, NA, 7L, 7L, 9L, 9L, 3L, 9L, 4L, 3L, 4L, 10L, 2L,
7L, 8L, 7L, 7L, 8L, 9L, 10L, NA, 2L, 9L, NA, 8L, 11L, 3L,
8L, 8L, 5L, 10L, 3L, 10L, 8L, 2L, 2L, 4L, 8L, 5L, 2L, 3L,
2L, 9L, NA, 10L, 6L, 5L, 4L, 3L, 9L, 6L, 5L, 9L, 10L, 9L,
NA, 9L, 9L, 8L), .Label = c("breakfast", "cleaning", "location",
"overall", "price", "restaurant", "room", "services", "staff",
"structure", "Wi-Fi"), class = "factor"), ratebt9 = c(1,
1, 1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1,
1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1, 1, 1,
1, 1, 1, -1, 1, 1, -1, -1, 1, 1, NA, NA, -1, 1, -1, 1, -1,
1, 1, 1, 1, -1, 1, 1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1,
-1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, NA, 1,
NA, -1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, NA, NA, NA, 1, -1,
-1, 1, -1, 1, 1, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA,
NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1,
1, -1, NA, 1, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, -1,
1, 1, 1, -1, 1, 1, -1, 1, -1, -1, 1, 1, 1, NA, NA, NA, 1,
-1, NA, 1, NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1, -1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, NA, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, -1,
1, -1, -1, 1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, 1, 1, -1,
1, -1, -1, 1, 1, 1, 1, 1, 1, 1, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1,
1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, -1, NA, NA, NA, 1, 1, 1, 1, NA, NA, 1, NA, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA,
NA, NA, NA, 1, 1, 1, 1, 1, 1, NA, -1, 1, 1, 1, 1, 1, 1, 1,
1, 1, -1, 1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, NA, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1,
-1, 1, 1, 1, 1, 1, NA, 1, 1, NA, 1, -1, 1, 1, 1, -1, 1, 1,
1, 1, -1, 0.1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 1, -1, 1, 1, 1,
1, -1, 1, 1, 1, NA, 1, 1, -1), bt10 = structure(c(11L, 8L,
8L, 9L, 6L, 4L, 4L, 9L, 7L, 7L, 4L, 3L, 7L, 9L, 8L, 8L, 8L,
10L, 3L, 8L, 8L, 3L, 10L, 8L, 5L, 9L, 11L, 10L, 4L, 2L, 7L,
4L, 10L, 8L, 10L, 4L, 9L, 8L, 6L, 8L, 10L, 7L, 6L, 8L, 5L,
3L, 11L, NA, NA, 7L, 11L, 8L, 3L, 10L, 8L, 8L, 9L, 4L, 7L,
7L, 2L, 4L, 3L, 5L, 5L, 4L, 10L, 4L, 9L, 11L, 4L, 6L, 4L,
10L, NA, 4L, 4L, 5L, 5L, 9L, 9L, 10L, 9L, 10L, 4L, 10L, 9L,
5L, NA, 9L, NA, 4L, 5L, 9L, 9L, 4L, 7L, 3L, 4L, 9L, NA, 8L,
NA, NA, NA, 8L, 11L, 4L, 4L, 6L, 8L, 8L, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 8L, NA, NA, NA,
NA, NA, 7L, 9L, 4L, 7L, 4L, 4L, 4L, 7L, NA, 1L, 10L, 4L,
3L, 9L, NA, 7L, 5L, 7L, 9L, 3L, 4L, 8L, 3L, 1L, 10L, 7L,
1L, 9L, 1L, 7L, 7L, 4L, 7L, 7L, NA, 9L, 4L, NA, NA, NA, 9L,
10L, NA, 1L, NA, NA, 3L, 5L, 3L, 9L, 1L, 3L, 4L, 6L, 8L,
10L, 8L, 9L, 4L, 1L, 1L, 9L, 10L, 7L, 9L, 7L, 7L, 9L, 7L,
10L, 4L, 4L, 4L, 4L, 7L, 4L, 4L, 3L, 8L, 7L, 8L, 3L, 3L,
8L, 4L, 9L, 9L, 2L, 10L, 10L, 2L, 5L, 2L, 9L, 4L, 4L, 4L,
3L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 9L, 4L, 9L, 4L, 4L, 2L,
4L, 4L, 4L, 4L, 3L, 4L, 8L, 9L, 5L, 4L, 5L, 5L, 3L, 4L, 3L,
3L, 4L, 2L, NA, 4L, 5L, 9L, 5L, 4L, 9L, 8L, 1L, 5L, 2L, 8L,
9L, 4L, 4L, 9L, 8L, 3L, 5L, 10L, 4L, 3L, 3L, 11L, 1L, 3L,
10L, 7L, 3L, 6L, 3L, 10L, 1L, 10L, 3L, 4L, 9L, 3L, 8L, 3L,
3L, 1L, 4L, 8L, 3L, 10L, 8L, 9L, 10L, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 6L,
4L, 2L, 4L, 9L, 4L, 4L, 9L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, NA, NA, NA, NA, 3L, 4L, NA, 4L,
NA, NA, NA, NA, 4L, 9L, 10L, 4L, 3L, 3L, 4L, 9L, 7L, 6L,
5L, 3L, 10L, 3L, 4L, 2L, 6L, 3L, 3L, 7L, 1L, 6L, NA, NA,
NA, NA, 8L, 8L, 3L, 3L, 5L, 8L, NA, 4L, 7L, 9L, 7L, 7L, 4L,
10L, NA, 10L, 4L, 7L, 7L, 3L, 4L, 8L, 8L, 10L, 4L, 6L, 4L,
8L, 7L, 8L, 9L, 4L, 4L, 4L, 9L, 8L, 8L, 11L, NA, 6L, 7L,
9L, 9L, 6L, 8L, 10L, 4L, 7L, 2L, 4L, 8L, 3L, 6L, 7L, 5L,
3L, 4L, NA, 8L, 11L, NA, 11L, 3L, 4L, 8L, 4L, 2L, 8L, 6L,
8L, 8L, 7L, 9L, 4L, 8L, 7L, 5L, 11L, 3L, 1L, NA, 3L, 5L,
6L, 3L, 2L, 3L, 5L, 3L, 4L, 3L, 9L, 3L, 4L, 5L, 5L), .Label = c("breakfast",
"cleaning", "location", "overall", "price", "restaurant",
"room", "services", "staff", "structure", "Wi-Fi"), class = "factor"),
ratebt10 = c(1L, -1L, -1L, 1L, -1L, 1L, 1L, -1L, -1L, -1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, -1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L,
-1L, -1L, -1L, 1L, 1L, -1L, 1L, 1L, -1L, NA, NA, -1L, -1L,
1L, 1L, -1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, NA, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, NA, 1L, 1L, 1L,
1L, 1L, -1L, 1L, -1L, 1L, NA, -1L, NA, NA, NA, 1L, -1L, -1L,
1L, 1L, 1L, -1L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 1L, NA, NA, NA, NA, NA, 1L, 1L, 1L, -1L,
1L, 1L, -1L, -1L, NA, -1L, -1L, -1L, 1L, 1L, NA, -1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, -1L, -1L,
-1L, -1L, -1L, NA, 1L, 1L, NA, NA, NA, 1L, 1L, NA, 1L, NA,
NA, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L,
1L, -1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
-1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L,
1L, 1L, -1L, -1L, -1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, -1L, -1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA, 1L, 1L, NA, 1L, NA, NA,
NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, NA,
1L, 1L, 1L, 1L, 1L, 1L, NA, -1L, 1L, 1L, 1L, 1L, 1L, -1L,
NA, 1L, 1L, 1L, -1L, 1L, 1L, 1L, -1L, -1L, 1L, 1L, 1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, -1L, 1L,
1L, -1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L, 1L,
1L, NA, 1L, -1L, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, -1L, 1L,
1L, 1L, 1L, 1L, 1L, -1L, 1L, -1L, 1L, 1L, NA, 1L, 1L, -1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("municipality",
"stars", "area", "seaLocation", "excellent", "good", "average",
"bad", "poor", "family", "couple", "single", "business", "MarMay",
"JunAug", "SepNov", "DecFeb", "location", "sleepQuality", "room",
"services", "priceQualityRate", "cleaning", "bt1", "ratebt1",
"bt2", "ratebt2", "bt3", "ratebt3", "bt4", "ratebt4", "bt5",
"ratebt5", "bt6", "ratebt6", "bt7", "ratebt7", "bt8", "ratebt8",
"bt9", "ratebt9", "bt10", "ratebt10"), row.names = c(NA, -518L
), class = "data.frame") |
ordranks <- function(dat.frame, paired = TRUE) {
cols <- ncol(dat.frame)
half <- cols/2
dat.orig <- as.matrix(dat.frame)
j=0; ranks=0; DLmax=0; data.1 <- dat.orig; data.0 <- dat.orig
if (paired) { for (i in seq(1, to=(cols-1), by=2)) {j = j+1
data.0[,j] <- dat.orig[,i]
data.0[,j+half] <- dat.orig[,i+1]
nvec <- colnames(data.0)
nvec <- nvec[seq(1, (cols-1), 2)]
nvec <- paste("rnk.", nvec, sep="")
} }
else {data.0 <- dat.orig
nvec <- colnames(data.0)
nvec <- nvec[1:half]
nvec <- paste("rnk.", nvec, sep="")
}
for (i in 1:half) {DLmax <- max(data.0[,i]*data.0[,i+half])
data.1[,i] <- data.0[,i]*(1-data.0[,i+half])
data.1[,i][data.1[,i] < DLmax] = 0
}
data.2 <- data.1[,1:half]
r.out <- data.2
for (i in 1:half) {ranks <- rank(data.2[,i])
if (i==1) {r.out <- ranks}
else {r.out <- cbind(r.out, ranks)}
}
colnames(r.out) <- nvec
return(r.out)
} |
library(brokenstick)
context("predict.brokenstick()")
obj <- fit_200
dat <- smocc_200
n <- nrow(dat)
m <- length(unique(dat$id))
k <- length(get_knots(obj))
test_that("returns proper number of rows", {
expect_equal(nrow(predict(obj, dat)), n)
expect_equal(nrow(predict(obj, dat, x = NA)), m)
expect_equal(nrow(predict(obj, x = NA, y = 10)), 1L)
expect_equal(nrow(predict(obj, x = c(NA, NA), y = c(-1, 10))), 2L)
})
test_that("returns proper number of rows with at = 'knots'", {
expect_equal(nrow(predict(obj, dat, x = "knots")), m * k)
expect_equal(nrow(predict(obj, dat, x = NA)), m)
expect_equal(nrow(predict(obj, x = NA, y = 10)), 1L)
})
test_that("returns proper number of rows with both data & knots", {
expect_equal(nrow(predict(obj, dat, x = "knots", strip_data = FALSE)), n + k * m)
expect_equal(nrow(predict(obj, dat, x = NA, y = 10, group = 10001, strip_data = FALSE)), 11)
expect_equal(nrow(predict(obj, dat, x = c(NA, NA), y = c(-1, 10), group = rep(10001, 2), strip_data = FALSE)), 12)
})
test_that("output = 'vector' and output = 'long' are consistent", {
expect_equivalent(
predict(obj, dat)[[".pred"]],
predict(obj, dat, shape = "vector")
)
expect_equal(
predict(obj, dat, x = 1)[[".pred"]],
predict(obj, dat, x = 1, shape = "vector")
)
expect_equal(
predict(obj, x = c(NA, 1), y = c(1, NA))[[".pred"]],
predict(obj, x = c(NA, 1), y = c(1, NA), 10, shape = "vector")
)
expect_equal(
predict(obj, dat, x = "knots")[[".pred"]],
predict(obj, dat, x = "knots", shape = "vector")
)
expect_equal(
predict(obj, dat, strip_data = FALSE)[[".pred"]],
predict(obj, dat, strip_data = FALSE, shape = "vector")
)
expect_equal(
predict(obj, x = NA, y = 10, strip_data = FALSE)[[".pred"]],
predict(obj, x = NA, y = 10, strip_data = FALSE, shape = "vector")
)
})
exp <- fit_200
dat <- smocc_200
test_that("returns proper number of rows", {
expect_equal(nrow(predict(exp, dat, x = NA)), 200L)
expect_equal(nrow(predict(exp, dat, x = c(NA, NA))), 400L)
expect_equal(nrow(predict(exp, dat, x = NA, y = 1)), 1L)
expect_equal(nrow(predict(exp, dat, x = c(NA, NA), y = c(-1, 10))), 2L)
expect_equal(nrow(predict(exp, dat, x = "knots")), 2200L)
expect_equal(nrow(predict(exp, dat, x = "knots", y = rep(1, 11))), 11L)
})
test_that("accepts intermediate NA in x", {
expect_equal(
unlist(predict(exp, x = 1, y = -1)[1, ]),
unlist(predict(exp, x = c(NA, 1), y = c(1, -1))[2, ])
)
expect_equal(
unlist(predict(exp, x = c(1, NA), y = c(-1, 1))[1, ]),
unlist(predict(exp, x = c(NA, 1), y = c(1, -1))[2, ])
)
expect_equal(
unlist(predict(exp, x = c(1, 2, NA), y = c(NA, -1, 1))[2, ]),
unlist(predict(exp, x = c(1, NA, 2), y = c(NA, 1, -1))[3, ])
)
})
test_that("accepts unordered x", {
expect_equal(
round(predict(exp, x = c(1, 2, 3), y = c(-1, 1, 0))[1, 5], 5),
round(predict(exp, x = c(2, 3, 1), y = c(1, 0, -1))[3, 5], 5)
)
})
xz <- data.frame(
id = c(NA_real_, NA_real_),
age = c(NA_real_, NA_real_),
hgt_z = c(NA_real_, NA_real_)
)
test_that("accepts all NA's in newdata", {
expect_silent(predict(exp, newdata = xz, x = "knots"))
})
context("predict_brokenstick factor")
fit <- fit_200
dat <- smocc_200
dat$id <- factor(dat$id)
test_that("works if id in newdata is a factor", {
expect_silent(predict(obj, newdata = dat))
}) |
ivif <-
function(lmobj) {
vif <- diag(vcov(lmobj))*diag(solve(vcov(lmobj)))
return(vif)
} |
betaExpert <-
function(best, lower, upper, p = 0.95, method = "mode"){
if (missing(best))
stop("'best' is missing")
if (missing(lower) & missing(upper))
stop("at least 'lower' or 'upper' must be specified")
checkInput(best, "best", range = c(0, 1))
checkInput(p, "p", range = c(0, 1))
if (!missing(lower))
checkInput(lower, "lower", range = c(0, 1), minEq = 0)
if (!missing(upper))
checkInput(upper, "upper", range = c(0, 1), maxEq = 1)
if (!missing(lower))
if (lower > best) stop("'lower' cannot be greater than 'best'")
if (!missing(upper))
if (upper < best) stop("'upper' cannot be smaller than 'best'")
if (!missing(lower) & !missing(upper))
if (lower > upper) stop("'lower' cannot be greater than 'upper'")
f_mode <-
function(x, mode, p, target){
return(
sum(
(qbeta(p = p,
shape1 = x,
shape2 = (x * (1 - mode) + 2 * mode - 1) / mode) -
target) ^ 2
))
}
f_mode_zero <-
function(x, p, target){
return((qbeta(p = p, shape1 = 1, shape2 = x) - target) ^ 2)
}
f_mode_one <-
function(x, p, target){
return((qbeta(p = p, shape1 = x, shape2 = 1) - target) ^ 2)
}
f_mean <-
function(x, mean, p, target){
return(
sum(
(qbeta(p = p,
shape1 = x,
shape2 = (x * (1 - mean)) / mean) -
target) ^ 2
))
}
if (!missing(lower) & missing(upper)){
target <- lower
p <- 1 - p
} else if (!missing(upper) & missing(lower)){
target <- upper
} else if (!missing(upper) & !missing(lower)){
target <- c(lower, upper)
p <- c(0, p) + (1 - p) / 2
}
if (method == "mode"){
if (best == 0){
a <- 1
b <- optimize(f_mode_zero, c(0, 1000), p = p, target = target)$minimum
} else if (best == 1) {
a <- optimize(f_mode_one, c(0, 1000), p = p, target = target)$minimum
b <- 1
} else {
a <- optimize(f_mode, c(0, 1000),
mode = best, p = p, target = target)$minimum
b <- (a * (1 - best) + 2 * best - 1) / best
}
} else if (method == "mean"){
a <- optimize(f_mean, c(0, 1000),
mean = best, p = p, target = target)$minimum
b <- (a * (1 - best)) / best
}
out <- list(alpha = a, beta = b)
class(out) <- "betaExpert"
return(out)
} |
air_dens_cf <- function(T.actual,
P.actual,
T.ref = 20,
P.ref = 760) {
T.ref.K <- 273.15 + T.ref
T.actual.K <- 273.15 + T.actual
T.actual.K / T.ref.K * 760 / P.actual
} |
context("is.significant")
test_that("test significance at 0.05",
{
expect_equal(
is_significant(c(.9, .8, .65, .33, .10, .06, .051, .05, .049, .001), .05),
c(rep(FALSE, 7), rep(TRUE, 3)))
})
test_that("test significance at 0.05",
{
expect_equal(
is_significant(c(.9, .8, .65, .33, .10, .06, .051, .05, .049, .001), .10),
c(rep(FALSE, 4), rep(TRUE, 6)))
}) |
"csiro_cobalt_marine" |
context("documentation")
test_that("can_read_pkg_description, data_version", {
file <- system.file("extdata", "tests", "subsetCars.Rmd",
package = "DataPackageR"
)
file2 <- system.file("extdata", "tests", "extra.rmd",
package = "DataPackageR"
)
datapackage_skeleton(
name = "subsetCars",
path = tempdir(),
code_files = c(file, file2),
force = TRUE,
r_object_names = c("cars_over_20", "pressure")
)
DataPackageR:::read_pkg_description(file.path(tempdir(), "subsetCars"))
devtools::load_all(file.path(tempdir(), "subsetCars"))
expected_version <-
structure(list(c(0L, 1L, 0L)),
class = c("package_version", "numeric_version")
)
expect_equal(data_version("subsetCars"), expected_version)
unlink(file.path(tempdir(), "subsetCars"),
recursive = TRUE,
force = TRUE
)
}) |
knitr::opts_chunk$set(
collapse = TRUE,
comment = "
message = FALSE,
warning = FALSE,
fig.height = 7,
fig.width = 7,
dpi = 75
)
check_namespaces <- function(pkgs){
return(all(unlist(sapply(pkgs, requireNamespace,quietly = TRUE))))
}
library(geofi)
library(dplyr)
d <- data(package = "geofi")
as_tibble(d$results) %>%
select(Item,Title) %>%
filter(grepl("municipality_key", Item))
names(geofi::municipality_key_2020)
geofi::municipality_key_2021 %>%
count(maakunta_code,maakunta_name_fi,maakunta_name_sv,maakunta_name_en)
municipalities <- get_municipalities(year = 2020, scale = 4500)
plot(municipalities["municipality_name_fi"], border = NA)
get_municipality_pop(year = 2020) %>%
subset(select = miehet_p) %>%
plot()
get_municipality_pop(year = 2020) %>%
group_by(hyvinvointialue_name_fi) %>%
summarise(vaesto = sum(vaesto)) %>%
subset(select = vaesto) %>%
plot()
get_municipality_pop(year = 2020) %>%
dplyr::group_by(hyvinvointialue_name_fi) %>%
summarise(vaesto = sum(vaesto),
miehet = sum(miehet)) %>%
mutate(share = miehet/vaesto*100) %>%
subset(select = share) %>%
plot()
zipcodes <- get_zipcodes(year = 2015)
plot(zipcodes["nimi"], border = NA)
stat_grid <- get_statistical_grid(resolution = 5, auxiliary_data = TRUE)
plot(stat_grid["euref_x"], border = NA)
pop_grid <- get_population_grid(year = 2018, resolution = 5)
plot(pop_grid["kunta"], border = NA)
plot(municipality_central_localities["teksti"])
d <- data(package = "geofi")
as_tibble(d$results) %>%
select(Item,Title) %>%
filter(grepl("grid", Item)) %>%
print(n = 100)
libs <- c("pxweb","geofacet","ggplot2")
if (check_namespaces(pkgs = libs)) {
library(pxweb)
pxweb_query_list <-
list("Alue 2019"=c("*"),
"Tiedot"=c("M411"),
"Vuosi"=c("*"))
px_data <-
pxweb_get(url = "https://pxnet2.stat.fi/PXWeb/api/v1/fi/Kuntien_avainluvut/2019/kuntien_avainluvut_2019_aikasarja.px",
query = pxweb_query_list)
px_data <- as.data.frame(px_data, column.name.type = "text", variable.value.type = "text")
names(px_data) <- c("kunta_name","year","value")
dat <- left_join(geofi::municipality_key_2021 %>% select(-year),
px_data) %>%
group_by(maakunta_code, maakunta_name_fi,year) %>%
summarise(population = sum(value, na.rm = TRUE)) %>%
na.omit() %>%
ungroup() %>%
rename(code = maakunta_code, name = maakunta_name_fi)
library(geofacet)
library(ggplot2)
ggplot(dat, aes(x = year, y = population/1000, group = name)) +
geom_line() +
facet_geo(facets = ~name, grid = grid_maakunta, scales = "free_y") +
theme(axis.text.x = element_text(size = 6)) +
scale_x_discrete(breaks = seq.int(from = 1987, to = 2018, by = 5)) +
labs(title = "Population 1987-2018", y = "population (1000)")
} else {
message("'pxweb' not available")
} |
ReadJAFROCOldFormat <- function(fileName, renumber) {
UNINITIALIZED <- RJafrocEnv$UNINITIALIZED
wb <- excel_sheets(fileName)
sheetNames <- toupper(wb)
truthFileIndex <- which(!is.na(match(sheetNames, "TRUTH")))
if (truthFileIndex == 0)
stop("TRUTH table cannot be found in the dataset.")
truthTable <- data.frame( read_xlsx(fileName, truthFileIndex, range = cell_cols(1:3) ) )
for (i in 1:3){
truthTable[grep("^\\s*$", truthTable[ , i]), i] <- NA
}
naRows <- colSums(is.na(truthTable))
if (max(naRows) > 0) {
if (max(naRows) == min(naRows)) {
truthTable <- truthTable[1:(nrow(truthTable) - max(naRows)), ]
}
}
for (i in 1:2) {
if (any((as.numeric(as.character(truthTable[, i]))) %% 1 != 0 )) {
naLines <- which(!is.integer(as.numeric(as.character(truthTable[, i])))) + 1
errorMsg <- paste0("There are non-integer values(s) for CaseID or LesionID at the line(s) ", paste(naLines, collapse = ", "), " in the TRUTH table.")
stop(errorMsg)
}
}
if (any(is.na(as.numeric(as.character(truthTable[, 3]))))) {
naLines <- which(is.na(as.numeric(as.character(truthTable[, 3])))) + 1
errorMsg <- paste0("There are non-numeric values(s) for weights at the line(s) ", paste(naLines, collapse = ", "), " in the TRUTH table.")
stop(errorMsg)
}
caseIDColumn <- as.integer(truthTable[[1]])
lesionIDColumn <- as.integer(truthTable[[2]])
weightsColumn <- truthTable[[3]]
normalCases <- sort(unique(caseIDColumn[lesionIDColumn == 0]))
abnormalCases <- sort(unique(caseIDColumn[lesionIDColumn > 0]))
allCases <- c(normalCases, abnormalCases)
K1 <- length(normalCases)
K2 <- length(abnormalCases)
K <- (K1 + K2)
if (anyDuplicated(cbind(caseIDColumn, lesionIDColumn))) {
naLines <- which(duplicated(cbind(caseIDColumn, lesionIDColumn))) + 1
errorMsg <- paste0("Line(s) ", paste(naLines, collapse = ", "), " in the TRUTH table are duplicated with previous line(s) .")
stop(errorMsg)
}
nlFileIndex <- which(!is.na(match(sheetNames, c("FP", "NL"))))
if (nlFileIndex == 0)
stop("FP table cannot be found in the dataset.")
NLTable <- data.frame( read_xlsx(fileName, nlFileIndex, range = cell_cols(1:4) ) )
for (i in 1:4){
NLTable[grep("^\\s*$", NLTable[ , i]), i] <- NA
}
naRows <- colSums(is.na(NLTable))
if (max(naRows) > 0) {
if (max(naRows) == min(naRows)) {
NLTable <- NLTable[1:(nrow(NLTable) - max(naRows)), ]
}
}
for (i in 3:4) {
if (any(is.na(as.numeric(as.character(NLTable[, i]))))) {
naLines <- which(is.na(as.numeric(as.character(NLTable[, i])))) + 1
errorMsg <- paste0("There are unavailable cell(s) at the line(s) ", paste(naLines, collapse = ", "), " in the FP table.")
stop(errorMsg)
}
}
NLReaderID <- as.character(NLTable[[1]])
NLModalityID <- as.character(NLTable[[2]])
NLCaseID <- NLTable[[3]]
if (any(!(NLCaseID %in% caseIDColumn))) {
naCases <- NLCaseID[which(!(NLCaseID %in% caseIDColumn))]
errorMsg <- paste0("Case(s) ", paste(unique(naCases), collapse = ", "), " in the FP table cannot be found in TRUTH table.")
stop(errorMsg)
}
NLRating <- as.numeric(NLTable[[4]])
llFileIndex <- which(!is.na(match(sheetNames, c("TP", "LL"))))
if (llFileIndex == 0)
stop("TP table cannot be found in the dataset.")
LLTable <- data.frame( read_xlsx(fileName, llFileIndex, range = cell_cols(1:5) ) )
for (i in 1:5){
LLTable[grep("^\\s*$", LLTable[ , i]), i] <- NA
}
naRows <- colSums(is.na(LLTable))
if (max(naRows) > 0) {
if (max(naRows) == min(naRows)) {
LLTable <- LLTable[1:(nrow(LLTable) - max(naRows)), ]
}
}
for (i in 3:5) {
if (any(is.na(as.numeric(as.character(LLTable[, i]))))) {
naLines <- which(is.na(as.numeric(as.character(LLTable[, i])))) + 1
errorMsg <- paste0("There are unavailable cell(s) at the line(s) ", paste(naLines, collapse = ", "), " in the TP table.")
stop(errorMsg)
}
}
LLReaderID <- as.character(LLTable[[1]])
LLModalityID <- as.character(LLTable[[2]])
LLCaseID <- LLTable[[3]]
LLLesionID <- LLTable[[4]]
for (i in 1:nrow(LLTable)) {
lineNum <- which((caseIDColumn == LLCaseID[i]) & (lesionIDColumn == LLLesionID[i]))
if (!length(lineNum)) {
errorMsg <- paste0("Modality ", LLTable[i, 2], " Reader(s) ", LLTable[i, 1], " Case(s) ", LLTable[i, 3], " Lesion(s) ", LLTable[i, 4], " cannot be found in TRUTH table .")
stop(errorMsg)
}
}
LLRating <- as.numeric(LLTable[[5]])
if (anyDuplicated(LLTable[, 1:4])) {
naLines <- which(duplicated(LLTable[, 1:4]))
errorMsg <- paste0("Modality ", paste(LLTable[naLines, 2], collapse = ", "), " Reader(s) ", paste(LLTable[naLines, 1], collapse = ", "), " Case(s) ", paste(LLTable[naLines, 3], collapse = ", "), " Lesion(s) ",
paste(LLTable[naLines, 4], collapse = ", "), " have multiple ratings in TP table .")
stop(errorMsg)
}
perCase <- as.vector(table(caseIDColumn[caseIDColumn %in% abnormalCases]))
weights <- array(dim = c(length(abnormalCases), max(perCase )))
IDs <- array(dim = c(length(abnormalCases), max(perCase )))
for (k2 in 1:length(abnormalCases)) {
k <- which(caseIDColumn == abnormalCases[k2])
IDs[k2, ] <- c(sort(lesionIDColumn[k]), rep(UNINITIALIZED, max(perCase ) - length(k)))
if (all(weightsColumn[k] == 0)) {
weights[k2, 1:length(k)] <- 1/perCase [k2]
} else {
weights[k2, ] <- c(weightsColumn[k][order(lesionIDColumn[k])], rep(UNINITIALIZED, max(perCase ) - length(k)))
sumWeight <- sum(weights[k2, weights[k2, ] != UNINITIALIZED])
if (sumWeight != 1){
if (sumWeight <= 1.01 && sumWeight >= 0.99){
weights[k2, ] <- weights[k2, ] / sumWeight
}else{
errorMsg <- paste0("The sum of the weights for Case ", k2, " is not 1.")
