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context("testcorr")
test_that("Correlation CorrMatCauchy works", {
x1 <- runif(5)
x2 <- runif(4)
th <- c(.05,.9,-.3)
th <- runif(3,-1,1)
expect_equal(CGGP_internal_CorrMatCauchy(return_numpara=TRUE), 3)
expect_error(CGGP_internal_CorrMatCauchy(x1=x1, x2=x2, theta = c(.1,.1)))
cauchy1 <- CGGP_internal_CorrMatCauchy(x1=x1, x2=x2, theta=th)
expect_is(cauchy1, "matrix")
expect_equal(dim(cauchy1), c(5,4))
cauchyfunc <- function(a,b,theta) {
expLS <- exp(3*theta[1])
expHE <- exp(3*theta[2])
alpha = 2*exp(3*theta[3]+2)/(1+exp(3*theta[3]+2))
diffmat <- outer(a, b, Vectorize(function(aa,bb) abs(aa-bb)))
h = diffmat/expLS
halpha = h^alpha
pow = -expHE/alpha
(1+halpha)^pow
}
cauchy2 <- cauchyfunc(x1, x2, theta=th)
expect_equal(cauchy1, cauchy2, tol=1e-5)
cauchy_C_dC <- CGGP_internal_CorrMatCauchy(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:3) {
thd <- c(0,0,0)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatCauchy(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatCauchy(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, cauchy_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Correlation CorrMatCauchySQT works", {
x1 <- runif(5)
x2 <- runif(4)
th <- c(.05,.9,-.3)
th <- runif(3,-1,1)
expect_equal(CGGP_internal_CorrMatCauchySQT(return_numpara=TRUE), 3)
expect_error(CGGP_internal_CorrMatCauchySQT(x1=x1, x2=x2, theta = c(.1,.1)))
cauchy1 <- CGGP_internal_CorrMatCauchySQT(x1=x1, x2=x2, theta=th)
expect_is(cauchy1, "matrix")
expect_equal(dim(cauchy1), c(5,4))
cauchyfunc <- function(x1,x2,theta) {
expTILT = exp((theta[3]))
expLS = exp(3*(theta[1]))
x1t = (x1+10^(-2))^expTILT
x2t = (x2+10^(-2))^expTILT
x1ts = x1t/expLS
x2ts = x2t/expLS
diffmat =abs(outer(x1ts,x2ts,'-'));
expHE = exp(3*(theta[2]))
h = diffmat
alpha = 2*exp(5)/(1+exp(5))
halpha = h^alpha
pow = -expHE/alpha
(1+halpha)^pow
}
cauchy2 <- cauchyfunc(x1, x2, theta=th)
expect_equal(cauchy1, cauchy2)
cauchy_C_dC <- CGGP_internal_CorrMatCauchySQT(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:3) {
thd <- c(0,0,0)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatCauchySQT(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatCauchySQT(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, cauchy_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Correlation CorrMatCauchySQ works", {
x1 <- runif(5)
x2 <- runif(4)
th <- runif(2,-1,1)
expect_equal(CGGP_internal_CorrMatCauchySQ(return_numpara=TRUE), 2)
expect_error(CGGP_internal_CorrMatCauchySQ(x1=x1, x2=x2, theta = c(.1,.1,.4)))
cauchy1 <- CGGP_internal_CorrMatCauchySQ(x1=x1, x2=x2, theta=th)
expect_is(cauchy1, "matrix")
expect_equal(dim(cauchy1), c(5,4))
cauchyfunc <- function(x1,x2,theta) {
diffmat =abs(outer(x1,x2,'-'));
expLS = exp(3*theta[1])
expHE = exp(3*theta[2])
h = diffmat/expLS
alpha = 2*exp(0+6)/(1+exp(0+6))
halpha = h^alpha
pow = -expHE/alpha
(1-10^(-10))*(1+halpha)^pow+10^(-10)*(diffmat<10^(-4))
}
cauchy2 <- cauchyfunc(x1, x2, theta=th)
expect_equal(cauchy1, cauchy2)
cauchy_C_dC <- CGGP_internal_CorrMatCauchySQ(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:2) {
thd <- c(0,0)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatCauchySQ(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatCauchySQ(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, cauchy_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Correlation CorrMatGaussian works", {
x1 <- runif(5)
x2 <- runif(4)
th <- runif(1,-1,1)
expect_equal(CGGP_internal_CorrMatGaussian(return_numpara=TRUE), 1)
expect_error(CGGP_internal_CorrMatGaussian(x1=x1, x2=x2, theta = c(.1,.1)))
corr1 <- CGGP_internal_CorrMatGaussian(x1=x1, x2=x2, theta=th)
expect_is(corr1, "matrix")
expect_equal(dim(corr1), c(5,4))
gaussianfunc <- function(x1,x2,theta) {
diffmat =abs(outer(x1,x2,'-'));
diffmat2 <- diffmat^2
expLS = exp(3*theta[1])
h = diffmat2/expLS
C = (1-10^(-10))*exp(-h) + 10^(-10)*(diffmat<10^(-4))
C
}
corr2 <- gaussianfunc(x1, x2, theta=th)
expect_equal(corr1, corr2)
corr_C_dC <- CGGP_internal_CorrMatGaussian(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:1) {
thd <- c(0)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatGaussian(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatGaussian(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, corr_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Correlation CorrMatMatern32 works", {
x1 <- runif(5)
x2 <- runif(4)
th <- runif(1,-1,1)
expect_equal(CGGP_internal_CorrMatMatern32(return_numpara=TRUE), 1)
expect_error(CGGP_internal_CorrMatMatern32(x1=x1, x2=x2, theta = c(.1,.1)))
corr1 <- CGGP_internal_CorrMatMatern32(x1=x1, x2=x2, theta=th)
expect_is(corr1, "matrix")
expect_equal(dim(corr1), c(5,4))
matern32func <- function(x1,x2,theta) {
diffmat =abs(outer(x1,x2,'-'))
expLS = exp(3*theta[1])
h = diffmat/expLS
C = (1-10^(-10))*(1+sqrt(3)*h)*exp(-sqrt(3)*h) + 10^(-10)*(diffmat<10^(-4))
C
}
corr2 <- matern32func(x1, x2, theta=th)
expect_equal(corr1, corr2)
corr_C_dC <- CGGP_internal_CorrMatMatern32(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:1) {
thd <- c(0)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatMatern32(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatMatern32(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, corr_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Correlation CorrMatMatern52 works", {
x1 <- runif(5)
x2 <- runif(4)
th <- runif(1,-1,1)
expect_equal(CGGP_internal_CorrMatMatern52(return_numpara=TRUE), 1)
expect_error(CGGP_internal_CorrMatMatern52(x1=x1, x2=x2, theta = c(.1,.1)))
corr1 <- CGGP_internal_CorrMatMatern52(x1=x1, x2=x2, theta=th)
expect_is(corr1, "matrix")
expect_equal(dim(corr1), c(5,4))
matern52func <- function(x1,x2,theta) {
diffmat =abs(outer(x1,x2,'-'))
expLS = exp(3*theta[1])
h = diffmat/expLS
C = (1-10^(-10))*(1+sqrt(5)*h+5/3*h^2)*exp(-sqrt(5)*h) + 10^(-10)*(diffmat<10^(-4))
C
}
corr2 <- matern52func(x1, x2, theta=th)
expect_equal(corr1, corr2)
corr_C_dC <- CGGP_internal_CorrMatMatern52(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:1) {
thd <- c(0)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatMatern52(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatMatern52(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, corr_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Correlation CorrMatPowerExp works", {
nparam <- 2
x1 <- runif(5)
x2 <- runif(4)
th <- runif(nparam,-1,1)
expect_equal(CGGP_internal_CorrMatPowerExp(return_numpara=TRUE), nparam)
expect_error(CGGP_internal_CorrMatPowerExp(x1=x1, x2=x2, theta = rep(0, nparam-1)))
expect_error(CGGP_internal_CorrMatPowerExp(x1=x1, x2=x2, theta = rep(0, nparam+1)))
corr1 <- CGGP_internal_CorrMatPowerExp(x1=x1, x2=x2, theta=th)
expect_is(corr1, "matrix")
expect_equal(dim(corr1), c(5,4))
PowerExpfunc <- function(x1,x2,theta) {
diffmat =abs(outer(x1,x2,'-'))
expLS = exp(3*theta[1])
minpower <- 1
maxpower <- 1.95
alpha <- minpower + (theta[2]+1)/2 * (maxpower - minpower)
h = diffmat/expLS
C = (1-10^(-10))*exp(-(h)^alpha) + 10^(-10)*(diffmat<10^(-4))
C
}
corr2 <- PowerExpfunc(x1, x2, theta=th)
expect_equal(corr1, corr2)
corr_C_dC <- CGGP_internal_CorrMatPowerExp(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:nparam) {
thd <- rep(0, nparam)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatPowerExp(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatPowerExp(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, corr_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Correlation CorrMatWendland0 works", {
x1 <- runif(5)
x2 <- runif(4)
th <- runif(1,-1,1)
expect_equal(CGGP_internal_CorrMatWendland0(return_numpara=TRUE), 1)
expect_error(CGGP_internal_CorrMatWendland0(x1=x1, x2=x2, theta = c(.1,.1)))
corr1 <- CGGP_internal_CorrMatWendland0(x1=x1, x2=x2, theta=th)
expect_is(corr1, "matrix")
expect_equal(dim(corr1), c(5,4))
wendland0func <- function(x1,x2,theta) {
diffmat =abs(outer(x1,x2,'-'))
expLS = exp(3*theta[1])
h = diffmat/expLS
C = pmax(1 - h, 0)
C
}
corr2 <- wendland0func(x1, x2, theta=th)
expect_equal(corr1, corr2)
corr_C_dC <- CGGP_internal_CorrMatWendland0(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:1) {
thd <- c(0)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatWendland0(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatWendland0(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, corr_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Correlation CorrMatWendland1 works", {