stop(errorMsg)
}
}
}
}
modalityID <- as.character(sort(unique(c(NLModalityID, LLModalityID))))
I <- length(modalityID)
readerID <- as.character(sort(unique(c(NLReaderID, LLReaderID))))
J <- length(readerID)
maxNL <- 0
for (i in modalityID) {
for (j in readerID) {
k <- (NLModalityID == i) & (NLReaderID == j)
if ((sum(k) == 0))
next
maxNL <- max(maxNL, max(table(NLCaseID[k])))
}
}
NL <- array(dim = c(I, J, K, maxNL))
for (i in 1:I) {
for (j in 1:J) {
k <- (NLModalityID == modalityID[i]) & (NLReaderID == readerID[j])
if ((sum(k) == 0))
next
caseNLTable <- table(NLCaseID[k])
temp <- as.numeric(unlist(attr(caseNLTable, "dimnames")))
for (k1 in 1:length(temp)) {
for (el in 1:caseNLTable[k1]) {
NL[i, j, which(temp[k1] == allCases), el] <- NLRating[k][which(NLCaseID[k] == temp[k1])][el]
}
}
}
}
LL <- array(dim = c(I, J, K2, max(perCase )))
for (i in 1:I) {
for (j in 1:J) {
k <- (LLModalityID == modalityID[i]) & (LLReaderID == readerID[j])
if ((sum(k) == 0))
next
caseLLTable <- table(LLCaseID[k])
temp <- as.numeric(unlist(attr(caseLLTable, "dimnames")))
for (k1 in 1:length(temp)) {
temp1 <- which(temp[k1] == abnormalCases)
for (el in 1:caseLLTable[k1]) {
temp2 <- which(LLLesionID[k][which(LLCaseID[k] == temp[k1])][el] == IDs[which(temp[k1] == abnormalCases), ])
LL[i, j, temp1, temp2] <- LLRating[k][which(LLCaseID[k] == temp[k1])][el]
}
}
}
}
weights[is.na(weights)] <- UNINITIALIZED
IDs[is.na(IDs)] <- UNINITIALIZED
NL[is.na(NL)] <- UNINITIALIZED
LL[is.na(LL)] <- UNINITIALIZED
isROI <- TRUE
for (i in 1:I) {
for (j in 1:J) {
if (any(NL[i, j, 1:K1, ] == UNINITIALIZED)) {
isROI <- FALSE
break
}
temp <- LL[i, j, , ] != UNINITIALIZED
dim(temp) <- c(K2, max(perCase ))
if (!all(perCase == rowSums(temp))) {
isROI <- FALSE
break
}
temp <- NL[i, j, (K1 + 1):K, ] == UNINITIALIZED
dim(temp) <- c(K2, maxNL)
if (!all(perCase == rowSums(temp))) {
isROI <- FALSE
break
}
}
}
if ((max(table(caseIDColumn)) == 1) && (maxNL == 1) && (all((NL[, , (K1 + 1):K, ] == UNINITIALIZED))) && (all((NL[, , 1:K1, ] != UNINITIALIZED)))) {
type <- "ROC"
} else {
if (isROI) {
type <- "ROI"
} else {
type <- "FROC"
}
}
modalityNames <- modalityID
readerNames <- readerID
if (renumber){
modalityID <- 1:I
readerID <- 1:J
}
names(modalityID) <- modalityNames
names(readerID) <- readerNames
truthTableStr <- array(dim = c(I, J, K, (max(lesionIDColumn)+1)))
truthTableStr[,,1:K1,1] <- 1
for (k2 in 1:K2) {
truthTableStr[,,k2+K1,(1:perCase[k2])+1] <- 1
}
name <- NA
design <- "FCTRL"
return(convert2dataset(NL, LL, LL_IL = NA,
perCase, IDs, weights,
fileName, type, name, truthTableStr, design,
modalityID, readerID))
} |
[
{
"title": "Data Science Competitions 101: Anatomy and Approach",
"href": "https://techandmortals.wordpress.com/2016/07/27/data-science-competitions-101-anatomy-and-approach/"
},
{
"title": "Getting knitr to work with StatET",
"href": "https://danganothererror.wordpress.com/2012/04/13/getting-knitr-to-work-with-statet/"
},
{
"title": "Maps with R (II)",
"href": "https://procomun.wordpress.com/2012/02/20/maps_with_r_2/"
},
{
"title": "Agent-based modeling in R – habitat diversity and species richness",
"href": "https://web.archive.org/web/http://mbjoseph.github.io/blog/2013/04/20/allouche/"
},
{
"title": "Asher’s enigma",
"href": "https://xianblog.wordpress.com/2010/07/26/asher%E2%80%99s%C2%A0enigma/"
},
{
"title": "RcppArmadillo 0.2.34",
"href": "http://dirk.eddelbuettel.com/blog/2011/12/13/"
},
{
"title": "Why R for Mass Spectrometrist and Computational Proteomics",
"href": "http://computationalproteomic.blogspot.com/2012/08/why-r-for-mass-spectrometrist-and.html"
},
{
"title": "The Mechanics of Data Visualization",
"href": "https://trinkerrstuff.wordpress.com/2013/07/02/the-mechanics-of-data-visualization/"
},
{
"title": "A Visualization of World Cuisines",
"href": "https://designdatadecisions.wordpress.com/2015/12/28/a-visualization-of-world-cuisines/"
},
{
"title": "What’s new in Revolution R Enterprise 6.1",
"href": "http://blog.revolutionanalytics.com/2012/11/whats-new-in-revolution-r-enterprise-61.html"
},
{
"title": "Aggregation with dplyr: summarise and summarise_each",
"href": "http://www.milanor.net/blog/aggregation-dplyr-summarise-summarise_each/"
},
{
"title": "Hierarchical Linear Model",
"href": "http://www.r-tutor.com/gpu-computing/rbayes/rhierlmc"
},
{
"title": "Reminder: Abstract submission for the 2014 ‘R in Insurance’ conference will close this Friday",
"href": "http://www.magesblog.com/2014/03/reminder-abstract-submission-for-2014-r.html"
},
{
"title": "Notice that even though output is in a log scale, output is…",
"href": "https://web.archive.org/web/http://isomorphism.es//post/615285554"
},
{
"title": "Predictive Analytics with R, PMML, ADAPA, and Excel",
"href": "http://www.predictive-analytics.info/2011/01/predictive-analytics-with-r-pmml-adapa.html"
},
{
"title": "Ganglia welcomes Google Summer of Code students for 2014",
"href": "https://danielpocock.com/ganglia-welcomes-gsoc-2014-students"
},
{
"title": "Sharing my work for “Advanced Methods III”",
"href": "https://feedproxy.google.com/~r/FellgernonBit-rstats/~3/8Q1ju_r44EE/sharing-my-work-for-advanced-methods-iii"
},
{
"title": "Exploring the Market with Hurst",
"href": "http://timelyportfolio.blogspot.com/2011/06/exploring-market-with-hurst.html"
},
{
"title": "Introductions to R and predictive analytics",
"href": "http://blog.revolutionanalytics.com/2016/03/introductions-to-r-and-predictive-analytics.html"
},
{
"title": "R Day at Strata NYC",
"href": "https://blog.rstudio.org/2014/07/08/r-day-at-strata-nyc/"
},
{
"title": "Quantitative Candlestick Pattern Recognition (Part 2 — What’s this Natural Language Processing stuff?)",
"href": "http://intelligenttradingtech.blogspot.com/2010/08/quantitative-candlestick-pattern.html"
},
{
"title": "Simple user interface in R to get login details",
"href": "http://www.magesblog.com/2014/07/simple-user-interface-in-r-to-get-login.html"
},
{
"title": "More Command-Line Text Munging Utilities",
"href": "http://www.gettinggeneticsdone.com/2011/05/more-command-line-text-munging.html"
},
{
"title": "Restaurant Inspection Results",
"href": "https://statofmind.wordpress.com/2014/02/04/restaurant-inspection-results/"
},
{
"title": "EARL2016, London – Deadline for Abstracts – 29th April",
"href": "http://www.mango-solutions.com/wp/2016/04/earl2016-london-deadline-for-abstracts-29th-april/"
},
{
"title": "More Haskell: a bootstrap",
"href": "https://martinsbioblogg.wordpress.com/2013/02/16/more-haskell-a-bootstrap/"
},
{
"title": "New Zealand school performance: beyond the headlines",
"href": "http://www.quantumforest.com/2012/09/new-zealand-school-performance-beyond-the-headlines/"
},
{
"title": "What is your favorite R feature?",
"href": "https://web.archive.org/web/http://cloudnumbers.com/what-is-your-favorite-r-feature"
},
{
"title": "Install and load missing specified/needed packages on the fly",
"href": "http://rscriptsandtips.blogspot.com/2014/02/install-and-load-missing.html"
},
{
"title": "Assessing Model Fit Through Simulation",
"href": "https://web.archive.org/web/http://polstat.org/blog/2011/12/assessing-model-fit-through-simulation"
},
{
"title": "rOpenSci awarded $300k from the Sloan Foundation",
"href": "http://ropensci.org/blog/2014/06/10/new-sloan-award/"
},
{
"title": "Installing StatET",
"href": "https://web.archive.org/web/http://flyordie.sin.khk.be/2011/03/16/installing-statet/"
},
{
"title": "R: microbenchmark, reshaping big data features",
"href": "http://hack-r.com/r-microbenchmark-reshaping-big-data-features/"
},
{
"title": "An Introduction to Collaborative Filtering",
"href": "http://www.tatvic.com/blog/an-introduction-to-collaborative-filtering/"
},
{
"title": "A Checkpoint Of Spanish Football League",
"href": "https://aschinchon.wordpress.com/2016/01/20/a-checkpoint-of-spanish-football-league/"
},
{
"title": "Learn to Manage Data at useR! 2014 or Online April 25",
"href": "http://r4stats.com/2014/03/18/learn-to-manage-data-at-user-2014-or-online-april-25/"
},
{
"title": "An xpd-tion into R plot margins",
"href": "https://feedproxy.google.com/~r/FellgernonBit-rstats/~3/fH97-LBa7Mw/add-logo-in-R"
},
{
"title": "R-based data maps in PowerBI",
"href": "http://blog.revolutionanalytics.com/2016/02/r-maps-in-powerbi.html"
},
{
"title": "Multiple cores in R, revisited",
"href": "http://zvfak.blogspot.com/2011/08/multiple-cores-in-r-revisited.html"
},
{
"title": "4 Tips to Learn More About ACS Data",
"href": "http://www.arilamstein.com/blog/2015/11/14/4-tips-learn-acs-data/"
},
{
"title": "A dirty hack for importing packages that use Depends",
"href": "http://r2d2.quartzbio.com/posts/package-depends-dirty-hack-solution.html"
},
{
"title": "The winds of Winter [Bayesian prediction]",
"href": "https://xianblog.wordpress.com/2014/10/07/the-winds-of-winter-bayesian-prediction/"
},
{
"title": "Making Static/Interactive Voronoi Map Layers In ggplot/leaflet",
"href": "http://rud.is/b/2015/07/26/making-staticinteractive-voronoi-map-layers-in-ggplotleaflet/"
},
{
"title": "Beautiful table-outputs: Summarizing mixed effects models
"href": "https://strengejacke.wordpress.com/2015/06/05/beautiful-table-outputs-summarizing-mixed-effects-models-rstats/"
},
{
"title": "The Rebirth",
"href": "http://dancingeconomist.blogspot.com/2011/07/rebirth.html"
},
{
"title": "Using R in Excel",
"href": "http://www.mathfinance.cn/using-R-in-excel/"
},
{
"title": "Easier Database Querying with R",
"href": "https://anrprogrammer.wordpress.com/2013/07/27/easier-database-querying-with-r/"
},
{
"title": "End User Computing and why R can help meeting Solvency II",
"href": "http://www.magesblog.com/2012/05/end-user-computing-and-why-r-can-help.html"
},
{
"title": "Big Data Bytes: How Open Source is Changing Business",
"href": "http://blog.revolutionanalytics.com/2013/09/big-data-bytes-replay.html"
},
{
"title": "Interactive Heatmaps (and Dendrograms) – A Shiny App",
"href": "https://imdevsoftware.wordpress.com/2013/07/07/interactive-heatmaps-and-dendrograms-a-shiny-app/"
}
] |
"are.pargev.valid" <-
function(para,nowarn=FALSE) {
if(! is.gev(para)) return(FALSE)
if(any(is.na(para$para))) return(FALSE)
A <- para$para[2]
G <- para$para[3]
op <- options()
GO <- TRUE
if(nowarn == TRUE) options(warn=-1)
if(A <= 0) {
warning("Parameter A is not > 0, invalid")
GO <- FALSE
}
if(G <= -1) {
warning("Parameter G is not > -1, invalid")
GO <- FALSE
}
options(op)
if(GO) return(TRUE)
return(FALSE)
} |
context("as_package")
test_that("it throws error if no package", {
expect_error(as_package("arst11234"), "`path` is invalid:.*arst11234")
})
test_that("it returns the package if given the root or child directory", {
expect_equal(as_package("TestS4")$package, "TestS4")
expect_equal(as_package("TestS4/")$package, "TestS4")
expect_equal(as_package("TestS4/R")$package, "TestS4")
expect_equal(as_package("TestS4/tests")$package, "TestS4")
expect_equal(as_package("TestS4/tests/testthat")$package, "TestS4")
})
context("local_branch")
test_that("it works as expected", {
with_mock(`covr:::system_output` = function(...) { "test_branch " }, {
expect_equal(local_branch("TestSummary"), "test_branch")
})
})
context("current_commit")
test_that("it works as expected", {
with_mock(`covr:::system_output` = function(...) { " test_hash" }, {
expect_equal(current_commit("TestSummary"), "test_hash")
})
})
context("get_source_filename")
test_that("it works", {
skip_if(getRversion() >= "4.0.0")
x <- eval(bquote(function() 1))
expect_identical(getSrcFilename(x), character())
expect_identical(get_source_filename(x), "")
})
test_that("per_line removes blank lines and lines with only punctuation (
skip_on_cran()
cov <- package_coverage(test_path("testFunctional"))
line_cov <- per_line(cov)
expect_equal(line_cov[[1]]$coverage, c(NA, 0, 0, 2, NA, 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA))
}) |
expected <- eval(parse(text="0L"));
test(id=0, code={
argv <- eval(parse(text="list(integer(0), 1L)"));
.Internal(`tabulate`(argv[[1]], argv[[2]]));
}, o=expected); |
llsearch.QQ.D <- function(x, y, n, jlo, jhi)
{
fj <- matrix(0, n)
fxy <- matrix(0, jhi - jlo + 1)
jgrid <- expand.grid(jlo:jhi)
k.ll <- apply(jgrid, 1, p.estFUN.QQ.D, x = x, y = y, n = n)
fxy <- matrix(k.ll, nrow = jhi-jlo+1)
rownames(fxy) <- jlo:jhi
z <- findmax(fxy)
jcrit <- z$imax + jlo - 1
list(jhat = jcrit, value = max(fxy))
}
p.estFUN.QQ.D <- function(j, x, y, n){
a <- p.est.QQ.D(x,y,n,j)
s2 <- a$sigma2
t2<- a$tau2
return(p.ll.D(n, j, s2,t2))
}
p.est.QQ.D <- function(x,y,n,j){
xa <- x[1:j]
ya <- y[1:j]
jp1 <- j+1
xb <- x[jp1:n]
yb <- y[jp1:n]
g1 <- lm(ya ~ xa + I(xa^2))
g2 <- lm(yb ~ xb + I(xb^2))
beta <-c(g1$coef[1],g1$coef[2],g1$coef[3],g2$coef[1],g2$coef[2],g2$coef[3])
s2 <- sum((ya-g1$fit)^2)/j
t2 <- sum((yb-g2$fit)^2)/(n-j)
list(a0=beta[1],a1=beta[2],a2=beta[3],b0=beta[4],b1=beta[5],b2=beta[6],sigma2=s2,tau2=t2,xj=x[j])
}
p.ll.D <- function(n, j, s2, t2){
q1 <- n * log(sqrt(2 * pi))
q2 <- 0.5 * j * (1 + log(s2))
q3 <- 0.5 * (n - j) * (1 + log(t2))
- (q1 + q2+ q3)
}
findmax <-function(a)
{
maxa<-max(a)
imax<- which(a==max(a),arr.ind=TRUE)[1]
jmax<-which(a==max(a),arr.ind=TRUE)[2]
list(imax = imax, jmax = jmax, value = maxa)
} |
handle_noise <- function(global_membership, timeID, minPts, minTime) {
cli::cli_div(theme = list(span.vrb = list(color = "yellow",
`font-weight` = "bold"),
span.unit = list(color = "magenta"),
.val = list(digits = 3),
span.side = list(color = "grey")))
cli::cli_h3("{.field minPts} = {.val {minPts}} {.unit hot spot{?s}} | {.field minTime} = {.val {minTime}} {.unit time index{?es}}")
n <- timelen <- NULL
`%>%` <- dplyr::`%>%`
membership_count <- data.frame(id = 1:length(global_membership),
timeID,
global_membership) %>%
dplyr::group_by(global_membership) %>%
dplyr::summarise(n = dplyr::n(), timelen = max(timeID) - min(timeID))
noise_clusters <- dplyr::filter(membership_count,
n < minPts | timelen < minTime)
noise_clusters <- noise_clusters[['global_membership']]
indexes <- global_membership %in% noise_clusters
global_membership[indexes] <- -1
if (all_noise_bool(global_membership)) {
cli::cli_alert_warning("All observations are noise!!!")
} else {
global_membership[!indexes] <-
adjust_membership(global_membership[!indexes], 0)
}
cli::cli_alert_success("{.vrb Handle} {.field noise}")
cli::cli_alert_info("{.val {max(c(global_membership, 0))}} cluster{?s} {.side left}")
cli::cli_alert_info("noise {.side proportion} : {.val {mean(global_membership == -1)*100} %} ")
cli::cli_end()
return(global_membership)
} |
setGeneric("distsumlp", function(o, x=0, y=0, p=2) standardGeneric("distsumlp"))
setMethod("distsumlp", "loca.p", function(o, x=0, y=0, p=2)
{
if (p>=1) return(sum(o@w*(abs(o@x-x)^p+abs(o@y-y)^p)^(1/p)))
else stop(paste(p, gettext("is not a valid value for p, use 1 <= p", domain = "R-orloca")))
}
)
setGeneric("distsumlpgra", function(o, x=0, y=0, p=2, partial=F) standardGeneric("distsumlpgra"))
setMethod("distsumlpgra", "loca.p", function(o, x=0, y=0, p=2, partial=F)
{
if (p>=1) {
n<- o@w*(abs(x-o@x)^p+abs(y-o@y)^p)^(1/p-1)
c(sum(sign(x-o@x)*abs(x-o@x)^(p-1)*n, na.rm=partial), sum(sign(y-o@y)*abs(y-o@y)^(p-1)*n, na.rm=partial))
}
else stop(paste(p, gettext("is not a valid value for p, use 1 <= p", domain = "R-orloca")))
}
) |
REB3m2<-function(andar,m2and,cbaixo,cand,nand,m2apt,nm21=0,nm22=0,nm23=0,nm24=0,nm25=0,nm26=0,nm27=0,nm28=0,nm29=0){
if(andar<=nand)
if(nand<9){
custo = cbaixo+cand*nand
custop = cbaixo+cand*andar
nm2 = nm21+nm22+nm23+nm24+nm25+nm26+nm27+nm28+nm29
sumci = ((cbaixo+cand)*nm21)+((cbaixo+2*cand)*nm22)+((cbaixo+3*cand)*nm23)+((cbaixo+4*cand)*nm24)+((cbaixo+5*cand)*nm25)+((cbaixo+6*cand)*nm26)+((cbaixo+7*cand)*nm27)+((cbaixo+8*cand)*nm28)+((cbaixo+9*cand)*nm29)
resultado=m2apt*(custop+((custo-sumci)/(m2and*nand)))
return(resultado)
}
else{
print("nand non debe superar 9")
}
else{
print("O nivel do andar non pode ser maior que nand")
}
} |
compileCode <- TRUE
library(rstan) ; library(HRW)
nWarm <- 10000
nKept <- 10000
nThin <- 10
fTrue <- function(x)
return(3*exp(-78*(x-0.38)^2)+exp(-200*(x-0.75)^2) - x)
muXTrue <- 0.5 ; sigmaXTrue <- 1/6
sigmaEpsTrue <- 0.7
phiTrue <- c(3,-3)
set.seed(2)
n <- 1000
x <- rnorm(n,muXTrue,sigmaXTrue)
y <- fTrue(x) + sigmaEpsTrue*rnorm(n)
probxObs <- 1/(1+exp(-(phiTrue[1]+phiTrue[2]*y)))
indicxObsTmp <- rbinom(n,1,probxObs)
xObs <- x[indicxObsTmp == 1]
nObs <- length(xObs)
yxObs <- fTrue(xObs) + sigmaEpsTrue*rnorm(nObs)
indicxObs <- rep(1,nObs)
xUnobsTrue <- x[indicxObsTmp == 0]
nUnobs <- length(xUnobsTrue)
yxUnobs <- fTrue(xUnobsTrue) + sigmaEpsTrue*rnorm(nUnobs)
indicxUnobs <- rep(0,nUnobs)
r <- c(indicxObs,indicxUnobs)
ncZ <- 30
knots <- seq(min(xObs),max(xObs),length=(ncZ+2))[-c(1,ncZ+2)]
ZxObs <- outer(xObs,knots,"-")
ZxObs <- ZxObs*(ZxObs>0)
npRegMARModel <-
'data
{
int<lower=1> nObs; int<lower=1> nUnobs;
int<lower=1> n; int<lower=1> ncZ;
vector[nObs] yxObs; vector[nUnobs] yxUnobs;
vector[nObs] xObs;
vector[ncZ] knots; matrix[nObs,ncZ] ZxObs;
int<lower=0,upper=1> r[n];
real<lower=0> sigmaBeta; real<lower=0> sigmaMu;
real<lower=0> sigmaPhi;
real<lower=0> Ax; real<lower=0> Aeps;
real<lower=0> Au;
}
transformed data
{
vector[n] y; matrix[n,2] Y;
for (i in 1:nObs)
{
y[i] = yxObs[i];
Y[i,1] = 1 ; Y[i,2] = yxObs[i];
}
for (i in 1:nUnobs)
{
y[i+nObs] = yxUnobs[i];
Y[i+nObs,1] = 1 ; Y[i+nObs,2] = yxObs[i];
}
}
parameters
{
vector[2] beta; vector[ncZ] u;
vector[2] phi;
real muX; real<lower=0> sigmaX;
real<lower=0> sigmaEps; real<lower=0> sigmaU;
real xUnobs[nUnobs];
}
transformed parameters
{
matrix[n,2] X; matrix[n,ncZ] Z;
for (i in 1:nObs)
{
X[i,1] = 1 ; X[i,2] = xObs[i] ;
Z[i] = ZxObs[i];
}
for (i in 1:nUnobs)
{
X[i+nObs,1] = 1 ; X[i+nObs,2] = xUnobs[i];
for (k in 1:ncZ)
Z[i+nObs,k] = (xUnobs[i]-knots[k])*step(xUnobs[i]-knots[k]);
}
}
model
{
y ~ normal(X*beta+Z*u,sigmaEps);
r ~ bernoulli_logit(Y*phi);
col(X,2) ~ normal(muX,sigmaX);
u ~ normal(0,sigmaU) ; beta ~ normal(0,sigmaBeta);
muX ~ normal(0,sigmaMu); phi ~ normal(0,sigmaPhi);
sigmaX ~ cauchy(0,Ax);
sigmaEps ~ cauchy(0,Aeps); sigmaU ~ cauchy(0,Au);
}'
allData <- list(nObs = nObs,nUnobs = nUnobs,n = (nObs+nUnobs),
ncZ = ncZ,xObs = xObs,yxObs = yxObs,yxUnobs = yxUnobs,knots = knots,
ZxObs = ZxObs,r = r,sigmaMu = 1e5,sigmaBeta = 1e5,sigmaPhi = 1e5,
sigmaEps = 1e5,sigmaX = 1e5,Ax = 1e5,Aeps = 1e5,Au = 1e5)
if (compileCode)
stanCompilObj <- stan(model_code = npRegMARModel,data = allData,
iter = 1,chains = 1)
stanObj <- stan(model_code = npRegMARModel,data = allData,warmup = nWarm,
iter = (nWarm + nKept),chains = 1,thin = nThin,refresh = 10,
fit = stanCompilObj)
betaMCMC <- NULL
for (j in 1:2)
{
charVar <- paste("beta[",as.character(j),"]",sep = "")
betaMCMC <- rbind(betaMCMC,extract(stanObj,charVar,permuted = FALSE))
}
uMCMC <- NULL
for (k in 1:ncZ)
{
charVar <- paste("u[",as.character(k),"]",sep = "")
uMCMC <- rbind(uMCMC,extract(stanObj,charVar,permuted = FALSE))
}
muXMCMC <- as.vector(extract(stanObj,"muX",permuted = FALSE))
sigmaXMCMC <- as.vector(extract(stanObj,"sigmaX",permuted = FALSE))
sigmaEpsMCMC <- as.vector(extract(stanObj,"sigmaEps",permuted = FALSE))
sigmaUMCMC <- as.vector(extract(stanObj,"sigmaU",permuted = FALSE))
phiMCMC <- NULL
for (j in 1:2)
{
charVar <- paste("phi[",as.character(j),"]",sep="")
phiMCMC <- rbind(phiMCMC,extract(stanObj,charVar,permuted = FALSE))
}
xUnobsMCMC <- NULL
for (i in 1:nUnobs)
{
charVar <- paste("xUnobs[",as.character(i),"]",sep = "")
xUnobsMCMC <- rbind(xUnobsMCMC,extract(stanObj,charVar,permuted = FALSE))
}
cexVal <- 0.3
obsCol <- "darkblue" ; misCol <- "lightskyblue"
estFunCol <- "darkgreen"; trueFunCol <- "indianred3"
varBandCol <- "palegreen"
ng <- 101
xg <- seq(min(x),max(x),length = ng)
Xg <- cbind(rep(1,ng),xg)
Zg <- outer(xg,knots,"-")
Zg <- Zg*(Zg>0)
fMCMC <- Xg%*%betaMCMC + Zg%*%uMCMC
credLower <- apply(fMCMC,1,quantile,0.025)
credUpper <- apply(fMCMC,1,quantile,0.975)
fg <- apply(fMCMC,1,mean)
par(mfrow = c(1,1),mai = c(1.02,0.82,0.82,0.42))
plot(xg,fg,type = "n",bty = "l",xlab = "x",ylab = "y",xlim = range(c(xObs,xUnobsTrue)),
ylim = range(c(yxObs,yxUnobs)))
polygon(c(xg,rev(xg)),c(credLower,rev(credUpper)),col = varBandCol,border = FALSE)
lines(xg,fg,lwd = 2,col = estFunCol)
lines(xg,fTrue(xg),lwd = 2,col = trueFunCol)
points(xObs,yxObs,col = obsCol,cex = cexVal)
points(xUnobsTrue,yxUnobs,col = misCol,cex = cexVal)
legend(0.75,1.5,c("fully observed","x value unobserved"),col = c(obsCol,misCol),
pch = rep(1,2),pt.lwd = rep(2,2))
legend(0.467,-1.2,c("true f","estimated f"),col = c(trueFunCol,estFunCol),
lwd = rep(2,2))
indQ1 <- length(xg[xg <= quantile(xObs,0.25)])
fQ1MCMC <- fMCMC[indQ1,]
indQ2 <- length(xg[xg <= quantile(xObs,0.50)])
fQ2MCMC <- fMCMC[indQ2,]
indQ3 <- length(xg[xg <= quantile(xObs,0.75)])
fQ3MCMC <- fMCMC[indQ3,]
parms <- list(cbind(muXMCMC,sigmaXMCMC,sigmaEpsMCMC,phiMCMC[1,],phiMCMC[2,],
fQ1MCMC,fQ2MCMC,fQ3MCMC))
parNamesVal <- list(c(expression(mu[x])),c(expression(sigma[x])),
c(expression(sigma[epsilon])),
c(expression(phi[0])),c(expression(phi[1])),
c("first quartile","of x"),
c("second quart.","of x"),
c("third quartile","of x"))
summMCMC(parms,parNames = parNamesVal,numerSummCex = 1,
KDEvertLine = FALSE,addTruthToKDE = c(muXTrue,sigmaXTrue,sigmaEpsTrue,
phiTrue[1],phiTrue[2],fTrue(xg[indQ1]),
fTrue(xg[indQ2]),fTrue(xg[indQ3])))
parms <- list(t(xUnobsMCMC[1:5,]))
parNamesVal <- list(c(expression(x[1]^{"unobs"})),
c(expression(x[2]^{"unobs"})),
c(expression(x[3]^{"unobs"})),
c(expression(x[4]^{"unobs"})),
c(expression(x[5]^{"unobs"})))
summMCMC(parms,parNames = parNamesVal,KDEvertLine = FALSE,addTruthToKDE = xUnobsTrue[1:5]) |
"plot.potd" <-
function(x, ...)
{
rawdata <- x$data
n <- length(as.numeric(rawdata))
times <- attributes(rawdata)$times
if(is.character(times) || inherits(times, "POSIXt") ||
inherits(x, "date") || inherits(x, "dates")) {
times <- as.POSIXlt(times)
gaps <- as.numeric(difftime(times[2:n], times[1:(n-1)],
units = "days")) * x$intensity
}
else gaps <- as.numeric(diff(times)) * x$intensity
data <- as.numeric(rawdata)
threshold <- x$threshold
par.ests <- x$par.ests
xi <- par.ests[1]
beta <- par.ests[4]
residuals <- logb(1 + (xi * (data - threshold))/beta)/xi
choices <- c("Point Process of Exceedances", "Scatterplot of Gaps",
"Qplot of Gaps", "ACF of Gaps", "Scatterplot of Residuals",
"Qplot of Residuals", "ACF of Residuals", "Go to GPD Plots")
tmenu <- paste("plot:", choices)
pick <- 1
lastcurve <- NULL
while(pick > 0) {
pick <- menu(tmenu, title =
"\nMake a plot selection (or 0 to exit):")
if(pick %in% 1:7) lastcurve <- NULL
switch(pick,
{
plot(times, rawdata, type = "h", sub = paste("Point process of",
length(as.numeric(rawdata)), "exceedances of threshold",
format(signif(threshold, 3))), ...)
},
{
plot(gaps, ylab = "Gaps", xlab = "Ordering", ...)
lines(lowess(1:length(gaps), gaps))
},
qplot(gaps, ...),
acf(gaps, lag.max = 20, ...),
{
plot(residuals, ylab = "Residuals", xlab = "Ordering", ...)
lines(lowess(1:length(residuals), residuals))
},
qplot(residuals, ...),
acf(residuals, lag.max = 20, ...),
lastcurve <- plot.gpd(x, ...))
}
invisible(lastcurve)
}
"pot" <-
function(data, threshold = NA, nextremes = NA, run = NA,
picture = TRUE, ...)
{
n <- length(as.numeric(data))
times <- attributes(data)$times
if(is.null(times)) {
times <- 1:n
attributes(data)$times <- times
start <- 1
end <- n
span <- end - start
}
else {
start <- times[1]
end <- times[n]
span <- as.numeric(difftime(as.POSIXlt(times)[n],
as.POSIXlt(times)[1], units = "days"))
}
if(is.na(nextremes) && is.na(threshold))
stop("Enter either a threshold or the number of upper extremes")
if(!is.na(nextremes) && !is.na(threshold))
stop("Enter EITHER a threshold or the number of upper extremes")
if(!is.na(nextremes))
threshold <- findthresh(as.numeric(data), nextremes)
if(threshold > 10) {
factor <- 10^(floor(log10(threshold)))
cat(paste("If singularity problems occur divide data",
"by a factor, perhaps", factor, "\n"))
}
exceedances.its <- structure(data[data > threshold], times =
times[data > threshold])
n.exceed <- length(as.numeric(exceedances.its))
p.less.thresh <- 1 - n.exceed/n
if(!is.na(run)) {
exceedances.its <- decluster(exceedances.its, run, picture)
n.exceed <- length(exceedances.its)
}
intensity <- n.exceed/span
exceedances <- as.numeric(exceedances.its)
xbar <- mean(exceedances) - threshold
s2 <- var(exceedances)
shape0 <- -0.5 * (((xbar * xbar)/s2) - 1)
extra <- ((length(exceedances)/span)^( - shape0) - 1)/shape0
betahat <- 0.5 * xbar * (((xbar * xbar)/s2) + 1)
scale0 <- betahat/(1 + shape0 * extra)
loc0 <- 0
theta <- c(shape0, scale0, loc0)
negloglik <- function(theta, exceedances, threshold, span)
{
if((theta[2] <= 0) || (min(1 + (theta[1] * (exceedances -
theta[3])) / theta[2]) <= 0))
f <- 1e+06
else {
y <- logb(1 + (theta[1] * (exceedances - theta[3])) / theta[2])
term3 <- (1/theta[1] + 1) * sum(y)
term1 <- span * (1 + (theta[1] * (threshold - theta[3])) /
theta[2])^(-1/theta[1])
term2 <- length(y) * logb(theta[2])
f <- term1 + term2 + term3
}
f
}
fit <- optim(theta, negloglik, hessian = TRUE, ..., exceedances =
exceedances, threshold = threshold, span = span)
if(fit$convergence)
warning("optimization may not have succeeded")
par.ests <- fit$par
varcov <- solve(fit$hessian)
par.ses <- sqrt(diag(varcov))
beta <- par.ests[2] + par.ests[1] * (threshold - par.ests[3])
par.ests <- c(par.ests, beta)
out <- list(n = length(data), period = c(start, end), data =
exceedances.its, span = span, threshold = threshold,
p.less.thresh = p.less.thresh, n.exceed = n.exceed, run = run,
par.ests = par.ests, par.ses = par.ses, varcov = varcov,
intensity = intensity, nllh.final = fit$value, converged
= fit$convergence)
names(out$par.ests) <- c("xi", "sigma", "mu", "beta")
names(out$par.ses) <- c("xi", "sigma", "mu")
class(out) <- "potd"
out
} |
check_tabulated_yield <- function(x, exclusive = TRUE, order = TRUE, x_name = substitute(x)) {
x_name <- deparse(x_name)
chk::check_data(
x,
values = list(
Type = c("actual", "actual", "optimal"),
pi = c(0, 1),
u = c(0, 1),
Yield = c(0, .Machine$double.xmax),
Age = c(0, 100),
Length = c(0, .Machine$double.xmax),
Weight = c(0, .Machine$double.xmax),
Effort = c(0, .Machine$double.xmax)
),
nrow = TRUE,
exclusive = exclusive,
order = order,
x_name = x_name
)
x
}
test_that("ypr_tabulate_yield", {
yield <- ypr_tabulate_yield(ypr_population())
expect_identical(check_tabulated_yield(yield), yield)
expect_identical(yield$Type, c("actual", "optimal"))
yield <- ypr_tabulate_yield(ypr_population(), all = TRUE)
expect_identical(check_tabulated_yield(yield, exclusive = FALSE), yield)
expect_identical(ncol(yield), 38L)
expect_identical(yield$Linf, c(100, 100))
yield <- ypr_tabulate_yield(ypr_populations(Rk = c(3, 5)))
expect_identical(check_tabulated_yield(yield, exclusive = FALSE), yield)
expect_identical(colnames(yield), c("Type", "pi", "u", "Yield", "Age", "Length", "Weight", "Effort", "Rk"))
expect_identical(nrow(yield), 4L)
yield <- ypr_tabulate_yield(ypr_populations(Rk = c(3, 5)), all = TRUE)
expect_identical(check_tabulated_yield(yield, exclusive = FALSE), yield)
expect_identical(ncol(yield), 38L)
expect_identical(nrow(yield), 4L)
yields <- ypr_tabulate_yields(ypr_population(n = ypr:::inst2inter(0.2)), pi = seq(0, 1, length.out = 10))
expect_identical(colnames(yields), c("pi", "u", "Yield", "Age", "Length", "Weight", "Effort"))
expect_identical(nrow(yields), 10L)
expect_error(chk::check_data(
yields,
values = list(
pi = c(0, 1),
u = c(0, 1),
Yield = c(0, .Machine$double.xmax),
Age = c(0, 100, NA),
Length = c(0, .Machine$double.xmax, NA),
Weight = c(0, .Machine$double.xmax, NA),
Effort = c(0, Inf)
),
nrow = TRUE,
exclusive = TRUE,
order = TRUE
), NA)
expect_identical(yields$pi[1:2], c(0, 1 / 9))
expect_equal(yields$Effort[1:2], c(0, 1.117905), tolerance = 1e-07)
expect_equal(yields$Yield[1:2], c(0, 0.0738), tolerance = 1e-04)
expect_equal(yields$Weight[1:2], c(NA, 3057.662), tolerance = 1e-07)
yields <- ypr_tabulate_yields(ypr_populations(Rk = c(3, 5)), pi = seq(0, 1, length.out = 2))
expect_identical(ncol(yields), 8L)
expect_identical(nrow(yields), 4L)
yields <- ypr_tabulate_yields(ypr_populations(Rk = c(3, 5)),
pi = seq(0, 1, length.out = 2),
all = TRUE
)
expect_identical(ncol(yields), 37L)
expect_identical(nrow(yields), 4L)
sr <- ypr_tabulate_sr(ypr_population())
expect_error(chk::check_data(
sr,
values = list(
Type = c("unfished", "actual", "optimal"),
pi = c(0, 1),
u = c(0, 1),
Eggs = c(0, .Machine$double.xmax),
Recruits = c(0, .Machine$double.xmax),
Spawners = c(0, .Machine$double.xmax),
Fecundity = c(0, .Machine$double.xmax)
),
nrow = TRUE,
exclusive = TRUE,
order = TRUE
), NA)
expect_identical(sr$Type, c("unfished", "actual", "optimal"))
fish <- ypr_tabulate_fish(ypr_population(n = ypr:::inst2inter(0.2)))
expect_identical(colnames(fish), c(
"Age", "Survivors", "Spawners", "Caught",
"Harvested", "Released", "HandlingMortalities"
))
expect_identical(fish[[1]], as.double(1:20))
expect_equal(fish$Survivors[1:2], c(0.134, 0.110), tolerance = 0.001)
fish <- ypr_tabulate_fish(ypr_population(), x = "Length")
expect_identical(colnames(fish), c(
"Length", "Survivors", "Spawners", "Caught",
"Harvested", "Released", "HandlingMortalities"
))
expect_identical(fish$Length[1:2], c(14, 26))
sr <- ypr_tabulate_sr(ypr_populations(Rk = c(3, 5)))
expect_error(chk::check_data(
sr,
values = list(
Type = c("unfished", "actual", "optimal"),
pi = c(0, 1),
u = c(0, 1),
Eggs = c(0, .Machine$double.xmax),
Recruits = c(0, .Machine$double.xmax),
Spawners = c(0, .Machine$double.xmax),
Fecundity = c(0, .Machine$double.xmax),
Rk = c(3, 3, 5)
),
nrow = TRUE,
exclusive = TRUE,
order = TRUE
), NA)
expect_identical(colnames(sr), c(
"Type", "pi", "u", "Eggs", "Recruits",
"Spawners", "Fecundity", "Rk"
))
expect_identical(sr$Rk, c(3, 3, 3, 5, 5, 5))
skip_if(length(tools::Rd_db("ypr")) == 0)
parameters <- ypr_tabulate_parameters(ypr_population())
expect_identical(parameters$Description[1], "The maximum age (yr).")