x1 <- runif(5)
x2 <- runif(4)
th <- runif(1,-1,1)
expect_equal(CGGP_internal_CorrMatWendland1(return_numpara=TRUE), 1)
expect_error(CGGP_internal_CorrMatWendland1(x1=x1, x2=x2, theta = c(.1,.1)))
corr1 <- CGGP_internal_CorrMatWendland1(x1=x1, x2=x2, theta=th)
expect_is(corr1, "matrix")
expect_equal(dim(corr1), c(5,4))
wendland1func <- function(x1,x2,theta) {
diffmat =abs(outer(x1,x2,'-'))
expLS = exp(3*theta[1])
h = diffmat/expLS
C = pmax(1 - h, 0)^3 * (3*h+1)
C
}
corr2 <- wendland1func(x1, x2, theta=th)
expect_equal(corr1, corr2)
corr_C_dC <- CGGP_internal_CorrMatWendland1(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:1) {
thd <- c(0)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatWendland1(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatWendland1(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, corr_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Correlation CorrMatWendland2 works", {
x1 <- runif(5)
x2 <- runif(4)
th <- runif(1,-1,1)
expect_equal(CGGP_internal_CorrMatWendland2(return_numpara=TRUE), 1)
expect_error(CGGP_internal_CorrMatWendland2(x1=x1, x2=x2, theta = c(.1,.1)))
corr1 <- CGGP_internal_CorrMatWendland2(x1=x1, x2=x2, theta=th)
expect_is(corr1, "matrix")
expect_equal(dim(corr1), c(5,4))
wendland2func <- function(x1,x2,theta) {
diffmat =abs(outer(x1,x2,'-'))
expLS = exp(3*theta[1])
h = diffmat/expLS
C = pmax(1 - h, 0)^5 * (8*h^2 + 5*h + 1)
C
}
corr2 <- wendland2func(x1, x2, theta=th)
expect_equal(corr1, corr2)
corr_C_dC <- CGGP_internal_CorrMatWendland2(x1=x1, x2=x2, theta=th, return_dCdtheta=TRUE)
eps <- 1e-6
for (i in 1:1) {
thd <- c(0)
thd[i] <- eps
numdC <- (CGGP_internal_CorrMatWendland2(x1=x1, x2=x2, theta=th+thd) -
CGGP_internal_CorrMatWendland2(x1=x1, x2=x2, theta=th-thd)) / (2*eps)
expect_equal(numdC, corr_C_dC$dCdtheta[,(1+4*i-4):(4*i)], info = paste("theta dimension with error is",i))
}
})
test_that("Logs work for all", {
corrs <- list(CGGP_internal_CorrMatCauchySQ, CGGP_internal_CorrMatCauchySQT,
CGGP_internal_CorrMatCauchy, CGGP_internal_CorrMatGaussian,
CGGP_internal_CorrMatMatern32, CGGP_internal_CorrMatMatern52,
CGGP_internal_CorrMatPowerExp, CGGP_internal_CorrMatWendland0,
CGGP_internal_CorrMatWendland1, CGGP_internal_CorrMatWendland2
)
works_well_on_log_scales <- c(rep(T, 7),
rep(F, 3))
n1 <- 5
n2 <- 6
for (use_log_scale in c(T,F)) {
for (icorr in rev(1:length(corrs))) {
corr <- corrs[[icorr]]
numpara <- corr(return_numpara = T)
x1 <- runif(n1)
x2 <- runif(n2)
theta <- runif(numpara)*2-1
c1 <- corr(x1 = x1, x2 = x2, theta = theta)
c1_log <- corr(x1 = x1, x2 = x2, theta = theta, returnlogs = T)
expect_is(c1, "matrix")
expect_is(c1_log, "matrix")
expect_equal(c1, exp(c1_log))
d1_log <- corr(x1 = x1, x2 = x2, theta = theta, returnlogs = T, return_dCdtheta = T)
expect_is(d1_log$dCdtheta, "matrix")
expect_is(d1_log, "list")
expect_equal(d1_log$C, c1_log)
corr_C_dC <- corr(x1 = x1, x2 = x2, theta = theta, return_dCdtheta=T)
eps <- 1e-6
for (i in 1:numpara) {
thd <- rep(0, numpara)
thd[i] <- eps
numdC <- (corr(x1=x1, x2=x2, theta=theta+thd) -
corr(x1=x1, x2=x2, theta=theta-thd)) / (2*eps)
expect_equal(numdC, corr_C_dC$dCdtheta[,(1+n2*i-n2):(n2*i)],
info = paste("theta dimension with error is",i, ", icor is", icorr,
"use_log_scale is", use_log_scale,
"theta is", theta))
}
corr_C_dC_logs <- corr(x1 = x1, x2 = x2, theta = theta, return_dCdtheta=T,
returnlogs=use_log_scale)
eps <- 1e-6
for (i in 1:numpara) {
thd <- rep(0, numpara)
thd[i] <- eps
numdC <- (corr(x1=x1, x2=x2, theta=theta+thd, returnlogs = use_log_scale) -
corr(x1=x1, x2=x2, theta=theta-thd, returnlogs = use_log_scale)) / (2*eps)
numdC <- ifelse(is.nan(numdC), 0, numdC)
if (F) {
plot(numdC, corr_C_dC_logs$dCdtheta[,(1+n2*i-n2):(n2*i)])
cbind(c(numdC), c(corr_C_dC_logs$dCdtheta[,(1+n2*i-n2):(n2*i)]))
(c(numdC) / c(corr_C_dC_logs$dCdtheta[,(1+n2*i-n2):(n2*i)]))
}
expect_equal(numdC, corr_C_dC_logs$dCdtheta[,(1+n2*i-n2):(n2*i)],
info = paste("theta dimension with error is", i, ", icor is", icorr,
"use_log_scale is", use_log_scale,
"theta is", theta),
tolerance = if (!use_log_scale || works_well_on_log_scales[icorr]) {1e-8} else {1e-4})
}
rm(numpara, c1, c1_log)
}
}
}) |
cdm_pem_include_ll_args <- function(ll_args, pem_parm, pem_pars, pem_parameter_index)
{
for (pp in pem_pars){
ll_args[[ pp ]] <- cdm_pem_extract_parameters( parm=pem_parm, parmgroup=pp, pem_parameter_index=pem_parameter_index )
}
return(ll_args)
} |
plot.nplr <- function(x, pcol="aquamarine1", lcol="red3",
showEstim=FALSE, showCI=TRUE, showGOF=TRUE, showInfl=FALSE,
showPoints = TRUE, showSDerr = FALSE,
B=1e4, conf.level=.95, unit="", ...){
.plot(x, ...)
if(showPoints) .addPoints(x, pcol, ...)
if(showSDerr) .addErr(x, pcol, ...)
if(showGOF) .addGOF(x)
if(!(!showEstim)) .addEstim(x, showEstim, unit, B, conf.level)
if(showCI) .addPolygon(x)
if(showInfl) points(getInflexion(x), pch=19, cex=2, col="blue")
.addCurve(x, lcol, ...)
if(x@LPweight != 0){
Sub = sprintf("Weighted %s-P logistic regr. (nplr package, version: %s)", x@npars, packageVersion("nplr"))
} else{
Sub = sprintf("Non-weighted %s-P logistic regr. (nplr package, version: %s)", x@npars, packageVersion("nplr"))
}
title(sub = Sub, cex.sub = .75)
} |
expected <- eval(parse(text="TRUE"));
test(id=0, code={
argv <- eval(parse(text="list(structure(list(`/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/lookup.xport.Rd` = structure(c(\"read.xport\", \"\"), .Dim = 1:2, .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.S.Rd` = structure(character(0), .Dim = c(0L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.arff.Rd` = structure(c(\"connection\", \"write.arff\", \"\", \"\"), .Dim = c(2L, 2L), .Dimnames = list( NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.dbf.Rd` = structure(c(\"make.names\", \"write.dbf\", \"\", \"\"), .Dim = c(2L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.dta.Rd` = structure(c(\"write.dta\", \"attributes\", \"Date\", \"factor\", \"\", \"\", \"\", \"\"), .Dim = c(4L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.epiinfo.Rd` = structure(c(\"Date\", \"DateTimeClasses\", \"\", \"\"), .Dim = c(2L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.mtp.Rd` = structure(character(0), .Dim = c(0L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.octave.Rd` = structure(character(0), .Dim = c(0L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.spss.Rd` = structure(c(\"sub\", \"iconv\", \"iconvlist\", \"\", \"\", \"\"), .Dim = c(3L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.ssd.Rd` = structure(c(\"read.xport\", \"\"), .Dim = 1:2, .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.systat.Rd` = structure(character(0), .Dim = c(0L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.xport.Rd` = structure(c(\"lookup.xport\", \"\"), .Dim = 1:2, .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/write.arff.Rd` = structure(c(\"make.names\", \"read.arff\", \"\", \"\"), .Dim = c(2L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/write.dbf.Rd` = structure(c(\"read.dbf\", \"\"), .Dim = 1:2, .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/write.dta.Rd` = structure(c(\"drop\", \"read.dta\", \"attributes\", \"DateTimeClasses\", \"abbreviate\", \"\", \"\", \"\", \"\", \"\"), .Dim = c(5L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\"))), `/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/write.foreign.Rd` = structure(character(0), .Dim = c(0L, 2L), .Dimnames = list(NULL, c(\"Target\", \"Anchor\")))), .Names = c(\"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/lookup.xport.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.S.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.arff.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.dbf.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.dta.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.epiinfo.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.mtp.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.octave.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.spss.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.ssd.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.systat.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/read.xport.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/write.arff.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/write.dbf.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/write.dta.Rd\", \"/home/lzhao/tmp/Rtmpe5iuYI/R.INSTALL2aa854a74188/foreign/man/write.foreign.Rd\")))"));