expect_error(chk::check_data(
parameters,
values = list(
Parameter = ypr:::.parameters$Parameter,
Value = c(min(ypr:::.parameters$Lower), max(ypr:::.parameters$Upper)),
Description = ""
),
exclusive = TRUE,
order = TRUE,
nrow = nrow(ypr:::.parameters),
key = "Parameter"
), NA)
expect_identical(
ypr_detabulate_parameters(ypr_tabulate_parameters(ypr_population(BH = 1L))),
ypr_population(BH = 1L)
)
})
test_that("ypr_tabulate_yield extinct population", {
yield <- ypr_tabulate_yield(ypr_population(Linf = 130), all = TRUE)
expect_equal(yield$pi, c(0.2, 0.102752704683603))
expect_equal(yield$Yield, c(0.0273043505036034, 0.0770860806461869))
yield <- ypr_tabulate_yield(ypr_population(Linf = 140), all = TRUE)
expect_equal(yield$pi, c(0.2, 0.0926725376181979))
expect_equal(yield$Yield, c(0, 0.0857039391276462))
})
test_that("ypr_tabulate_biomass", {
biomass <- ypr_tabulate_biomass(ypr_population())
expect_error(chk::check_data(
biomass,
values = list(
Age = c(1L, 100L),
Length = c(0, .Machine$double.xmax),
Weight = c(0, .Machine$double.xmax),
Fecundity = c(0, .Machine$double.xmax),
Survivors = c(0, 1),
Spawners = c(0, .Machine$double.xmax),
Biomass = c(0, .Machine$double.xmax),
Eggs = c(0, .Machine$double.xmax)
),
nrow = TRUE,
exclusive = TRUE,
order = TRUE
), NA)
}) |
system <- function(command, intern = FALSE,
ignore.stdout = FALSE, ignore.stderr = FALSE,
wait = TRUE, input = NULL,
show.output.on.console = TRUE, minimized = FALSE,
invisible = TRUE)
{
if(!missing(show.output.on.console) || !missing(minimized)
|| !missing(invisible))
message("arguments 'show.output.on.console', 'minimized' and 'invisible' are for Windows only")
if(!is.logical(intern) || is.na(intern))
stop("'intern' must be TRUE or FALSE")
if(!is.logical(ignore.stdout) || is.na(ignore.stdout))
stop("'ignore.stdout' must be TRUE or FALSE")
if(!is.logical(ignore.stderr) || is.na(ignore.stderr))
stop("'ignore.stderr' must be TRUE or FALSE")
if(!is.logical(wait) || is.na(wait))
stop("'wait' must be TRUE or FALSE")
if(ignore.stdout) command <- paste(command, ">/dev/null")
if(ignore.stderr) command <- paste(command, "2>/dev/null")
if(!is.null(input)) {
if(!is.character(input))
stop("'input' must be a character vector or 'NULL'")
f <- tempfile()
on.exit(unlink(f))
writeLines(input, f)
command <- paste("<", shQuote(f), command)
}
if(!wait && !intern) command <- paste(command, "&")
.Internal(system(command, intern))
}
system2 <- function(command, args = character(),
stdout = "", stderr = "", stdin = "", input = NULL,
env = character(),
wait = TRUE, minimized = FALSE, invisible = TRUE)
{
if(!missing(minimized) || !missing(invisible))
message("arguments 'minimized' and 'invisible' are for Windows only")
if(!is.logical(wait) || is.na(wait))
stop("'wait' must be TRUE or FALSE")
intern <- FALSE
command <- paste(c(env, shQuote(command), args), collapse = " ")
if(is.null(stdout)) stdout <- FALSE
if(is.null(stderr))
stderr <- FALSE
else if (isTRUE(stderr)) {
if (!isTRUE(stdout)) warning("setting stdout = TRUE")
stdout <- TRUE
}
if (identical(stdout, FALSE))
command <- paste(command, ">/dev/null")
else if(isTRUE(stdout))
intern <- TRUE
else if(is.character(stdout)) {
if(length(stdout) != 1L) stop("'stdout' must be of length 1")
if(nzchar(stdout)) {
command <- if (identical(stdout, stderr))
paste (command, ">", shQuote(stdout), "2>&1")
else paste(command, ">", shQuote(stdout))
}
}
if (identical(stderr, FALSE))
command <- paste(command, "2>/dev/null")
else if(isTRUE(stderr)) {
command <- paste(command, "2>&1")
} else if(is.character(stderr)) {
if(length(stderr) != 1L) stop("'stderr' must be of length 1")
if(nzchar(stderr) && !identical(stdout, stderr))
command <- paste(command, "2>", shQuote(stderr))
}
if(!is.null(input)) {
if(!is.character(input))
stop("'input' must be a character vector or 'NULL'")
f <- tempfile()
on.exit(unlink(f))
writeLines(input, f)
command <- paste(command, "<", shQuote(f))
} else if (nzchar(stdin)) command <- paste(command, "<", stdin)
if(!wait && !intern) command <- paste(command, "&")
.Internal(system(command, intern))
}
Sys.which <- function(names)
{
res <- character(length(names)); names(res) <- names
which <- "@WHICH@"
if (!nzchar(which)) {
warning("'which' was not found on this platform")
return(res)
}
for(i in seq_along(names)) {
if(is.na(names[i])) {res[i] <- NA; next}
ans <- suppressWarnings(system(paste(which, shQuote(names[i])),
intern = TRUE, ignore.stderr = TRUE))
if(grepl("solaris", R.version$os)) {
tmp <- strsplit(ans[1], " ", fixed = TRUE)[[1]]
if(identical(tmp[1:3], c("no", i, "in"))) ans <- ""
}
res[i] <- if(length(ans)) ans[1] else ""
if(!file.exists(res[i])) res[i] <- ""
}
res
} |
draw_pie <- function(x = 0.5, y = 0.5, radius = 1, cols = c("red", "green"), border_col = "black", node_border_lwd = 1,
labels = NULL, edges = 200, label_cex = 1, xlim = NULL, ylim = NULL, add = TRUE){
pies <- rep(1, length(cols))
init_angle = 0
if(is.null(labels)){labels <- rep("", length(cols))}
pies <- c(0, cumsum(pies)/sum(pies))
d_pie <- diff(pies)
n_pie <- length(d_pie)
twopi <- 2 * pi
t2xy <- function(x,y,t){
t2p <- twopi * t + init_angle * pi/180
list(x = (radius * cos(t2p))+x, y = (radius * sin(t2p))+y)
}
if(!add){
plot.new()
if(is.null(xlim)){xlim = c(x-radius, x+radius)}
if(is.null(ylim)){ylim = c(y-radius, y+radius)}
plot.window(xlim = xlim, ylim = ylim)
}
for(i in 1:n_pie){
n <- max(2, floor(edges * d_pie[i]))
P <- t2xy(x = x, y = y, seq.int(pies[i], pies[i + 1], length.out = n))
polygon(c(P$x, x), c(P$y, y), col = cols[i], border = border_col, lwd = node_border_lwd)
text_x <- mean(c(min(P$x), max(P$x)))
text_y <- mean(c(min(P$y), max(P$y)))
lab <- as.character(labels[i])
if (!is.na(lab) && nzchar(lab)){
text(text_x, text_y, labels[i], xpd = TRUE, adj = 0.5, cex = label_cex)
}
}
} |
fSquared <- function(seminr_model, iv, dv) {
if (length(seminr_model$constructs) == 2) {
rsq <- (seminr_model$rSquared["Rsq", dv])
return((rsq - 0) / (1 - rsq))
}
with_sm <- seminr_model$smMatrix
without_sm <- subset(with_sm, !((with_sm[, "source"] == iv) & (with_sm[, "target"] == dv)))
suppressMessages(
without_pls <- estimate_pls(data = seminr_model$rawdata,
measurement_model = seminr_model$measurement_model,
structural_model = without_sm,
inner_weights = seminr_model$inner_weights,
missing = seminr_model$settings$missing,
missing_value = seminr_model$settings$missing_value,
maxIt = seminr_model$settings$maxIt,
stopCriterion = seminr_model$settings$stopCriterion)
)
with_r2 <- seminr_model$rSquared["Rsq", dv]
ifelse(any(without_sm[,"target"] == dv),
without_r2 <- without_pls$rSquared["Rsq", dv],
without_r2 <- 0)
return((with_r2 - without_r2) / (1 - with_r2))
}
model_fsquares <- function(seminr_model) {
path_matrix <- seminr_model$path_coef
fsquared_matrix <- path_matrix
for (dv in all_endogenous(seminr_model$smMatrix)) {
ifelse(length(interactions_of(dv, seminr_model$smMatrix) ) > 0,
int_components <- unique(unlist(strsplit(interactions_of(dv, seminr_model$smMatrix), "\\*"))),
int_components <- NA)
for (iv in setdiff(all_exogenous(seminr_model$smMatrix), int_components)) {
fsquared_matrix[iv, dv] <- fSquared(seminr_model = seminr_model,
iv = iv,
dv = dv)
fsquared_matrix[int_components,dv] <- NA
}
}
if (length(all_interactions(seminr_model$smMatrix) > 0)) {
comment(fsquared_matrix) <- "The fSquare for certain relationships cannot be calculated as the model contains an interaction term and omitting either the antecedent or moderator in the interaction term will cause model estimation to fail"
}
convert_to_table_output(fsquared_matrix)
} |
qln3 <- function(u = NULL, RP = 1/(1 - u), para){
if (is.null(u) & length(RP) >= 1) {u <- 1 - 1/RP}
x <- qualn3(f = u, para = para)
return(x)
} |
inspect_data_dichotomous <-
function(data,
success,
allow_nas = TRUE,
warning_nas = FALSE) {
inspect_true_or_false(allow_nas)
inspect_true_or_false(warning_nas)
data_output_name <- deparse(substitute(data))
s_output_name <- deparse(substitute(success))
if (is.null(data)) {
stop(paste("Invalid argument:", data_output_name, "is NULL."))
}
if (is.null(success)) {
stop(paste("Invalid argument:", s_output_name, "is NULL."))
}
if (isFALSE(is.atomic(data))) {
stop(paste("Invalid argument:", data_output_name, "must be atomic."))
}
if (length(data) == 0) {
stop(paste("Invalid argument:", data_output_name, "is empty."))
}
if (any(isFALSE(is.atomic(success)), isFALSE(length(success) == 1))) {
stop(paste(
"Invalid argument:",
s_output_name,
"must be atomic and have length 1."
))
}
if (isFALSE(typeof(data) %in% c("logical",
"integer",
"double",
"character"))) {
stop(
paste(
"Invalid argument: the type of",
data_output_name,
"must be 'logical', 'integer', 'double' or 'character'."
)
)
}
if (isFALSE(typeof(success) %in% c("logical",
"integer",
"double",
"character"))) {
stop(
paste(
"Invalid argument: the type of",
s_output_name,
"must be 'logical', 'integer', 'double' or 'character'."
)
)
}
if (is.na(success)) {
stop(paste("Invalid argument:", s_output_name, "is NA or NaN"))
}
data_factor <-
factor(data, levels = unique(c(levels(factor(
success
)), levels(factor(
unique(data)
)))))
if (isTRUE(nlevels(data_factor) > 2)) {
stop(paste("Invalid argument: there are more than two levels'."))
}
if (all(is.na(data))) {
stop(paste(
"Invalid argument: all elements of",
data_output_name,
"are NA or NaN."
))
}
if (any(is.na(data))) {
if (isFALSE(allow_nas)) {
stop(paste(
"Invalid argument: there are NA or NaN values in ",
paste0(data_output_name, ".")
))
} else {
if (isTRUE(warning_nas)) {
warning(paste(
"There are NA or NaN values in",
paste0(data_output_name, ".")
))
}
}
}
if (isFALSE(success %in% unique(data))) {
warning(paste(
s_output_name,
"not observed in",
paste0(data_output_name, ".")
))
}
}
inspect_data_categorical <-
function(data,
allow_nas = TRUE,
warning_nas = FALSE) {
inspect_true_or_false(allow_nas)
inspect_true_or_false(warning_nas)
data_output_name <- deparse(substitute(data))
if (is.null(data)) {
stop(paste("Invalid argument:", data_output_name, "is NULL."))
}
if (isFALSE(is.atomic(data))) {
stop(paste("Invalid argument:", data_output_name, "must be atomic."))
}
if (length(data) == 0) {
stop(paste("Invalid argument:", data_output_name, "is empty."))
}
if (isFALSE(typeof(data) %in% c("logical",
"integer",
"double",
"character"))) {
stop(
paste(
"Invalid argument: the type of",
data_output_name,
"must be 'logical', 'integer', 'double' or 'character'."
)
)
}
if (all(is.na(data))) {
stop(paste(
"Invalid argument: all elements of",
data_output_name,
"are NA or NaN."
))
}
if (any(is.na(data))) {
if (isFALSE(allow_nas)) {
stop(paste(
"Invalid argument: there are NA or NaN values in ",
paste0(data_output_name, ".")
))
} else {
if (isTRUE(warning_nas)) {
warning(paste(
"There are NA or NaN values in",
paste0(data_output_name, ".")
))
}
}
}
}
inspect_data_cat_as_dichotom <-
function(data,
success,
allow_nas = TRUE,
warning_nas = FALSE) {
inspect_true_or_false(allow_nas)
inspect_true_or_false(warning_nas)
data_output_name <- deparse(substitute(data))
s_output_name <- deparse(substitute(success))
if (is.null(data)) {
stop(paste("Invalid argument:", data_output_name, "is NULL."))
}
if (is.null(success)) {
stop(paste("Invalid argument:", s_output_name, "is NULL."))
}
if (isFALSE(is.atomic(data))) {
stop(paste("Invalid argument:", data_output_name, "must be atomic."))
}
if (length(data) == 0) {
stop(paste("Invalid argument:", data_output_name, "is empty."))
}
if (any(isFALSE(is.atomic(success)), isFALSE(length(success) == 1))) {
stop(paste(
"Invalid argument:",
s_output_name,
"must be atomic and have length 1."
))
}
if (isFALSE(typeof(data) %in% c("logical",
"integer",
"double",
"character"))) {
stop(
paste(
"Invalid argument: the type of",
data_output_name,
"must be 'logical', 'integer', 'double' or 'character'."
)
)
}
if (isFALSE(typeof(success) %in% c("logical",
"integer",
"double",
"character"))) {
stop(
paste(
"Invalid argument: the type of",
s_output_name,
"must be 'logical', 'integer', 'double' or 'character'."
)
)
}
if (all(is.na(data))) {
stop(paste(
"Invalid argument: all elements of",
data_output_name,
"are NA or NaN."
))
}
if (is.na(success)) {
stop(paste("Invalid argument:", s_output_name, "is NA or NaN."))
}
if (any(is.na(data))) {
if (isFALSE(allow_nas)) {
stop(paste(
"Invalid argument: there are NA or NaN values in ",
paste0(data_output_name, ".")
))
} else {
if (isTRUE(warning_nas)) {
warning(paste(
"There are NA or NaN values in",
paste0(data_output_name, ".")
))
}
}
}
if (isFALSE(success %in% unique(data))) {
warning(paste(
s_output_name,
"not observed in",
paste0(data_output_name, ".")
))
}
} |
update_membership <- function(lon,
lat,
global_membership,
local_membership,
indexes) {
if (sum(global_membership[indexes]) == 0) {
global_membership[indexes] <- adjust_membership(local_membership,
max(global_membership))
return(global_membership)
}
if (all(global_membership[indexes] != 0)) {
return(global_membership)
}
fin_membership <- global_membership[indexes]
local_lon <- lon[indexes]
local_lat <- lat[indexes]
new_p <- which(fin_membership == 0)
old_p <- which(fin_membership != 0)
shared_clusteres <- unique(local_membership[old_p])
type1 <- new_p[local_membership[new_p] %in% shared_clusteres]
type2 <- new_p[!local_membership[new_p] %in% shared_clusteres]
for (i in type1) {
bool <- local_membership[old_p] == local_membership[i]
current_old <- old_p[bool]
dist_vector <- dist_point_to_vector(local_lon[i],
local_lat[i],
local_lon[current_old],
local_lat[current_old])
target <- current_old[which.min(dist_vector)]
fin_membership[i] <- fin_membership[target]
}
if (length(type2) != 0) {
fin_membership[type2] <- adjust_membership(local_membership[type2],
max(global_membership))
}
global_membership[indexes] <- fin_membership
return(global_membership)
} |
InterVA5 <- function (Input, HIV, Malaria, write = TRUE, directory = NULL, filename = "VA5_result",
output = "classic", append = FALSE, groupcode = FALSE, sci = NULL,
returnCheckedData = FALSE, ...)
{
va5 <- function(ID, MALPREV, HIVPREV, PREGSTAT, PREGLIK, CAUSE1, LIK1, CAUSE2, LIK2, CAUSE3, LIK3, INDET,
COMCAT, COMNUM, wholeprob, ...) {
ID <- ID
MALPREV <- as.character(MALPREV)
HIVPREV <- as.character(HIVPREV)
PREGSTAT <- PREGSTAT
PREGLIK <- PREGLIK
COMCAT <- as.character(COMCAT)
COMNUM <- COMNUM
wholeprob <- wholeprob
va5.out <- list(ID = ID, MALPREV = MALPREV, HIVPREV = HIVPREV, PREGSTAT = PREGSTAT, PREGLIK = PREGLIK,
CAUSE1 = CAUSE1, LIK1 = LIK1, CAUSE2 = CAUSE2, LIK2 = LIK2, CAUSE3 = CAUSE3, LIK3 = LIK3, INDET = INDET,
COMCAT = COMCAT, COMNUM = COMNUM, wholeprob = wholeprob)
va5.out
}
save.va5 <- function(x, filename, write) {
if (!write) {
return()
}
x <- x[-15]
x <- as.matrix(x)
filename <- paste(filename, ".csv", sep = "")
write.table(t(x), file = filename, sep = ",", append = TRUE, row.names = FALSE, col.names = FALSE)
}
save.va5.prob <- function(x, filename, write) {
if (!write) {
return()
}
prob <- unlist(x[15])
x <- x[-15]
x <- unlist(c(as.matrix(x), as.matrix(prob)))
filename <- paste(filename, ".csv", sep = "")
write.table(t(x), file = filename, sep = ",", append = TRUE, row.names = FALSE, col.names = FALSE)
}
if (is.null(directory) & write)
stop("error: please provide a directory (required when write = TRUE)")
if (is.null(directory))
directory = getwd()
dir.create(directory, showWarnings = FALSE)
globle.dir <- getwd()
setwd(directory)
if (is.null(sci)) {
data("probbaseV5", envir = environment())
probbaseV5 <- get("probbaseV5", envir = environment())
probbaseV5 <- as.matrix(probbaseV5)
probbaseV5Version <- probbaseV5[1,3]
}
if (!is.null(sci)) {
validSCI <- TRUE
if (!is.data.frame(sci) & !is.matrix(sci)) validSCI <- FALSE
if (nrow(sci) != 354) validSCI <- FALSE
if (ncol(sci) != 87) validSCI <- FALSE
if (!validSCI) {
stop("error: invalid sci (must be data frame or matrix with 354 rows and 87 columns).")
}
probbaseV5 <- as.matrix(sci)
probbaseV5Version <- probbaseV5[1,3]
}
message("Using Probbase version: ", probbaseV5Version)
data("causetextV5", envir = environment())
causetextV5 <- get("causetextV5", envir = environment())
if (groupcode) {
causetextV5 <- causetextV5[, -2]
} else {
causetextV5 <- causetextV5[, -3]
}
if (write) {
cat(paste("Error & warning log built for InterVA5", Sys.time(), "\n"),
file = "errorlogV5.txt", append = FALSE)
}
if ( "i183o" %in% colnames(Input)) {
colnames(Input)[which(colnames(Input) == "i183o")] <- "i183a"
message("Due to the inconsistent names in the early version of InterVA5, the indicator 'i183o' has been renamed as 'i183a'.")
}
Input <- as.matrix(Input)
if (dim(Input)[1] < 1) {
stop("error: no data input")
}
N <- dim(Input)[1]
S <- dim(Input)[2]
if (S != dim(probbaseV5)[1]) {
stop("error: invalid data input format. Number of values incorrect")
}
if (tolower(colnames(Input)[S]) != "i459o") {
stop("error: the last variable should be 'i459o'")
}
data("RandomVA5", envir = environment())
RandomVA5 <- get("RandomVA5", envir = environment())
valabels = colnames(RandomVA5)
count.changelabel = 0
for (i in 1:S) {
if (tolower(colnames(Input)[i]) != tolower(valabels)[i]) {
warning(paste("Input column '", colnames(Input)[i], "' does not match InterVA5 standard: '", valabels[i], "'", sep = ""),
call. = FALSE, immediate. = TRUE)
count.changelabel = count.changelabel + 1
}
}
if (count.changelabel > 0) {
warning(paste(count.changelabel, "column names changed in input. \n If the change in undesirable, please change in the input to match standard InterVA5 input format."),
call. = FALSE, immediate. = TRUE)
colnames(Input) <- valabels
}
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "I" ] <- 1
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "A+" ] <- 0.8
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "A" ] <- 0.5
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "A-" ] <- 0.2
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "B+" ] <- 0.1
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "B" ] <- 0.05
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "B-" ] <- 0.02
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "B -"] <- 0.02
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "C+" ] <- 0.01
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "C" ] <- 0.005
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "C-" ] <- 0.002
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "D+" ] <- 0.001
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "D" ] <- 5e-04
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "D-" ] <- 1e-04
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "E" ] <- 1e-05
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "N" ] <- 0
probbaseV5[,18:ncol(probbaseV5)][probbaseV5[,18:ncol(probbaseV5)] == "" ] <- 0
probbaseV5[1, 1:17] <- rep(0, 17)
Sys_Prior <- as.numeric(probbaseV5[1, ])
D <- length(Sys_Prior)
HIV <- tolower(HIV)
Malaria <- tolower(Malaria)
if (!(HIV %in% c("h", "l", "v")) || !(Malaria %in% c("h","l", "v"))) {
stop("error: the HIV and Malaria indicator should be one of the three: 'h', 'l', and 'v'")
}
if (HIV == "h")
Sys_Prior[23] <- 0.05
if (HIV == "l")
Sys_Prior[23] <- 0.005
if (HIV == "v")
Sys_Prior[23] <- 1e-05
if (Malaria == "h") {
Sys_Prior[25] <- 0.05
Sys_Prior[45] <- 0.05
}
if (Malaria == "l") {
Sys_Prior[25] <- 0.005
Sys_Prior[45] <- 1e-05
}
if (Malaria == "v") {
Sys_Prior[25] <- 1e-05
Sys_Prior[45] <- 1e-05
}
ID.list <- rep(NA, N)
VAresult <- vector("list", N)
if (write && append == FALSE) {
header = c("ID", "MALPREV", "HIVPREV", "PREGSTAT", "PREGLIK",
"CAUSE1", "LIK1", "CAUSE2", "LIK2", "CAUSE3", "LIK3", "INDET", "COMCAT", "COMNUM")
if (output == "extended")
header = c(header, as.character(causetextV5[, 2]))
write.table(t(header), file = paste(filename, ".csv", sep = ""), row.names = FALSE, col.names = FALSE, sep = ",")
}
nd <- max(1, round(N/100))
np <- max(1, round(N/10))
if (write) {
cat(paste("\n\n", "the following records are incomplete and excluded from further processing:", "\n\n",
sep=""), file = "errorlogV5.txt", append = TRUE)
}
firstPass <- NULL
secondPass <- NULL
errors <- NULL
if (returnCheckedData) {
checkedData <- NULL
idInputs <- Input[,1]
}
for (i in 1:N) {
if (i%%nd == 0) {
cat(".")
}
if (i%%np == 0) {
cat(paste(round(i/N * 100), "% completed\n", sep = ""))
}
if (i == N) {
cat(paste("100% completed\n", sep = ""))
}
index.current <- as.character(Input[i, 1])
Input[i, which(toupper(Input[i, ]) == "N")] <- "0"
Input[i, which(toupper(Input[i, ]) == "Y")] <- "1"
Input[i, which(Input[i, ] != "1" & Input[i, ] != "0")] <- NA
input.current <- as.numeric(Input[i, ])
input.current[1] <- 0
if (sum(input.current[6:12], na.rm=TRUE) < 1) {
if (write) {
errors <- rbind(errors, paste(index.current, " Error in age indicator: Not Specified "))
}
next
}
if (sum(input.current[4:5], na.rm=TRUE) < 1) {
if (write) {
errors <- rbind(errors, paste(index.current, " Error in sex indicator: Not Specified "))
}
next
}
if (sum(input.current[21:328], na.rm=TRUE) < 1) {
if (write) {
errors <- rbind(errors, paste(index.current, " Error in indicators: No symptoms specified "))
}
next
}
tmp <- DataCheck5(input.current, id=index.current, probbaseV5=probbaseV5, write=write)
if (returnCheckedData) {
checkedData <- rbind(checkedData,
c(idInputs[i], tmp$Output[2:S]))
}
input.current <- tmp$Output
firstPass <- rbind(firstPass, tmp$firstPass)
secondPass <- rbind(secondPass, tmp$secondPass)
subst.vector <- rep(NA, length=S)
subst.vector[probbaseV5[,6]=="N"] <- 0
subst.vector[probbaseV5[,6]=="Y"] <- 1
new.input <- rep(0, S)
for (y in 2:S) {
if (!is.na(input.current[y])) {
if (input.current[y]==subst.vector[y]) {
new.input[y] <- 1
}
}
}
input.current[input.current==0] <- 1
input.current[1] <- 0
input.current[is.na(input.current)] <- 0
reproductiveAge <- 0
preg_state <- " "
lik.preg <- " "
if (input.current[5] == 1 && (input.current[17] == 1 || input.current[18] == 1 || input.current[19] == 1)) {
reproductiveAge <- 1
}
prob <- Sys_Prior[18:D]
temp <- which(new.input[2:length(input.current)] == 1)
for (jj in 1:length(temp)) {
temp_sub <- temp[jj]
for (j in 18:D) {
prob[j - 17] <- prob[j - 17] * as.numeric(probbaseV5[temp_sub + 1, j])
}
if (sum(prob[1:3]) > 0)
prob[1:3] <- prob[1:3]/sum(prob[1:3])
if (sum(prob[4:64]) > 0)
prob[4:64] <- prob[4:64]/sum(prob[4:64])
if (sum(prob[65:70]) > 0)
prob[65:70] <- prob[65:70]/sum(prob[65:70])
}
names(prob) <- causetextV5[, 2]
prob_A <- prob[ 1: 3]
prob_B <- prob[ 4:64]
prob_C <- prob[65:70]
if (sum(prob_A) == 0 || reproductiveAge == 0) {
preg_state <- "n/a"
lik.preg <- " "
}
if (max(prob_A) < 0.1 & reproductiveAge == 1) {
preg_state <- "indeterminate"
lik.preg <- " "
}
if (which.max(prob_A) == 1 && prob_A[1] >= 0.1 && reproductiveAge == 1) {
preg_state <- "Not pregnant or recently delivered"
lik.preg <- as.numeric(round(prob_A[1]/sum(prob_A) * 100))
}
if (which.max(prob_A) == 2 && prob_A[2] >= 0.1 && reproductiveAge == 1) {
preg_state <- "Pregnancy ended within 6 weeks of death"
lik.preg <- as.numeric(round(prob_A[2]/sum(prob_A) * 100))
}
if (which.max(prob_A) == 3 && prob_A[3] >= 0.1 && reproductiveAge == 1) {
preg_state <- "Pregnant at death"
lik.preg <- as.numeric(round(prob_A[3]/sum(prob_A) * 100))
}
prob.temp <- prob_B
if (max(prob.temp) < 0.4) {
cause1 <- lik1 <- cause2 <- lik2 <- cause3 <- lik3 <- " "
indet <- 100
}
if (max(prob.temp) >= 0.4) {
lik1 <- round(max(prob.temp) * 100)
cause1 <- names(prob.temp)[which.max(prob.temp)]
prob.temp <- prob.temp[-which.max(prob.temp)]
lik2 <- round(max(prob.temp) * 100)
cause2 <- names(prob.temp)[which.max(prob.temp)]
if (max(prob.temp) < 0.5 * max(prob_B))
lik2 <- cause2 <- " "
prob.temp <- prob.temp[-which.max(prob.temp)]
lik3 <- round(max(prob.temp) * 100)
cause3 <- names(prob.temp)[which.max(prob.temp)]
if (max(prob.temp) < 0.5 * max(prob_B))
lik3 <- cause3 <- " "
top3 <- as.numeric(c(lik1, lik2, lik3))
indet <- round(100 - sum(top3, na.rm=TRUE))
}
if (sum(prob_C) > 0) prob_C <- prob_C/sum(prob_C)
if (max(prob_C)<.5) {
comcat <- "Multiple"
comnum <- " "
}
if (max(prob_C)>=.5) {
comcat <- names(prob_C)[which.max(prob_C)]
comnum <- round(max(prob_C)*100)
}
ID.list[i] <- index.current
VAresult[[i]] <- va5(ID = index.current, MALPREV = Malaria, HIVPREV = HIV,
PREGSTAT = preg_state, PREGLIK = lik.preg,
CAUSE1 = cause1, LIK1 = lik1, CAUSE2 = cause2, LIK2 = lik2, CAUSE3 = cause3, LIK3 = lik3,
INDET = indet, COMCAT=comcat, COMNUM=comnum, wholeprob = c(prob_A, prob_B, prob_C))
if (output == "classic")
save.va5(VAresult[[i]], filename = filename, write)
if (output == "extended")
save.va5.prob(VAresult[[i]], filename = filename, write)
}
if (write) {
cat(errors, paste("\n", "the following data discrepancies were identified and handled:", "\n"),
firstPass, paste("\n", "Second pass", "\n"), secondPass, sep="\n", file="errorlogV5.txt", append=TRUE)
}
setwd(globle.dir)
if (!returnCheckedData) {
checkedData <- "returnCheckedData = FALSE"
} else {
colnames(checkedData) <- colnames(Input)
}
out <- list(ID = ID.list[which(!is.na(ID.list))], VA5 = VAresult[which(!is.na(ID.list))],
Malaria = Malaria, HIV = HIV, checkedData = checkedData)
class(out) <- "interVA5"
return(out)
} |
rm(list=ls()); gc()
setwd("C:/Users/Tom/Documents/Kaggle/Santander")
library(data.table)
library(bit64)
library(xgboost)
library(Matrix)
library(Ckmeans.1d.dp)
library(beepr)
library(ggplot2)
library(stringr)
set.seed(14)
targetDate <- "12-11-2016"
trainModelFolder <- "train"
saveFolderExtension <- " Top 100 monthProduct 200 rounds 20 Folds"
testModel <- grepl("test", trainModelFolder)
overwrite <- FALSE
featureSelection <- TRUE
topFeatures <- 100
featureSelectionMode <- c("monthProduct", "product")[1]
targetIds <- 0:24
excludeNoNewProducts <- FALSE
jointModelNoNewProducts <- FALSE
excludeNoPosFlanks <- FALSE
excludeString <- ifelse(excludeNoNewProducts, "",
ifelse(excludeNoPosFlanks, "PosFlankCusts", "TrainAll"))
underSampleNomPensNoNomina <- FALSE
maxMonthNomPensNoNomina <- 150
nrounds <- 2e2
hyperparSetSimple <- list(nrounds = nrounds, etaC = 10, subsample = 1,
colsample_bytree = 0.5, max.depth = 6,
min_child_weight = 0, gamma = 0)
hyperparSetExtended <- list(nrounds = nrounds, etaC = 10, subsample = 1,
colsample_bytree = 0.5, max.depth = 8,
min_child_weight = 0, gamma = 0.1)
baseK <- 20
if(baseK <= 1) browser()
K <- 20
saveBaseModels <- TRUE
skipCommonModel <- TRUE
bootstrap <- FALSE
nbBoots <- ifelse(bootstrap, 5, 1)
extraBootstrapDepth <- ifelse(bootstrap, 1, 0)
if(bootstrap && K>1){
stop("Please Tom, don't combine bootstrap with cross validation")
}
useStackingFolds <- TRUE
stackingIdsFn <- paste("first level ncodpers", baseK, "folds.rds")
maxTrainRecords <- Inf
showVariableImportance <- FALSE
showMeanLabelByMayFlag <- FALSE
timeRange <- c(0, 24)
simpleModeling <- FALSE
dropProductFeaturesPosFlankProd <- FALSE
dropOtherIndFeatures <- FALSE
baseProducts <- c("ahor_fin", "aval_fin", "cco_fin", "cder_fin",
"cno_fin", "ctju_fin", "ctma_fin", "ctop_fin",
"ctpp_fin", "deco_fin", "deme_fin", "dela_fin",
"ecue_fin", "fond_fin", "hip_fin", "plan_fin",
"pres_fin", "reca_fin", "tjcr_fin", "valo_fin",
"viv_fin", "nomina", "nom_pens", "recibo"
)
targetVars <- paste0("ind_", baseProducts, "_ult1")
featuresPath <- file.path(getwd(), "Feature engineering", targetDate,
trainModelFolder)
featureFiles <- list.files(featuresPath)[c(3, 10)]
featureFiles <- featureFiles[grepl(paste0(ifelse(testModel, "Lag17 ", ""),
"features.rds$"), featureFiles)]
trainFnBases <- gsub(" features.rds$", "", featureFiles)
batchFeatures <- all(grepl("batch", trainFnBases, ignore.case = TRUE))
nbFeatureFiles <- length(featureFiles)
trainFeaturePaths <- paste(featuresPath, featureFiles, sep="/")
if(!excludeNoNewProducts || testModel){
targetIds <- targetIds[targetIds!=0]
}
firstNonZeroTarget <- which(targetIds!=0)[1]
inspectVarImpTopModels <- FALSE
inspectIds <- c(3, 5, 7, 13, 18, 19, 22, 23, 24)
lowPriorityIds <- NULL
nbTargetIds <- length(targetIds)
dropPredictors <- c(
"trainWeight"
, "hasNewProduct", "nbNewProducts", "hasAnyPosFlank"
, "ncodpers"
, "lastDate"
, "gapsFrac", "dataMonths", "monthsFrac", "nbLagRecords "
, "grossIncome"
, "seniorityDensity"
, paste0(targetVars, "MAPRatioJune15")
, paste0(targetVars, "RelMAP")
, "familyId"
, targetVars
)
source("Common/getModelWeights.R")
source("Common/getHyperParDescr.R")
while(TRUE){
if(as.numeric(format(Sys.time(),"%H")) >= timeRange[1] &&
as.numeric(format(Sys.time(),"%H")) <= timeRange[2]){
break
}
cat(paste0("Invalid time range, sleeping for five minutes @"),
as.character(Sys.time()), "\n")
Sys.sleep(300)
}
dateTargetWeights <- readRDS(file.path(getwd(), "Model weights", targetDate,
"model weights first.rds"))
if(featureSelection){
if(featureSelectionMode == "monthProduct"){
featureOrders <-
readRDS(file.path(getwd(), "first level learners", targetDate,
"product month feature order.rds"))
} else{
featureOrders <-
readRDS(file.path(getwd(), "first level learners", targetDate,
"product feature order.rds"))
}
}
if(useStackingFolds && K>1){
stackingFoldsPath <- file.path(getwd(), "Second level learners", targetDate,
stackingIdsFn)
stackingFolds <- readRDS(stackingFoldsPath)
}
for(modelGroupId in 1:nbFeatureFiles){
cat("Learning xgboost models for month", modelGroupId, "of",
nbFeatureFiles , "@", as.character(Sys.time()), "\n\n")
trainOrig <- readRDS(trainFeaturePaths[modelGroupId])
if(testModel){
submission <- readRDS(bestSubmissionFn)
submissionProds <- sort(unique(submission$product))
for(i in 1:length(submissionProds)){
targetSubmissionProduct <- submissionProds[i]
submissionProdRows <- submission[product == targetSubmissionProduct, ]
trainOrig[[targetSubmissionProduct]] <-
submissionProdRows[match(trainOrig$ncodpers,
submissionProdRows$ncodpers),
totalProb]
}
}
predictors <- setdiff(names(trainOrig), unique(dropPredictors))
predictorsOrig <- predictors
saveDir <- file.path(getwd(), "First level learners", targetDate)
dir.create(saveDir, showWarnings = FALSE)
baseModelDir <- file.path(saveDir, paste0(trainModelFolder, excludeString,
saveFolderExtension))
dir.create(baseModelDir, showWarnings = FALSE)
if(nbFeatureFiles==1){
saveModelDir <- baseModelDir
} else{
saveModelDir <- file.path(baseModelDir, trainFnBases[modelGroupId])
dir.create(saveModelDir, showWarnings = FALSE)
}
if(excludeNoNewProducts && min(targetIds)>0){
trainOrig <- trainOrig[hasNewProduct == TRUE, ]
}
posFlankClientsFn <- file.path(getwd(), "Feature engineering", targetDate,
"positive flank clients.rds")
posFlankClients <- readRDS(posFlankClientsFn)
if(excludeNoPosFlanks && min(targetIds)>0){
trainOrig <- trainOrig[ncodpers %in% posFlankClients, ]
}
meanPosFlanks <- rep(NA, nbTargetIds)
for(targetIndex in 1:nbTargetIds){
targetId <- targetIds[targetIndex]
if(targetId==0){
targetVar <- "hasNewProduct"
} else{
targetVar <- targetVars[targetId]
}
cat("Learning xgboost models for target variable", targetVar, targetIndex,
"of", nbTargetIds , "@", as.character(Sys.time()), "\n")
if(!is.null(firstNonZeroTarget) && targetIndex==firstNonZeroTarget &&
excludeNoNewProducts && jointModelNoNewProducts && !testModel &&
nbFeatureFiles>1){
if(modelGroupId==1){
trainOrig <- trainOrig[hasNewProduct == TRUE, ]
trainOrig[, trainWeight := NULL]
for(j in 2:nbFeatureFiles){
trainBatch <- readRDS(trainFeaturePaths[j])
trainOrig <- rbind(trainOrig, trainBatch[hasNewProduct == TRUE, ])
}
} else{
break
}
}
observationWeights <- getModelWeights(trainOrig$lastDate, targetVar,
dateTargetWeights)
trainOrig[, trainWeight := observationWeights]
saveDirFiles <- list.files(saveModelDir)
if(overwrite || !any(grepl(targetVar, saveDirFiles) & (
!bootstrap | grepl("Boot", saveDirFiles)))){
predictors <- predictorsOrig
if(simpleModeling){
consideredFeatures <- c(grep(targetVar, predictors, ignore.case=TRUE,
value = TRUE), paste0(targetVars, "Lag1"))
predictors <- unique(consideredFeatures)
}
if(targetId == 0 && dropProductFeaturesPosFlankProd){
predictors <- predictors[!grepl("ult1", predictors)]
}
if(dropOtherIndFeatures && targetId != 0){
predictors <- predictors[!grepl("ult1", predictors) |
grepl(baseProducts[targetId], predictors)]
}
if(featureSelection){
targetVarLoop <- targetVar
monthsBackLoop <- 12*2016 + 5 - 12*year(trainOrig$lastDate[1]) -
month(trainOrig$lastDate[1])
if(featureSelectionMode == "monthProduct"){
featureOrder <-
featureOrders[targetVar == targetVarLoop &
monthsBack == monthsBackLoop, feature]
} else{
featureOrder <- featureOrders[targetVar == targetVarLoop, feature]
}
featureOrder <- featureOrder[featureOrder %in% predictors]
if(topFeatures < length(featureOrder)){
excludedFeatures <- featureOrder[-(1:topFeatures)]
predictors <- setdiff(predictors, excludedFeatures)
}
}
for(bootId in 1:nbBoots){
bootExtension <- ifelse(bootstrap, paste0(" - Boot ", bootId), "")
if(nbBoots>1){
cat("Bootstrap replicate", bootId, "of", nbBoots, "@",
as.character(Sys.time()), "\n")
}
if(excludeNoNewProducts){
train <- trainOrig[targetId==0 | (hasNewProduct == TRUE), ]
} else{
if(excludeNoPosFlanks){
train <- trainOrig[targetId==0 | (ncodpers %in% posFlankClients), ]
} else{
train <- trainOrig
}
}
if(targetId==0 && nrow(train)>maxTrainRecords){
train <- train[sample(1:nrow(train), maxTrainRecords)]
}
gc()
if(targetId>0){
dropTarget <- is.na(train[[targetVar]]) |
(train[[paste0(targetVar, "Lag1")]] == 1)
train <- train[!dropTarget, ]
}
if(underSampleNomPensNoNomina && targetVar=="ind_nom_pens_ult1"){
posFlankIdsNpNoNom <- which(train[[paste0(targetVar, "Lag1")]] == 0 &
train[[targetVar]] == 1 &
!is.na(train[["ind_nomina_ult1"]]) &
(train[["ind_nomina_ult1"]] == 0 |
train[["ind_nomina_ult1Lag1"]] == 1))
if(length(posFlankIdsNpNoNom)>maxMonthNomPensNoNomina){
keepTarget <- sample(posFlankIdsNpNoNom, maxMonthNomPensNoNomina)
dropTarget <- setdiff(posFlankIdsNpNoNom, keepTarget)
train <- train[-dropTarget, ]
}
}
nbPosFlanks <- sum(train[[targetVar]])
plotTitle <- paste0(targetId, " - ", targetVar, " (", nbPosFlanks, ")")
if(showMeanLabelByMayFlag){
plotData <- data.frame(MayFlag = train$hasMay15Data,
JuneFlag = train$hasJune15Data,
labels = as.numeric(train[[targetVar]]))
p <- ggplot(plotData, aes(x=MayFlag, y=labels, fill=MayFlag)) +
stat_summary(fun.y="mean", geom="bar") +
ggtitle(plotTitle)
print(p)
}
meanPosFlanks[targetIndex] <- sum(train[[targetVar]])/nrow(trainOrig)
if(bootstrap){
train <- train[sample(1:nrow(train), nrow(train), replace = TRUE), ]
}
nbModels <- ifelse(K==1, 1, ifelse(skipCommonModel, K, K+1))
foldIds <- vector(mode = "list", length = nbModels)
allNcodpers <- sort(unique(train$ncodpers))
if(K>1){
if(useStackingFolds){
for(j in 1:K){
stackingFold <- stackingFolds[[j]]
foldIds[[j]] <- stackingFold[stackingFold %in% allNcodpers]
}
} else{
folds <- sample(cut(seq(1, length(allNcodpers)), breaks = K,
labels = FALSE))
for(j in 1:K){
foldIds[[j]] <- allNcodpers[folds==j]
}
}
}
allPredictions <- rep(NA, nrow(train))
for(i in 1:nbModels){
excludeIds <- foldIds[[i]]
if(nbModels>1){
cat("Learning xgboost model for fold", i, "of", nbModels , "@",
as.character(Sys.time()), "\n")
}
features <- train[!ncodpers %in% excludeIds,]
predictorData <- features[, predictors, with=FALSE]
labels <- features[, targetVar, with=FALSE][[1]]
trainWeights <- features[, "trainWeight", with=FALSE][[1]]
if(all(trainWeights==0)){
trainWeights <- rep(1, length(trainWeights))
}
predictorData <- data.matrix(predictorData)
if(sum(labels)>=1e3){
hyperpar <- hyperparSetExtended
} else{
hyperpar <- hyperparSetSimple
}
hyperpar$max.depth <- hyperpar$max.depth + extraBootstrapDepth
foldRounds <- hyperpar$nrounds *
(1 + ifelse(i==nbModels & (K==1 | !skipCommonModel), 1/baseK, 0))
if(targetId %in% lowPriorityIds){
foldRounds <- round(foldRounds/10)
} else{
foldRounds <- round(foldRounds)
}
model <- xgboost(data = predictorData, label = labels
, eta = hyperpar$etaC/foldRounds
, nrounds = foldRounds
, subsample = hyperpar$subsample
, colsample_bytree = hyperpar$colsample_bytree
, max.depth =
hyperpar$max.depth
, min_child_weight = hyperpar$min_child_weight
, gamma = hyperpar$gamma
, objective = "reg:logistic"
, eval_metric = "logloss"
, missing = NA
, verbose = 0
, save_period = NULL
, weight = (trainWeights/mean(trainWeights))
)
oobFeatures <- train[train$ncodpers %in% excludeIds,]
predictorDataOob <- oobFeatures[, predictors, with=FALSE]
predictorDataOob <- data.matrix(predictorDataOob)
if(nrow(predictorDataOob)>0){
predOob <- predict(model, predictorDataOob, missing=NA)
allPredictions[train$ncodpers %in% excludeIds] <- predOob
}
if(nrow(predictorDataOob)>0){
foldPredIds <- train$ncodpers %in% excludeIds
analyzedPreds <- allPredictions[foldPredIds]
analyzedLabels <- train[[targetVar]][foldPredIds]
foldLL <- -(sum(log(analyzedPreds[analyzedLabels==1])) +
sum(log(1-analyzedPreds[analyzedLabels==0])))
cat("Fold log loss:", foldLL, "\n")
foldTP <- sum(analyzedLabels==1)
foldPredRatio <- round(mean(analyzedPreds[analyzedLabels==1])/
mean(analyzedPreds[analyzedLabels==0]), 2)
cat("Fold prediction ratio TP/FP:", foldPredRatio, "\n")
} else{
foldPredRatio <- NA
foldTP <- NA
}
assessFeatureImportance <- showVariableImportance ||
(inspectVarImpTopModels && targetId %in% inspectIds)
if(assessFeatureImportance){
importanceMatrix <- xgb.importance(predictors, model = model)
p <- xgb.plot.importance(importanceMatrix)
print(p)
}
if((i==nbModels || saveBaseModels) && !assessFeatureImportance){
importanceMatrix <- xgb.importance(predictors, model = model)
}
if(saveBaseModels && i<(K+1) && (!(i==1 && K==1))){
hyperParDescr <- getHyperParDescr(hyperpar)
saveBasePath <- file.path(saveModelDir,
paste0(targetVar, " Fold ", i, " of ",
baseK, " - ", hyperParDescr,
bootExtension, ".rds"))
saveRDS(list(targetVar=targetVar, model=model,
predictors=predictors, hyperpar=hyperpar,
importanceMatrix=importanceMatrix,
timeStamp = as.character(Sys.time()),
foldPredRatio = foldPredRatio, foldTP = foldTP),
saveBasePath)
}
}
nbPosFlanks <- sum(train[[targetVar]])
foldPredRatio <- NA
foldTP <- NA
if(any(!is.na(allPredictions))){
if(testModel){
newProductIds <- !is.na(allPredictions)
} else{
newProductIds <- !is.na(allPredictions) & (train$hasNewProduct |
(targetId == 0) |
!excludeNoNewProducts)
}
analyzeRecordIds <- newProductIds &
(train$trainWeight == max(train$trainWeight))
analyzedPreds <- allPredictions[analyzeRecordIds]
analyzedLabels <- train[[targetVar]][analyzeRecordIds]
nbPosFlanks <- sum(analyzedLabels)
if(testModel){
predLabelCorr <- cor(analyzedPreds, analyzedLabels)
plot(analyzedPreds, analyzedLabels,
main = paste("Predicted vs label correlation:",
round(predLabelCorr, 3)))
} else{
boxplot(analyzedPreds ~ analyzedLabels,
main=paste0(targetIndex, " - ", targetVar, " (",
nbPosFlanks, ")"))
meanLL <- -(sum(log(analyzedPreds[analyzedLabels==1])) +
sum(log(1-analyzedPreds[analyzedLabels==0]))) /
length(analyzedPreds)
cat("Out of bag mean log loss for", paste0(targetVar, ":"), meanLL,
"\n")
foldTP <- sum(analyzedLabels==1)
foldPredRatio <- round(mean(analyzedPreds[analyzedLabels==1])/
mean(analyzedPreds[analyzedLabels==0]), 2)
cat("Prediction ratio TP/FP:", foldPredRatio, "\n")
cat("(Mean predictions, mean target)",
c(mean(allPredictions[analyzeRecordIds]),
mean(train[[targetVar]][analyzeRecordIds])), "\n")
}
}
cat("\n")
if(K==1 || !skipCommonModel){
savePath <- file.path(saveModelDir, paste0(targetVar,
bootExtension, ".rds"))
saveRDS(list(targetVar=targetVar, model=model, predictors=predictors,
hyperpar=hyperpar, importanceMatrix=importanceMatrix,
timeStamp=as.character(Sys.time()),
foldPredRatio=foldPredRatio, foldTP=foldTP),
savePath)
}
}
}
}
}
beep(sound = "fanfare") |
test_that('print.dg treats variable as categorical if guide has length > 1',{
file <- system.file(package = 'yamlet', 'extdata','quinidine.csv')
library(ggplot2)
library(dplyr)
library(magrittr)
file %>% decorate %>% filter(!is.na(conc)) %>%
ggplot(aes(x = time, y = conc, color = Heart)) + geom_point()
})
test_that('print.dg uses conditional labels and guides',{
file <- system.file(package = 'yamlet', 'extdata','phenobarb.csv')
file %>% decorate %>%
filter(event == 'conc') %>%
ggplot(aes(x = time, y = value, color = ApgarInd)) + geom_point()
})
test_that('ggplot.decorated works with multiple layers',{
library(yamlet)
library(ggplot2)
library(magrittr)
library(csv)
a <- io_csv(system.file(package = 'yamlet', 'extdata','phenobarb.csv'))
b <- io_csv(system.file(package = 'yamlet', 'extdata','quinidine.csv'))
c <- as.csv(system.file(package = 'yamlet', 'extdata','phenobarb.csv'))
d <- as.csv(system.file(package = 'yamlet', 'extdata','quinidine.csv'))
x <-
a %>% filter(event == 'conc') %>%
ggplot(aes(x = time, y = value, color = ApgarInd)) + geom_point() +
b %>% filter(!is.na(conc)) %>%
geom_point(data = ., aes(x = time/10, y = conc*10, color = Heart))
y <-
c %>% filter(event == 'conc') %>%
ggplot2:::ggplot.default(aes(x = time, y = value, color = ApgarInd)) + geom_point() +
d %>% filter(!is.na(conc)) %>%
geom_point(data = ., aes(x = time/10, y = conc*10, color = Heart))
})
test_that('ggready supports axis label line breaks',{
library(yamlet)
library(ggplot2)
library(magrittr)
library(dplyr)
library(encode)
data(mtcars)
mtcars %>%
select(mpg, vs, am) %>%
data.frame %>%
mutate(
plotgroup = case_when(
vs == 0 & am == 0 ~ 'v-shaped\nautomatic',
vs == 0 & am == 1 ~ 'v-shaped\nmanual',
vs == 1 & am == 0 ~ 'straight\nautomatic',
vs == 1 & am == 1 ~ 'straight\nmanual'
)
) %>%
redecorate("
mpg: [ milage, mi/gal ]
plotgroup: [ engine\\ntransmission, [v-shaped\n\nautomatic,v-shaped\n\nmanual,straight\n\nautomatic,straight\n\nmanual]]
") %>%
ggready %>%
ggplot(aes(x = plotgroup, y = mpg)) +
geom_boxplot()
})
test_that('subplots respect metadata assignments',{
library(ggplot2)
library(magrittr)
library(dplyr)
library(gridExtra)
a <- io_csv(system.file(package = 'yamlet', 'extdata','phenobarb.csv'))
b <- io_csv(system.file(package = 'yamlet', 'extdata','quinidine.csv'))
c <- as.csv(system.file(package = 'yamlet', 'extdata','phenobarb.csv'))
d <- as.csv(system.file(package = 'yamlet', 'extdata','quinidine.csv'))
x <-
a %>% filter(event == 'conc') %>%
ggplot(aes(x = time, y = value, color = ApgarInd)) + geom_point() +
b %>% filter(!is.na(conc)) %>%
geom_point(data = ., aes(x = time/10, y = conc*10, color = Heart))
y <-
a %>% filter(event == 'conc') %>%
ggplot2:::ggplot.default(aes(x = time, y = value, color = ApgarInd)) + geom_point() +
d %>% filter(!is.na(conc)) %>%
geom_point(data = ., aes(x = time/10, y = conc*10, color = Heart))
grid.arrange(x, y)
p <- x %>% ggplot_build
q <- p %>% ggplot_gtable
plot(q)
expect_equal_to_reference(file = '098.rds', p)
}) |
workingDir <- getwd()
dataDir <- paste(workingDir, "data-publisher/", sep = "/")
modelDir <- paste(workingDir, "model", sep = "/")
fundyDir <- paste(workingDir, "fundamentals", sep = "/")
setwd(modelDir)
n.days <- 30
just.today <- T
n.sims <- 50000
source("senate-model-2014.R")
if (just.today) source("combine-data.R") |
local_bru_testthat_setup()
test_that("2D LGCP fitting and prediction: Plot sampling", {
skip_on_cran()
local_bru_safe_inla()
options <- list(
control.inla = list(
int.strategy = "eb"
)
)
data(gorillas, package = "inlabru", envir = environment())
matern <- INLA::inla.spde2.pcmatern(gorillas$mesh,
prior.sigma = c(0.1, 0.01),
prior.range = c(5, 0.01)
)
cmp <- coordinates ~ my.spde(main = coordinates, model = matern)
fit <- lgcp(cmp,
data = gorillas$plotsample$nests,
samplers = gorillas$plotsample$plots,
domain = list(coordinates = gorillas$mesh),
options = options
)
expect_equal(
sum(fit$bru_info$lhoods[[1]]$E),
7.096605,
tolerance = lowtol
)
expect_snapshot_value(
fit$summary.fixed["Intercept", "mean"] + fit$summary.random$my.spde$mean[c(19, 100, 212)],
tolerance = midtol,
style = "serialize"
)
expect_snapshot_value(
fit$summary.fixed["Intercept", "sd"] + fit$summary.random$my.spde$sd[c(19, 100, 212)],
tolerance = hitol,
style = "serialize"
)
}) |
sfacross <- function(formula, muhet, uhet, vhet, logDepVar = TRUE, data, subset,
S = 1L, udist = "hnormal", scaling = FALSE, start = NULL,
method = "bfgs", hessianType = 1L, simType = "halton",
Nsim = 100, prime = 2L, burn = 10, antithetics = FALSE, seed = 12345,
itermax = 2000, printInfo = FALSE, tol = 1e-12, gradtol = 1e-06,
stepmax = 0.1, qac = "marquardt") {
udist <- tolower(udist)
if (!(udist %in% c(
"hnormal", "exponential", "tnormal", "rayleigh",
"uniform", "gamma", "lognormal", "weibull", "genexponential",
"tslaplace"
))) {
stop("Unknown inefficiency distribution: ", paste(udist),
call. = FALSE
)
}
if (length(Formula(formula))[2] != 1) {
stop("argument 'formula' must have one RHS part", call. = FALSE)
}
cl <- match.call()
mc <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data", "subset"), names(mc), nomatch = 0L)
mc <- mc[c(1L, m)]
mc$drop.unused.levels <- TRUE
formula <- interCheckMain(formula = formula)
if (!missing(muhet)) {
muhet <- lhsCheck_mu(formula = muhet, scaling = scaling)
} else {
muhet <- ~1
}
if (!missing(uhet)) {
uhet <- lhsCheck_u(formula = uhet, scaling = scaling)
} else {
uhet <- ~1
}
if (!missing(vhet)) {
vhet <- lhsCheck_v(formula = vhet)
} else {
vhet <- ~1
}
formula <- formDist_sfacross(
udist = udist, formula = formula,
muhet = muhet, uhet = uhet, vhet = vhet
)
if (missing(data)) {
data <- environment(formula)
}
mc$formula <- formula
mc$na.action <- na.pass
mc[[1L]] <- quote(model.frame)
mc <- eval(mc, parent.frame())
validObs <- rowSums(is.na(mc) | is.infinite.data.frame(mc)) ==
0
Yvar <- model.response(mc, "numeric")
Yvar <- Yvar[validObs]
mtX <- terms(formula, data = data, rhs = 1)
Xvar <- model.matrix(mtX, mc)
Xvar <- Xvar[validObs, , drop = FALSE]
nXvar <- ncol(Xvar)
N <- nrow(Xvar)
if (N == 0L) {
stop("0 (non-NA) cases", call. = FALSE)
}
if (length(Yvar) != nrow(Xvar)) {
stop(paste("the number of observations of the dependent variable (",
length(Yvar), ") must be the same to the number of observations of the exogenous variables (",
nrow(Xvar), ")",
sep = ""
), call. = FALSE)
}
if (udist %in% c("tnormal", "lognormal")) {
mtmuH <- delete.response(terms(formula,
data = data,
rhs = 2
))
muHvar <- model.matrix(mtmuH, mc)
muHvar <- muHvar[validObs, , drop = FALSE]
nmuZUvar <- ncol(muHvar)
mtuH <- delete.response(terms(formula, data = data, rhs = 3))
uHvar <- model.matrix(mtuH, mc)
uHvar <- uHvar[validObs, , drop = FALSE]
nuZUvar <- ncol(uHvar)
mtvH <- delete.response(terms(formula, data = data, rhs = 4))
vHvar <- model.matrix(mtvH, mc)
vHvar <- vHvar[validObs, , drop = FALSE]
nvZVvar <- ncol(vHvar)
} else {
mtuH <- delete.response(terms(formula, data = data, rhs = 2))
uHvar <- model.matrix(mtuH, mc)
uHvar <- uHvar[validObs, , drop = FALSE]
nuZUvar <- ncol(uHvar)
mtvH <- delete.response(terms(formula, data = data, rhs = 3))
vHvar <- model.matrix(mtvH, mc)
vHvar <- vHvar[validObs, , drop = FALSE]
nvZVvar <- ncol(vHvar)
}
if (length(S) != 1 || !(S %in% c(-1L, 1L))) {
stop("argument 'S' must equal either 1 or -1: 1 for production or profit frontier
and -1 for cost frontier",
call. = FALSE
)
}
typeSfa <- if (S == 1L) {
"Stochastic Production/Profit Frontier, e = v - u"
} else {
"Stochastic Cost Frontier, e = v + u"
}
if (length(scaling) != 1 || !is.logical(scaling[1])) {
stop("argument 'scaling' must be a single logical value",
call. = FALSE
)
}
if (scaling) {
if (udist != "tnormal") {
stop("argument 'udist' must be 'tnormal' when scaling option is TRUE",
call. = FALSE
)
}
if (nuZUvar != nmuZUvar) {
stop("argument 'muhet' and 'uhet' must have the same length",
call. = FALSE
)
}
if (!all(colnames(uHvar) == colnames(muHvar))) {
stop("argument 'muhet' and 'uhet' must contain the same variables",
call. = FALSE
)
}
if (nuZUvar == 1 || nmuZUvar == 1) {
if (attr(terms(muhet), "intercept") == 1 || attr(
terms(uhet),
"intercept"
) == 1) {
stop("at least one exogeneous variable must be provided for the scaling option",
call. = FALSE
)
}
}
}
if (length(logDepVar) != 1 || !is.logical(logDepVar[1])) {
stop("argument 'logDepVar' must be a single logical value",
call. = FALSE
)
}
nParm <- if (udist == "tnormal") {
if (scaling) {
if (attr(terms(muhet), "intercept") == 1 || attr(
terms(uhet),
"intercept"
) == 1) {
nXvar + (nmuZUvar - 1) + 2 + nvZVvar
} else {
nXvar + nmuZUvar + 2 + nvZVvar
}
} else {
nXvar + nmuZUvar + nuZUvar + nvZVvar
}
} else {
if (udist == "lognormal") {
nXvar + nmuZUvar + nuZUvar + nvZVvar
} else {
if (udist %in% c("gamma", "weibull", "tslaplace")) {
nXvar + nuZUvar + nvZVvar + 1
} else {
nXvar + nuZUvar + nvZVvar
}
}
}
if (!is.null(start)) {
if (length(start) != nParm) {
stop("Wrong number of initial values: model has ",
nParm, " parameters",
call. = FALSE
)
}
}
if (nParm > N) {
stop("Model has more parameters than observations", call. = FALSE)
}
method <- tolower(method)
if (!(method %in% c(
"ucminf", "bfgs", "bhhh", "nr", "nm",
"sr1", "mla", "sparse", "nlminb"
))) {
stop("Unknown or non-available optimization algorithm: ",
paste(method),
call. = FALSE
)
}
if (length(hessianType) != 1 || !(hessianType %in% c(
1L,
2L, 3L
))) {
stop("argument 'hessianType' must equal either 1 or 2 or 3",
call. = FALSE
)
}
if (udist %in% c("gamma", "lognormal", "weibull")) {
if (!(simType %in% c("halton", "ghalton", "sobol", "uniform"))) {
stop("Unknown or non-available random draws method",
call. = FALSE
)
}
if (!is.numeric(Nsim) || length(Nsim) != 1) {
stop("argument 'Nsim' must be a single numeric scalar",
call. = FALSE
)
}
if (!is.numeric(burn) || length(burn) != 1) {
stop("argument 'burn' must be a single numeric scalar",
call. = FALSE
)
}
if (!is_prime(prime)) {
stop("argument 'prime' must be a single prime number",
call. = FALSE
)
}
if (length(antithetics) != 1 || !is.logical(antithetics[1])) {
stop("argument 'antithetics' must be a single logical value",
call. = FALSE
)
}
if (antithetics && (Nsim %% 2) != 0) {
Nsim <- Nsim + 1
}
simDist <- if (simType == "halton") {
"Halton"
} else {
if (simType == "ghalton") {
"Generalized Halton"
} else {
if (simType == "sobol") {
"Sobol"
} else {
if (simType == "uniform") {
"Uniform"
}
}
}
}
cat("Initialization of", Nsim, simDist, "draws per observation ...\n")
FiMat <- drawMat(
N = N, Nsim = Nsim, simType = simType,
prime = prime, burn = burn + 1, antithetics = antithetics,
seed = seed
)
}
if (!is.numeric(itermax) || length(itermax) != 1) {
stop("argument 'itermax' must be a single numeric scalar",
call. = FALSE
)
}
if (itermax != round(itermax)) {
stop("argument 'itermax' must be an integer", call. = FALSE)
}
if (itermax <= 0) {
stop("argument 'itermax' must be positive", call. = FALSE)
}
itermax <- as.integer(itermax)
if (length(printInfo) != 1 || !is.logical(printInfo[1])) {
stop("argument 'printInfo' must be a single logical value",
call. = FALSE
)
}
if (!is.numeric(tol) || length(tol) != 1) {
stop("argument 'tol' must be numeric", call. = FALSE)
}
if (tol < 0) {
stop("argument 'tol' must be non-negative", call. = FALSE)
}
if (!is.numeric(gradtol) || length(gradtol) != 1) {
stop("argument 'gradtol' must be numeric", call. = FALSE)
}
if (gradtol < 0) {
stop("argument 'gradtol' must be non-negative", call. = FALSE)
}
if (!is.numeric(stepmax) || length(stepmax) != 1) {
stop("argument 'stepmax' must be numeric", call. = FALSE)
}
if (stepmax < 0) {
stop("argument 'stepmax' must be non-negative", call. = FALSE)
}
if (!(qac %in% c("marquardt", "stephalving"))) {
stop("argument 'qac' must be either 'marquardt' or 'stephalving'",
call. = FALSE
)
}
olsRes <- if (colnames(Xvar)[1] == "(Intercept)") {
lm(Yvar ~ ., data = as.data.frame(Xvar[, -1]))
} else {
lm(Yvar ~ -1 + ., data = as.data.frame(Xvar))
}
if (any(is.na(olsRes$coefficients))) {
stop("at least one of the OLS coefficients is NA: ",
paste(colnames(Xvar)[is.na(olsRes$coefficients)],
collapse = ", "
), "This may be due to a singular matrix
due to potential perfect multicollinearity",
call. = FALSE
)
}
olsParam <- c(olsRes$coefficients)
olsSigmasq <- summary(olsRes)$sigma^2
olsStder <- sqrt(diag(vcov(olsRes)))
olsLoglik <- logLik(olsRes)[1]
if (inherits(data, "plm.dim")) {
dataTable <- data[validObs, 1:2]
} else {
dataTable <- data.frame(IdObs = c(1:sum(validObs)))
}
dataTable <- as_tibble(cbind(dataTable, data[validObs, all.vars(terms(formula))]))
dataTable <- mutate(dataTable,
olsResiduals = residuals(olsRes),
olsFitted = fitted(olsRes)
)
olsSkew <- skewness(dataTable[["olsResiduals"]])
olsM3Okay <- if (S * olsSkew < 0) {
"Residuals have the expected skeweness"
} else {
"Residuals do not have the expected skeweness"
}
if (S * olsSkew > 0) {
warning("The residuals of the OLS are ", if (S == 1) {
" right"
} else {
"left"
}, "-skewed. This may indicate the absence of inefficiency or
model misspecification or sample 'bad luck'",
call. = FALSE
)
}
CoelliM3Test <- c(z = moment(dataTable[["olsResiduals"]],
order = 3
) / sqrt(6 * moment(dataTable[["olsResiduals"]],
order = 2
)^3 / N), p.value = 2 * pnorm(-abs(moment(dataTable[["olsResiduals"]],
order = 3
) / sqrt(6 * moment(dataTable[["olsResiduals"]],
order = 2
)^3 / N))))
AgostinoTest <- dagoTest(dataTable[["olsResiduals"]])
class(AgostinoTest) <- "dagoTest"
FunArgs <- if (udist == "tnormal") {
if (scaling) {
list(
start = start, olsParam = olsParam, dataTable = dataTable,
nXvar = nXvar, nuZUvar = nuZUvar, nvZVvar = nvZVvar,
uHvar = uHvar, vHvar = vHvar, Yvar = Yvar, Xvar = Xvar,
S = S, method = method, printInfo = printInfo,
itermax = itermax, stepmax = stepmax, tol = tol,
gradtol = gradtol, hessianType = hessianType,
qac = qac
)
} else {
list(
start = start, olsParam = olsParam, dataTable = dataTable,
nXvar = nXvar, nmuZUvar = nmuZUvar, nuZUvar = nuZUvar,
nvZVvar = nvZVvar, muHvar = muHvar, uHvar = uHvar,
vHvar = vHvar, Yvar = Yvar, Xvar = Xvar, S = S,
method = method, printInfo = printInfo, itermax = itermax,
stepmax = stepmax, tol = tol, gradtol = gradtol,
hessianType = hessianType, qac = qac
)
}
} else {
if (udist == "lognormal") {
list(
start = start, olsParam = olsParam, dataTable = dataTable,
nXvar = nXvar, nmuZUvar = nmuZUvar, nuZUvar = nuZUvar,
nvZVvar = nvZVvar, muHvar = muHvar, uHvar = uHvar,
vHvar = vHvar, Yvar = Yvar, Xvar = Xvar, S = S,
N = N, FiMat = FiMat, method = method, printInfo = printInfo,
itermax = itermax, stepmax = stepmax, tol = tol,
gradtol = gradtol, hessianType = hessianType,
qac = qac
)
} else {
if (udist %in% c("gamma", "weibull")) {
list(
start = start, olsParam = olsParam, dataTable = dataTable,
nXvar = nXvar, nuZUvar = nuZUvar, nvZVvar = nvZVvar,
uHvar = uHvar, vHvar = vHvar, Yvar = Yvar,
Xvar = Xvar, S = S, N = N, FiMat = FiMat, method = method,
printInfo = printInfo, itermax = itermax, stepmax = stepmax,
tol = tol, gradtol = gradtol, hessianType = hessianType,
qac = qac
)
} else {
list(
start = start, olsParam = olsParam, dataTable = dataTable,
nXvar = nXvar, nuZUvar = nuZUvar, nvZVvar = nvZVvar,
uHvar = uHvar, vHvar = vHvar, Yvar = Yvar,
Xvar = Xvar, S = S, method = method, printInfo = printInfo,
itermax = itermax, stepmax = stepmax, tol = tol,
gradtol = gradtol, hessianType = hessianType,
qac = qac
)
}
}
}
mleList <- tryCatch(switch(udist, hnormal = do.call(
halfnormAlgOpt,
FunArgs
), exponential = do.call(exponormAlgOpt, FunArgs),
tnormal = if (scaling) {
do.call(truncnormscalAlgOpt, FunArgs)
} else {
do.call(
truncnormAlgOpt,
FunArgs
)
}, rayleigh = do.call(raynormAlgOpt, FunArgs),
gamma = do.call(gammanormAlgOpt, FunArgs), uniform = do.call(
uninormAlgOpt,
FunArgs
), lognormal = do.call(lognormAlgOpt, FunArgs),
weibull = do.call(weibullnormAlgOpt, FunArgs), genexponential = do.call(
genexponormAlgOpt,
FunArgs
), tslaplace = do.call(tslnormAlgOpt, FunArgs)
),
error = function(e) e
)
if (inherits(mleList, "error")) {
stop("The current error occurs during optimization:\n",
mleList$message,
call. = FALSE
)
}
mleList$invHessian <- vcovObj(
mleObj = mleList$mleObj, hessianType = hessianType,
method = method, nParm = nParm
)
mleList <- c(mleList, if (method == "ucminf") {
list(
type = "ucminf max.", nIter = unname(mleList$mleObj$info["neval"]),
status = mleList$mleObj$message, mleLoglik = -mleList$mleObj$value,
gradient = mleList$mleObj$gradient
)
} else {
if (method %in% c("bfgs", "bhhh", "nr", "nm")) {
list(
type = substr(mleList$mleObj$type, 1, 27), nIter = mleList$mleObj$iterations,
status = mleList$mleObj$message, mleLoglik = mleList$mleObj$maximum,
gradient = mleList$mleObj$gradient
)
} else {
if (method == "sr1") {
list(
type = "SR1 max.", nIter = mleList$mleObj$iterations,
status = mleList$mleObj$status, mleLoglik = -mleList$mleObj$fval,
gradient = mleList$mleObj$gradient
)
} else {
if (method == "mla") {
list(
type = "Lev. Marquardt max.", nIter = mleList$mleObj$ni,
status = switch(mleList$mleObj$istop, `1` = "convergence criteria were satisfied",
`2` = "maximum number of iterations was reached",
`4` = "algorithm encountered a problem in the function computation"
),
mleLoglik = -mleList$mleObj$fn.value, gradient = mleList$mleObj$grad
)
} else {
if (method == "sparse") {
list(
type = "Sparse Hessian max.", nIter = mleList$mleObj$iterations,
status = mleList$mleObj$status, mleLoglik = -mleList$mleObj$fval,
gradient = mleList$mleObj$gradient
)
} else {
if (method == "nlminb") {
list(
type = "nlminb max.", nIter = mleList$mleObj$iterations,
status = mleList$mleObj$message, mleLoglik = -mleList$mleObj$objective,
gradient = mleList$mleObj$gradient
)
}
}
}
}
}
})
if (udist %in% c("tnormal", "lognormal")) {
names(mleList$startVal) <- fName_mu_sfacross(
Xvar = Xvar,
udist = udist, muHvar = muHvar, uHvar = uHvar, vHvar = vHvar,
scaling = scaling
)
} else {
names(mleList$startVal) <- fName_uv_sfacross(
Xvar = Xvar,
udist = udist, uHvar = uHvar, vHvar = vHvar
)
}
names(mleList$mlParam) <- names(mleList$startVal)
rownames(mleList$invHessian) <- colnames(mleList$invHessian) <- names(mleList$mlParam)
names(mleList$gradient) <- names(mleList$mlParam)
colnames(mleList$mleObj$gradL_OBS) <- names(mleList$mlParam)
mlDate <- format(Sys.time(), "Model was estimated on : %b %a %d, %Y at %H:%M")
dataTable$mlResiduals <- Yvar - as.numeric(crossprod(
matrix(mleList$mlParam[1:nXvar]),
t(Xvar)
))
dataTable$mlFitted <- as.numeric(crossprod(
matrix(mleList$mlParam[1:nXvar]),
t(Xvar)
))
dataTable$logL_OBS <- mleList$mleObj$logL_OBS
returnObj <- list()
returnObj$call <- cl
returnObj$formula <- formula
returnObj$S <- S
returnObj$typeSfa <- typeSfa
returnObj$Nobs <- N
returnObj$nXvar <- nXvar
if (udist %in% c("tnormal", "lognormal")) {
returnObj$nmuZUvar <- nmuZUvar
}
returnObj$scaling <- scaling
returnObj$logDepVar <- logDepVar
returnObj$nuZUvar <- nuZUvar
returnObj$nvZVvar <- nvZVvar
returnObj$nParm <- nParm
returnObj$udist <- udist
returnObj$startVal <- mleList$startVal
returnObj$dataTable <- dataTable
returnObj$olsParam <- olsParam
returnObj$olsStder <- olsStder
returnObj$olsSigmasq <- olsSigmasq
returnObj$olsLoglik <- olsLoglik
returnObj$olsSkew <- olsSkew
returnObj$olsM3Okay <- olsM3Okay
returnObj$CoelliM3Test <- CoelliM3Test
returnObj$AgostinoTest <- AgostinoTest
returnObj$optType <- mleList$type
returnObj$nIter <- mleList$nIter
returnObj$optStatus <- mleList$status
returnObj$startLoglik <- mleList$startLoglik
returnObj$mlLoglik <- mleList$mleLoglik
returnObj$mlParam <- mleList$mlParam
returnObj$gradient <- mleList$gradient
returnObj$gradL_OBS <- mleList$mleObj$gradL_OBS
returnObj$gradientNorm <- sqrt(sum(mleList$gradient^2))
returnObj$invHessian <- mleList$invHessian
returnObj$hessianType <- if (hessianType == 1) {
"Analytic/Numeric Hessian"
} else {
if (hessianType == 2) {
"BHHH Hessian"
} else {
if (hessianType == 3) {
"Robust Hessian"
}
}
}
returnObj$mlDate <- mlDate
if (udist %in% c("gamma", "lognormal", "weibull")) {
returnObj$simDist <- simDist
returnObj$Nsim <- Nsim
returnObj$FiMat <- FiMat
}
rm(mleList)
class(returnObj) <- "sfacross"
return(returnObj)
}
print.sfacross <- function(x, ...) {
cat("Call:\n")
cat(deparse(x$call ))
cat("\n\n")
cat("Likelihood estimates using", x$optType, "\n")
cat(sfadist(x$udist), "\n")
cat("Status:", x$optStatus, "\n\n")
cat(x$typeSfa, "\n")
print.default(format(x$mlParam), print.gap = 2, quote = FALSE)
invisible(x)
} |
NULL
ReducedDimensionPlot <- function(...) {
new("ReducedDimensionPlot", ...)