do.call(`is.list`, argv);
}, o=expected); |
library(lmerTest)
has_pbkrtest <- requireNamespace("pbkrtest", quietly = TRUE) && getRversion() >= "3.3.3"
load(system.file("testdata","test_paper_objects.RData", package="lmerTest"))
tv <- lmer(Sharpnessofmovement ~ TVset * Picture + (1 | Assessor) +
(1 | Assessor:TVset) + (1 | Assessor:Picture), data = TVbo,
control=lmerControl(optimizer="bobyqa"))
(an8.2 <- anova(tv))
if(has_pbkrtest)
(ankr8.2 <- anova(tv, type=2, ddf="Kenward-Roger"))
m.carrots <- lmer(Preference ~ sens1 + sens2 + (1 + sens1 + sens2 | Consumer) +
(1 | Product), data=carrots,
control=lmerControl(optimizer="bobyqa"))
(sum8.3 <- coef(summary(m.carrots)))
tv <- lmer(Sharpnessofmovement ~ TVset * Picture +
(1 | Assessor:TVset) + (1 | Assessor:Picture) +
(1 | Assessor:Picture:TVset) + (1 | Repeat) + (1 | Repeat:Picture) +
(1 | Repeat:TVset) + (1 | Repeat:TVset:Picture) + (1 | Assessor),
data = TVbo,
control=lmerControl(optimizer="bobyqa"))
st <- step(tv)
(elim_tab_random8.4 <- st$random)
(elim_tab_fixed8.4 <- st$fixed)
(an8.4 <- anova(get_model(st)))
L <- cbind(array(0, dim=c(6, 6)), diag(6))
(con1_8.5 <- calcSatterth(tv, L))
(con2_8.5 <- contest(tv, L))
(ran_C <- ranova(m.carrots))
TOL <- 1e-4
stopifnot(
isTRUE(all.equal(an8.2_save, an8.2, check.attributes = FALSE, tolerance=TOL)),
isTRUE(all.equal(sum8.3_save, sum8.3, check.attributes = FALSE, tolerance=TOL)),
isTRUE(all.equal(elim_tab_random8.4_save, elim_tab_random8.4,
check.attributes = FALSE, tolerance=TOL)),
isTRUE(all.equal(elim_tab_fixed8.4_save, elim_tab_fixed8.4,
check.attributes = FALSE, tolerance=TOL)),
isTRUE(all.equal(an8.4_save, an8.4, check.attributes = FALSE, tolerance=TOL)),
isTRUE(all.equal(con1_8.5_save, con1_8.5, check.attributes = FALSE, tolerance=TOL)),
isTRUE(all.equal(con2_8.5_save, con2_8.5, check.attributes = FALSE, tolerance=TOL))
)
if(has_pbkrtest) {
stopifnot(
isTRUE(all.equal(ankr8.2_save, ankr8.2, check.attributes = FALSE, tolerance=TOL))
)
} |
RVtest <- function(Dx, Dy, nperm){
n <- dim(Dx)[1]
C <- diag(n) - ((rep(1, n) %*% t(rep(1, n)))/n)
RVObs <- as.vector(RVcoeff(mDx = Dx, mDy = Dy, mC = C))
if(nperm != 0 ){
permStats <- rep(NA, nperm)
s <-lapply(1:nperm, function(x) c(sample(nrow(Dy))))
for(i in 1:nperm){
permStats[i] <- as.vector(RVcoeff(mDx = Dx, mDy = Dy[s[[i]], s[[i]]], mC = C))
}
pVal <- (sum(permStats > RVObs) + 1)/(nperm + 1)
return(list(Stat = RVObs, pValue = pVal, permStats = permStats))
}
else{
return(list(Stat = RVObs))
}
} |
"Brainsz" |
context("Spectral methods")
test_that("get_first_eigs returns correct values on dense matrix", {
G <- matrix(c(7, -4, 14, 0, -4, 19, 10, 0,
14, 10, 10, 0, 0, 0, 0, 100), nrow = 4)
ans_vals <- c(-9, 18, 27)
ans_vects <- matrix(
c(-2, -1, 2, 0, -2, 2, -1, 0, -1, -2, -2, 0) / 3, nrow = 4)
spect <- get_first_eigs(G, 3)
vals <- spect$vals
vects <- spect$vects
for (i in seq_len(length(vals))) {
if (sign(vects[1, i]) != sign(ans_vects[1, i])) {
vects[, i] <- -vects[, i]
}
}
expect_equal(vals, ans_vals)
expect_equal(vects, ans_vects)
})
test_that("get_first_eigs returns correct values on sparse matrix", {
G <- drop0(matrix(c(7, -4, 14, 0, -4, 19, 10, 0,
14, 10, 10, 0, 0, 0, 0, 100), nrow = 4))
ans_vals <- c(-9, 18, 27)
ans_vects <- matrix(c(
-2, -1, 2, 0, -2, 2, -1, 0, -1, -2, -2, 0) / 3, nrow = 4)
spect <- get_first_eigs(G, 3)
vals <- spect$vals
vects <- spect$vects
for (i in seq_len(length(vals))) {
if (sign(vects[1, i]) != sign(ans_vects[1, i])) {
vects[, i] <- -vects[, i]
}
}
expect_equal(vals, ans_vals)
expect_equal(vects, ans_vects)
})
test_that("build_laplacian returns correct matrices on dense matrix", {
G <- matrix(c(0:8), nrow = 3)
G <- G + t(G)
degs_mat <- diag(c(12, 24, 36))
comb_lap <- drop0(degs_mat - G)
rw_lap <- drop0(solve(degs_mat) %*% (degs_mat - G))
expect_equal(build_laplacian(G, type_lap = "comb"), comb_lap)
expect_equal(build_laplacian(G, type_lap = "rw"), rw_lap)
})
test_that("build_laplacian returns correct matrices on sparse matrix", {
G <- matrix(c(0:8), nrow = 3)
G <- drop0(G + t(G))
degs_mat <- diag(c(12, 24, 36))
comb_lap <- degs_mat - G
rw_lap <- drop0(solve(degs_mat) %*% (degs_mat - G))
expect_equal(build_laplacian(G, type_lap = "comb"), comb_lap)
expect_equal(build_laplacian(G, type_lap = "rw"), rw_lap)
})
test_that("build_laplacian gives correct error if row sums are zero", {
G <- drop0(matrix(c(0, 1, 0, 2)))
expect_error(build_laplacian(G, type_lap = "rw"),
"row sums of adj_mat must be non-zero")
})
test_that("run_laplace_embedding returns correct spectrum on dense matrix", {
set.seed(9235)
G <- matrix(c(0:8), nrow = 3)
G <- G + t(G)
ans_vals_comb <- c(0, 17.07)
ans_vects_comb <- matrix(c(
0.577, 0.789,
0.577, -0.577,
0.577, -0.211
), nrow = 3, byrow = TRUE)
ans_vals_rw <- c(0, 1)
ans_vects_rw <- matrix(c(
0.577, 0.408,
0.577, -0.816,
0.577, 0.408
), nrow = 3, byrow = TRUE)
spectrum_comb <- run_laplace_embedding(G, 2, "comb")
spectrum_rw <- run_laplace_embedding(G, 2, "rw")
vals_comb <- spectrum_comb$vals
vects_comb <- spectrum_comb$vects
vals_rw <- spectrum_rw$vals
vects_rw <- spectrum_rw$vects
for (i in seq_len(length(vals_comb))) {
if (sign(vects_comb[1, i]) != sign(ans_vects_comb[1, i])) {
vects_comb[, i] <- -vects_comb[, i]
}
if (sign(vects_rw[1, i]) != sign(ans_vects_rw[1, i])) {
vects_rw[, i] <- -vects_rw[, i]
}
}
expect_equal(vals_comb, ans_vals_comb, tolerance = 0.01)
expect_equal(vects_comb, ans_vects_comb, tolerance = 0.01)
expect_equal(vals_rw, ans_vals_rw, tolerance = 0.01)
expect_equal(vects_rw, ans_vects_rw, tolerance = 0.01)
})
test_that("run_laplace_embedding returns correct spectrum on sparse matrix", {
set.seed(9235)
G <- matrix(c(0:8), nrow = 3)
G <- drop0(G + t(G))
ans_vals_comb <- c(0, 17.07)
ans_vects_comb <- matrix(c(
0.577, 0.789,
0.577, -0.577,
0.577, -0.211
), nrow = 3, byrow = TRUE)
ans_vals_rw <- c(0, 1)
ans_vects_rw <- matrix(c(
0.577, 0.408,
0.577, -0.816,
0.577, 0.408
), nrow = 3, byrow = TRUE)
spectrum_comb <- run_laplace_embedding(G, 2, "comb")
spectrum_rw <- run_laplace_embedding(G, 2, "rw")
vals_comb <- spectrum_comb$vals
vects_comb <- spectrum_comb$vects
vals_rw <- spectrum_rw$vals
vects_rw <- spectrum_rw$vects
for (i in seq_len(length(vals_comb))) {
if (sign(vects_comb[1, i]) != sign(ans_vects_comb[1, i])) {
vects_comb[, i] <- -vects_comb[, i]
}
if (sign(vects_rw[1, i]) != sign(ans_vects_rw[1, i])) {
vects_rw[, i] <- -vects_rw[, i]
}
}
expect_equal(vals_comb, ans_vals_comb, tolerance = 0.01)
expect_equal(vects_comb, ans_vects_comb, tolerance = 0.01)
expect_equal(vals_rw, ans_vals_rw, tolerance = 0.01)
expect_equal(vects_rw, ans_vects_rw, tolerance = 0.01)
})
test_that("run_motif_embedding correct on dense matrix with restrict", {
set.seed(9235)
adj_mat <- matrix(c(
0, 2, 0, 0,
0, 0, 3, 0,
4, 0, 0, 0,
0, 0, 0, 0
), nrow = 4, byrow = TRUE)
ans_adj_mat <- drop0(matrix(c(
0, 2, 0, 0,
0, 0, 3, 0,
4, 0, 0, 0,
0, 0, 0, 0
), nrow = 4, byrow = TRUE))
ans_motif_adj_mat <- drop0(matrix(c(
0, 2, 4, 0,
2, 0, 3, 0,
4, 3, 0, 0,
0, 0, 0, 0
), nrow = 4, byrow = TRUE))
ans_comps <- 1:3
ans_adj_mat_comps <- drop0(matrix(c(
0, 2, 0,
0, 0, 3,
4, 0, 0
), nrow = 3, byrow = TRUE))
ans_motif_adj_mat_comps <- drop0(matrix(c(
0, 2, 4,
2, 0, 3,
4, 3, 0
), nrow = 3, byrow = TRUE))
ans_vals <- c(0, 1.354)
ans_vects <- matrix(c(
0.577, 0.544,
0.577, -0.830,
0.577, 0.126
), nrow = 3, byrow = TRUE)
emb_list <- run_motif_embedding(adj_mat, "Ms", "func", "mean", "dense", 2,
"rw", restrict = TRUE)
for (i in seq_len(length(ans_vals))) {
if (sign(emb_list$vects[1, i]) != sign(ans_vects[1, i])) {
emb_list$vects[, i] <- -emb_list$vects[, i]
}
}
expect_equal(ans_adj_mat, emb_list$adj_mat)
expect_equal(ans_motif_adj_mat, emb_list$motif_adj_mat)
expect_equal(ans_comps, emb_list$comps)
expect_equal(ans_adj_mat_comps, emb_list$adj_mat_comps)
expect_equal(ans_motif_adj_mat_comps, emb_list$motif_adj_mat_comps)
expect_equal(ans_vals, emb_list$vals, tolerance = 0.01)
expect_equal(ans_vects, emb_list$vects, tolerance = 0.01)
})
test_that("run_motif_embedding correct on sparse matrix with restrict", {
set.seed(9235)
adj_mat <- matrix(c(
0, 2, 0, 0,
0, 0, 3, 0,
4, 0, 0, 0,
0, 0, 0, 0
), nrow = 4, byrow = TRUE)
ans_adj_mat <- drop0(matrix(c(
0, 2, 0, 0,
0, 0, 3, 0,
4, 0, 0, 0,
0, 0, 0, 0
), nrow = 4, byrow = TRUE))
ans_motif_adj_mat <- drop0(matrix(c(
0, 2, 4, 0,
2, 0, 3, 0,
4, 3, 0, 0,
0, 0, 0, 0
), nrow = 4, byrow = TRUE))