}
setMethod("initialize", "ReducedDimensionPlot", function(.Object, ...) {
args <- list(...)
args <- .emptyDefault(args, .redDimType, NA_character_)
args <- .emptyDefault(args, .redDimXAxis, 1L)
args <- .emptyDefault(args, .redDimYAxis, 2L)
do.call(callNextMethod, c(list(.Object), args))
})
setMethod(".cacheCommonInfo", "ReducedDimensionPlot", function(x, se) {
if (!is.null(.getCachedCommonInfo(se, "ReducedDimensionPlot"))) {
return(se)
}
se <- callNextMethod()
if (is(se, "SingleCellExperiment")) {
available <- reducedDimNames(se)
for (y in seq_along(available)) {
if (ncol(reducedDim(se, y))==0L) {
available[y] <- NA_character_
}
}
available <- available[!is.na(available)]
} else {
available <- character(0)
}
.setCachedCommonInfo(se, "ReducedDimensionPlot",
valid.reducedDim.names=available)
})
setMethod(".refineParameters", "ReducedDimensionPlot", function(x, se) {
x <- callNextMethod()
if (is.null(x)) {
return(NULL)
}
available <- .getCachedCommonInfo(se, "ReducedDimensionPlot")$valid.reducedDim.names
if (!is.na(chosen <- slot(x, .redDimType)) &&
chosen %in% available &&
slot(x, .redDimXAxis) <= ncol(reducedDim(se, chosen)) &&
slot(x, .redDimYAxis) <= ncol(reducedDim(se, chosen)))
{
} else {
if (length(available)==0L) {
warning(sprintf("no 'reducedDims' with non-zero dimensions for '%s'", class(x)[1]))
return(NULL)
}
y <- available[1]
slot(x, .redDimType) <- y
slot(x, .redDimXAxis) <- 1L
slot(x, .redDimYAxis) <- min(ncol(reducedDim(se, y)), 2L)
}
x
})
setValidity2("ReducedDimensionPlot", function(object) {
msg <- character(0)
msg <- .singleStringError(msg, object, .redDimType)
for (field in c(.redDimXAxis, .redDimYAxis)) {
if (length(val <- object[[field]])!=1 || is.na(val) || val <= 0L) {
msg <- c(msg, sprintf("'%s' must be a single positive integer", field))
}
}
if (length(msg)>0) {
return(msg)
}
TRUE
})
setMethod(".defineDataInterface", "ReducedDimensionPlot", function(x, se, select_info) {
cur_reddim <- slot(x, .redDimType)
max_dim <- ncol(reducedDim(se, cur_reddim))
choices <- seq_len(max_dim)
.addSpecificTour(class(x)[1], .redDimType, function(plot_name) {
data.frame(
rbind(
c(
element=paste0("
intro="Here, we can select the type of dimensionality reduction result to show.
The choices are extracted from the <code>reducedDims</code> of a <code>SingleCellExperiment</code> object.
These results should be loaded into the object prior to calling <strong>iSEE</strong> - they are not computed on the fly."
)
)
)
})
.addSpecificTour(class(x)[1], .redDimXAxis, function(plot_name) {
data.frame(
rbind(
c(
element=paste0("
intro="Given a particular <code>reducedDim</code> entry to visualize, this field specifies the dimension to show on the x-axis."
)
)
)
})
.addSpecificTour(class(x)[1], .redDimYAxis, function(plot_name) {
data.frame(
rbind(
c(
element=paste0("
intro="Given a particular <code>reducedDim</code> entry to visualize, this field specifies the dimension to show on the y-axis."
)
)
)
})
list(
.selectInput.iSEE(x, .redDimType,
label="Type:",
choices=.getCachedCommonInfo(se, "ReducedDimensionPlot")$valid.reducedDim.names,
selected=cur_reddim),
.selectInput.iSEE(x, .redDimXAxis,
label="Dimension 1:",
choices=choices,
selected=slot(x, .redDimXAxis)),
.selectInput.iSEE(x, .redDimYAxis,
label="Dimension 2:",
choices=choices,
selected=slot(x, .redDimYAxis))
)
})
setMethod(".createObservers", "ReducedDimensionPlot", function(x, se, input, session, pObjects, rObjects) {
callNextMethod()
plot_name <- .getEncodedName(x)
.createProtectedParameterObservers(plot_name,
fields=c(.redDimXAxis, .redDimYAxis),
input=input, pObjects=pObjects, rObjects=rObjects)
cur_field <- paste0(plot_name, "_", .redDimType)
dim_fieldX <- paste0(plot_name, "_", .redDimXAxis)
dim_fieldY <- paste0(plot_name, "_", .redDimYAxis)
observeEvent(input[[cur_field]], {
matched_input <- as(input[[cur_field]], typeof(pObjects$memory[[plot_name]][[.redDimType]]))
if (identical(matched_input, pObjects$memory[[plot_name]][[.redDimType]])) {
return(NULL)
}
pObjects$memory[[plot_name]][[.redDimType]] <- matched_input
new_max <- ncol(reducedDim(se, matched_input))
capped_X <- pmin(new_max, pObjects$memory[[plot_name]][[.redDimXAxis]])
capped_Y <- pmin(new_max, pObjects$memory[[plot_name]][[.redDimYAxis]])
pObjects$memory[[plot_name]][[.redDimXAxis]] <- capped_X
pObjects$memory[[plot_name]][[.redDimYAxis]] <- capped_Y
new_choices <- seq_len(new_max)
updateSelectInput(session, dim_fieldX, choices=new_choices, selected=capped_X)
updateSelectInput(session, dim_fieldY, choices=new_choices, selected=capped_Y)
.requestCleanUpdate(plot_name, pObjects, rObjects)
}, ignoreInit=TRUE)
invisible(NULL)
})
setMethod(".fullName", "ReducedDimensionPlot", function(x) "Reduced dimension plot")
setMethod(".panelColor", "ReducedDimensionPlot", function(x) "
setMethod(".generateDotPlotData", "ReducedDimensionPlot", function(x, envir) {
data_cmds <- list()
data_cmds[["reducedDim"]] <- sprintf(
"red.dim <- reducedDim(se, %s);", deparse(slot(x, .redDimType)))
data_cmds[["xy"]] <- sprintf(
"plot.data <- data.frame(X=red.dim[, %i], Y=red.dim[, %i], row.names=colnames(se));",
slot(x, .redDimXAxis), slot(x, .redDimYAxis))
plot_title <- slot(x, .redDimType)
x_lab <- sprintf("Dimension %s", slot(x, .redDimXAxis))
y_lab <- sprintf("Dimension %s", slot(x, .redDimYAxis))
data_cmds <- unlist(data_cmds)
.textEval(data_cmds, envir)
list(commands=data_cmds, labels=list(title=plot_title, X=x_lab, Y=y_lab))
})
setMethod(".definePanelTour", "ReducedDimensionPlot", function(x) {
collated <- character(0)
collated <- rbind(
c(paste0("
.addTourStep(x, .dataParamBoxOpen, "The <i>Data parameters</i> box shows the available parameters that can be tweaked in this plot.<br/><br/><strong>Action:</strong> click on this box to open up available options.")
)
rbind(
data.frame(element=collated[,1], intro=collated[,2], stringsAsFactors=FALSE),
callNextMethod()
)
}) |
timestamp <- Sys.time()
library(caret)
library(plyr)
library(recipes)
library(dplyr)
model <- "svmLinearWeights2"
set.seed(2)
training <- twoClassSim(50, linearVars = 2)
testing <- twoClassSim(500, linearVars = 2)
trainX <- training[, -ncol(training)]
trainY <- training$Class
rec_cls <- recipe(Class ~ ., data = training) %>%
step_center(all_predictors()) %>%
step_scale(all_predictors())
cctrl1 <- trainControl(method = "cv", number = 3, returnResamp = "all")
cctrl2 <- trainControl(method = "LOOCV")
cctrl3 <- trainControl(method = "none")
set.seed(849)
test_class_cv_model <- train(trainX, trainY,
method = "svmLinearWeights2",
trControl = cctrl1,
tuneLength = 2,
preProc = c("center", "scale"))
set.seed(849)
test_class_cv_form <- train(Class ~ ., data = training,
method = "svmLinearWeights2",
trControl = cctrl1,
tuneLength = 2,
preProc = c("center", "scale"))
test_class_pred <- predict(test_class_cv_model, testing[, -ncol(testing)])
test_class_pred_form <- predict(test_class_cv_form, testing[, -ncol(testing)])
set.seed(849)
test_class_loo_model <- train(trainX, trainY,
method = "svmLinearWeights2",
trControl = cctrl2,
tuneLength = 2,
preProc = c("center", "scale"))
set.seed(849)
test_class_none_model <- train(trainX, trainY,
method = "svmLinearWeights2",
trControl = cctrl3,
tuneGrid = test_class_cv_model$bestTune,
preProc = c("center", "scale"))
test_class_none_pred <- predict(test_class_none_model, testing[, -ncol(testing)])
set.seed(849)
test_class_rec <- train(x = rec_cls,
data = training,
method = "svmLinearWeights2",
tuneLength = 2,
trControl = cctrl1)
if(
!isTRUE(
all.equal(test_class_cv_model$results,
test_class_rec$results))
)
stop("CV weights not giving the same results")
test_class_imp_rec <- varImp(test_class_rec)
test_class_pred_rec <- predict(test_class_rec, testing[, -ncol(testing)])
test_levels <- levels(test_class_cv_model)
if(!all(levels(trainY) %in% test_levels))
cat("wrong levels")
test_class_predictors1 <- predictors(test_class_cv_model)
test_class_predictors2 <- predictors(test_class_cv_model$finalModel)
tests <- grep("test_", ls(), fixed = TRUE, value = TRUE)
sInfo <- sessionInfo()
timestamp_end <- Sys.time()
save(list = c(tests, "sInfo", "timestamp", "timestamp_end"),
file = file.path(getwd(), paste(model, ".RData", sep = "")))
if(!interactive())
q("no") |
abc = 123
ghi <- 456 |
gen_new_active_sets <- function(s, delta) {
k <- length(s)
Z <- list()
if (length(delta) == 0) {
return (Z)
}
delta_k <- list()
counter <- 1
for (i in seq_along(delta)) {
if (length(delta[[i]]) == k) {
delta_k[[counter]] <- delta[[i]]
counter <- counter + 1
}
}
delta_k[[counter]] <- s
supp <- table(unlist(delta_k))
omega <- setdiff(strtoi(names(supp[supp >= k])), s)
counter <- 1
if (all(supp[match(s, names(supp))] >= k)) {
for (a in omega) {
r <- sort(c(s, a))
if (all(combn(r, k, simplify = FALSE) %in% delta_k)) {
Z[[counter]] <- r
counter <- counter + 1
}
}
}
return(Z)
} |
RunModel_GR4J <- function(InputsModel, RunOptions, Param) {
NParam <- 4
FortranOutputs <- .FortranOutputs(GR = "GR4J")$GR
if (!inherits(InputsModel, "InputsModel")) {
stop("'InputsModel' must be of class 'InputsModel'")
}
if (!inherits(InputsModel, "daily")) {
stop("'InputsModel' must be of class 'daily'")
}
if (!inherits(InputsModel, "GR")) {
stop("'InputsModel' must be of class 'GR'")
}
if (!inherits(RunOptions, "RunOptions")) {
stop("'RunOptions' must be of class 'RunOptions'")
}
if (!inherits(RunOptions, "GR")) {
stop("'RunOptions' must be of class 'GR'")
}
if (!is.vector(Param) | !is.numeric(Param)) {
stop("'Param' must be a numeric vector")
}
if (sum(!is.na(Param)) != NParam) {
stop(paste("'Param' must be a vector of length", NParam, "and contain no NA"))
}
Param <- as.double(Param)
Param_X1X3_threshold <- 1e-2
Param_X4_threshold <- 0.5
if (Param[1L] < Param_X1X3_threshold) {
warning(sprintf("Param[1] (X1: production store capacity [mm]) < %.2f\n X1 set to %.2f", Param_X1X3_threshold, Param_X1X3_threshold))
Param[1L] <- Param_X1X3_threshold
}
if (Param[3L] < Param_X1X3_threshold) {
warning(sprintf("Param[3] (X3: routing store capacity [mm]) < %.2f\n X3 set to %.2f", Param_X1X3_threshold, Param_X1X3_threshold))
Param[3L] <- Param_X1X3_threshold
}
if (Param[4L] < Param_X4_threshold) {
warning(sprintf("Param[4] (X4: unit hydrograph time constant [d]) < %.2f\n X4 set to %.2f", Param_X4_threshold, Param_X4_threshold))
Param[4L] <- Param_X4_threshold
}
if (identical(RunOptions$IndPeriod_WarmUp, 0L)) {
RunOptions$IndPeriod_WarmUp <- NULL
}
IndPeriod1 <- c(RunOptions$IndPeriod_WarmUp, RunOptions$IndPeriod_Run)
LInputSeries <- as.integer(length(IndPeriod1))
if ("all" %in% RunOptions$Outputs_Sim) {
IndOutputs <- as.integer(1:length(FortranOutputs))
} else {
IndOutputs <- which(FortranOutputs %in% RunOptions$Outputs_Sim)
}
IndPeriod2 <- (length(RunOptions$IndPeriod_WarmUp)+1):LInputSeries
ExportDatesR <- "DatesR" %in% RunOptions$Outputs_Sim
ExportStateEnd <- "StateEnd" %in% RunOptions$Outputs_Sim
if (!is.null(RunOptions$IniResLevels)) {
RunOptions$IniStates[1] <- RunOptions$IniResLevels[1] * Param[1]
RunOptions$IniStates[2] <- RunOptions$IniResLevels[2] * Param[3]
}
RESULTS <- .Fortran("frun_gr4j", PACKAGE = "airGR",
LInputs = LInputSeries,
InputsPrecip = InputsModel$Precip[IndPeriod1],
InputsPE = InputsModel$PotEvap[IndPeriod1],
NParam = as.integer(length(Param)),
Param = Param,
NStates = as.integer(length(RunOptions$IniStates)),
StateStart = RunOptions$IniStates,
NOutputs = as.integer(length(IndOutputs)),
IndOutputs = IndOutputs,
Outputs = matrix(as.double(-99e9), nrow = LInputSeries, ncol = length(IndOutputs)),
StateEnd = rep(as.double(-99e9), length(RunOptions$IniStates))
)
RESULTS$Outputs[RESULTS$Outputs <= -99e8] <- NA
RESULTS$StateEnd[RESULTS$StateEnd <= -99e8] <- NA
if (ExportStateEnd) {
RESULTS$StateEnd[-3L] <- ifelse(RESULTS$StateEnd[-3L] < 0, 0, RESULTS$StateEnd[-3L])
RESULTS$StateEnd <- CreateIniStates(FUN_MOD = RunModel_GR4J, InputsModel = InputsModel,
ProdStore = RESULTS$StateEnd[1L], RoutStore = RESULTS$StateEnd[2L], ExpStore = NULL,
UH1 = RESULTS$StateEnd[(1:20) + 7],
UH2 = RESULTS$StateEnd[(1:40) + (7+20)],
GCemaNeigeLayers = NULL, eTGCemaNeigeLayers = NULL,
verbose = FALSE)
}
if (!ExportDatesR & !ExportStateEnd) {
OutputsModel <- lapply(seq_len(RESULTS$NOutputs), function(i) RESULTS$Outputs[IndPeriod2, i])
names(OutputsModel) <- FortranOutputs[IndOutputs]
}
if (ExportDatesR & !ExportStateEnd) {
OutputsModel <- c(list(InputsModel$DatesR[RunOptions$IndPeriod_Run]),
lapply(seq_len(RESULTS$NOutputs), function(i) RESULTS$Outputs[IndPeriod2, i]))
names(OutputsModel) <- c("DatesR", FortranOutputs[IndOutputs])
}
if (!ExportDatesR & ExportStateEnd) {
OutputsModel <- c(lapply(seq_len(RESULTS$NOutputs), function(i) RESULTS$Outputs[IndPeriod2, i]),
list(RESULTS$StateEnd))
names(OutputsModel) <- c(FortranOutputs[IndOutputs], "StateEnd")
}
if ((ExportDatesR & ExportStateEnd) | "all" %in% RunOptions$Outputs_Sim) {
OutputsModel <- c(list(InputsModel$DatesR[RunOptions$IndPeriod_Run]),
lapply(seq_len(RESULTS$NOutputs), function(i) RESULTS$Outputs[IndPeriod2, i]),
list(RESULTS$StateEnd))
names(OutputsModel) <- c("DatesR", FortranOutputs[IndOutputs], "StateEnd")
}
rm(RESULTS)
class(OutputsModel) <- c("OutputsModel", "daily", "GR")
return(OutputsModel)
} |
skip_on_cran()
test_that("The ouput is correct", {
data <- SDMtune:::t
data@data <- data@data[, 1:4]
m <- trainANN(data = data, size = 3)
pred <- predict(m@model, data@data)
expect_equal(sum(pred >= 0), nrow(data@data))
expect_equal(sum(pred <= 1), nrow(data@data))
}) |
library(tidyverse)
library(fs)
library(here)
library(glue)
library(magick)
image_table <- tibble(path = dir_ls(here(), glob = "*.png", recurse = 2)) %>%
mutate(file = basename(path),
year = as.numeric(str_extract(path, "[0-9]{4}(?=\\/)")),
week = as.numeric(str_extract(path, "(?<=week)\\d{1,2}"))) %>%
arrange(year, week, file, path) %>%
group_by(year, week) %>%
mutate(idx = row_number()) %>%
ungroup() %>%
mutate(tag = glue("tt_{year}_{week}{ifelse(length(idx) > 1, glue('_{idx}'), '')}.png")) %>%
mutate(path = as.character(path)) |
rsq.f <-
function(x)
{
n1<-dim(x)[1]
n2<-dim(x)[2]
return(.Fortran("rs_rsq",r=as.double(0),as.matrix(x),
as.integer(n1),as.integer(n2),PACKAGE="stima")$r)
} |
require(OpenMx)
require(MASS)
set.seed(200)
a2<-0.5
c2<-0.3
e2<-0.2
rMZ <- a2+c2
rDZ <- .5*a2+c2
DataMZ <- mvtnorm::rmvnorm (1000, c(0,0), matrix(c(1,rMZ,rMZ,1),2,2))
DataDZ <- mvtnorm::rmvnorm (1000, c(0,0), matrix(c(1,rDZ,rDZ,1),2,2))
selVars <- c('t1','t2')
dimnames(DataMZ) <- list(NULL,selVars)
dimnames(DataDZ) <- list(NULL,selVars)
summary(DataMZ)
summary(DataDZ)
colMeans(DataMZ,na.rm=TRUE)
colMeans(DataDZ,na.rm=TRUE)
cov(DataMZ,use="complete")
cov(DataDZ,use="complete")
twinSatModel <- mxModel("twinSat",
mxModel("MZ",
mxMatrix("Full", 1, 2, T, c(0,0), name="expMeanMZ"),
mxMatrix("Lower", 2, 2, T, .5, name="CholMZ"),
mxAlgebra(CholMZ %*% t(CholMZ), name="expCovMZ"),
mxData(DataMZ, type="raw"),
mxFitFunctionML(),mxExpectationNormal("expCovMZ", "expMeanMZ", selVars)),
mxModel("DZ",
mxMatrix("Full", 1, 2, T, c(0,0), name="expMeanDZ"),
mxMatrix("Lower", 2, 2, T, .5, name="CholDZ"),
mxAlgebra(CholDZ %*% t(CholDZ), name="expCovDZ"),
mxData(DataDZ, type="raw"),
mxFitFunctionML(),mxExpectationNormal("expCovDZ", "expMeanDZ", selVars)),
mxAlgebra(MZ.objective + DZ.objective, name="twin"),
mxFitFunctionAlgebra("twin"))
twinSatFit <- mxRun(twinSatModel, suppressWarnings=TRUE)
ExpMeanMZ <- mxEval(MZ.expMeanMZ, twinSatFit)
ExpCovMZ <- mxEval(MZ.expCovMZ, twinSatFit)
ExpMeanDZ <- mxEval(DZ.expMeanDZ, twinSatFit)
ExpCovDZ <- mxEval(DZ.expCovDZ, twinSatFit)
LL_Sat <- mxEval(objective, twinSatFit)
twinSatModelSub1 <- twinSatModel
twinSatModelSub1$MZ$expMeanMZ <- mxMatrix("Full", 1, 2, T, 0, "mMZ", name="expMeanMZ")
twinSatModelSub1$DZ$expMeanDZ <- mxMatrix("Full", 1, 2, T, 0, "mDZ", name="expMeanDZ")
twinSatFitSub1 <- mxRun(twinSatModelSub1, suppressWarnings=TRUE)
twinSatModelSub2 <- twinSatModelSub1
twinSatModelSub2$MZ$expMeanMZ <- mxMatrix("Full", 1, 2, T, 0, "mean", name="expMeanMZ")
twinSatModelSub2$DZ$expMeanDZ <- mxMatrix("Full", 1, 2, T, 0, "mean", name="expMeanDZ")
twinSatFitSub2 <- mxRun(twinSatModelSub2, suppressWarnings=TRUE)
LL_Sat <- mxEval(objective, twinSatFit)
LL_Sub1 <- mxEval(objective, twinSatFitSub1)
LRT1 <- LL_Sub1 - LL_Sat
LL_Sub2 <- mxEval(objective, twinSatFitSub2)
LRT2 <- LL_Sub2 - LL_Sat
twinACEModel <- mxModel("twinACE",
mxMatrix("Full", 1, 2, T, 20, "mean", name="expMean"),
mxMatrix("Full", nrow=1, ncol=1, free=TRUE, values=.6, label="a", name="X"),
mxMatrix("Full", nrow=1, ncol=1, free=TRUE, values=.6, label="c", name="Y"),
mxMatrix("Full", nrow=1, ncol=1, free=TRUE, values=.6, label="e", name="Z"),
mxAlgebra(X * t(X), name="A"),
mxAlgebra(Y * t(Y), name="C"),
mxAlgebra(Z * t(Z), name="E"),
mxAlgebra(rbind(cbind(A+C+E , A+C),
cbind(A+C , A+C+E)), name="expCovMZ"),
mxModel("MZ",
mxData(DataMZ, type="raw"),
mxFitFunctionML(),mxExpectationNormal("twinACE.expCovMZ", "twinACE.expMean",selVars)),
mxAlgebra(rbind(cbind(A+C+E , .5%x%A+C),
cbind(.5%x%A+C , A+C+E)), name="expCovDZ"),
mxModel("DZ",
mxData(DataDZ, type="raw"),
mxFitFunctionML(),mxExpectationNormal("twinACE.expCovDZ", "twinACE.expMean",selVars)),
mxAlgebra(MZ.objective + DZ.objective, name="twin"),
mxFitFunctionAlgebra("twin"))
twinACEFit <- mxRun(twinACEModel, suppressWarnings=TRUE)
LL_ACE <- mxEval(objective, twinACEFit)
LRT_ACE= LL_ACE - LL_Sat
MZc <- mxEval(expCovMZ, twinACEFit)
DZc <- mxEval(expCovDZ, twinACEFit)
M <- mxEval(expMean, twinACEFit)
A <- mxEval(A, twinACEFit)
C <- mxEval(C, twinACEFit)
E <- mxEval(E, twinACEFit)
V <- (A+C+E)
a2 <- A/V
c2 <- C/V
e2 <- E/V
ACEest <- rbind(cbind(A,C,E),cbind(a2,c2,e2))
ACEest <- data.frame(ACEest, row.names=c("Variance Components","Standardized VC"))
names(ACEest)<-c("A", "C", "E")
ACEest; LL_ACE; LRT_ACE
twinAEModel <- twinACEModel
twinAEModel$twinACE$Y <- mxMatrix("Full", 1, 1, F, 0, "c", name="Y")
twinAEFit <- mxRun(twinAEModel, suppressWarnings=TRUE)
LL_AE <- mxEval(objective, twinAEFit)
MZc <- mxEval(expCovMZ, twinAEFit)
DZc <- mxEval(expCovDZ, twinAEFit)
M <- mxEval(expMean, twinAEFit)
A <- mxEval(A, twinAEFit)
C <- mxEval(C, twinAEFit)
E <- mxEval(E, twinAEFit)
V <- (A+C+E)
a2 <- A/V
c2 <- C/V
e2 <- E/V
AEest <- rbind(cbind(A,C,E),cbind(a2,c2,e2))
AEest <- data.frame(ACEest, row.names=c("Variance Components","Standardized VC"))
names(ACEest)<-c("A", "C", "E")
AEest; LL_AE;
LRT_ACE_AE <- LL_AE - LL_ACE
LRT_ACE_AE
|
test_that(
desc = "checking gghistostats plot and parametric stats - data with NAs",
code = {
skip_on_cran()
set.seed(123)
p <- gghistostats(
data = dplyr::starwars,
x = height,
xlab = "character height",
title = "starwars: character heights",
binwidth = 20,
bin.args = list(
col = "black",
fill = "orange",
alpha = 0.7
),
test.value = 150,
bf.prior = 0.9
)
pb <- ggplot2::ggplot_build(p)
set.seed(123)
expect_snapshot(pb$data)
expect_null(pb$layout$panel_params[[1]]$y.sec.labels, NULL)
set.seed(123)
p_subtitle <- statsExpressions::one_sample_test(
data = dplyr::starwars,
x = height,
type = "p",
test.value = 150
)$expression[[1]]
set.seed(123)
p_cap <- statsExpressions::one_sample_test(
data = dplyr::starwars,
x = height,
type = "bayes",
test.value = 150,
bf.prior = 0.9
)$expression[[1]]
expect_equal(pb$plot$labels$subtitle, p_subtitle, ignore_attr = TRUE)
expect_equal(pb$plot$labels$caption, p_cap, ignore_attr = TRUE)
expect_snapshot(within(pb$plot$labels, rm(subtitle, caption)))
}
)
test_that(
desc = "checking gghistostats and non-parametric stats - data without NAs",
code = {
skip_on_cran()
set.seed(123)
p <- gghistostats(
data = ggplot2::mpg,
x = cty,
xlab = "city miles per gallon",
title = "fuel economy",
caption = "source: government website",
binwidth = 5,
test.value = 20,
k = 3,
type = "np",
results.subtitle = FALSE
)
pb <- ggplot2::ggplot_build(p)
set.seed(123)
expect_snapshot(pb$data)
expect_equal(pb$layout$panel_params[[1]]$x$limits, c(7.5, 37.5))
expect_equal(
pb$layout$panel_params[[1]]$x$breaks,
c(NA, 10, 20, 30, NA)
)
expect_equal(
pb$layout$panel_params[[1]]$y$breaks,
c(0, 25, 50, 75, 100)
)
expect_snapshot(pb$layout$panel_params[[1]]$y.sec$break_info)
expect_snapshot(pb$plot$labels)
}
)
test_that(
desc = "checking robust stats and proportions",
code = {
skip_on_cran()
set.seed(123)
p <- gghistostats(
data = mtcars,
x = wt,
binwidth = 0.5,
test.value = 2.5,
type = "r"
) +
scale_x_continuous(limits = c(1, 6))
pb <- ggplot2::ggplot_build(p)
set.seed(123)
p_subtitle <- statsExpressions::one_sample_test(
data = mtcars,
x = wt,
test.value = 2.5,
type = "r"
)$expression[[1]]
expect_equal(pb$plot$labels$subtitle, p_subtitle, ignore_attr = TRUE)
set.seed(123)
expect_snapshot(pb$data)
expect_snapshot(within(pb$plot$labels, rm(subtitle)))
}
)
test_that(
desc = "checking if normal curve work",
code = {
skip_on_cran()
set.seed(123)
p1 <- gghistostats(
data = ggplot2::msleep,
x = awake,
binwidth = 1,
results.subtitle = FALSE,
normal.curve = TRUE,
normal.curve.args =
list(
color = "red",
size = 0.8
)
)
pb1 <- ggplot2::ggplot_build(p1)
set.seed(123)
expect_snapshot(pb1$data)
expect_snapshot(pb1$plot$labels)
}
)
test_that(
desc = "subtitle output",
code = {
skip_on_cran()
set.seed(123)
p_sub <- gghistostats(
data = ggplot2::msleep,
x = brainwt,
type = "np",
output = "subtitle",
test.value = 0.25
)
set.seed(123)
sub <-
statsExpressions::one_sample_test(
data = ggplot2::msleep,
x = brainwt,
type = "np",
test.value = 0.25
)$expression[[1]]
expect_equal(p_sub, sub, ignore_attr = TRUE)
}
) |
do_stats_teams <- function(df_games, season, competition, type_season){
Compet <- Season <- Type_season <- Name <- CombinID <- NULL
Position <- Nationality <- Type_season <- Type_stats <- NULL
GP <- GS <- MP <- Team <- Game <- Type <- PTSrv <- PTSrv_mean <- NULL
FGPerc <- FGA <- FG <- TwoPPerc <- TwoPA <- TwoP <- NULL
ThreePPerc <- ThreePA <- ThreeP <- FTPerc <- FTA <- NULL
FT <- EFGPerc <- PTS <- NULL
if (type_season == "All") {
df_games1 <- df_games %>%
filter(Compet == competition,
Season == season)
}else{
df_games1 <- df_games %>%
filter(Compet == competition,
Season == season,
Type_season == type_season)
}
df_games1 <- do_add_adv_stats(df_games1)
games_played <- df_games1 %>%
group_by(Team) %>%
distinct(Game) %>%
count()
if (type_season == "All") {
df_games2 <- do_stats(df_games1,
"Total",
unique(df_games1$Season),
unique(df_games1$Compet),
"All")
}else{
df_games2 <- do_stats(df_games1,
"Total",
unique(df_games1$Season),
unique(df_games1$Compet),
unique(df_games1$Type_season))
}
df_team <- df_games2 %>%
ungroup() %>%
select(-c(Name, CombinID, Position, Nationality,
Season, Compet, Type_season, Type_stats)) %>%
select(-GP, -GS, -MP, -contains("Perc")) %>%
group_by(Team) %>%
summarise_all(sum)
df_team1 <- left_join(df_team, games_played) %>%
rename(GP = n)
df_team2 <- df_team1 %>%
select(Team, GP, everything()) %>%
mutate(FGPerc = ifelse(FGA == 0, 0, round((FG / FGA) * 100))) %>%
mutate(TwoPPerc = ifelse(TwoPA == 0, 0, round((TwoP / TwoPA) * 100))) %>%
mutate(ThreePPerc = ifelse(ThreePA == 0, 0, round((ThreeP / ThreePA) * 100))) %>%
mutate(FTPerc = ifelse(FTA == 0, 0, round((FT / FTA) * 100))) %>%
mutate(EFGPerc = ifelse(FGA == 0, 0, (FG + 0.5 * ThreeP) / FGA)) %>%
mutate(EFGPerc = round(EFGPerc * 100)) %>%
mutate(EFGPerc = ifelse(EFGPerc > 100, 100, EFGPerc)) %>%
select(1:5, FGPerc, 6:7, TwoPPerc, 8:9, ThreePPerc, 10:11, FTPerc, 12:27, EFGPerc, everything())
df_team_mean <- df_team2 %>%
select(-contains("Perc"))
df_team_mean_aux <- apply(df_team_mean[,-c(1,2)], 2, "/", df_team_mean$GP)
df_team_mean_aux1 <- as.data.frame(df_team_mean_aux)
df_team_mean1 <- cbind(df_team_mean[,1:2], df_team_mean_aux1)
df_team_mean2 <- df_team_mean1 %>%
mutate(FGPerc = ifelse(FGA == 0, 0, round((FG / FGA) * 100))) %>%
mutate(TwoPPerc = ifelse(TwoPA == 0, 0, round((TwoP / TwoPA) * 100))) %>%
mutate(ThreePPerc = ifelse(ThreePA == 0, 0, round((ThreeP / ThreePA) * 100))) %>%
mutate(FTPerc = ifelse(FTA == 0, 0, round((FT / FTA) * 100))) %>%
mutate(EFGPerc = ifelse(FGA == 0, 0, (FG + 0.5 * ThreeP) / FGA)) %>%
mutate(EFGPerc = round(EFGPerc * 100)) %>%
mutate(EFGPerc = ifelse(EFGPerc > 100, 100, EFGPerc)) %>%
select(1:5, FGPerc, 6:7, TwoPPerc, 8:9, ThreePPerc, 10:11, FTPerc, 12:27, EFGPerc, everything())
df_team_mean2[, 3:ncol(df_team_mean2)] <- round(df_team_mean2[, 3:ncol(df_team_mean2)], 1)
teams <- df_team_mean2$Team
df_defense <- data.frame()
for (i in teams) {
team_Game <- unique(df_games1$Game[df_games1$Team == i])
df_defense_team <- df_games1 %>%
filter(Game %in% team_Game) %>%
group_by(Team) %>%
mutate(Type = ifelse(Team == i, "Offense", "Defense"))
df_defense_team1 <- df_defense_team %>%
filter(Type == "Defense") %>%
ungroup() %>%
summarise(PTSrv = sum(PTS)) %>%
mutate(Team = i) %>%
mutate(PTSrv_mean = round(PTSrv / games_played$n[games_played$Team == i], 1))
df_defense <- bind_rows(df_defense, df_defense_team1)
}
df_team3 <- left_join(df_team2, df_defense) %>%
select(-PTSrv_mean) %>%
select(Team, GP, PTS, PTSrv, everything())
df_team_mean3 <- left_join(df_team_mean2, df_defense) %>%
select(-PTSrv) %>%
select(Team, GP, PTS, PTSrv_mean, everything()) %>%
rename(PTSrv = PTSrv_mean)
return(list(df_team_total = df_team3, df_team_mean = df_team_mean3))
} |
bias_correction <- function(components, b){
A <- grab_bread(components)
A_i <- grab_bread_list(components)
B_i <- grab_meat_list(components)
Ainv <- solve(A)
H_i <- lapply(A_i, function(m){
diag( (1 - pmin(b, diag(m %*% Ainv) ) )^(-0.5) )
})
Bbc_i <- lapply(seq_along(B_i), function(i){
H_i[[i]] %*% B_i[[i]] %*% H_i[[i]]
})
Bbc <- apply(simplify2array(Bbc_i), 1:2, sum)
compute_sigma(A = A, B = Bbc)
}
gee_eefun <- function(data, formula, family){
X <- model.matrix(object = formula, data = data)
Y <- model.response(model.frame(formula = formula, data = data))
n <- nrow(X)
function(theta, alpha, psi){
mu <- family$linkinv(X %*% theta)
Dt <- t(X) %*% diag(as.numeric(mu), nrow = n)
A <- diag(as.numeric(family$variance(mu)), nrow = n)
R <- matrix(alpha, nrow = n, ncol = n)
diag(R) <- 1
V <- psi * (sqrt(A) %*% R %*% sqrt(A))
Dt %*% solve(V) %*% (Y - mu)
}
}
g <- gee::gee(breaks~tension, id=wool, data=warpbreaks, corstr="exchangeable")
guo <- saws::geeUOmega(g)
library(geex)
results <- m_estimate(
estFUN = gee_eefun, data = warpbreaks,
units = 'wool', roots = coef(g), compute_roots = FALSE,
outer_args = list(formula = breaks ~ tension,
family = gaussian()),
inner_args = list(alpha = g$working.correlation[1,2],
psi = g$scale),
corrections = list(
bias_correction_.1 = correction(bias_correction, b = .1),
bias_correction_.3 = correction(bias_correction, b = .3)))
saws::saws(guo, method = 'd1')$V
vcov(results)
saws::saws(guo, method = 'd4', bound = 0.1)$V
get_corrections(results)[[1]]
saws::saws(guo, method = 'd4', bound = 0.3)$V
get_corrections(results)[[2]] |
aovperm_rnd <- function( formula, data, method, type, np, P, coding_sum, rnd_rotation, new_method = NULL){
if(is.null(coding_sum)){coding_sum = T}
if(is.null(new_method)){new_method = F}
if(is.null(method)){method = "Rd_kheradPajouh_renaud"}
if(!new_method){
method = match.arg(method,c("Rd_kheradPajouh_renaud","Rde_kheradPajouh_renaud","Rd_replic_kheradPajouh_renaud"))
}
switch(method,
"Rd_kheradPajouh_renaud"={funP=function(...){fisher_Rd_kheradPajouh_renaud_rnd(...)}},
"Rde_kheradPajouh_renaud"={funP=function(...){fisher_Rde_kheradPajouh_renaud_rnd(...)}},
"Rd_replic_kheradPajouh_renaud" = {funP=function(...){fisher_Rd_replic_kheradPajouh_renaud_rnd(...)}},
{warning(paste("the method",method, "is not defined. Choose between Rd_kheradPajouh_renaud or Rde_kheradPajouh_renaud. Set new_method = TRUE to allow user-defined functions."))