ans_comps <- 1:3
ans_adj_mat_comps <- drop0(matrix(c(
0, 2, 0,
0, 0, 3,
4, 0, 0
), nrow = 3, byrow = TRUE))
ans_motif_adj_mat_comps <- drop0(matrix(c(
0, 2, 4,
2, 0, 3,
4, 3, 0
), nrow = 3, byrow = TRUE))
ans_vals <- c(0, 1.354)
ans_vects <- matrix(c(
0.577, 0.544,
0.577, -0.830,
0.577, 0.126
), nrow = 3, byrow = TRUE)
emb_list <- run_motif_embedding(adj_mat, "Ms", "func", "mean", "dense", 2,
"rw", restrict = TRUE)
for (i in seq_len(length(ans_vals))) {
if (sign(emb_list$vects[1, i]) != sign(ans_vects[1, i])) {
emb_list$vects[, i] <- -emb_list$vects[, i]
}
}
expect_equal(ans_adj_mat, emb_list$adj_mat)
expect_equal(ans_motif_adj_mat, emb_list$motif_adj_mat)
expect_equal(ans_comps, emb_list$comps)
expect_equal(ans_adj_mat_comps, emb_list$adj_mat_comps)
expect_equal(ans_motif_adj_mat_comps, emb_list$motif_adj_mat_comps)
expect_equal(ans_vals, emb_list$vals, tolerance = 0.01)
expect_equal(ans_vects, emb_list$vects, tolerance = 0.01)
})
test_that("run_motif_embedding correct on dense matrix without restrict", {
set.seed(9235)
adj_mat <- matrix(c(
0, 2, 0,
0, 0, 3,
4, 0, 0
), nrow = 3, byrow = TRUE)
ans_adj_mat <- drop0(matrix(c(
0, 2, 0,
0, 0, 3,
4, 0, 0
), nrow = 3, byrow = TRUE))
ans_motif_adj_mat <- drop0(matrix(c(
0, 2, 4,
2, 0, 3,
4, 3, 0
), nrow = 3, byrow = TRUE))
ans_vals <- c(0, 1.354)
ans_vects <- matrix(c(
0.577, 0.544,
0.577, -0.830,
0.577, 0.126
), nrow = 3, byrow = TRUE)
emb_list <- run_motif_embedding(adj_mat, "Ms", "func", "mean", "dense", 2,
"rw", restrict = FALSE)
for (i in seq_len(length(ans_vals))) {
if (sign(emb_list$vects[1, i]) != sign(ans_vects[1, i])) {
emb_list$vects[, i] <- -emb_list$vects[, i]
}
}
expect_equal(ans_adj_mat, emb_list$adj_mat)
expect_equal(ans_motif_adj_mat, emb_list$motif_adj_mat)
expect_equal(ans_vals, emb_list$vals, tolerance = 0.01)
expect_equal(ans_vects, emb_list$vects, tolerance = 0.01)
})
test_that("run_motif_embedding correct on sparse matrix without restrict", {
set.seed(9235)
adj_mat <- matrix(c(
0, 2, 0,
0, 0, 3,
4, 0, 0
), nrow = 3, byrow = TRUE)
ans_adj_mat <- drop0(matrix(c(
0, 2, 0,
0, 0, 3,
4, 0, 0
), nrow = 3, byrow = TRUE))
ans_motif_adj_mat <- drop0(matrix(c(
0, 2, 4,
2, 0, 3,
4, 3, 0
), nrow = 3, byrow = TRUE))
ans_vals <- c(0, 1.354)
ans_vects <- matrix(c(
0.577, 0.544,
0.577, -0.830,
0.577, 0.126
), nrow = 3, byrow = TRUE)
emb_list <- run_motif_embedding(adj_mat, "Ms", "func", "mean", "dense", 2,
"rw", restrict = FALSE)
for (i in seq_len(length(ans_vals))) {
if (sign(emb_list$vects[1, i]) != sign(ans_vects[1, i])) {
emb_list$vects[, i] <- -emb_list$vects[, i]
}
}
expect_equal(ans_adj_mat, emb_list$adj_mat)
expect_equal(ans_motif_adj_mat, emb_list$motif_adj_mat)
expect_equal(ans_vals, emb_list$vals, tolerance = 0.01)
expect_equal(ans_vects, emb_list$vects, tolerance = 0.01)
}) |
"estDesign" <-
function( n, smax, p.tr, biasrest=0.05)
{
if(length(n)!=1 || (n<=1 | abs(round(n)-n) > 1e-07)){stop("number of groups n must be specified as a single integer greater than 1")}
if(length(p.tr)!=1 || p.tr>1 || p.tr<0){stop("true proportion p.tr must be specified as a single number between 0 and 1")}
if(length(smax)!=1 || (smax<=1 | abs(round(smax)-smax) > 1e-07)){stop("the maximal group size allowed in calculations must be a single integer greater than 1")}
if(length(biasrest)!=1 || biasrest>=1 || biasrest<0){stop("the maximally allowed bias(p) specified in biasrest must be a single number between 0 and 1, usually should be close to 0")}
for (i in 2:smax)
{
temp<-msep(n=n, p.tr=p.tr,s=i)
if(temp$bias > biasrest)
{cat("maximal group size within bias restriction is s =",i-1,"\n")
return(msep(n=n, p.tr=p.tr,s=i-1))}
if(i>=2 && temp$mse > msep(n=n, p.tr=p.tr,s=i-1)$mse)
{cat("group size s with minimal mse(p) =",i-1,"\n")
return(msep(n=n, p.tr=p.tr,s=i-1))}
if (i==smax && temp$mse <= msep(n=n, p.tr=p.tr,s=i-1)$mse)
{cat(" minimal mse(p) is achieved with group size s >= smax","\n")
return(msep(n=n, p.tr=p.tr,s=i-1))}
}
} |
library(tinytest)
.runThisTest <- Sys.getenv("RunRblpapiUnitTests") == "yes"
if (!.runThisTest) exit_file("Skipping this file")
library(Rblpapi)
isweekend <- as.POSIXlt(Sys.Date())$wday %in% c(0,6)
res <- getTicks("ESA Index", startTime=Sys.time() - isweekend*48*60*60 - 60*60,
endTime=Sys.time() - isweekend*48*60*60, returnAs="data.frame")
expect_true(inherits(res, "data.frame"), info = "checking return type")
expect_true(dim(res)[1] > 10, info = "check return of at least ten rows")
expect_true(dim(res)[2] == 5, info = "check return of five columns")
expect_true(all(c("times", "value", "size") %in% colnames(res)), info = "check column names")
isweekend <- as.POSIXlt(Sys.Date())$wday %in% c(0,6)
res <- getTicks("ESA Index", startTime=Sys.time() - isweekend*48*60*60 - 60*60,
endTime=Sys.time() - isweekend*48*60*60, returnAs="xts")
expect_true(inherits(res, "xts"), info = "checking return type")
expect_true(dim(res)[1] > 10, info = "check return of at least ten rows")
expect_true(dim(res)[2] == 2, info = "check return of two columns")
expect_true(all(c("value", "size") %in% colnames(res)), info = "check column names")
isweekend <- as.POSIXlt(Sys.Date())$wday %in% c(0,6)
res <- getTicks("ESA Index", startTime=Sys.time() - isweekend*48*60*60 - 60*60,
endTime=Sys.time() - isweekend*48*60*60, returnAs="data.table")
expect_true(inherits(res, "data.table"), info = "checking return type")
expect_true(dim(res)[1] > 10, info = "check return of at least ten rows")
expect_true(dim(res)[2] == 7, info = "check return of seven columns")
expect_true(all(c("pt", "date", "time", "type", "value", "size", "condcode") %in% colnames(res)),
info = "check column names") |
if(requireNamespace("limma", quietly = TRUE) &
requireNamespace("SDMTools", quietly = TRUE)){
testFmod <- function(counts, group, a = .05){
library(propr)
library(limma)
M <- counts
gr <- group
ce=which(group == unique(group)[1])
co=which(group == unique(group)[2])
cere=ce
cort=co
nce=length(cere)
nco=length(cort)
design=matrix(0,dim(M)[1],2)
design[1:length(cere),1]=rep(1,length(cere))
design[(length(cere)+1):dim(M)[1],2]=rep(1,length(cort))
z=exp(apply(log(M),1,mean))
Mz=M/z
scaledcounts=t(Mz*mean(z))
u=voom(scaledcounts, design=design, plot=TRUE)
param=lmFit(u,design)
param=eBayes(param)
dz=param$df.prior
s2z=param$s2.prior
counts=t(M[c(cere,cort),])
v=voom(counts, design=design, plot=TRUE)
colnames(v$weights)=rownames(M)
su=apply(v$weights,2,sum)
w=t(v$weights)/su
zw=exp(apply(w*log(M)*dim(M)[2],1,mean))
wz=log(zw)/log(z)
res=propd(counts=M, group=gr, p = 1)
Res=propd(counts=M, group=gr, p = 1, weighted = TRUE)
resa=propd(counts=M, group=gr, p = 1, alpha = a)
Resa=propd(counts=M, group=gr, p = 1, weighted = TRUE, alpha = a)
st=res@results[,c("lrv","theta")]
stw=Res@results[,c("lrv","theta")]
sta=resa@results[,c("lrv","theta")]
stwa=Resa@results[,c("lrv","theta")]
mod=dz*s2z/st[,"lrv"]
Fpmod=(1-st[,"theta"])*(dz+nce+nco)/((nce+nco)*st[,"theta"]+mod)
thetamod=1/(1+Fpmod)
Fmod=(nce+nco+dz-2)*Fpmod
modw=dz*s2z/stw[,"lrv"]
Fpmodw=(1-stw[,"theta"])*(dz+nce+nco)/((nce+nco)*stw[,"theta"]+modw)
thetamodw=1/(1+Fpmodw)
Fmodw=(nce+nco+dz-2)*Fpmodw
moda=dz*s2z/sta[,"lrv"]
Fpmoda=(1-sta[,"theta"])*(dz+nce+nco)/((nce+nco)*sta[,"theta"]+moda)
thetamoda=1/(1+Fpmoda)
Fmoda=(nce+nco+dz-2)*Fpmoda
modwa=dz*s2z/stwa[,"lrv"]
Fpmodwa=(1-stwa[,"theta"])*(dz+nce+nco)/((nce+nco)*stwa[,"theta"]+modwa)
thetamodwa=1/(1+Fpmodwa)
Fmodwa=(nce+nco+dz-2)*Fpmodwa
return(list(thetamod, thetamodw, thetamoda, thetamodwa,
Fmod, Fmodw, Fmoda, Fmodwa))
}
library(propr)
data(iris)
keep <- iris$Species %in% c("setosa", "versicolor")
counts <- iris[keep, 1:4] * 10
group <- ifelse(iris[keep, "Species"] == "setosa", "A", "B")
pd.nn <- propd(counts, group)
pd.wn <- propd(counts, group, weighted = TRUE)
pd.na <- propd(counts, group, alpha = .05)
pd.wa <- propd(counts, group, weighted = TRUE, alpha = .05)
pd.nn <- updateF(pd.nn, moderated = TRUE)
pd.wn <- updateF(pd.wn, moderated = TRUE)
pd.na <- updateF(pd.na, moderated = TRUE)
pd.wa <- updateF(pd.wa, moderated = TRUE)
ref <- testFmod(counts, group, a = .05)
test_that("updateF matches code provided by Ionas", {
expect_equal(
pd.nn@results$theta_mod,
ref[[1]]
)
expect_equal(
pd.nn@results$Fstat,
ref[[5]]
)
expect_equal(
pd.na@results$theta_mod,
ref[[3]]
)
expect_equal(
pd.na@results$Fstat,
ref[[7]]
)
})
} |
ProDenICA <-
function(x, k=p, W0=NULL, whiten=FALSE, maxit = 20, thresh = 1e-7, restarts = 0,
trace = FALSE, Gfunc=GPois, eps.rank=1e-7, ...)