funP=function(...){eval(parse(text=paste("fisher_",method,"_rnd(...)",sep="",collpase="")))}})
terms<-terms(formula,special="Error",data=data)
ind_error <- attr(terms, "specials")$Error
error_term <- attr(terms, "variables")[[1 + ind_error]]
formula_f <- update(formula, paste(". ~ .-",deparse(error_term, width.cutoff = 500L, backtick = TRUE)))
e_term <- deparse(error_term[[2L]], width.cutoff = 500L,backtick = TRUE)
formula_allfixed <- as.formula(paste(c(formula_f[[2]],"~",formula_f[[3]],"+",e_term),collapse=""))
formula_within <- formula(paste("~", e_term, collapse = ""))
formula_within<- formula(paste("~",deparse(error_term[[2]][[3]]),collapse=""))
formula_id <- formula(paste("~",deparse(error_term[[2]][[2]]),collapse = ""))
mf <- model.frame(formula = formula_allfixed, data = data)
if(coding_sum){mf <- changeContrast(mf, contr = contr.sum)}
mf_f <- model.frame(formula = formula_f, data = mf)
mf_id <- model.frame(formula = formula_id, data = as.data.frame(lapply(mf,function(col){
col = as.factor(col)
contrasts(col) = contr.sum
col})))
y <- model.response(mf)
link = link(formula_f=formula_f,formula_within=formula_within)
mm_f <- model.matrix(attr(mf_f, "terms"), data = mf_f)
mm_id <- model.matrix(attr(mf_id, "terms"), data = mf_id)[,-1,drop=F]
name <- colnames(mm_f)
checkBalancedData(fixed_formula = formula_f, data = cbind(y,mf))
if (is.null(P)) {P = Pmat(np = np, n = length(y), type = type)}
type = attr(P,"type")
np = np(P)
args <- list(y = y, mm = mm_f, mm_id = mm_id, link = link, P = P)
distribution<-sapply(1:max(attr(mm_f,"assign")),function(i){
args$i = i
funP(args = args)})
distribution = matrix(distribution,nrow=np)
colnames(distribution) = attr(attr(mf_f,"terms"),"term.labels")
check_distribution(distribution = distribution, digits = 10, n_unique = 200)
table = anova_table_rnd(args)
rownames(table) = attr(attr(mf_f,"terms"),"term.labels")
permutation_pvalue = apply(distribution,2,function(d){compute_pvalue(distribution = d,alternative="two.sided", na.rm = T)})
table$'resampled P(>F)' = permutation_pvalue
table = table[order(link[3,], link[1,]),]
distribution = distribution[,order(link[3,], link[1,])]
attr(table,"type") <- paste0("Resampling test using ",method," to handle nuisance variables and ",np," ", attr(P,"type"),"s.")
out=list()
out$y = y
out$model.matrix = mm_f
out$model.matrix_id = mm_id = mm_id
out$link = link
out$P = P
out$np = np
out$table = table
out$distribution = distribution
out$data=mf
out$method = method
out$coding_sum = coding_sum
class(out)<-"lmperm"
return(out)
} |
context("Check dashes")
test_that("Does not error in math mode", {
expect_null(check_dashes(filename = "./check-dashes/ok-despite-math.tex"))
expect_null(check_dashes(filename = "./check-dashes/ok-despite-math-2.tex"))
expect_null(check_dashes(filename = "./check-dashes/ok-despite-math-3.tex"))
expect_null(check_dashes(filename = "./check-dashes/ok-despite-math-4.tex"))
})
test_that("Errors if hyphen wrongly typed", {
expect_error(check_dashes(filename = "./check-dashes/bad-hyphen.tex"),
regexp = "[Hh]yphen")
expect_error(check_dashes(filename = "./check-dashes/bad-outside-math-1.tex"),
regexp = "[Hh]yphen")
})
test_that("Hyphens adjacent are noticed", {
expect_error(check_dashes("./check-dashes/hyphens-adj-dash-1.tex"),
regexp = "[Hh]yphen adjacent to en-dash.")
expect_error(check_dashes("./check-dashes/hyphens-adj-dash-2.tex"),
regexp = "[Hh]yphen adjacent to en-dash.")
})
test_that("Emdashes detected", {
expect_error(check_dashes("./check-dashes/has-emdash-1.tex"),
regexp = "[Ee]m-dash")
})
test_that("emdashes are ok when protasis", {
expect_error(check_dashes("./check-dashes/ok-if-protasis.tex",
protases_ok = FALSE),
regexp = "[Ee]m-dash")
expect_null(check_dashes("./check-dashes/ok-if-protasis.tex",
protases_ok = TRUE))
})
test_that("emdashes are ok when requested", {
skip_on_cran()
tempf <- tempfile(fileext = ".tex")
writeLines(c("A -- B", "C---D", "x-y", "\\(x - y\\)"), tempf)
expect_error(check_dashes(tempf), "Em")
expect_null(check_dashes(tempf, dash.consistency = c("en-dash", "em-dash")))
expect_error(check_dashes(tempf, dash.consistency = c("em-dash")), "En")
}) |
selectnhighest <-
function(dif, inputter, nlim) {
rnk <- rank(dif)
if (length(nlim) > length(inputter)) {
stop(paste("cannot rake on", nlim, "variables. Too few targets specified"))
}
found <- inputter[rnk <= nlim]
found
} |
simTermTaxa <- function(ntaxa, sumRate = 0.2){
tree <- deadTree(ntaxa = ntaxa, sumRate = sumRate)
termEdge <- sapply(tree$edge[,2],function(x)
any(x == (1:ntaxa))
)
taxonDurations <- tree$edge.length[termEdge]
nodeDist <- node.depth.edgelength(tree)
taxonLADs <- tree$root.time-nodeDist[1:ntaxa]
taxonFADs <- taxonLADs+taxonDurations
taxonRanges <- cbind(taxonFADs,taxonLADs)
rownames(taxonRanges) <- tree$tip.label[tree$edge[termEdge,2]]
res <- list(taxonRanges = taxonRanges,tree = tree)
return(res)
}
simTermTaxaAdvanced <- function(
p = 0.1,
q = 0.1,
mintaxa = 1, maxtaxa = 1000,
mintime = 1,maxtime = 1000,
minExtant = 0,maxExtant = NULL,
min.cond = TRUE
){
record <- simFossilRecord(
p = p, q = q, r = 0, nruns = 1,
nTotalTaxa = c(mintaxa,maxtaxa),
totalTime = c(mintime,maxtime),
nExtant = c(minExtant,maxExtant),
anag.rate = 0, prop.bifurc = 0,
prop.cryptic = 0,
modern.samp.prob = 1,
startTaxa = 1,
nSamp = c(0, 1000),
tolerance = 10^-4,
maxStepTime = 0.01,
shiftRoot4TimeSlice = "withExtantOnly",
count.cryptic = FALSE,
negRatesAsZero = TRUE,
print.runs = FALSE,
sortNames = FALSE,
plot = FALSE
)
taxa <- fossilRecord2fossilTaxa(record)
tree <- dropZLB(taxa2phylo(taxa))
ntaxa <- Ntip(tree)
termEdge <- sapply(tree$edge[,2],
function(x) any(x == (1:ntaxa))
)
termNodes <- tree$edge[termEdge,2]
taxonDurations <- tree$edge.length[termEdge]
nodeDist <- node.depth.edgelength(tree)
taxonLADs <- tree$root.time-nodeDist[termNodes]
taxonFADs <- taxonLADs+taxonDurations
taxonRanges <- cbind(taxonFADs,taxonLADs)
rownames(taxonRanges) <- tree$tip.label[termNodes]
res <- list(taxonRanges = taxonRanges,tree = tree)
return(res)
}
trueTermTaxaTree <- function(TermTaxaRes,time.obs){
taxR <- TermTaxaRes$taxonRanges
nameMatch <- match(names(time.obs),rownames(taxR))
if(any(is.na(nameMatch))){
stop("ERROR: names on time.obs and in TermTaxaRes don't match")
}
timeObsOutsideRanges <- sapply(1:length(time.obs),function(x)
if(is.na(time.obs[x])){
FALSE
}else{
(time.obs[x]>taxR[nameMatch[x],1])|(time.obs[x]<taxR[nameMatch[x],2])
})
if(any(timeObsOutsideRanges)){
stop("ERROR: Given time.obs are outside of the original taxon ranges")
}
tree1 <- TermTaxaRes$tree
newDurations <- taxR[nameMatch,1]-time.obs
if(is.null(names(time.obs))){
stop("ERROR: No taxon names on observation vector?")
}
tipMatch <- sapply(1:Ntip(tree1),function(x)
which(tree1$tip.label[x] == names(time.obs)))
dropTaxa <- character()
for(i in 1:Ntip(tree1)){
newDur <- newDurations[tipMatch[i]]
if(!is.na(newDur)){
if(tree1$edge.length[tree1$edge[,2] == i]<newDur){
stop("New duration longer than original taxon ranges?")
}
tree1$edge.length[tree1$edge[,2] == i] <- newDur
}else{
dropTaxa <- c(dropTaxa,tree1$tip.label[i])
}
}
treeNoDrop <- tree1
if(length(dropTaxa)>0){
tree1 <- drop.tip(tree1,dropTaxa)
}
tree1 <- fixRootTime(treeNoDrop,tree1)
return(tree1)
}
deadTree <- function(ntaxa,sumRate = 0.2){
tree <- ape::rtree(ntaxa)
tree$edge.length <- rexp(ntaxa+ntaxa-2,sumRate)
tree$root.time <- max(node.depth.edgelength(tree))+200
return(tree)
} |
tam_np_2pl_irf_probs <- function(x, par0, index, desmat, ...)
{
par0[index] <- x
pred <- desmat %*% par0
TP <- nrow(desmat)
p1 <- as.vector( stats::plogis(pred) )
probs <- matrix(0, nrow=2, ncol=TP)
probs[2,] <- p1
probs[1,] <- 1 - p1
return(probs)
} |
render.sigr_cortest <- function(statistic,
...,
format,
statDigits=4,
sigDigits=4,
pLargeCutoff=0.05,
pSmallCutoff=1.0e-5) {
wrapr::stop_if_dot_args(substitute(list(...)), "sigr::render.sigr_cortest")
if (missing(format) || is.null(format)) {
format <- getRenderingFormat()
}
if(!isTRUE(format %in% formats)) {
format <- "ascii"
}
fsyms <- syms[format,]
stat_format_str <- paste0('%.',statDigits,'g')
ct <- statistic$ct
pString <- render(wrapSignificance(ct$p.value,
symbol='p'),
format=format,
pLargeCutoff=pLargeCutoff,
pSmallCutoff=pSmallCutoff)
formatStr <- paste0(fsyms['startB'],ct$method,fsyms['endB'],
': (',fsyms['startI'],'r',fsyms['endI'],
'=',sprintf(stat_format_str,ct$estimate),
', ',pString,').')
formatStr
}
wrapCorTest <- function(x,...) {
UseMethod('wrapCorTest')
}
wrapCorTest.htest <- function(x,
...) {
wrapr::stop_if_dot_args(substitute(list(...)), "sigr::wrapCorTest.htest")
r <- list(ct=x,
test='cor.test')
class(r) <- c('sigr_cortest', 'sigr_statistic')
r
}
wrapCorTest.data.frame <- function(x,
Column1Name,
Column2Name,
...,
alternative = c("two.sided", "less", "greater"),
method = c("pearson", "kendall", "spearman"),
exact = NULL, conf.level = 0.95, continuity = FALSE,
na.rm= FALSE) {
if(!is.numeric(x[[Column1Name]])) {
stop("wrapr::wrapCorTest.data.frame column 1 must be numeric")
}
if(!is.numeric(x[[Column2Name]])) {
stop("wrapr::wrapCorTest.data.frame column 2 must be numeric")
}
c1 <- x[[Column1Name]]
c2 <- x[[Column2Name]]
nNA <- sum(is.na(c1) | is.na(c2))
if(na.rm) {
goodPosns <- (!is.na(c1)) & (!is.na(c2))
c1 <- c1[goodPosns]
c2 <- c2[goodPosns]
}
n <- length(c1)
ct <- stats::cor.test(x=c1,y=c2,
alternative = alternative,
method = method,
exact = exact, conf.level = conf.level, continuity = continuity,
...)
r <- list(ct=ct,
test='cor.test',
Column1Name=Column1Name,
Column2Name=Column2Name,
n=n,
nNA=nNA)
class(r) <- c('sigr_cortest', 'sigr_statistic')
r
} |
Stacked <- function(data, id.vars = NULL, var.stubs, sep,
keep.all = TRUE, keyed = TRUE,
keep.rownames = FALSE, ...) {
temp1 <- vGrep(var.stubs, names(data))
s <- sort.list(sapply(names(temp1), nchar), decreasing = TRUE)
for (i in s) {
matches <- temp1[[i]]
for (j in 1:length(temp1)) {
if (j != i && any(matches %in% temp1[[j]])) {
temp1[[j]] <- temp1[[j]][-which(temp1[[j]] %in% matches)]
}
}
}
temp <- Names(data, unlist(temp1))
if (sep == ".") sep <- "\\."
if (sep == "var.stubs") sep <- .collapseMe(var.stubs, ...)
if (is.null(id.vars)) {
id.vars <- othernames(data, temp)
} else {
id.vars <- Names(data, id.vars)
}
onames <- othernames(data, c(id.vars, temp))
if (!isTRUE(keep.all)) onames <- NULL
if (length(onames) == 0) onames <- NULL
if (!isTRUE(is.data.table(data))) {
data <- as.data.table(data, keep.rownames = keep.rownames)
} else {
data <- copy(data)
}
ZZ <- vector("list", length(var.stubs))
names(ZZ) <- var.stubs
.SD <- .N <- count <- a <- NULL
TimeCols <-
lapply(seq_along(var.stubs), function(i) {
x <- do.call(rbind, strsplit(names(data)[temp1[[i]]], sep))
if (ncol(x) == 1L) {
colnames(x) <- ".time_1"
x
} else {
colnames(x) <- c(
".var", paste(".time", 1:(ncol(x)-1), sep = "_"))
x[, -1, drop = FALSE]
}
})
for (i in seq_along(var.stubs)) {
ZZ[[i]] <- cbind(
data[, c(id.vars, onames), with = FALSE],
data[, list(.values = unlist(.SD, use.names=FALSE)),
.SDcols = temp1[[i]]])
setnames(ZZ[[i]], ".values", var.stubs[[i]])
setkeyv(ZZ[[i]], id.vars)
ZZ[[i]] <- cbind(ZZ[[i]], TimeCols[[i]])
if (isTRUE(keyed)) {
setkeyv(ZZ[[i]], c(key(ZZ[[i]]), colnames(TimeCols[[i]])))
setcolorder(ZZ[[i]], c(key(ZZ[[i]]), var.stubs[[i]], onames))
}
}
if (length(ZZ) == 1) ZZ[[1]]
else ZZ
}
NULL
merged.stack <- function(data, id.vars = NULL, var.stubs, sep, keep.all = TRUE, ...) {
temp <- Stacked(data = data, id.vars = id.vars, var.stubs = var.stubs,
sep = sep, keep.all = keep.all, keyed = TRUE, ...)
if (!is.null(dim(temp))) temp
else Reduce(function(x, y) merge(x, y, all = TRUE), temp)
} |
calc_APA_factor <- function(pRes, Res){
pRes_selection <- pRes[pRes$Source %in% Res$Source, ]
pRes_selection$Source <- as.vector(pRes_selection$Source)
Res_selection <-
Res[Res$Source %in% pRes_selection$Source, ]
APA <-
by(Res_selection, Res_selection$Source, nrow) / by(pRes_selection, pRes_selection$Source, nrow)
APAfactor <-
round(as.numeric(apply(Res, 1, function(x)
APA[which(names(APA) == x["Source"])])), 2)
return(APAfactor)
} |
CountsEPPM <-
function(formula,data,subset=NULL,na.action=NULL,weights=NULL,
model.type="mean and scale-factor",model.name="general",
link="log",initial=NULL,ltvalue=NA,utvalue=NA,
method="Nelder-Mead",control=NULL,
fixed.b=NA) {
if (model.type!="mean only") {
if ((model.name!="general") & (model.name!="general fixed b") & (model.name!="limiting")) {
cat("\n","unknown model.name for this model.type","\n")
return(object=NULL) }
} else {
if ((model.name!="Poisson") & (model.name!="negative binomial") &
(model.name!="negative binomial fixed b") & (model.name!="Faddy distribution") &
(model.name!="Faddy distribution fixed b")) {
cat("\n","unknown model.name for this model.type","\n")
return(object=NULL) } }
if ((method!="Nelder-Mead") & (method!="BFGS")) {
cat("\n","unknown function optim method","\n")
return(object=NULL) }
if ((is.data.frame(data)==FALSE) & (is.list(data)==FALSE)) {
cat("\n","Input data is neither data frame nor list.","\n")
return(object=NULL) }
cl <- match.call()
if ((link!="log")) {
cat("\n","unknown link function","\n")
return(object=NULL)
} else {
attr(link, which="mean") <- make.link(link) }
if (missing(data)) { data <- environment(formula) }
mf <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data", "subset", "na.action", "weights"), names(mf), 0L)
mf <- mf[c(1L, m)]
mf$drop.unused.levels <- TRUE
FBoth <- Formula(formula)
lenFB <- length(FBoth)
mf[[1L]] <- as.name("model.frame")
wk.name <- attr(FBoth, which="lhs")
if (length(wk.name)>1) {
cat("\n","more than one variable name on lhs of the formula","\n")
return(object=NULL) }
if (is.data.frame(data)==TRUE) {
data.type <- TRUE
mfBoth <- model.frame(FBoth,data=data)
resp.var <- model.part(FBoth,data=mfBoth,lhs=1)
nvar <- length(data)
nobs <- length(data[[1]])
if (nvar==1) { covariates <- NULL
} else { covariates <- data }
list.data <- lapply(1:nobs, function(i)
c(rep(0,resp.var[[1]][i]),1) )
mean.obs <- resp.var[[1]]
variance.obs <- rep(0,nobs)
scalef.obs <- rep(1,nobs)
wkdata <- data.frame(mean.obs,scalef.obs,data)
} else {
data.type <- FALSE
wk.name <- attr(FBoth, which="lhs")
nvar <- length(data)
nobs <- length(data[[1]])
if (nvar==1) { covariates <- NULL }
if ((nvar==1) & (is.list(data[[1]])==TRUE)) {
if ((wk.name==names(data)[1])==TRUE) {
list.set <- TRUE
list.data <- data[[1]]
} else {
cat("\n","single list with list of data is not named ",wk.name,"\n")
return(object=NULL) }
} else { list.set <- FALSE
for ( i in 1:nvar ) {
if (is.list(data[[i]])==TRUE) {
if ((wk.name==names(data)[i])==TRUE) {
if (list.set==TRUE) {
cat("\n","More than one list named ",wk.name," within list of data so","\n")
cat("not clear which is the dependent variable.","\n")
return(object=NULL)
} else { list.data <- data[[i]]
list.set <- TRUE }
}
} else {
if (i==1) { covariates <- data.frame(data[1])
names(covariates[1]) <- names(data[1])
} else {
if ((i==2) & ((list.set)==TRUE)) {
covariates <- data.frame(data[2])
names(covariates[1]) <- names(data[2])
} else {
wks <- length(covariates) + 1
covariates <- data.frame(covariates,data[i])
names(covariates[wks]) <- names(data[i])
}}} }
}
if (list.set==FALSE) {
cat("\n","No list named ",wk.name," within list of data.","\n")
return(object=NULL) }
mean.obs <- rep(0,nobs)
variance.obs <- rep(0,nobs)
scalef.obs <- rep(1,nobs)
for ( i in 1:nobs ) {
count <- list.data[[i]]
ncount <- sum(count)
nmax1 <- length(count)
nmax <- nmax1 - 1
cnum <- 0:nmax
if (ncount==1) { mean.obs[i] <- t(cnum)%*%count
} else {
mean.obs[i] <- t(cnum)%*%count/ncount
variance.obs[i] <- (t(cnum*cnum)%*%count -
ncount*mean.obs[i]*mean.obs[i]) / (ncount - 1)
if (mean.obs[i]>0) {
scalef.obs[i] <- variance.obs[i]/mean.obs[i] }
if (is.na(variance.obs[i])==TRUE) { variance.obs[i] <- 0 }
if (is.na(scalef.obs[i])==TRUE) { scalef.obs[i] <- 1 }
} }
if (is.null(covariates)==TRUE) { wkdata <- data.frame(mean.obs,scalef.obs)
} else { wkdata <- data.frame(mean.obs,scalef.obs,covariates) }
}
if (is.null(weights)==FALSE) {
if (is.null(attributes(weights))==TRUE) {
attr(weights, which="normalize") <- FALSE }
if (attr(weights, which="normalize")==TRUE) {
wkv <- c(rep(0, nobs))
wkv <- sapply(1:nobs, function(i) {
wkv[i] <- sum(list.data[[i]]) } )
total.n <- sum(wkv)
if (is.null(attr(weights, which="norm.to.n"))==FALSE) {
total.n <- as.numeric(attr(weights, which="norm.to.n")) }
if (data.type==TRUE) {
weights <- total.n*weights/sum(weights)
} else {
weights <- lapply(1:nobs, function(i) { weights[[i]] <-
(list.data[[i]]>0)*weights[[i]] } )
wkv <- sapply(1:nobs, function(i) { wkv[i] <-
sum(as.numeric(list.data[[i]]*weights[[i]])) } )
weights <- lapply(1:nobs, function(i) {
weights[[i]] <- total.n*weights[[i]] / sum(wkv) } )
} }
}
vnmax <- sapply(list.data,length) - 1
mf$data <- wkdata
mf$resp.var <- mean.obs
mf$formula <- update(formula(FBoth,lhs=NULL,rhs=1), mean.obs ~ . )
if (data.type==FALSE) { mf$weights=vnmax }
wkdata <- eval(mf, parent.frame())
if (model.type=="mean and scale-factor") {
mf.scalef <- mf
mf.scalef$resp.var <- scalef.obs
if (lenFB[2]==2) {
mf.scalef$formula <- update(formula(FBoth,lhs=NULL,rhs=2), scalef.obs ~ . )
} else {
mf.scalef$formula <- update(formula(FBoth,lhs=NULL,rhs=NULL), scalef.obs ~ 1 )
}
temp.wkdata <- eval(mf.scalef, parent.frame())
intersection.var <- intersect(names(wkdata), names(temp.wkdata))
dup.var <- duplicated(c(intersection.var, names(temp.wkdata)))
wks <- length(intersection.var) + 1
wke <- length(dup.var)
dup.var <- dup.var[wks:wke]
temp.wkdata <- subset(temp.wkdata, select=(dup.var==FALSE))
wkdata <- merge(wkdata, temp.wkdata, by=0, incomparables=TRUE, sort=FALSE)
}
wkdata <- subset(wkdata, select=((names(wkdata)!="(resp.var)") &
(names(wkdata)!="Row.names")))
if (is.null(subset)==FALSE) {
mean.obs <- mean.obs[subset]
variance.obs <- variance.obs[subset]
scalef.obs <- scalef.obs[subset]
weights <- weights[subset]
list.data <- list.data[subset]
if (data.type==TRUE) { resp.var <- resp.var[subset]
n.var <- n.var[subset] } }
if (data.type==TRUE) { wkdata <- data.frame(wkdata, resp.var) }
if (is.null(subset)==FALSE) { nobs <- length(wkdata[[1]]) }
if (is.null(subset)==FALSE) { nobs <- length(wkdata[[1]])
list.data <- list.data[subset] }
offset.var <- NULL
wks <- length(wkdata)
for ( i in 1:wks) { nchar <- nchar(names(wkdata[i]))
if ((nchar>7) & (((substr(names(wkdata[i]), 1, 7)=="offset(")==TRUE) |
(substr(names(wkdata[i]), 1, 7)=="offset.")==TRUE)) {
offset.var <- wkdata[i]
names(offset.var) <- substr(names(wkdata[i]), 8, (nchar-1))
wkdata <- data.frame(wkdata, offset.var)
} }
wks <- length(wkdata)
names(wkdata) <- sapply(1:wks, function(i) {
nchar <- nchar(names(wkdata[i]))
if ((nchar>7) & ((substr(names(wkdata[i]), 1, 7)=="offset.")==TRUE)) {
new.label <- paste("offset(", substr(names(wkdata[i]), 8, (nchar-1)), ")", sep="")
names(wkdata[i]) <- new.label
} else {
names(wkdata[i]) <- names(wkdata[i]) }
} )
on.exit(rm(wkdata))
if ((model.type!="mean only") & (model.type!="mean and scale-factor")) {
cat("\n","unknown model.type","\n")
return(object=NULL) }
if (model.type=="mean only") {
if (lenFB[2]==2) {
cat("\n","model.type is mean only but rhs of formula has two parts to it")
cat("\n","2nd part of rhs of formula is ignored","\n") }
FBoth <- update(FBoth,mean.obs ~ .) }
if (model.type=="mean and scale-factor") {
if (lenFB[2]==1) {
cat("\n","Model for scale-factor set to intercept only.","\n")
FBoth <- update(FBoth,mean.obs | scalef.obs ~ . | 1 )
} else {
FBoth <- update(FBoth,mean.obs | scalef.obs ~ . | . )
} }
terms.mean <- terms(formula(FBoth,lhs=1,rhs=1))
if (model.type=="mean only") {
terms.scalef <- terms(formula( 0 ~ 1 ))
} else {
if (lenFB[2]==1) { terms.scalef <- terms(formula( 0 ~ 1 ))
} else { terms.scalef <- terms(formula(FBoth,lhs=1,rhs=2)) } }
terms.full <- terms(formula(FBoth))
if (is.list(list.data)==FALSE) { cat("\n","list.data is not a list","\n")
return(object=NULL) }
mf.mean <- model.frame(formula(FBoth,lhs=1,rhs=1), data=wkdata)
covariates.matrix.mean <- model.matrix(mf.mean, data=wkdata)
offset.mean <- model.offset(mf.mean)
covariates.matrix.scalef <- matrix(c(rep(1,nrow(covariates.matrix.mean))),ncol=1)
offset.scalef <- NULL
if (model.type=="mean and scale-factor") {
if (lenFB[2]==2) {
mf.scalef <- model.frame(formula(FBoth,lhs=2,rhs=2), data=wkdata)
covariates.matrix.scalef <- model.matrix(mf.scalef, data=wkdata)
offset.scalef <- model.offset(mf.scalef) } }
if (is.null(offset.mean)==TRUE) { offset.mean <- c(rep(0,nobs)) }
if (is.null(offset.scalef)==TRUE) { offset.scalef <- c(rep(0,nobs)) }
if (is.null(initial)==TRUE) {
options(warn=-1)
if (data.type==TRUE) {
if (is.null(weights)==TRUE) {
glm.Results <- glm(formula(FBoth,lhs=1,rhs=1),family=poisson(link=link),
data=wkdata)
} else {
wkdata <- data.frame(wkdata, weights)
glm.Results <- glm(formula(FBoth,lhs=1,rhs=1),family=poisson(link=link),
data=wkdata, weights=weights) }
} else {
weights.mean <- c(rep(1,nobs))
if (is.null(weights)==FALSE) {
weights.mean <- as.vector(sapply(1:nobs, function(i) {
weights.mean[i] <- t(weights[[i]])%*%list.data[[i]] } ) ) }
weights.mean <- weights.mean*vnmax
wkdata <- data.frame(wkdata, weights.mean)
glm.Results <- glm(formula(FBoth,lhs=1,rhs=1),family=poisson(link=link),
data=wkdata, weights=weights.mean)
}
options(warn=0)
initial.mean <- coefficients(glm.Results)
names(initial.mean) <- names(coefficients(glm.Results))
if (model.type=="mean and scale-factor") {
glm.Results <- glm(formula(FBoth,lhs=2,rhs=2), family=gaussian(link="log"),
subset=(scalef.obs>0), data=wkdata)
initial.scalef <- coefficients(glm.Results)
names(initial.scalef) <- names(coefficients(glm.Results))
if (model.name=="general") { parameter <- c(initial.mean,initial.scalef,0)
names(parameter) <- c(names(initial.mean),names(initial.scalef),"log(b)") }
if ((model.name=="general fixed b") | (model.name=="limiting")) {
parameter <- c(initial.mean,initial.scalef)
names(parameter) <- c(names(initial.mean),names(initial.scalef)) }
loglikelihood <- LL.Regression.Counts(parameter,model.type,model.name,
link=link,list.data,covariates.matrix.mean,covariates.matrix.scalef,
offset.mean,offset.scalef,ltvalue,utvalue,fixed.b,weights,grad.method)
wks <- length(initial.scalef)
if (model.name=="general") { wk.parameter <- c(initial.mean,rep(0,wks),0)
names(wk.parameter) <- c(names(initial.mean),names(initial.scalef),"log(b)") }
if ((model.name=="general fixed b") | (model.name=="limiting")) {
wk.parameter <- c(initial.mean,rep(0,wks))
names(wk.parameter) <- c(names(initial.mean),names(initial.scalef)) }
wk.loglikelihood <- LL.Regression.Counts(wk.parameter,model.type,model.name,
link=link,list.data,covariates.matrix.mean,covariates.matrix.scalef,
offset.mean,offset.scalef,ltvalue,utvalue,fixed.b,weights,grad.method)
if (wk.loglikelihood>loglikelihood) { parameter <- wk.parameter }
}
if (model.type=="mean only") {
if ((model.name=="Poisson") | (model.name=="negative binomial fixed b")) {
parameter <- initial.mean
names(parameter) <- names(initial.mean) }
if (model.name=="negative binomial") { parameter <- c(initial.mean,0)
names(parameter) <- c(names(initial.mean),"log(b)") }
if (model.name=="Faddy distribution") { parameter <- c(initial.mean,0,0)
names(parameter) <- c(names(initial.mean),"c","log(b)") }
if (model.name=="Faddy distribution fixed b") { parameter <- c(initial.mean,0)
names(parameter) <- c(names(initial.mean),"c") }
}
} else {
npar.one <- ncol(covariates.matrix.mean)
numpar <- length(initial)
if (model.type=="mean only") { npar <- npar.one
loc.c <- npar + 1
if ((model.name=="negative binomial") |
(model.name=="Faddy distribution fixed b")) { npar <- loc.c }
if (model.name=="Faddy distribution") { npar <- npar + 2 } }
if (model.type=="mean and scale-factor") {
npar.two <- ncol(covariates.matrix.scalef)
npar <- npar.one + npar.two
if (model.name=="general") { npar <- npar + 1 } }
if (numpar!=npar) { cat("\n","number of parameters error","\n")
return(object=NULL) }
parameter <- initial
if (is.null(names(initial))==TRUE) {
cat("\n","WARNING: initial has no associated names","\n")
names(parameter) <- 1:numpar }
}
start <- parameter
wks <- length(list.data)
if (wks!=nobs) {
cat("\n","number of rows of covariates.matrix.mean not equal to length of list.data","\n")
return(object=NULL) }
output <- Model.Counts(parameter,model.type,model.name,link=link,covariates.matrix.mean,
covariates.matrix.scalef,offset.mean,offset.scalef,
fixed.b,vnmax)
for ( i in 1:nobs) { probability <- output$probabilities[[i]]
nmax1 <- vnmax[i] + 1
wks <- sum(probability)
if (is.finite(wks)==TRUE) {
wks <- round(sum(probability),digits=10)
if ((wks<0) | (wks>1)) { cat("\n","improper distribution produced by initial estimates","\n")
return(object=NULL) }
wks <- sum((round(probability,digits=10)<0) |
(round(probability,digits=10)>1))
if (wks>0) { cat("\n","improper distribution produced by initial estimates","\n")
return(object=NULL) }
} else { cat("\n","improper distribution produced by initial estimates","\n")
return(object=NULL) }
}
if (((model.name=="general fixed b") |
(model.name=="negative binomial fixed b") |
(model.name=="Faddy distribution fixed b")) &
((is.na(fixed.b)==TRUE) | (fixed.b<=0))) { cat("\n","value of fixed.b is NA or <=0","\n")
return(object=NULL) }
if ((is.null(initial)==FALSE) & ((model.name=="Faddy distribution") |
(model.name=="Faddy distribution fixed b"))) {
if (round(parameter[loc.c],digits=4)>=1) { cat("\n","initial c>=1 is not allowed for the Faddy distribution","\n")
return(object=NULL) }}
npar.mean <- ncol(covariates.matrix.mean)
npar <- npar.mean
r.parameter.mean <- parameter[1:npar.mean]
lp.mean <- covariates.matrix.mean%*%r.parameter.mean + offset.mean
if (model.type=="mean and scale-factor") {
npar.scalef <- ncol(covariates.matrix.scalef)
npar <- npar.mean + npar.scalef
wks <- npar.mean + 1
r.parameter.scalef <- parameter[wks:npar]
lp.scalef <- covariates.matrix.scalef%*%r.parameter.scalef + offset.scalef }
if (method=="BFGS") {
if (is.null(attr(method,which="grad.method"))==TRUE) {
attr(method,which="grad.method") <- "simple" }
if ((attr(method,which="grad.method")!="simple") &
(attr(method,which="grad.method")!="Richardson")) {
cat("\n","unknown gradient method with method BFGS so reset to simple","\n")
attr(method,which="grad.method") <- "simple" }
grad.method <- attr(method,which="grad.method")
} else {
grad.method <- NULL
}
if (is.null(control)==TRUE) {
control=list(fnscale=-1, trace=0)
} else {
ncontrol <- length(control)
wk.control=list(fnscale=-1, trace=0)
for ( i in 1:ncontrol) {
if ((names(control[i])!="fnscale") & (names(control[i])!="trace") &
(names(control[i])!="maxit") & (names(control[i])!="abstol") &
(names(control[i])!="reltol") & (names(control[i])!="alpha") &
(names(control[i])!="beta") & (names(control[i])!="gamma") &
(names(control[i])!="REPORT")) {
wkname <- names(control[i])
cat("\n","WARNING: Argument in control is unknown so ignored.","\n")
}
if (names(control[i])=="fnscale") { wk.control$fnscale <- control[[i]] }
if (names(control[i])=="trace") { wk.control$trace <- control[[i]] }
if (names(control[i])=="maxit") { wk.control$maxit <- control[[i]] }
if (names(control[i])=="abstol") { wk.control$abstol <- control[[i]] }
if (names(control[i])=="reltol") { wk.control$reltol <- control[[i]] }
if (names(control[i])=="alpha") { wk.control$alpha <- control[[i]] }
if (names(control[i])=="beta") { wk.control$beta <- control[[i]] }
if (names(control[i])=="gamma") { wk.control$gamma <- control[[i]] }
if (names(control[i])=="REPORT") { wk.control$REPORT <- control[[i]] }
}
control <- wk.control
}
if (length(parameter)==1) {
options(warn=-1)
if (data.type==TRUE) {
if (is.null(weights)==TRUE) {
FBoth_one <- update(FBoth, mean.obs ~ .)