{
this.call=match.call()
p <- ncol(x)
n <- nrow(x)
x <- scale(x, T, F)
if(whiten){
sx <- svd(x)
condnum=sx$d;condnum=condnum/condnum[1]
good=condnum >eps.rank
rank=sum(good)
if(k>rank){
warning(paste("Rank of x is ",rank,"; k reduced from",k," to ",rank,sep=""))
k=rank
}
x <- sqrt(n) * sx$u[,good]
whitener=sqrt(n)*scale(sx$v[,good],FALSE,sx$d[good])
}
else whitener=NULL
if(is.null(W0)) W0 <- matrix(rnorm(p * k), p, k) else k=ncol(W0)
W0 <- ICAorthW(W0)
GS <- matrix(0., n, k)
gS <- GS
gpS <- GS
s <- x %*% W0
flist <- as.list(1.:k)
for(j in 1.:k)
flist[[j]] <- Gfunc(s[, j], ...)
flist0 <- flist
crit0 <- mean(sapply(flist0, "[[", "Gs"))
while(restarts) {
W1 <- matrix(rnorm(p * k), p, k)
W1 <- ICAorthW(W1)
s <- x %*% W1
for(j in 1.:k)
flist[[j]] <- Gfunc(s[, j], ...)
crit <- mean(sapply(flist, "[[", "Gs"))
if(trace)
cat("old crit", crit0, "new crit", crit, "\n")
if(crit > crit0) {
crit0 <- crit
W0 <- W1
flist0 <- flist
}
restarts <- restarts - 1.
}
nit <- 0
nw <- 10
repeat {
nit <- nit + 1
gS <- sapply(flist0, "[[", "gs")
gpS <- sapply(flist0, "[[", "gps")
t1 <- t(x) %*% gS/n
t2 <- apply(gpS, 2, mean)
W1 <- t1 - scale(W0, F, 1/t2)
W1 <- ICAorthW(W1)
nw <- amari(W0, W1)
if(trace)
cat("Iter", nit, "G", crit0, "crit", nw, "\n")
W0 <- W1
if((nit > maxit) | (nw < thresh))
break
s <- x %*% W0
for(j in 1:k)
flist0[[j]] <- Gfunc(s[, j], ...)
crit0 <- mean(sapply(flist0, "[[", "Gs"))
}
rl=list(W = W0, negentropy = crit0,s= x %*% W0,whitener=whitener,call=this.call)
rl$density=lapply(flist0,"[[","density")
class(rl)="ProDenICA"
rl
} |
as_nlist <- function(x, ...) {
UseMethod("as_nlist")
}
as.nlist <- function(x, ...) {
deprecate_soft("0.1.1",
what = "nlist::as.nlist()",
with = "nlist::as_nlist()"
)
UseMethod("as_nlist")
}
as_nlist.numeric <- function(x, ...) {
chk_named(x)
chk_term(as_term(names(x)), validate = "consistent", x_name = "`names(x)`")
chk_not_any_na(names(x))
chk_unique(names(x))
chk_unused(...)
if (!length(x)) {
return(nlist())
}
terms <- as_term(names(x))
if (is_incomplete_terms(terms)) {
terms <- complete_terms(terms)
y <- rep(NA_integer_, length(terms))
names(y) <- terms
y[names(x)] <- x
x <- y
}
x <- split(x, pars_terms(terms))
x <- lapply(x, function(x) x[order(as_term(names(x)))])
x <- lapply(x, function(x) set_dim(x, pdims(as_term(names(x)))[[1]]))
as_nlist(x)
}
as_nlist.list <- function(x, ...) {
chk_unused(...)
if (!length(x)) {
return(nlist())
}
x <- numericise(x)
class(x) <- "nlist"
chk_nlist(x)
x
}
as_nlist.data.frame <- function(x, ...) as_nlist(as.list(x))
as_nlist.mcmc <- function(x, ...) {
chk_unused(...)
if(!identical(nrow(x), 1L)) abort_chk("`x` must have one iteration.")
x <- complete_terms(x)
pars <- pars(x)
x <- lapply(pars, function(p, x) subset(x, pars = p), x = x)
names(x) <- pars
x <- lapply(x, function(x) set_dim(as.vector(x), pdims(x)[[1]]))
as_nlist(x)
}
as_nlist.mcmc.list <- function(x, ...) {
as_nlist(as_mcmc(x), ...)
}
as_nlist.nlist <- function(x, ...) x |
CatDynPred <-
function(x, method,partial=TRUE)
{
fleet.name <- x$Data$Properties$Fleets$Fleet;
p <- x$Model[[method]]$Type;
options(warn=-1)
if(class(x) != "catdyn")
{stop("Pass an object of class 'catdyn' to CatDynPred")}
if(class(method) != "character")
{stop("method must be a character corresponding to one of the numerical methods passed to the CatDyn function")}
if(length(method) != 1)
{stop("Provide the name of just one the numerical methods used to fit the model")}
if(sum(method == names(x$Model)) == 0)
{stop("The method provided in 'method' was not used to numerically fit the model")}
if(is.na(x$Model[[method]]$AIC))
{stop("The selected method failed. Consider trying a different method")}
if(length(fleet.name) == 1)
{
parlist <- list(par=log(as.numeric(x$Model[[method]]$bt.par)),
dates=x$Model[[method]]$Dates,
obscat1=x$Data$Data[[fleet.name]][,5],
obseff1=x$Data$Data[[fleet.name]][,2],
obsmbm1=x$Data$Data[[fleet.name]][,4],
distr=x$Model[[method]]$Distr,
properties=x$Data$Properties,
output="predict",
partial=partial)
if(p == 0)
{
results <- do.call(.CDMN0P, parlist);
}
else if(p == 1)
{
results <- do.call(.CDMN1P, parlist);
}
else if(p == -1)
{
results <- do.call(.CDMNT1P, parlist);
}
else if(p == 2)
{
results <- do.call(.CDMN2P, parlist);
}
else if(p == -2)
{
results <- do.call(.CDMNT2P, parlist);
}
else if(p == 3)
{
results <- do.call(.CDMN3P, parlist);
}
else if(p == -3)
{
results <- do.call(.CDMNT3P, parlist);
}
else if(p == 4)
{
results <- do.call(.CDMN4P, parlist);
}
else if(p == -4)
{
results <- do.call(.CDMNT4P, parlist);
}
else if(p == 5)
{
results <- do.call(.CDMN5P, parlist);
}
else if(p == -5)
{
results <- do.call(.CDMNT5P, parlist);
}
else if(p == 6)
{
results <- do.call(.CDMN6P, parlist);
}
else if(p == -6)
{
results <- do.call(.CDMNT6P, parlist);
}
else if(p == 7)
{
results <- do.call(.CDMN7P, parlist);
}
else if(p == -7)
{
results <- do.call(.CDMNT7P, parlist);
}
else if(p == 8)
{
results <- do.call(.CDMN8P, parlist);
}
else if(p == -8)
{
results <- do.call(.CDMNT8P, parlist);
}
else if(p == 9)
{
results <- do.call(.CDMN9P, parlist);
}
else if(p == -9)
{
results <- do.call(.CDMNT9P, parlist);
}
else if(p == 10)
{
results <- do.call(.CDMN10P, parlist);
}
else if(p == -10)
{
results <- do.call(.CDMNT10P, parlist);
}
else if(p == 11)
{
results <- do.call(.CDMN11P, parlist);
}
else if(p == -11)
{
results <- do.call(.CDMNT11P, parlist);
}
else if(p == 12)
{
results <- do.call(.CDMN12P, parlist);
}
else if(p == -12)
{
results <- do.call(.CDMNT12P, parlist);
}
else if(p == 13)
{
results <- do.call(.CDMN13P, parlist);
}
else if(p == -13)
{
results <- do.call(.CDMNT13P, parlist);
}
else if(p == 14)
{
results <- do.call(.CDMN14P, parlist);
}
else if(p == -14)
{
results <- do.call(.CDMNT14P, parlist);
}
else if(p == 15)
{
results <- do.call(.CDMN15P, parlist);
}
else if(p == -15)
{
results <- do.call(.CDMNT15P, parlist);
}
else if(p == 16)
{
results <- do.call(.CDMN16P, parlist);
}
else if(p == -16)
{
results <- do.call(.CDMNT16P, parlist);
}
else if(p == 17)
{
results <- do.call(.CDMN17P, parlist);
}
else if(p == -17)
{
results <- do.call(.CDMNT17P, parlist);
}
else if(p == 18)
{
results <- do.call(.CDMN18P, parlist);
}
else if(p == -18)
{
results <- do.call(.CDMNT18P, parlist);
}
else if(p == 19)
{
results <- do.call(.CDMN19P, parlist);
}
else if(p == -19)
{
results <- do.call(.CDMNT19P, parlist);
}
else if(p == 20)
{
results <- do.call(.CDMN20P, parlist);
}
else if(p == -20)
{
results <- do.call(.CDMNT20P, parlist);
}
else if(p == 21)
{
results <- do.call(.CDMN21P, parlist);
}
else if(p == -21)
{
results <- do.call(.CDMNT21P, parlist);
}
else if(p == 22)
{
results <- do.call(.CDMN22P, parlist);
}
else if(p == -22)
{
results <- do.call(.CDMNT22P, parlist);
}
else if(p == 23)
{
results <- do.call(.CDMN23P, parlist);
}
else if(p == -23)
{
results <- do.call(.CDMNT23P, parlist);
}
else if(p == 24)
{
results <- do.call(.CDMN24P, parlist);
}
else if(p == -24)
{
results <- do.call(.CDMNT24P, parlist);
}
else if(p == 25)
{
results <- do.call(.CDMN25P, parlist);
}
else if(p == -25)
{
results <- do.call(.CDMNT25P, parlist);
}
}
else if(length(fleet.name) == 2)
{
parlist <- list(par=log(as.numeric(x$Model[[method]]$bt.par)),
dates=x$Model[[method]]$Dates,
obscat1=x$Data$Data[[fleet.name[1]]][,5],
obseff1=x$Data$Data[[fleet.name[1]]][,2],
obsmbm1=x$Data$Data[[fleet.name[1]]][,4],
obscat2=x$Data$Data[[fleet.name[2]]][,5],
obseff2=x$Data$Data[[fleet.name[2]]][,2],
obsmbm2=x$Data$Data[[fleet.name[2]]][,4],
distr=x$Model[[method]]$Distr,
properties=x$Data$Properties,
output="predict")
if(sum(p==c(0,0)) == length(p))
{
results <- do.call(.CDMN0P0P, parlist);
}
else if(sum(p==c(0,1)) == length(p))
{
results <- do.call(.CDMN0P1P, parlist);
}
else if(sum(p==c(0,2)) == length(p))
{
results <- do.call(.CDMN0P2P, parlist);
}
else if(sum(p==c(0,3)) == length(p))
{
results <- do.call(.CDMN0P3P, parlist);
}
else if(sum(p==c(0,4)) == length(p))
{
results <- do.call(.CDMN0P4P, parlist);
}
else if(sum(p==c(0,5)) == length(p))
{
results <- do.call(.CDMN0P5P, parlist);
}
else if(sum(p==c(1,1)) == length(p))
{
results <- do.call(.CDMN1P1P, parlist);
}
else if(sum(p==c(1,2)) == length(p))
{
results <- do.call(.CDMN1P2P, parlist);
}
else if(sum(p==c(1,3)) == length(p))
{
results <- do.call(.CDMN1P3P, parlist);
}
else if(sum(p==c(1,4)) == length(p))
{
results <- do.call(.CDMN1P4P, parlist);
}
else if(sum(p==c(1,5)) == length(p))
{
results <- do.call(.CDMN1P5P, parlist);
}
else if(sum(p==c(2,2)) == length(p))
{
results <- do.call(.CDMN2P2P, parlist);
}
else if(sum(p==c(2,3)) == length(p))
{
results <- do.call(.CDMN2P3P, parlist);
}
else if(sum(p==c(2,4)) == length(p))
{
results <- do.call(.CDMN2P4P, parlist);
}
else if(sum(p==c(2,5)) == length(p))
{
results <- do.call(.CDMN2P5P, parlist);
}
else if(sum(p==c(3,3)) == length(p))
{
results <- do.call(.CDMN3P3P, parlist);
}
else if(sum(p==c(3,4)) == length(p))
{
results <- do.call(.CDMN3P4P, parlist);
}
else if(sum(p==c(3,5)) == length(p))
{
results <- do.call(.CDMN3P5P, parlist);
}
else if(sum(p==c(4,4)) == length(p))
{
results <- do.call(.CDMN4P4P, parlist);
}
else if(sum(p==c(4,5)) == length(p))
{
results <- do.call(.CDMN4P5P, parlist);
}
else if(sum(p==c(5,5)) == length(p))
{
results <- do.call(.CDMN5P5P, parlist);
}
else if(sum(p==c(6,6)) == length(p))
{
results <- do.call(.CDMN6P6P, parlist);
}
else if(sum(p==c(7,7)) == length(p))
{
results <- do.call(.CDMN7P7P, parlist);
}
else if(sum(p==c(8,8)) == length(p))
{
results <- do.call(.CDMN8P8P, parlist);
}
else if(sum(p==c(9,9)) == length(p))
{
results <- do.call(.CDMN9P9P, parlist);
}
else if(sum(p==c(10,10)) == length(p))
{
results <- do.call(.CDMN10P10P, parlist);
}
else if(sum(p==c(11,11)) == length(p))
{
results <- do.call(.CDMN11P11P, parlist);
}
else if(sum(p==c(12,12)) == length(p))
{
results <- do.call(.CDMN12P12P, parlist);
}
else if(sum(p==c(13,13)) == length(p))
{
results <- do.call(.CDMN13P13P, parlist);
}
else if(sum(p==c(14,14)) == length(p))
{
results <- do.call(.CDMN14P14P, parlist);
}
else if(sum(p==c(15,15)) == length(p))
{
results <- do.call(.CDMN15P15P, parlist);
}
else if(sum(p==c(16,16)) == length(p))
{
results <- do.call(.CDMN16P16P, parlist);
}
else if(sum(p==c(17,17)) == length(p))
{
results <- do.call(.CDMN17P17P, parlist);
}
else if(sum(p==c(18,18)) == length(p))
{
results <- do.call(.CDMN18P18P, parlist);
}
else if(sum(p==c(19,19)) == length(p))
{
results <- do.call(.CDMN19P19P, parlist);
}
else if(sum(p==c(20,20)) == length(p))
{
results <- do.call(.CDMN20P20P, parlist);
}
else if(sum(p==c(21,21)) == length(p))
{
results <- do.call(.CDMN21P21P, parlist);
}
else if(sum(p==c(22,22)) == length(p))
{
results <- do.call(.CDMN22P22P, parlist);
}
else if(sum(p==c(23,23)) == length(p))
{
results <- do.call(.CDMN23P23P, parlist);
}
else if(sum(p==c(24,24)) == length(p))
{
results <- do.call(.CDMN24P24P, parlist);
}
else if(sum(p==c(25,25)) == length(p))
{
results <- do.call(.CDMN25P25P, parlist);
}
}
results$Model$Method <- method;
results$Model$AIC <- x$Model[[method]]$AIC;
class(results) <- "CatDynMod";
return(results);
} |
library("testthat")
library("tidywikidatar")
test_that("check if image returned when valid id given", {
testthat::skip_if_offline()
expect_true(
object = {
tw_get_image(
id = "Q2",
cache = FALSE
) %>%
tidyr::drop_na() %>%
nrow() %>%
as.logical()
}
)
tw_set_cache_folder(path = tempdir())
expect_true(
object = {
tw_get_image(
id = "Q2",
cache = TRUE
) %>%
tidyr::drop_na() %>%
nrow() %>%
as.logical()
}
)
})
test_that("check if image returned when invalid id given", {
testthat::skip_if_offline()
expect_true(
object = {
tw_get_image(id = "non_qid_string") %>%
is.null()
}
)
expect_true(
object = {
tw_get_image_same_length(id = "non_qid_string") %>%
is.na()
}
)
expect_equal(
object = {
tw_get_image_same_length(id = c("non_qid_string", NA)) %>%
is.na() %>%
sum()
}, expected = 2
)
expect_equal(
object = {
tw_get_image_same_length(id = c("non_qid_string", "Q2", "non_qid_string", NA)) %>%
is.na() %>%
which()
}, expected = c(1, 3, 4)
)
})
test_that("check if image metadata returned correctly with or without cache", {
testthat::skip_if_offline()
testthat::skip_on_cran()
expect_equal(
object = {
df <- tw_get_image_metadata_single(
id = c("Q2"),
only_first = TRUE,
cache = FALSE
)
list(
ncol = ncol(df),
nrow = nrow(df),
id = df %>% dplyr::pull(id)
)
}, expected = list(
ncol = 19,
nrow = 1,
id = "Q2"
)
)
expect_equal(
object = {
tw_set_cache_folder(path = tempdir())
df <- tw_get_image_metadata(
id = c("Q2", NA, "not_an_id", "Q5"),
only_first = TRUE,
cache = TRUE
)
list(
ncol = ncol(df),
nrow = nrow(df),
id = df %>% dplyr::pull(id),
missing_image = which(is.na(df$image_filename))
)
}, expected = list(
ncol = 19,
nrow = 4,
id = c("Q2", NA, "not_an_id", "Q5"),
missing_image = c(2, 3)
)
)
expect_equal(
object = {
tw_set_cache_folder(path = tempdir())
df <- tw_get_image_metadata(
id = c("Q2", NA, "not_an_id", "Q5"),
only_first = TRUE,
cache = FALSE
)
list(
ncol = ncol(df),
nrow = nrow(df),
id = df %>% dplyr::pull(id)
)
}, expected = list(
ncol = 19,
nrow = 4,
id = c("Q2", NA, "not_an_id", "Q5")
)
)
}) |
CovInfo <- function(data_part, sigma) {
n0 <- data_part$Dims$n0
n1 <- data_part$Dims$n1
n2 <- data_part$Dims$n2
sigma_inv <- matInv(sigma)
out <- array(0, dim = c(3, 3))
if (n0 > 0) {
obs_info <- array(0, dim = c(3, 3))
obs_info[1, 1] <- sigma_inv[1, 1]^2
obs_info[2, 2] <- 2 * (sigma_inv[1, 2]^2 + sigma_inv[1, 1] * sigma_inv[2, 2])
obs_info[3, 3] <- sigma_inv[2, 2]^2
obs_info[1, 2] <- obs_info[2, 1] <- 2 * sigma_inv[1, 1] * sigma_inv[1, 2]
obs_info[2, 3] <- obs_info[3, 2] <- 2 * sigma_inv[1, 2] * sigma_inv[2, 2]
obs_info[1, 3] <- obs_info[3, 1] <- sigma_inv[1, 2]^2
out <- out + 0.5 * n0 * obs_info
}
if (n1 > 0) {
out[3, 3] <- out[3, 3] + 0.5 * n1 / (sigma[2, 2]^2)
}
if (n2 > 0) {
out[1, 1] <- out[1, 1] + 0.5 * n2 / (sigma[1, 1]^2)
}
return(out)
} |
diffquo <- function(data,tobs) {
n = length(tobs);
dtobs = diff(tobs);
data = as.matrix(data);
N = dim(data)[1];
n = dim(data)[2];
YI = matrix(nrow=N,ncol=(n-1));
diffdatak = rep(0,(n-1));
for (k in 1:N) {
YI[k,] = diff(data[k,])/dtobs;
}
Xtilda = matrix(nrow=n,ncol=N);
w = rep(0,n-2);
for (i in 2:(n-1)) {
w[i] = dtobs[i]/(dtobs[i-1]+dtobs[i]);
for (j in 1:N) {
Xtilda[i,j] = w[i]*YI[j,i-1]+(1-w[i])*YI[j,i];
}
}
for (j in 1:N) {
Xtilda[1,j] = YI[j,1];
Xtilda[n,j] = YI[j,n-1];
}
result = list(YI=YI,Xtilda=Xtilda);
return(result);
} |
msdecompose <- function(y, lags=c(12), type=c("additive","multiplicative")){
type <- match.arg(type);
ma <- function(y, order){
if (order%%2 == 0){
weigths <- c(0.5, rep(1, order - 1), 0.5) / order;
}
else {
weigths <- rep(1, order) / order;
}
return(filter(y, weigths))
}
obsInSample <- length(y);
yNAValues <- is.na(y);
if(type=="multiplicative"){
shiftedData <- FALSE;
if(any(y[!yNAValues]<=0)){
yNAValues[] <- yNAValues | y<=0;
}
yInsample <- suppressWarnings(log(y));
}
else{
yInsample <- y;
}
if(any(yNAValues)){
X <- cbind(1,poly(c(1:obsInSample),degree=min(max(trunc(obsInSample/10),1),5)),
sinpi(matrix(c(1:obsInSample)*rep(c(1:max(lags)),each=obsInSample)/max(lags), ncol=max(lags))));
lmFit <- .lm.fit(X[!yNAValues,,drop=FALSE], matrix(yInsample[!yNAValues],ncol=1));
yInsample[yNAValues] <- (X %*% coef(lmFit))[yNAValues];
rm(X)
}
yName <- paste0(deparse(substitute(y)),collapse="");
obs <- length(y);
lags <- sort(unique(lags));
lagsLength <- length(lags);
ySmooth <- vector("list",lagsLength+1);