glm.Results <- glm(formula(FBoth_one,lhs=1,rhs=1),family=poisson(link=link),
data=wkdata)
} else {
wkdata <- data.frame(wkdata, weights)
FBoth_one <- update(FBoth, mean.obs ~ .)
glm.Results <- glm(formula(FBoth_one,lhs=1,rhs=1),family=poisson(link=link),
data=wkdata, weights=weights) }
} else {
weights.mean <- c(rep(1,nobs))
if (is.null(weights)==FALSE) {
weights.mean <- as.vector(sapply(1:nobs, function(i) {
weights.mean[i] <- t(weights[[i]])%*%list.data[[i]] } ) ) }
weights.mean <- weights.mean*vnmax
wkdata <- data.frame(wkdata, weights.mean)
FBoth_one <- update(FBoth, mean.obs ~ . )
glm.Results <- glm(formula(FBoth_one,lhs=1,rhs=1),family=poisson(link=link),
data=wkdata, weights=weights.mean)
}
estimates <- coefficients(glm.Results)
options(warn=0)
wks <- LL.Regression.Counts(parameter,model.type,model.name,link,list.data,
covariates.matrix.mean,covariates.matrix.scalef,offset.mean,offset.scalef,
ltvalue,utvalue,fixed.b,weights,grad.method)
converged <- FALSE
attr(converged, which="code") <- 0
iterations <- glm.Results$iter
wk.optim <- list(par=glm.Results$coefficients,value=wks,counts=c(glm.Results$iter, NA),
convergence=0,message=NULL)
} else {
if (method=="Nelder-Mead") {
wk.method <- "Nelder-Mead" } else {
wk.method <- "BFGS" }
wk.optim <- optim(parameter,fn=LL.Regression.Counts,gr=LL.gradient,
model.type,model.name,link=link,list.data,
covariates.matrix.mean,covariates.matrix.scalef,
offset.mean,offset.scalef,ltvalue,utvalue,
fixed.b,weights,grad.method,
method=wk.method,hessian=FALSE,control=control)
if (wk.optim$convergence==0) { converged <- TRUE
} else { converged <- FALSE }
attr(converged, which="code") <- wk.optim$convergence
if (is.null(wk.optim$message)==FALSE) {
cat(" message ",wk.optim$message,"\n") }
iterations <- wk.optim$counts[1]
estimates <- wk.optim$par
}
names(estimates) <- names(parameter)
npar <- length(wk.optim$par)
nobs <- nrow(covariates.matrix.mean)
mean.par <- rep(0,nobs)
variance.par <- rep(1,nobs)
variance.limit <- rep(0,nobs)
npar.mean <- ncol(covariates.matrix.mean)
r.parameter.mean <- rep(0,npar.mean)
r.parameter.mean <- wk.optim$par[1:npar.mean]
mean.par <- attr(link, which="mean")$linkinv(lp.mean)
if (model.type=="mean only") {
if ((model.name=="Poisson") | (model.name=="negative binomial fixed b")) { wkv.coefficients <-
list(mean.est=wk.optim$par[1:npar.mean], scalef.est=NULL)
} else { wks <- npar.mean + 1
wkv.coefficients <-
list(mean.est=wk.optim$par[1:npar.mean], scalef.est=wk.optim$par[wks:npar]) }
} else {
npar.scalef <- ncol(covariates.matrix.scalef)
wks <- npar.mean + 1
wkv.coefficients <- list(mean.est=wk.optim$par[1:npar.mean], scalef.est=wk.optim$par[wks:npar])
scalef.par <- exp(lp.scalef)
}
model.hessian <- hessian(LL.Regression.Counts,x=wk.optim$par,
method="Richardson",method.args=list(r=6,eps=1.e-4),
model.type=model.type,model.name=model.name,link=link,
list.data=list.data,
covariates.matrix.mean=covariates.matrix.mean,
covariates.matrix.scalef=covariates.matrix.scalef,
offset.mean=offset.mean,offset.scalef=offset.scalef,
ltvalue=ltvalue,utvalue=utvalue,fixed.b=fixed.b,
weights=weights,grad.method=grad.method)
if ((model.name=="Faddy distribution") |
(model.name=="Faddy distribution fixed b")) { nparm1 <- npar - 1
nparm1sq <- nparm1*nparm1
nparm2 <- nparm1 - 1
wk.hessian <- matrix(c(rep(0,nparm1sq)),ncol=nparm1)
if (model.name=="Faddy distribution") { wk.c <- round(estimates[nparm1],digits=7)
} else { wk.c <- round(estimates[npar],digits=7) }
if ((model.name=="Faddy distribution") & (wk.c==1)) {
wk.hessian[1:nparm2,1:nparm2] <- model.hessian[1:nparm2,1:nparm2]
wk.hessian[nparm1,] <- c(model.hessian[npar,1:nparm2],model.hessian[npar,npar])
wk.hessian[,nparm1] <- c(model.hessian[1:nparm2,npar],model.hessian[npar,npar])
model.hessian <- wk.hessian
}
if ((model.name=="Faddy distribution fixed b") & (wk.c==1)) {
wk.hessian[1:nparm1,1:nparm1] <- model.hessian[1:nparm1,1:nparm1]
model.hessian <- wk.hessian
}
}
deter <- det(model.hessian)
wk.npar <- nrow(model.hessian)
if ((is.finite(deter)==FALSE) | (deter==0)) { vcov <- matrix(c(rep(NA,(wk.npar*wk.npar))),ncol=wk.npar)
} else { if (wk.npar==1) { vcov <- -1/model.hessian
} else { condition <- rcond(model.hessian)
if (condition>1e-16) {
vcov <- - solve(model.hessian)
} else { vcov <- matrix(c(rep(NA,(wk.npar*wk.npar))),ncol=wk.npar) }}}
colnames(vcov) <- rownames(vcov) <- names(wk.optim$par[1:wk.npar])
output.model <- Model.Counts(wk.optim$par,model.type,model.name,link,covariates.matrix.mean,
covariates.matrix.scalef,offset.mean,offset.scalef,
fixed.b,vnmax)
mean.prob <- rep(0, nobs)
vone <- rep(1,nobs)
if (model.name=="limiting") {
valpha <- output.model$FDparameters$out.valpha
vbeta <- output.model$FDparameters$out.vbeta
} else {
va <- output.model$FDparameters$out.va
vb <- output.model$FDparameters$out.vb
vc <- output.model$FDparameters$out.vc
v1mc <- vone - output.model$FDparameters$out.vc }
if ((model.name=="Poisson") | (model.name=="negative binomial") |
(model.name=="negative binomial fixed b")) {
if (model.name=="Poisson") { mean.prob <- va
} else {
mean.prob <- vb*(attr(link,which="mean")$linkinv(va) - vone) }
} else {
if (model.name=="limiting") {
mean.prob <- sapply(1:nobs, function(j)
mean.prob[j] <- - log(1 - valpha[j]*vbeta[j]) / vbeta[j] )
} else {
mean.prob <- sapply(1:nobs, function(j)
if ((abs(v1mc[j])<1.e-6)==TRUE) {
mean.prob[j] <- vb[j]*(attr(link,which="mean")$linkinv(va[j]) - 1)
} else {
mean.prob[j] <- vb[j]*((1+va[j]*v1mc[j]/(vb[j]**v1mc[j]))**(1/v1mc[j])-1) } )
} }
if (data.type==TRUE) { total.ninlist <- nobs
} else {
vninlist <- c(rep(0,length(list.data)))
vninlist <- sapply(1:length(list.data), function(ilist)
vninlist[ilist] <- sum(list.data[[ilist]]) )
total.ninlist <- sum(vninlist)
}
object <- list(data.type=data.type, list.data=list.data, call=cl,
formula=formula, model.type=model.type, model.name=model.name,
link=link,covariates.matrix.mean=covariates.matrix.mean,
covariates.matrix.scalef=covariates.matrix.scalef,
offset.mean=offset.mean,offset.scalef=offset.scalef,
ltvalue=ltvalue,utvalue=utvalue,fixed.b=fixed.b,
coefficients=wkv.coefficients,loglik=wk.optim$value,vcov=vcov,
n=nobs, nobs=nobs, df.null=total.ninlist, df.residual=(total.ninlist-length(wk.optim$par)),
vnmax=vnmax, weights=weights,converged=converged, method=method,
start=start, optim=wk.optim, control=control,
fitted.values=mean.prob, y=mean.obs,
terms=list(mean=terms.mean,scale.factor=terms.scalef,full=terms.full))
attr(object, "class") <- c("CountsEPPM")
return(object) } |
NMixPlugDensMarg.GLMM_MCMC <- function(x, grid, lgrid=500, scaled=FALSE, ...)
{
if (x$prior.b$priorK != "fixed") stop("only implemented for models with fixed number of components")
if (missing(grid)){
grid <- list()
if (scaled){
if (x$dimb == 1){
rangeGrid <- 0 + c(-3.5, 3.5)*1
grid[[1]] <- seq(rangeGrid[1], rangeGrid[2], length=lgrid)
}else{
for (i in 1:x$dimb){
rangeGrid <- 0 + c(-3.5, 3.5)*1
grid[[i]] <- seq(rangeGrid[1], rangeGrid[2], length=lgrid)
}
}
}else{
if (x$dimb == 1){
rangeGrid <- x$summ.b.Mean["Median"] + c(-3.5, 3.5)*x$summ.b.SDCorr["Median"]
grid[[1]] <- seq(rangeGrid[1], rangeGrid[2], length=lgrid)
}else{
for (i in 1:x$dimb){
rangeGrid <- x$summ.b.Mean["Median", i] + c(-3.5, 3.5)*x$summ.b.SDCorr["Median", (i-1)*(2*x$dimb - i + 2)/2 + 1]
grid[[i]] <- seq(rangeGrid[1], rangeGrid[2], length=lgrid)
}
}
}
names(grid) <- paste("b", 1:x$dimb, sep="")
}
if (x$dimb == 1) if (is.numeric(grid)) grid <- list(b1=grid)
if (!is.list(grid)) stop("grid must be a list")
if (scaled) scale <- list(shift=0, scale=1)
else scale <- x$scale.b
return(NMixPlugDensMarg.default(x=grid, scale=scale, w=x$poster.mean.w_b, mu=x$poster.mean.mu_b, Sigma=x$poster.mean.Sigma_b))
} |
bimonotone <- function( x, w = matrix( 1, nrow( x ), ncol( x ) ), maxiter = 65536, eps = 1.4901161193847656e-08 ) {
n <- nrow( x )
m <- ncol( x )
return( matrix( .C( "bimonotoneC", as.integer( n ), as.integer( m ), x = as.double( x ), as.double( w ), as.integer( maxiter ), as.double( eps ), PACKAGE = "monotone" )$x, n, m ) )
} |
.getRawData <- function() {
name <- tclvalue(tkgetOpenFile(
filetypes = "{ {RData Files} {.RData} {.rda}} { {All Files} * }"))
if (name == "")
return(data.frame())
temp=print(load(name))
dat=eval(parse(text=temp))
assign("DF", dat, envir = .JFEEnv)
importedFileName=last(unlist(strsplit(name,"/")))
assign("importedFileName", importedFileName, envir = .JFEEnv)
print(paste("You are loading ",importedFileName,sep=" "))
print(head(dat,3))
}
.saveWorkSpace<-function() {
file_name <- tkgetSaveFile(defaultextension = "Rsave")
if(nchar(fname <- as.character(file_name)))
save.image(file = file_name)
}
.getJFE <- function(x, mode="any", fail=TRUE){
if ((!fail) && (!exists(x, mode=mode, envir=.JFEEnv, inherits=FALSE))) return(NULL)
get(x, envir=.JFEEnv, mode=mode, inherits=FALSE)
}
.variable.list.height=6
.variable.list.width=c(20,Inf)
.title.color = as.character(.Tcl("ttk::style lookup TLabelframe.Label -foreground"))
.getFrame <- function(object) UseMethod(".getFrame")
.getFrame.listbox <- function(object){
object$frame
}
.defmacro <- function(..., expr){
expr <- substitute(expr)
len <- length(expr)
expr[3:(len+1)] <- expr[2:len]
expr[[2]] <- quote(on.exit(remove(list=objects(pattern="^\\.\\.", all.names=TRUE))))
a <- substitute(list(...))[-1]
nn <- names(a)
if (is.null(nn)) nn <- rep("", length(a))
for (i in seq(length.out=length(a))){
if (nn[i] == "") {
nn[i] <- paste(a[[i]])
msg <- paste(a[[i]], gettext("not supplied", domain="R-JFE"))
a[[i]] <- substitute(stop(foo), list(foo = msg))
}
}
names(a) <- nn
a <- as.list(a)
ff <- eval(substitute(
function(){
tmp <- substitute(body)
eval(tmp, parent.frame())
},
list(body = expr)))
formals(ff) <- a
mm <- match.call()
mm$expr <- NULL
mm[[1]] <- as.name("macro")
expr[[2]] <- NULL
attr(ff, "source") <- c(deparse(mm), deparse(expr))
ff
}
.variableListBox <- function(parentWindow, variableList, bg="white",selectmode="single", export="FALSE", initialSelection=NULL, listHeight=.variable.list.height, title){
if (selectmode == "multiple") selectmode <- .getJFE("multiple.select.mode")
if (length(variableList) == 1 && is.null(initialSelection)) initialSelection <- 0
frame <- tkframe(parentWindow)
minmax <- .variable.list.width
listbox <- tklistbox(frame, height=min(listHeight, length(variableList)),
selectmode=selectmode, background=bg, exportselection=export,
width=min(max(minmax[1], nchar(variableList)), minmax[2]))
scrollbar <- tkscrollbar(frame, command=function(...) tkyview(listbox, ...),repeatinterval=5)
tkconfigure(listbox, yscrollcommand=function(...) tkset(scrollbar, ...))
for (var in variableList) tkinsert(listbox, "end", var)
if (is.numeric(initialSelection)) for (sel in initialSelection) tkselection.set(listbox, sel)
firstChar <- tolower(substr(variableList, 1, 1))
len <- length(variableList)
onClick <- function() tkfocus(listbox)
toggleSelection <- function(){
active <- tclvalue(tkindex(listbox, "active"))
selected <- tclvalue(tkcurselection(listbox))
if (selected == active) tkselection.clear(listbox, "active") else tkselection.set(listbox, "active")
}
tkbind(listbox, "<ButtonPress-1>", onClick)
if (selectmode == "single") tkbind(listbox, "<Control-ButtonPress-1>", toggleSelection)
tkgrid(tklabel(frame, text=title, fg=.title.color, font="JFETitleFont"), columnspan=2, sticky="w")
tkgrid(listbox, scrollbar, sticky="nw")
tkgrid.configure(scrollbar, sticky="wns")
tkgrid.configure(listbox, sticky="ewns")
result <- list(frame=frame, listbox=listbox, scrollbar=scrollbar,
selectmode=selectmode, varlist=variableList)
class(result) <- "listbox"
result
}
.getSelection <- function(object) UseMethod(".getSelection")
.getSelection.listbox <- function(object){
object$varlist[as.numeric(tkcurselection(object$listbox)) + 1]
}
.radioButtons <- .defmacro(window, name, buttons, values=NULL, initialValue=..values[1], labels, title="", title.color=.title.color, right.buttons=FALSE, command=function(){},
expr={
..values <- if (is.null(values)) buttons else values
..frame <- paste(name, "Frame", sep="")
assign(..frame, tkframe(window))
..variable <- paste(name, "Variable", sep="")
assign(..variable, tclVar(initialValue))
if(title != ""){
tkgrid(tklabel(eval(parse(text=..frame)), text=title, foreground=.title.color, font="JFETitleFont"), columnspan=2, sticky="w")
}
for (i in 1:length(buttons)) {
..button <- paste(buttons[i], "Button", sep="")
if (right.buttons) {
assign(..button, ttkradiobutton(eval(parse(text=..frame)), variable=eval(parse(text=..variable)),
value=..values[i], command=command))
tkgrid(tklabel(eval(parse(text=..frame)), text=labels[i], justify="left"), eval(parse(text=..button)), sticky="w")
}
else{
assign(..button, ttkradiobutton(eval(parse(text=..frame)), variable=eval(parse(text=..variable)),
value=..values[i], text=labels[i], command=command))
tkgrid(eval(parse(text=..button)), sticky="w")
}
}
}
)
.seriesPlotX <-
function(x, labels = TRUE, type = "l", col = "indianred2",title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, ...)
{
N = NCOL(x)
Units = colnames(x)
if (length(col) == 1) col = rep(col, times = N)
for (i in 1:N) {
X = x[, i]
plot(x = X, type = type, col = col[i], ann = FALSE, ...)
if (title) {
title(main = Units[i])
} else {
title(...)
}
if(grid) grid()
if(box) box()
if(rug) rug(as.vector(X), ticksize = 0.01, side = 2, quiet = TRUE)
}
invisible()
}
downloadStockAI <- function (key="5edl69aag5", var.name="TWECO", from="2006-01-01", to="2015-12-31",
showdata=TRUE){
path<-"https://stock-ai.com/history-data-download?symbol="
url<-paste0(path,var.name, "&export=csv&startDate=",from,"&endDate=",to,"&key=", key,"&export=.csv")
filname<-paste0(var.name,".csv")
temp0 <- read.csv(filname)
timeID<-temp0[,1]
y<-as.matrix(temp0[,2])
colnames(y)<-var.name
row.names(y)<-timeID
y<-timeSeries::as.timeSeries(y)
return(y)
if (showdata) {
print(head(y))
print(tail(y))
}
}
ttsDS <- function (y,x=NULL, arOrder=2,xregOrder=0,type=NULL) {
if (!is.null(x)) {
x=timeSeries::as.timeSeries(x)
if ( nrow(y) != nrow(x) ) {print("Variables must have the same rows.")}
}
if (!timeSeries::is.timeSeries(y)) {stop("Data must be a timeSeries object.")}
if (is.null(type)) {type="none" }
p=max(arOrder,xregOrder)
colNAMES=c(outer(paste0(names(x),"_L"),0:p,FUN=paste0))
if (p==0) {
y=y
datasetX=x
ar0=NULL
} else {
datasetY=timeSeries::as.timeSeries(embed(y,p+1),timeSeries::time(y)[-c(1:p)])
y=datasetY[,1]
ar0=datasetY[,-1]
colnames(ar0)=paste0("ar",1:p)
if (is.null(x)) {datasetX=NULL
} else {
datasetX=timeSeries::as.timeSeries(embed(x,p+1),timeSeries::time(x)[-c(1:p)])
colnames(datasetX)=colNAMES
}
}
colnames(y)="y"
if (min(arOrder)==0) {ar=NULL
} else {ar=ar0[,paste0("ar",arOrder)]}
if (is.null(x)) {X=datasetX} else {
L.ID=paste0("L",xregOrder)
IDx=NULL
for (i in L.ID) {IDx=c(IDx,grep(colNAMES,pattern=i))}
X=datasetX[,IDx]
}
DF <- na.omit(cbind(y,ar,X))
trend <- 1:nrow(DF)
if (timeSeries::isRegular(y)) {
seasonDummy <- data.frame(forecast::seasonaldummy(as.ts(y)))
DF0 <- cbind(ar0,X,seasonDummy,trend)
} else {DF0 <- cbind(ar0,X,trend)}
if (type=="trend") {DF<-cbind(DF,trend)} else if (type=="sesaon") {DF<-cbind(DF,seasonDummy)
} else if (type=="both") {DF<-cbind(DF,trend,seasonDummy)
} else {DF <- DF}
return(DF)
} |
gl2related <- function(x, outfile="related.txt", outpath=tempdir(), save=TRUE)
{
gd <- as.matrix(x)
mm <- matrix(NA, nrow=nrow(gd), ncol =ncol(gd)*2 )
mm[, seq(1,nLoc(x)*2,2)] <- gd
mm <- ifelse(mm==1,0,mm)
mm[, seq(2,nLoc(x)*2,2)] <- gd
mm <- ifelse(mm==1,2,mm)
mm <- mm+1
mm <- ifelse(is.na(mm),0,mm)
gtd <- data.frame(V1=as.character(indNames(x)), mm)
gtd$V1 <- as.character(gtd$V1)
fn <- file.path(outpath, outfile)
if (save) write.table(gtd, file = fn, sep = "\t",col.names = F, row.names = F)
return(gtd)
} |
"iwres.dist.qq" <-
function(object,
...) {
if(is.null(check.vars(c("iwres"),object))) {
return(NULL)
}
xplot <- xpose.plot.qq(xvardef("iwres",object),
object,
...)
return(xplot)
}
|
with_mock_api({
test_that("dp_values are fetched", {
skippy()
values <- dp_values()
player_values <- dp_values("values-players.csv")
player_ids <- dp_playerids()
expect_tibble(values, min.rows = 1)
expect_tibble(player_ids, min.rows = 1)
expect_tibble(player_values, min.rows = 1)
})
})
test_that("dp_cleannames removes periods, apostrophes, and suffixes", {
player_names <- c("A.J. Green", "Odell Beckham Jr.", "Le'Veon Bell Sr.")
cleaned_names <- dp_cleannames(player_names)
lowercase_clean <- dp_cleannames(player_names, lowercase = TRUE)
expect_equal(cleaned_names, c("AJ Green", "Odell Beckham", "LeVeon Bell"))
expect_equal(lowercase_clean, c("aj green", "odell beckham", "leveon bell"))
})
test_that("dp_cleannames fixes MFL names and does custom name substitutions", {
mixed_names <- c(
"Trubisky, Mitch", "Chatarius Atwell",
"Zeke Elliott", "Devante Parker",
"A.J. Green", "Beckham Jr., Odell"
)
custom_cleaned_names <- dp_cleannames(mixed_names, convert_lastfirst = TRUE, use_name_database = TRUE)
expect_equal(
custom_cleaned_names,
c("Mitchell Trubisky", "Tutu Atwell", "Ezekiel Elliott", "DeVante Parker", "AJ Green", "Odell Beckham")
)
}) |
`scores.symcoca` <- function(x, choices = c(1, 2),
display = c("sites", "species"),
scaling = FALSE, ...) {
if (!inherits(x, "symcoca"))
stop("x must be of class \"symcoca\"")
opts <- c("species", "sites", "loadings", "xmatrix")
names(opts) <- c("species", "sites", "loadings", "xmatrix")
take <- opts[display]
retval <- list()
if ("species" %in% take) {
retval$species <- if(scaling) {
rescale(x, choices = choices, display = "species")
} else {
list(Y = x$scores$species$Y[, choices, drop = FALSE],
X = x$scores$species$X[, choices, drop = FALSE])
}
}
if ("sites" %in% take) {
retval$sites <- if(scaling) {
rescale(x, choices = choices, display = "sites")
} else {
list(Y = x$scores$site$Y[, choices, drop = FALSE],
X = x$scores$site$X[, choices, drop = FALSE])
}
}
retval$loadings <- if ("loadings" %in% take)
list(Y = x$loadings$Y[, choices, drop = FALSE],
X = x$loadings$X[ choices, drop = FALSE])
retval$xmatrix <- if ("xmatrix" %in% take)
x$X[, choices, drop = FALSE]
retval
} |
stopifnot(require("tikzDevice"))
require("sfsmisc")
x <- (-3:10) * 10^10
y <- abs(x / 1e9)
(t.file <- tempfile("tikz-eaxis", fileext = ".tex"))
tikz(file = t.file, standAlone=TRUE)
plot(x, y, axes=FALSE, type = "b")
eaxis(1, at=x, lab.type="latex")
eaxis(2, lab.type="latex")
dev.off()
helvet.lns <- c("\\renewcommand{\\familydefault}{\\sfdefault}",
"\\usepackage{helvet}")
str(ll <- readLines(t.file))
writeLines(c(ll[1:4], "", "%% Added from R (pkg 'sfsmisc', demo 'pretty-lab'):",
helvet.lns, "", ll[-(1:5)]), t.file)
system(paste(paste0("pdflatex -output-directory=", dirname(t.file)),
t.file))
if(file.exists(p.file <- sub("tex$", "pdf", t.file)) && interactive())
system(paste(getOption("pdfviewer"), p.file), wait=FALSE) |
download_format <- function(country,
urls,
ess_email = NULL,
only_download = FALSE,
output_dir = NULL) {
if (is.null(ess_email)) ess_email <- get_email()
authenticate(ess_email)
ess_round <- string_extract(urls, "ESS[[:digit:]]")
if (only_download && is.null(output_dir)) {
stop("`output_dir` should be a valid directory")
}
alt_dir <- ifelse(only_download, output_dir, tempdir())
if (!missing(country)) {
td <- file.path(alt_dir, paste0("ESS_", country), ess_round)
} else {
td <- file.path(alt_dir, ess_round)
}
for (dire in td) dir.create(dire, recursive = TRUE, showWarnings = FALSE)
mapply(round_downloader, urls, ess_round, td)
if (only_download) message("All files saved to ", normalizePath(output_dir))
td
}
authenticate <- function(ess_email) {
if(missing(ess_email)) {
stop(
"`ess_email` parameter must be specified. Create an account at https://www.europeansocialsurvey.org/user/new"
)
}
if (nchar(ess_email) == 0) {
stop(
"The email address you provided is not associated with any registered user. Create an account at https://www.europeansocialsurvey.org/user/new"
)
}
values <- list(u = ess_email)
url_login <- paste0(.global_vars$ess_website, .global_vars$path_login)
authen <- httr::POST(url_login,
body = values)
check_authen <-
safe_GET(url_login,
query = values)
authen_xml <- xml2::read_html(check_authen)
error_node <- xml2::xml_find_all(authen_xml, '//p [@class="error"]')
if (length(error_node) != 0) {
stop(xml2::xml_text(error_node),
" Create an account at https://www.europeansocialsurvey.org/user/new")
}
}
round_downloader <- function(each_url, which_round, which_folder) {
message(paste("Downloading", which_round))
temp_download <- file.path(which_folder, paste0(which_round, ".zip"))
current_file <- safe_GET(each_url, httr::progress())
writeBin(httr::content(current_file, as = "raw") , temp_download)
utils::unzip(temp_download, exdir = which_folder)
}
safe_GET <- function(url, config = list(), ...) {
resp_conn <- httr::GET(url = url, config = config, ...)
if (httr::status_code(resp_conn) > 300) {
stop("We're unable to reach 'www.europeansocialsurvey.org'. Are you connected to the internet or is the website down?'")
}
resp_conn
} |
library(RUnit)
run_tests <- function() {
dirs <- "analysis/R"
test_suite <- defineTestSuite("rappor", dirs, testFileRegexp = "_test.R$",
testFuncRegexp = "^Test")
stopifnot(isValidTestSuite(test_suite))
test_result <- runTestSuite(test_suite)
printTextProtocol(test_result)
result <- test_result[[1]]
if (result$nTestFunc == 0) {
cat("No tests found.\n")
return(FALSE)
}
if (result$nFail != 0 || result$nErr != 0) {
cat("Some tests failed.\n")
return(FALSE)
}
return(TRUE)
}
if (!run_tests()) {
quit(status = 1)
} |
emf <- function(file = "Rplot.emf", width=7, height=7,
bg = "transparent", fg = "black", pointsize=12,
family = "Helvetica", coordDPI = 300,
custom.lty=emfPlus, emfPlus=TRUE,
emfPlusFont = FALSE, emfPlusRaster = FALSE,
emfPlusFontToPath = FALSE)
{
if (is.na(width) || width < 0 || is.na(height) || height < 0) {
stop("emf: both width and height must be positive numbers.");
}
if (emfPlusFont && emfPlusFontToPath) {
stop("emf: at most one of 'emfPlusFont' and 'emfPlusFontToPath' can be TRUE")
}
.External(devEMF, file, bg, fg, width, height, pointsize,
family, coordDPI, custom.lty, emfPlus, emfPlusFont, emfPlusRaster,
emfPlusFontToPath)
invisible()
} |
posterior_summary <- function(RD_est, RR_est, OR_est) {
RD_mean = mean(RD_est)
RD_se = stats::sd(RD_est)
RD_lower = stats::quantile(RD_est, probs=0.025, na.rm = T)
RD_upper = stats::quantile(RD_est, probs=0.975, na.rm = T)
RR_mean = mean(RR_est)
RR_se = stats::sd(RR_est)
RR_lower = stats::quantile(RR_est, probs=0.025, na.rm = T)
RR_upper = stats::quantile(RR_est, probs=0.975, na.rm = T)
OR_mean = mean(OR_est)
OR_se = stats::sd(OR_est)
OR_lower = stats::quantile(OR_est, probs=0.025, na.rm = T)
OR_upper = stats::quantile(OR_est, probs=0.975, na.rm = T)
RD = c(RD_mean, RD_se, RD_lower, RD_upper)
RR = c(RR_mean, RR_se, RR_lower, RR_upper)
OR = c(OR_mean, OR_se, OR_lower, OR_upper)
res = rbind(RD, RR, OR)
colnames(res) = c("EST","SE","LOWER","UPPER")
return(res)
} |
test_that("`plot.see_p_direction()` works", {
if (require("bayestestR") && require("rstanarm") && require("ggridges")) {
set.seed(123)
m <<- stan_glm(Sepal.Length ~ Petal.Width * Species,
data = iris,
refresh = 0
)
result <- p_direction(m)
expect_s3_class(plot(result), "gg")
}
}) |
discrepancyCriteria <- function(design, type='all'){
X <- as.matrix(design)
dimension <- dim(X)[2]
n <- dim(X)[1]
if ( n < dimension ){
stop('Warning : the number of points is lower than the dimension.')