ySmooth[[1]] <- yInsample;
yClear <- vector("list",lagsLength);
for(i in 1:lagsLength){
ySmooth[[i+1]] <- ma(yInsample,lags[i]);
}
trend <- ySmooth[[lagsLength+1]];
for(i in 1:lagsLength){
yClear[[i]] <- ySmooth[[i]] - ySmooth[[i+1]];
}
patterns <- vector("list",lagsLength);
for(i in 1:lagsLength){
patterns[[i]] <- vector("numeric",lags[i]);
for(j in 1:lags[i]){
patterns[[i]][j] <- mean(yClear[[i]][(1:(obs/lags[i])-1)*lags[i]+j],na.rm=TRUE);
}
patterns[[i]][] <- patterns[[i]] - mean(patterns[[i]]);
}
initial <- c(ySmooth[[lagsLength]][!is.na(ySmooth[[lagsLength]])][1],
mean(diff(ySmooth[[lagsLength]]),na.rm=T));
initial[1] <- initial[1] - initial[2]*floor(max(lags)/2);
names(initial) <- c("level","trend");
if(type=="multiplicative"){
initial[] <- exp(initial);
trend <- exp(trend);
patterns[] <- lapply(patterns,exp);
if(shiftedData){
initial[1] <- initial[1] - 1;
trend[] <- trend -1;
}
}
return(structure(list(y=y, initial=initial, trend=trend, seasonal=patterns, loss="MSE",
lags=lags, type=type, yName=yName), class=c("msdecompose","smooth")));
}
actuals.msdecompose <- function(object, ...){
return(object$y);
}
errorType.msdecompose <- function(object, ...){
if(object$type=="additive"){
return("A");
}
else{
return("M");
}
}
fitted.msdecompose <- function(object, ...){
yFitted <- object$trend;
obs <- nobs(object);
if(object$type=="additive"){
for(i in 1:length(object$lags)){
yFitted <- yFitted + rep(object$seasonal[[i]],ceiling(obs/object$lags[i]))[1:obs];
}
}
else{
for(i in 1:length(object$lags)){
yFitted <- yFitted * rep(object$seasonal[[i]],ceiling(obs/object$lags[i]))[1:obs];
}
}
return(yFitted);
}
forecast.msdecompose <- function(object, h=10,
interval=c("parametric","semiparametric","nonparametric","none"),
level=0.95, model=NULL, ...){
interval <- match.arg(interval,c("parametric","semiparametric","nonparametric","none"));
if(is.null(model)){
model <- switch(errorType(object),
"A"="XXX",
"M"="YYY");
}
obs <- nobs(object);
yDeseasonalised <- actuals(object);
yForecastStart <- time(yDeseasonalised)[length(time(yDeseasonalised))]+1/frequency(yDeseasonalised);
if(errorType(object)=="A"){
for(i in 1:length(object$lags)){
yDeseasonalised <- yDeseasonalised - rep(object$seasonal[[i]],ceiling(obs/object$lags[i]))[1:obs];
}
}
else{
for(i in 1:length(object$lags)){
yDeseasonalised <- yDeseasonalised / rep(object$seasonal[[i]],ceiling(obs/object$lags[i]))[1:obs];
}
}
yesModel <- suppressWarnings(es(yDeseasonalised,model=model,h=h,interval=interval,level=level,initial="b",...));
yValues <- ts(c(yDeseasonalised,yesModel$forecast),start=start(yDeseasonalised),frequency=frequency(yDeseasonalised));
if(interval!="none"){
lower <- ts(c(yDeseasonalised,yesModel$lower),start=start(yDeseasonalised),frequency=frequency(yDeseasonalised));
upper <- ts(c(yDeseasonalised,yesModel$upper),start=start(yDeseasonalised),frequency=frequency(yDeseasonalised));
}
else{
lower <- upper <- NA;
}
if(errorType(object)=="A"){
for(i in 1:length(object$lags)){
yValues <- yValues + rep(object$seasonal[[i]],ceiling((obs+h)/object$lags[i]))[1:(obs+h)];
if(interval!="none"){
lower <- lower + rep(object$seasonal[[i]],ceiling((obs+h)/object$lags[i]))[1:(obs+h)];
upper <- upper + rep(object$seasonal[[i]],ceiling((obs+h)/object$lags[i]))[1:(obs+h)];
}
}
}
else{
for(i in 1:length(object$lags)){
yValues <- yValues * rep(object$seasonal[[i]],ceiling((obs+h)/object$lags[i]))[1:(obs+h)];
if(interval!="none"){
lower <- lower * rep(object$seasonal[[i]],ceiling((obs+h)/object$lags[i]))[1:(obs+h)];
upper <- upper * rep(object$seasonal[[i]],ceiling((obs+h)/object$lags[i]))[1:(obs+h)];
}
}
}
yForecast <- window(yValues,yForecastStart);
if(interval!="none"){
lower <- window(lower,yForecastStart);
upper <- window(upper,yForecastStart);
}
return(structure(list(model=object, esmodel=yesModel, method=paste0("ETS(",modelType(yesModel),") with decomposition"),
mean=yForecast, forecast=yForecast, lower=lower, upper=upper,
level=level, interval=interval),class=c("msdecompose.forecast","smooth.forecast","forecast")));
}
is.msdecompose <- function(x){
return(inherits(x,"msdecompose"))
}
is.msdecompose.forecast <- function(x){
return(inherits(x,"msdecompose.forecast"))
}
lags.msdecompose <- function(object, ...){
return(object$lags);
}
modelType.msdecompose <- function(object, ...){
return("Multiple Seasonal Decomposition");
}
nobs.msdecompose <- function(object, ...){
return(length(actuals(object)));
}
nparam.msdecompose <- function(object, ...){
return(length(object$lags)+1);
}
plot.msdecompose <- function(x, which=c(1,2,4,6), level=0.95, legend=FALSE,
ask=prod(par("mfcol")) < length(which) && dev.interactive(),
lowess=TRUE, ...){
ellipsis <- list(...);
obs <- nobs(x);
if(ask){
oask <- devAskNewPage(TRUE);
on.exit(devAskNewPage(oask));
}
if(any(which %in% c(1:6))){
plot.smooth(x, which=which[which %in% c(1:6)], level=level,
legend=legend, ask=FALSE, lowess=lowess, ...);
}
if(any(which==7)){
ellipsis$x <- actuals(x);
if(!any(names(ellipsis)=="ylab")){
ellipsis$ylab <- x$yName;
}
yFitted <- fitted(x);
do.call(plot,ellipsis);
lines(yFitted, col="red");
}
if(any(which %in% c(8:11))){
plot.smooth(x, which=which[which %in% c(8:11)], level=level,
legend=legend, ask=FALSE, lowess=lowess, ...);
}
if(any(which==12)){
yDecomposed <- cbind(actuals(x),x$trend);
for(i in 1:length(x$seasonal)){
yDecomposed <- cbind(yDecomposed,rep(x$seasonal[[i]],ceiling(obs/x$lags[i]))[1:obs]);
}
yDecomposed <- cbind(yDecomposed, residuals(x));
colnames(yDecomposed) <- c("Actuals","Trend",paste0("Seasonal ",c(1:length(x$seasonal))),"Residuals");
if(!any(names(ellipsis)=="main")){
ellipsis$main <- paste0("Decomposition of ", x$yName);
}
ellipsis$x <- yDecomposed;
do.call(plot,ellipsis);
}
}
print.msdecompose <- function(x, ...){
cat(paste0("Multiple seasonal decomposition of ",x$yName," using c(",paste0(x$lags,collapse=","),") lags.\n"));
cat("Type of decomposition:",x$type);
}
residuals.msdecompose <- function(object, ...){
if(errorType(object)=="A"){
return(actuals(object)-fitted(object));
}
else{
return(log(actuals(object)/fitted(object)));
}
}
sigma.msdecompose <- function(object, ...){
if(errorType(object)=="A"){
return(sqrt(mean(residuals(object)^2,na.rm=TRUE)));
}
else{
return(sqrt(mean(residuals(object)^2,na.rm=TRUE)));
}
} |
ex_data_table_step.relop_set_indicator <- function(optree,
...,
tables = list(),
source_usage = NULL,
source_limit = NULL,
env = parent.frame()) {
force(env)
wrapr::stop_if_dot_args(substitute(list(...)), "rqdatatable::ex_data_table_step.relop_set_indicator")
if(is.null(source_usage)) {
source_usage <- columns_used(optree)
}
x <- ex_data_table_step(optree$source[[1]],
tables = tables,
source_usage = source_usage,
source_limit = source_limit,
env = env)
x[[optree$rescol]] <- ifelse(x[[optree$testcol]] %in% optree$testvalues, 1, 0)
x
} |
get_UT <- function(network, comid, distance = NULL) {
if ("sf" %in% class(network)) network <- sf::st_set_geometry(network, NULL)
network <- network %>% check_names("get_UT") %>%
dplyr::select(get("get_UT_attributes", nhdplusTools_env))
start_comid <- get_start_comid(network, comid)
if (!is.null(distance)) {
if (distance < start_comid$LENGTHKM) return(comid)
}
all <- private_get_UT(network, comid)
if (!is.null(distance)) {
stop_pathlength <- start_comid$Pathlength -
start_comid$LENGTHKM +
distance
network <- filter(network, COMID %in% all)
return(filter(network, Pathlength <= stop_pathlength)$COMID)
} else {
return(all)
}
}
private_get_UT <- function(network, comid) {
main <- filter(network, COMID %in% comid)
if (length(main$Hydroseq) == 1) {
full_main <- filter(network,
LevelPathI %in% main$LevelPathI &
Hydroseq >= main$Hydroseq)