}
if ( min(X)<0 || max(X)>1 ){
warning("The design is rescaling into the unit cube [0,1]^d.")
M <- apply(X,2,max)
m <- apply(X,2,min)
for (j in 1:dim(X)[2]){
X[,j] <- (X[,j]-m[j])/(M[j]-m[j])
}
}
R <- list()
DisC2 <- FALSE
DisL2 <- FALSE
DisL2star <- FALSE
DisM2 <- FALSE
DisS2 <- FALSE
DisW2 <- FALSE
DisMix2 <- FALSE
if (length(type)==1 && type=='all'){
type <- c('C2','L2','L2star','M2','S2','W2','Mix2')
}
for(i in 1:length(type)){
type_ <- type[i]
switch(type_,
C2 = {DisC2 <- TRUE},
L2 = {DisL2 <- TRUE},
L2star = {DisL2star <- TRUE},
M2 = {DisM2 <- TRUE},
S2 = {DisS2 <- TRUE},
W2 = {DisW2 <- TRUE},
Mix2 = {DisMix2 <- TRUE})
}
if(DisC2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- prod((1+0.5*abs(X[i,]-0.5)-0.5*((abs(X[i,]-0.5))^2)))
s1 <- s1+p
for (k in 1:n){
q <- prod((1+0.5*abs(X[i,]-0.5)+0.5*abs(X[k,]-0.5)-0.5*abs(X[i,]-X[k,])))
s2 <- s2+q
}
}
R <- c(R,DisC2 = sqrt(((13/12)^dimension)-((2/n)*s1) + ((1/n^2)*s2)))
}
if(DisL2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- prod(X[i,]*(1-X[i,]))
s1 <- s1+p
for (k in 1:n){
q <- 1
for (j in 1:dimension){
q <- q*(1-max(X[i,j],X[k,j]))*min(X[i,j],X[k,j])
}
s2 <- s2+q
}
}
R <- c(R,DisL2 = sqrt(12^(-dimension) - (((2^(1-dimension))/n)*s1) + ((1/n^2)*s2)))
}
if(DisL2star == TRUE){
dL2<-0
for (j in 1:n){
for (i in 1:n){
if(i!=j){
t<-c()
for (l in 1:dimension) t<-c(t,1-max(X[i,l],X[j,l]))
t<-(prod(t))/(n^2)
}
else{
t1<-1-X[i,]
t1<-prod(t1)
t2<-1-X[i,]^2
t2<-prod(t2)
t<-t1/(n^2)-((2^(1-dimension))/n)*t2
}
dL2<-dL2+t}
}
R <- c(R,DisL2star = sqrt(3^(-dimension)+dL2))
}
if(DisM2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- 1
p <- prod((3-(X[i,]*X[i,])))
s1 <- s1+p
for (k in 1:n){
q <- 1
for (j in 1:dimension){
q <- q*(2-max(X[i,j],X[k,j]))
}
s2 <- s2+q
}
}
R <- c(R,DisM2 = sqrt(((4/3)^dimension) - (((2^(1-dimension))/n)*s1) + ((1/n^2)*s2)))
}
if(DisS2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- prod((1+(2*X[i,])-(2*X[i,]*X[i,])))
s1 <- s1+p
for (k in 1:n){
q <- prod((1-abs(X[i,]-X[k,])))
s2 <- s2+q
}
}
R <- c(R,DisS2 = sqrt(((4/3)^dimension) - ((2/n)*s1) + ((2^dimension/n^2)*s2)))
}
if(DisW2 == TRUE){
s1 <- 0
for (i in 1:n){
for (k in 1:n){
p <- prod((1.5-((abs(X[i,]-X[k,]))*(1-abs(X[i,]-X[k,])))))
s1 <- s1+p
}
}
R <- c(R , DisW2 = sqrt((-((4/3)^dimension) + ((1/n^2)*s1))))
}
if(DisMix2 == TRUE){
s1 <- 0; s2 <- 0
for (i in 1:n){
p <- prod((5/3-0.25*abs(X[i,]-0.5)-0.25*((abs(X[i,]-0.5))^2)))
s1 <- s1+p
for (k in 1:n){
q <- prod((15/8-0.25*abs(X[i,]-0.5)-0.25*abs(X[k,]-0.5)-0.75*abs(X[i,]-X[k,])+0.5*((abs(X[i,]-X[k,]))^2)))
s2 <- s2+q
}
}
R <- c(R,DisMix2 = sqrt(((19/12)^dimension)-((2/n)*s1) + ((1/n^2)*s2)))
}
return(R)
} |
ctStanPlotPost<-function(obj, rows='all', npp=6,priorwidth=TRUE,
smoothness=1,priorsamples=10000,
plot=TRUE,wait=FALSE,...){
if(!(class(obj) %in% c('ctStanFit','ctStanModel'))) stop('not a ctStanFit or ctStanModel object!')
plots <- list()
densiter <- 1e5
ps <- cbind(obj$setup$popsetup, obj$setup$popvalues)
ps <- ps[ps$when %in% c(0,-1) & ps$param > 0 & ps$copyrow < 1 & ps$matrix < 11,]
ps <- ps[!duplicated(ps$param),]
ps<-ps[order(ps$param),]
e<-ctExtract(obj)
priors <- ctStanGenerate(cts = obj,parsonly=TRUE,nsamples=priorsamples,...)
priors <- priors$stanfit$transformedpars
posteriors <- ctExtract(obj)
if(rows[1]=='all') rows<-1:nrow(ps)
nplots<-ceiling(length(rows) /4)
if(1==99) Par.Value <- type <- quantity <- Density <- NULL
quantity <- c('Posterior','Prior')
for(ploti in 1:nplots){
dat <- data.table(quantity='',Par.Value=0, Density=0,type='',param='')
for(ri in if(length(rows) > 1) rows[as.integer(cut_number(rows,nplots))==ploti] else rows){
pname <- ps$parname[ri]
pari <- ps[ri,'param']
meanpost <- posteriors$popmeans[,pari]
meanprior <- priors$popmeans[,pari]
if(priorwidth) xlimsindex <- 'all' else xlimsindex <- 1
mdens <- ctDensityList(list(meanpost, meanprior),probs=c(.05,.95),plot=FALSE,
xlimsindex=xlimsindex,cut=TRUE)
quantity <- c('Posterior','Prior')
for(i in 1:length(mdens$density)){
dat <- rbind(dat,data.table(quantity=quantity[i],Par.Value=mdens$density[[i]]$x,
Density=mdens$density[[i]]$y, type='Pop. Mean',param=pname))
}
if(ps[ri,'indvarying']>0){
posteriorsd <- posteriors$popsd[,ps$indvarying[ri]]
priorsd <- priors$popsd[,ps$indvarying[ri]]
sddens <- ctDensityList(list(posteriorsd, priorsd),probs=c(.05,.95),plot=FALSE,
xlimsindex=xlimsindex)
for(i in 1:length(sddens$density)){
dat <- rbind(dat,data.table(quantity=quantity[i],Par.Value=sddens$density[[i]]$x,
Density=sddens$density[[i]]$y, type='Pop. SD',param=pname))
}
}
}
dat <- dat[-1,]
plots<-c(plots,list(
ggplot(dat,aes(x=Par.Value,fill=quantity,ymax=Density,y=Density) )+
geom_line(alpha=.3) +
geom_ribbon(alpha=.4,ymin=0) +
scale_fill_manual(values=c('red','blue')) +
theme_minimal()+
theme(legend.title = element_blank(),
panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = .2),
strip.text.x = element_text(margin = margin(.01, 0, .01, 0, "cm"))) +
facet_wrap(vars(type,param),scales='free')
))
}
if(plot) {
firstplot=TRUE
lapply(plots,function(x){
if(wait && !firstplot) readline("Press [return] for next plot.")
firstplot <<- FALSE
suppressWarnings(print(x))
})
return(invisible(NULL))
} else return(plots)
} |
library(parallel)
library(hamcrest)
test.detectCores <- function() {
cores <- detectCores()
print(cores)
assertTrue(detectCores() > 0)
} |
material_spinner_show <- function(session, output_id){
js_code <-
paste0(
"$('
)
js_code <- gsub(pattern = "DOUBLEQUOTE", replacement = '"', x = js_code)
session$sendCustomMessage(
type = "shinymaterialJS",
js_code
)
}
material_spinner_hide <- function(session, output_id){
session$sendCustomMessage(
type = "shinymaterialJS",
paste0(
"$('
)
)
session$sendCustomMessage(
type = "shinymaterialJS",
paste0(
"$('
)
)
} |
library(sparklyr)
rsApiUpdateDialog <- function(code) {
if (exists(".rs.api.updateDialog")) {
updateDialog <- get(".rs.api.updateDialog")
updateDialog(code = code)
}
}
rsApiShowDialog <- function(title, message, url = "") {
if (exists(".rs.api.showDialog")) {
showDialog <- get(".rs.api.showDialog")
showDialog(title, message, url)
}
}
rsApiShowPrompt <- function(title, message, default) {
if (exists(".rs.api.showPrompt")) {
showPrompt <- get(".rs.api.showPrompt")
showPrompt(title, message, default)
}
}
rsApiShowQuestion <- function(title, message, ok, cancel) {
if (exists(".rs.api.showQuestion")) {
showPrompt <- get(".rs.api.showQuestion")
showPrompt(title, message, ok, cancel)
}
}
rsApiReadPreference <- function(name, default) {
if (exists(".rs.api.readPreference")) {
readPreference <- get(".rs.api.readPreference")
value <- readPreference(name)
if (is.null(value)) default else value
}
}
rsApiWritePreference <- function(name, value) {
if (!is.character(value)) {
stop("Only character preferences are supported")
}
if (exists(".rs.api.writePreference")) {
writePreference <- get(".rs.api.writePreference")
writePreference(name, value)
}
}
rsApiVersionInfo <- function() {
if (exists(".rs.api.versionInfo")) {
versionInfo <- get(".rs.api.versionInfo")
versionInfo()
}
}
is_java_available <- function() {
nzchar(spark_get_java())
}
spark_home <- function() {
home <- Sys.getenv("SPARK_HOME", unset = NA)
if (is.na(home))
home <- NULL
home
}
spark_ui_avaliable_versions <- function() {
tryCatch({
spark_available_versions(show_hadoop = TRUE, show_minor = TRUE)
}, error = function(e) {
warning(e)
spark_installed_versions()[,c("spark","hadoop")]
})
}
spark_ui_spark_choices <- function() {
availableVersions <- spark_ui_avaliable_versions()
selected <- spark_default_version()[["spark"]]
choiceValues <- unique(availableVersions[["spark"]])
choiceNames <- choiceValues
choiceNames <- lapply(
choiceNames,
function(e) if (e == selected) paste(e, "(Default)") else e
)
names(choiceValues) <- choiceNames
choiceValues
}
spark_ui_hadoop_choices <- function(sparkVersion) {
availableVersions <- spark_ui_avaliable_versions()
selected <- spark_install_find(version = sparkVersion, installed_only = FALSE)$hadoopVersion
choiceValues <- unique(availableVersions[availableVersions$spark == sparkVersion,][["hadoop"]])
choiceNames <- choiceValues
choiceNames <- lapply(
choiceNames,
function(e) if (length(selected) > 0 && e == selected) paste(e, "(Default)") else e
)
names(choiceValues) <- choiceNames
choiceValues
}
spark_ui_default_connections <- function() {
getOption(
"sparklyr.ui.connections",
getOption("rstudio.spark.connections")
)
}
connection_spark_ui <- function() {
elementSpacing <- if (.Platform$OS.type == "windows") 2 else 7
tags$div(
tags$head(
tags$style(
HTML(paste("
body {
background: none;
font-family : \"Lucida Sans\", \"DejaVu Sans\", \"Lucida Grande\", \"Segoe UI\", Verdana, Helvetica, sans-serif;
font-size : 12px;
-ms-user-select : none;
-moz-user-select : none;
-webkit-user-select : none;
user-select : none;
margin: 0;
margin-top: 7px;
}
select {
background:
}
.shiny-input-container {
min-width: 100%;
margin-bottom: ", elementSpacing, "px;
}
.shiny-input-container > .control-label {
display: table-cell;
width: 195px;
}
.shiny-input-container > div {
display: table-cell;
width: 300px;
}
display: none;
}
", sep = ""))
)
),
div(style = "table-row",
selectInput(
"master",
"Master:",
choices = c(
list("local" = "local"),
spark_ui_default_connections(),
list("Cluster..." = "cluster")
),
selectize = FALSE
),
selectInput(
"dbinterface",
"DB Interface:",
choices = c(
"dplyr" = "dplyr",
"(None)" = "none"
),
selectize = FALSE,
selected = rsApiReadPreference("sparklyr_dbinterface", "dplyr")
)
),
div(
style = paste("display: table-row; height: 10px")
),
conditionalPanel(
condition = "!output.notShowVersionsUi",
div(style = "table-row",
selectInput(
"sparkversion",
"Spark version:",
choices = spark_ui_spark_choices(),
selected = spark_default_version()$spark,
selectize = FALSE
),
selectInput(
"hadoopversion",
"Hadoop version:",
choices = spark_ui_hadoop_choices(spark_default_version()$spark),
selected = spark_default_version()$hadoop,
selectize = FALSE
)
)
)
)
}
connection_spark_server <- function(input, output, session) {
hasDefaultSparkVersion <- reactive({
input$sparkversion == spark_default_version()$spark
})
hasDefaultHadoopVersion <- reactive({
input$hadoopversion == spark_default_version()$hadoop
})
output$notShowVersionsUi <- reactive({
!identical(spark_home(), NULL)
})
userInstallPreference <- NULL
checkUserInstallPreference <- function(master, sparkSelection, hadoopSelection, prompt) {
if (identical(master, "local") &&
identical(rsApiVersionInfo()$mode, "desktop") &&
identical(spark_home(), NULL)) {
installed <- spark_installed_versions()
isInstalled <- nrow(installed[installed$spark == sparkSelection & installed$hadoop == hadoopSelection, ])
if (!isInstalled) {
if (prompt && identical(userInstallPreference, NULL)) {
userInstallPreference <<- rsApiShowQuestion(
"Install Spark Components",
paste(
"Spark ",
sparkSelection,
" for Hadoop ",
hadoopSelection,
" is not currently installed.",
"\n\n",
"Do you want to install this version of Spark?",
sep = ""
),
ok = "Install",
cancel = "Cancel"
)
userInstallPreference
}
else if (identical(userInstallPreference, NULL)) {
FALSE
}
else {
userInstallPreference
}
}
else {
FALSE
}
}
else {
FALSE
}
}
generateCode <- function(master, dbInterface, sparkVersion, hadoopVersion, installSpark) {
paste(
"library(sparklyr)\n",
if(dbInterface == "dplyr") "library(dplyr)\n" else "",
if(installSpark)
paste(
"spark_install(version = \"",
sparkVersion,
"\", hadoop_version = \"",
hadoopVersion,
"\")\n",
sep = ""
)
else "",
"sc ",
"<- ",
"spark_connect(master = \"",
master,
"\"",
if (!hasDefaultSparkVersion())
paste(
", version = \"",
sparkVersion,
"\"",
sep = ""
)
else "",
if (!hasDefaultHadoopVersion())
paste(
", hadoop_version = \"",
hadoopVersion,
"\"",
sep = ""
)
else "",
")",
sep = ""
)
}
stateValuesReactive <- reactiveValues(codeInvalidated = 1)
codeReactive <- reactive({
master <- input$master
dbInterface <- input$dbinterface
sparkVersion <- input$sparkversion
hadoopVersion <- input$hadoopversion
codeInvalidated <- stateValuesReactive$codeInvalidated
installSpark <- checkUserInstallPreference(master, sparkVersion, hadoopVersion, FALSE)
generateCode(master, dbInterface, sparkVersion, hadoopVersion, installSpark)
})
installLater <- reactive({
master <- input$master
sparkVersion <- input$sparkversion
hadoopVersion <- input$hadoopversion
}) %>% debounce(200)
observe({
installLater()
isolate({
master <- input$master
sparkVersion <- input$sparkversion
hadoopVersion <- input$hadoopversion
checkUserInstallPreference(master, sparkVersion, hadoopVersion, TRUE)
})
})
observe({
rsApiUpdateDialog(codeReactive())
})
observe({
if (identical(input$master, "cluster")) {
if (identical(rsApiVersionInfo()$mode, "desktop")) {
rsApiShowDialog(
"Connect to Spark",
paste(
"Connecting with a remote Spark cluster requires ",
"an RStudio Server instance that is either within the cluster ",
"or has a high bandwidth connection to the cluster.</p>",
"<p>Please see the <strong>Using Spark with RStudio</strong> help ",
"link below for additional details.</p>",
sep = ""
)
)
updateSelectInput(
session,
"master",
selected = "local"
)
}
else if (identical(spark_home(), NULL)) {
rsApiShowDialog(
"Connect to Spark",
paste(
"Connecting with a Spark cluster requires that you are on a system ",
"able to communicate with the cluster in both directions, and ",
"requires that the SPARK_HOME environment variable refers to a ",
"locally installed version of Spark that is configured to ",
"communicate with the cluster.",
"<p>Your system doesn't currently have the SPARK_HOME environment ",
"variable defined. Please contact your system administrator to ",
"ensure that the server is properly configured to connect with ",
"the cluster.<p>",
sep = ""
)
)
updateSelectInput(
session,
"master",
selected = "local"
)
}
else {
master <- rsApiShowPrompt(
"Connect to Cluster",
"Spark master:",
"spark://local:7077"
)
updateSelectInput(
session,
"master",
choices = c(
list(master = "master"),
master,
spark_ui_default_connections(),
list("Cluster..." = "cluster")
),
selected = master
)
}
}
})
currentSparkSelection <- NULL
session$onFlushed(function() {
if (!is_java_available()) {
url <- ""
message <- paste(
"In order to connect to Spark ",
"your system needs to have Java installed (",
"no version of Java was detected or installation ",
"is invalid).",
sep = ""
)
if (identical(rsApiVersionInfo()$mode, "desktop")) {
message <- paste(
message,
"<p>Please contact your server administrator to request the ",
"installation of Java on this system.</p>",
sep = "")
url <- java_install_url()
} else {
message <- paste(
message,
"<p>Please contact your server administrator to request the ",
"installation of Java on this system.</p>",
sep = "")
}
rsApiShowDialog(
"Java Required for Spark Connections",
message,
url
)
}
currentSparkSelection <<- spark_default_version()$spark
})
observe({
sparkVersion <- input$sparkversion
master <- input$master
if (!identical(currentSparkSelection, NULL)) {
currentSparkSelection <<- sparkVersion
hadoopDefault <- spark_install_find(version = currentSparkSelection, installed_only = FALSE)$hadoopVersion
updateSelectInput(
session,
"hadoopversion",
choices = spark_ui_hadoop_choices(currentSparkSelection),
selected = hadoopDefault
)
stateValuesReactive$codeInvalidated <<- isolate({
stateValuesReactive$codeInvalidated + 1
})
}
})
observe({
rsApiWritePreference("sparklyr_dbinterface", input$dbinterface)
})
outputOptions(output, "notShowVersionsUi", suspendWhenHidden = FALSE)
}
shinyApp(connection_spark_ui, connection_spark_server) |
create_participantgroup_widedata <- function(raw_df,
n_trials_per_grapheme=3,
participant_col_name,
symbol_col_regex,
color_col_regex="colou*r",
time_col_regex=NULL,
testdate_col_name=NULL,
color_space_spec="Luv") {
new_pgroup <- ParticipantGroup$new()
participant_vector <- as.character(raw_df[[participant_col_name]])
symbol_col_bool <- grepl(symbol_col_regex, colnames(raw_df))
symbol_mat <- as.matrix(raw_df[, symbol_col_bool])
color_col_bool <- grepl(color_col_regex, colnames(raw_df))
color_mat <- as.matrix(raw_df[, color_col_bool])
if (!is.null(time_col_regex)) {
time_col_bool <- grepl(time_col_regex, colnames(raw_df))
time_mat <- as.matrix(raw_df[, time_col_bool])
}
if (!is.null(testdate_col_name)) {
testdate_vector <- raw_df[[testdate_col_name]]
}
unique_symbols <- unique(as.vector(symbol_mat))
for (row_index in 1:nrow(raw_df)) {
participant_id <- participant_vector[row_index]
symbol_vector <- as.vector(symbol_mat[row_index, ])
color_vector <- as.vector(color_mat[row_index, ])
if (!is.null(time_col_regex)) {
time_vector <- as.vector(time_mat[row_index, ])
} else {time_vector <- NULL}
if (!is.null(testdate_col_name)) {
test_date <- testdate_vector[row_index]
} else {test_date <- NULL}
new_p <- create_participant(participant_id=participant_id,
grapheme_symbols=unique_symbols,
n_trials_per_grapheme=n_trials_per_grapheme,
trial_symbols=symbol_vector,
response_times=time_vector,
response_colors=color_vector,
color_space_spec=color_space_spec,
test_date=test_date)
new_pgroup$add_participant(new_p)
}
return(new_pgroup)
} |
"checkPackageLoadability" <-
function (pkg, quiet)
{
RevoIOQ:::testPackageLoadability(pkg = pkg, lib.loc = .Library, quiet = quiet)
} |
context("checks large data warning in rCompare")
test_that("Silent for small data", {
expect_silent(warnLargeData(nrow(iris),ncol(iris),nrow(iris),ncol(iris)))
expect_silent(warnLargeData(nrow(iris),ncol(iris),nrow(pressure),ncol(pressure)))
expect_silent(warnLargeData(nrow(pressure),ncol(pressure), nrow(pressure),ncol(pressure)))
expect_silent(warnLargeData(2E2,1E3,1E3,5E3))
})
test_that("Warns for large data", {
expect_message(warnLargeData(1E9,1E9,1E9,1E9))
expect_message(warnLargeData(5E6,5E6,5E6,6E6))
}) |
OUwie.sim <- function(phy=NULL, data=NULL, simmap.tree=FALSE, root.age=NULL, scaleHeight=FALSE, alpha=NULL, sigma.sq=NULL, theta0=NULL, theta=NULL, mserr="none", shift.point=0.5, fitted.object=NULL){
if(!is.null(fitted.object)) {
if(grepl("BM", fitted.object$model) | grepl("OU1", fitted.object$model)) {
stop(paste("not implemented yet for ", fitted.object$model))
}
if(!is.null(alpha) | !is.null(theta0) | !is.null(theta)) {
stop("You're passing in parameters to simulate from AND a fitted object to simulate under. You can do one or the other")
}
phy <- fitted.object$phy
data <- cbind(phy$tip.label, fitted.object$data)
alpha <- fitted.object$solution['alpha',]
alpha[which(is.na(alpha))] <- 0
sigma.sq <- fitted.object$solution['sigma.sq',]
if(mserr != "none"){
warning("measurement error is not yet handled for simulations from fitted.object")
}
if (fitted.object$root.station == TRUE | fitted.object$root.station==FALSE){
if (fitted.object$model == "OU1"){
theta <- matrix(t(fitted.object$theta[1,]), 2, length(levels(fitted.object$tot.states)))[1,]
theta0 <- theta[phy$node.label[1]]
}
}
if (fitted.object$root.station == TRUE | !grepl("OU", fitted.object$model)){
if (fitted.object$model != "OU1"){
theta <- matrix(t(fitted.object$theta), 2, length(levels(fitted.object$tot.states)))[1,]
theta0 <- theta[phy$node.label[1]]
}
}
if (fitted.object$root.station == FALSE & grepl("OU", fitted.object$model)){
if (fitted.object$model != "OU1"){
if(fitted.object$get.root.theta == TRUE){
theta.all <- matrix(t(fitted.object$theta), 2, 1:length(levels(fitted.object$tot.states))+1)[1,]
theta <- theta.all[2:length(theta.all)]
theta0 <- theta.all[1]
}else{
theta <- matrix(t(fitted.object$theta), 2, length(levels(fitted.object$tot.states)))[1,]
theta0 <- theta[phy$node.label[1]]
}
}
}
}
if(is.null(root.age)){
if(any(branching.times(phy)<0)){
stop("Looks like your tree is producing negative branching times. Must input known root age of tree.", .call=FALSE)
}
}
if(simmap.tree == FALSE){
if(mserr == "none"){
data <- data.frame(data[,2], data[,2], row.names=data[,1])
}
if(mserr == "known"){
data <- data.frame(data[,2], data[,3], row.names=data[,1])
}
data <- data[phy$tip.label,]
n <- max(phy$edge[,1])
ntips <- length(phy$tip.label)
int.states <- factor(phy$node.label)
phy$node.label <- as.numeric(int.states)
tip.states <- factor(data[,1])
data[,1] <- as.numeric(tip.states)
tot.states <- factor(c(phy$node.label,as.character(data[,1])))
k <- length(levels(tot.states))
regime <- matrix(rep(0,(n-1)*k), n-1, k)
root.state <- phy$node.label[1]
int.state <- phy$node.label[-1]
edges <- cbind(c(1:(n-1)),phy$edge,MakeAgeTable(phy, root.age=root.age))
if(scaleHeight == TRUE){
edges[,4:5] <- edges[,4:5]/max(MakeAgeTable(phy, root.age=root.age))
root.age <- 1
}
edges <- edges[sort.list(edges[,3]),]
mm <- c(data[,1],int.state)
regime <- matrix(0,nrow=length(mm),ncol=length(unique(mm)))
for (i in 1:length(mm)) {
regime[i,mm[i]] <- 1
}
edges <- cbind(edges,regime)
edges <- edges[sort.list(edges[,1]),]
oldregime <- root.state
alpha <- alpha
alpha[alpha==0] <- 1e-10
sigma <- sqrt(sigma.sq)
theta <- theta
x <- matrix(0, n, 1)
TIPS <- 1:ntips
ROOT <- ntips + 1L
x[ROOT,] <- theta0
for(i in 1:length(edges[,1])){
anc <- edges[i,2]
desc <- edges[i,3]
oldtime <- edges[i,4]
newtime <- edges[i,5]
if(anc%in%edges[,3]){
start <- which(edges[,3]==anc)
oldregime <- which(edges[start,6:(k+5)]==1)
}else{
oldregime <- root.state
}
newregime=which(edges[i,6:(k+5)]==1)
if(oldregime==newregime){
x[edges[i,3],] <- (x[edges[i,2],]*exp(-alpha[oldregime]*(newtime-oldtime))) + (theta[oldregime]*(1-exp(-alpha[oldregime]*(newtime-oldtime)))) + (sigma[oldregime]*rnorm(1,0,1)*sqrt((1-exp(-2*alpha[oldregime]*(newtime-oldtime)))/(2*alpha[oldregime])))
}else{
shifttime <- newtime-((newtime-oldtime) * shift.point)
epoch1 <- (x[edges[i,2],]*exp(-alpha[oldregime]*(shifttime-oldtime))) + (theta[oldregime]*(1-exp(-alpha[oldregime]*(shifttime-oldtime)))) + (sigma[oldregime]*rnorm(1,0,1)*sqrt((1-exp(-2*alpha[oldregime]*(shifttime-oldtime)))/(2*alpha[oldregime])))
oldtime <- shifttime
newtime <- newtime
x[edges[i,3],] <- (epoch1*exp(-alpha[newregime]*(newtime-oldtime))) + (theta[newregime]*(1-exp(-alpha[newregime]*(newtime-oldtime)))) + (sigma[newregime]*rnorm(1,0,1)*sqrt((1-exp(-2*alpha[newregime]*(newtime-oldtime)))/(2*alpha[newregime])))
}
}
sim.dat <- matrix(,ntips,3)
sim.dat <- data.frame(sim.dat)
sim.dat[,1] <- phy$tip.label
sim.dat[,2] <- data[,1]
sim.dat[,3] <- x[TIPS]
if(mserr == "known"){
for(i in TIPS){
sim.dat[i,3] <- rnorm(1,sim.dat[i,3],data[i,2])
}
}
colnames(sim.dat)<-c("Genus_species","Reg","X")
}
if(simmap.tree==TRUE){
n=max(phy$edge[,1])
ntips=length(phy$tip.label)
k=length(colnames(phy$mapped.edge))
regimeindex <- colnames(phy$mapped.edge)
branch.lengths=rep(0,(n-1))
branch.lengths[(ntips+1):(n-1)]=branching.times(phy)[-1]/max(branching.times(phy))
root.state<-which(colnames(phy$mapped.edge)==names(phy$maps[[1]][1]))
edges=cbind(c(1:(n-1)),phy$edge,MakeAgeTable(phy, root.age=root.age))
if(scaleHeight==TRUE){
edges[,4:5]<-edges[,4:5]/max(MakeAgeTable(phy, root.age=root.age))
root.age <- max(MakeAgeTable(phy, root.age=root.age))
phy$maps <- lapply(phy$maps, function(x) x/root.age)
root.age = 1
}
edges=edges[sort.list(edges[,3]),]
edges=edges[sort.list(edges[,1]),]
oldregime=root.state
oldtime=0
alpha=alpha
sigma=sqrt(sigma.sq)
theta=theta
n.cov=matrix(rep(0,n*n), n, n)
nodecode=matrix(c(ntips+1,1),1,2)
x <- matrix(0, n, 1)
TIPS <- 1:ntips
ROOT <- ntips + 1L
x[ROOT,] <- theta0
for(i in 1:length(edges[,1])){
currentmap<-phy$maps[[i]]
oldtime=edges[i,4]
if(length(phy$maps[[i]])==1){
regimeduration<-currentmap[1]
newtime<-oldtime+regimeduration
regimenumber<-which(colnames(phy$mapped.edge)==names(currentmap)[1])
x[edges[i,3],]=x[edges[i,2],]*exp(-alpha[regimenumber]*(newtime-oldtime))+(theta[regimenumber])*(1-exp(-alpha[regimenumber]*(newtime-oldtime)))+sigma[regimenumber]*rnorm(1,0,1)*sqrt((1-exp(-2*alpha[regimenumber]*(newtime-oldtime)))/(2*alpha[regimenumber]))
}
if(length(phy$maps[[i]])>1){
regimeduration<-currentmap[1]
newtime<-oldtime+regimeduration
regimenumber<-which(colnames(phy$mapped.edge)==names(currentmap)[1])
x[edges[i,3],]=x[edges[i,2],]*exp(-alpha[regimenumber]*(newtime-oldtime))+(theta[regimenumber])*(1-exp(-alpha[regimenumber]*(newtime-oldtime)))+sigma[regimenumber]*rnorm(1,0,1)*sqrt((1-exp(-2*alpha[regimenumber]*(newtime-oldtime)))/(2*alpha[regimenumber]))
oldtime<-newtime
for (regimeindex in 2:length(currentmap)){
regimeduration<-currentmap[regimeindex]
newtime<-oldtime+regimeduration
regimenumber<-which(colnames(phy$mapped.edge)==names(currentmap)[regimeindex])
x[edges[i,3],]=x[edges[i,3],]*exp(-alpha[regimenumber]*(newtime-oldtime))+(theta[regimenumber])*(1-exp(-alpha[regimenumber]*(newtime-oldtime)))+sigma[regimenumber]*rnorm(1,0,1)*sqrt((1-exp(-2*alpha[regimenumber]*(newtime-oldtime)))/(2*alpha[regimenumber]))
oldtime<-newtime
newregime<-regimenumber
}
}
}
sim.dat<-matrix(,ntips,2)
sim.dat<-data.frame(sim.dat)
sim.dat[,1]<-phy$tip.label
sim.dat[,2]<-x[TIPS,]
if(mserr == "known"){
for(i in TIPS){
sim.dat[i,2] <- rnorm(1,sim.dat[i,2],data[i,3])
}
}
colnames(sim.dat)<-c("Genus_species","X")
}
sim.dat
} |
downloadCloudData <- function(pathRemote = "https://raw.githubusercontent.com/kurator-org",
pathGithub = "/kurator-validation/master/packages/kurator_dwca/data/vocabularies/",
pathFile = "darwin_cloud.txt",
columnField = "fieldname",
columnStand = "standard") {
pathCloud <- paste0(pathRemote, pathGithub, pathFile)
data <- read.csv(pathCloud, sep = "\t")
data <- subset(data, select = c(columnField, columnStand))
colnames(data) <- c(columnField, columnStand)
return(data)
} |
checkMatrix = function(x, mode = NULL, any.missing = TRUE, all.missing = TRUE, min.rows = NULL, max.rows = NULL, min.cols = NULL, max.cols = NULL, nrows = NULL, ncols = NULL, row.names = NULL, col.names = NULL, null.ok = FALSE) {
.Call(c_check_matrix, x, mode, any.missing, all.missing, min.rows, max.rows, min.cols, max.cols, nrows, ncols, row.names, col.names, null.ok)
}
check_matrix = checkMatrix
assertMatrix = makeAssertionFunction(checkMatrix, c.fun = "c_check_matrix", use.namespace = FALSE)
assert_matrix = assertMatrix
testMatrix = makeTestFunction(checkMatrix, c.fun = "c_check_matrix")
test_matrix = testMatrix
expect_matrix = makeExpectationFunction(checkMatrix, c.fun = "c_check_matrix", use.namespace = FALSE) |
CExp <- function(m, Time, x, sign=1.) {
return(0.5*(exp(-m*(Time-x)) + sign*exp(-m*x)))
}
dCExpdm <- function(m, Time, x, sign=1.) {
return(0.5*(-(Time-x)*exp(-m*(Time-x)) -x* sign*exp(-m*x)))
} |
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