trib_lpid <- filter(network, DnHydroseq %in% full_main$Hydroseq &
!LevelPathI %in% main$LevelPathI &
Hydroseq >= main$Hydroseq)$LevelPathI
} else {
full_main <- filter(network, LevelPathI %in% main$LevelPathI)
trib_lpid <- filter(network, DnHydroseq %in% full_main$Hydroseq &
!LevelPathI %in% main$LevelPathI)$LevelPathI
}
trib_comid <- filter(network, LevelPathI %in% trib_lpid)$COMID
if (length(trib_comid) > 0) {
return(c(full_main$COMID, private_get_UT(network, trib_comid)))
} else {
return(full_main$COMID)
}
}
get_UM <- function(network, comid, distance = NULL, sort = FALSE, include = TRUE) {
network <- check_names(network, "get_UM")
main <- network %>%
filter(COMID %in% comid) %>%
select(COMID, LevelPathI, Hydroseq, Pathlength, LENGTHKM)
main_us <- network %>%
filter(LevelPathI %in% main$LevelPathI & Hydroseq >= main$Hydroseq) %>%
select(COMID, Hydroseq, Pathlength, LENGTHKM)
if (!is.null(distance)) {
if (length(main$LENGTHKM) == 1) {
if (main$LENGTHKM > distance) {
return(main$COMID)
}
}
stop_pathlength <- main$Pathlength - main$LENGTHKM + distance
main_us <- filter(main_us, Pathlength <= stop_pathlength)
}
if(sort) { main_us <- arrange(main_us, Hydroseq) }
if(!include) { main_us = filter(main_us, COMID != comid) }
return(main_us$COMID)
}
get_DM <- function(network, comid, distance = NULL, sort = FALSE, include = TRUE) {
if ("sf" %in% class(network)) { network <- sf::st_set_geometry(network, NULL) }
type <- ifelse(is.null(distance), "get_DM_nolength", "get_DM")
network <- network %>%
check_names(type) %>%
select(get(paste0(type, "_attributes"), nhdplusTools_env))
start_comid <- get_start_comid(network, comid)
if (!is.null(distance)) {
if (distance < start_comid$LENGTHKM){
return(comid)
}
}
main_ds <- private_get_DM(network, comid)
if (!is.null(distance)) {
stop_pathlength <- start_comid$Pathlength + start_comid$LENGTHKM - distance
main_ds <- network %>%
filter(COMID %in% main_ds$COMID, (Pathlength + LENGTHKM) >= stop_pathlength)
}
if(sort){ main_ds <- arrange(main_ds, desc(Hydroseq)) }
if(!include){ main_ds <- filter(main_ds, COMID != comid) }
return(main_ds$COMID)
}
private_get_DM <- function(network, comid) {
main <- ds_main <- filter(network, COMID %in% comid)
if (length(main$Hydroseq) == 1) {
ds_main <- network %>%
filter(LevelPathI %in% main$LevelPathI &
Hydroseq <= main$Hydroseq)
}
ds_hs <- ds_main %>%
filter(!DnLevelPat %in% main$LevelPathI) %>%
select(DnHydroseq)
if (nrow(ds_hs) > 0) {
ds_lpid <- network %>%
filter(Hydroseq == ds_hs$DnHydroseq) %>%
select(LevelPathI)
if (nrow(ds_lpid) > 0) {
ds_comid <- network %>%
filter(LevelPathI == ds_lpid$LevelPathI & Hydroseq <= ds_hs$DnHydroseq) %>%
select(COMID)
return(rbind(
select(ds_main, COMID, Hydroseq),
private_get_DM(network, comid = ds_comid$COMID)
))
}
}
return(select(ds_main, COMID, Hydroseq))
}
get_DD <- function(network, comid, distance = NULL) {
if ("sf" %in% class(network)) network <- sf::st_set_geometry(network, NULL)
network <- network %>% check_names("get_DD") %>%
dplyr::select(get("get_DD_attributes", nhdplusTools_env))
start_comid <- get_start_comid(network, comid)
stop_pathlength <- 0
if (!is.null(distance)) {
if (distance < start_comid$LENGTHKM) return(comid)
stop_pathlength <- start_comid$Pathlength +
start_comid$LENGTHKM -
distance
}
all <- private_get_DD(network, comid, stop_pathlength)
if (!is.null(distance)) {
network <- filter(network, COMID %in% unique(all))
return(filter(network, (Pathlength + LENGTHKM) >= stop_pathlength)$COMID)
} else {
return(unique(all))
}
}
private_get_DD <- function(network, comid, stop_pathlength = 0) {
main <- ds_main <- filter(network, COMID %in% comid)
if (length(main$Hydroseq) == 1) {
ds_main <- filter(network,
LevelPathI %in% main$LevelPathI &
Hydroseq <= main$Hydroseq)
}
ds_hs <- c(filter(ds_main, !DnLevelPat %in% main$LevelPathI)$DnHydroseq,
filter(ds_main, !DnMinorHyd == 0)$DnMinorHyd)
ds_lpid <- filter(network, Hydroseq %in% ds_hs)$LevelPathI
if (length(ds_lpid) > 0) {
if (length(ds_hs) == 1) {
ds_comid <- filter(network,
LevelPathI %in% ds_lpid &
Hydroseq <= ds_hs)$COMID
} else {
ds_hs <- filter(network, Hydroseq %in% ds_hs)
ds_comid <- filter(network, LevelPathI %in% ds_lpid) %>%
dplyr::left_join(select(ds_hs, LevelPathI, max_Hydroseq = Hydroseq),
by = "LevelPathI") %>%
filter(Hydroseq <= .data$max_Hydroseq)
ds_comid <- ds_comid$COMID
}
if (all(ds_main$Pathlength <= stop_pathlength)) return(ds_main$COMID)
c(ds_main$COMID, private_get_DD(network, ds_comid, stop_pathlength))
} else {
return(ds_main$COMID)
}
}
get_start_comid <- function(network, comid) {
start_comid <- filter(network, COMID == comid)
if(nrow(start_comid) > 1) {
stop("Found duplicate ID for starting catchment. Duplicate rows in network?")
}
start_comid
} |
make.directories <- function(homedir) {
dir <- paste0(homedir, "/FAMoS-Results")
if(!dir.exists(dir)){
dir.create(dir, showWarnings = F)
}
if(!dir.exists(paste0(dir, "/BestModel"))){
dir.create(paste0(dir, "/BestModel"), showWarnings = F)
}
if(!dir.exists(paste0(dir, "/Figures"))){
dir.create(paste0(dir, "/Figures"), showWarnings = F)
}
if(!dir.exists(paste0(dir, "/Fits"))){
dir.create(paste0(dir, "/Fits"), showWarnings = F)
}
if(!dir.exists(paste0(dir, "/TestedModels"))){
dir.create(paste0(dir, "/TestedModels"), showWarnings = F)
}
} |
.runge_kutta4<- function(t, biomasses, model){
bioms <- matrix(NA, ncol = length(biomasses), nrow = length(t))
biom.step<- biomasses
delta.t <- t[2] - t[1]
for (i in 1:length(t)){
bioms[i, ] <- biom.step
k1 <- model$ODE(biom.step, i*delta.t)
k2 <- model$ODE(biom.step + 0.5*delta.t * k1, (i+0.5)*delta.t)
k3 <- model$ODE(biom.step + 0.5*delta.t * k2, (i+0.5)*delta.t)
k4 <- model$ODE(biom.step + delta.t * k3, i*delta.t)
biom.step <- biom.step + (delta.t/6) * (k1 + 2*k2 + 2*k3 + k4)
}
return(cbind(t, bioms))
} |
rln_clock_model_to_xml_mean_rate_prior <- function(rln_clock_model) {
testit::assert(beautier::is_rln_clock_model(rln_clock_model))
id <- rln_clock_model$id
testit::assert(beautier::is_id(id))
text <- NULL
text <- c(text, paste0("<prior id=\"MeanRatePrior.c:", id, "\" ",
"name=\"distribution\" x=\"@ucldMean.c:", id, "\">"))
text <- c(text,
beautier::indent(
beautier::distr_to_xml(
distr = rln_clock_model$mean_rate_prior_distr
)
)
)
text <- c(text, paste0("</prior>"))
text
} |
stepbins <- function(bEdges, bCounts, m=NULL, tailShape = c("onebin", "pareto", "exponential"),
nTail=16, numIterations=20, pIndex=1.160964, tbRatio=0.8) {
L <- length(bCounts)
if(!(is.na(bEdges[L]) | is.infinite(bEdges[L])))
warning("Top bin is bounded. Expect inaccurate results.\n")
if(is.null(m)) {
warning("No mean provided: expect inaccurate results.\n")
m <- sum(0.5*(c(bEdges[1:(L-1)],2.0*bEdges[L-1])+c(0, bEdges[1:(L-1)]))*bCounts/sum(bCounts))
}
tailShape <- match.arg(tailShape)
if(tailShape == "onebin")
stepbinsNotail(bEdges, bCounts, m)
else
stepbinsTail(bEdges, bCounts, m, tailShape, nTail, numIterations, pIndex, tbRatio)
}
stepbinsTail <- function(bEdges, bCounts, m, tailShape = c("pareto", "exponential"),
nTail, numIterations, pIndex, tbRatio) {
tailShape <- match.arg(tailShape)
L <- length(bCounts)
tot <- sum(bCounts)
e <- c(0,bEdges[1:(L-1)],numeric(nTail))
shrinkFactor <- 1
shrinkMultiplier <- 0.995
tailCount <- bCounts[L]
bbtot <- tot-tailCount
bbMean <- sum((e[2:(L)]+e[1:(L-1)])*bCounts[1:(L-1)])/(2*bbtot)
while(m<bbMean) {
e <- e*shrinkMultiplier
bbMean <- sum((e[2:(L)]+e[1:(L-1)])*bCounts[1:(L-1)])/(2*bbtot)
shrinkFactor <- shrinkFactor*shrinkMultiplier
}
if(tailCount>0) {
L <- L+nTail-1
tailArea <- tailCount/tot
bAreas <- c(bCounts/tot, numeric(nTail-1))
tbWidth <- e[L-nTail+1]-e[L-nTail]
h <- bAreas[L-nTail]/tbWidth
if(tailShape=="pareto")
tailUnscaled <- (1:nTail)^(-1-pIndex)
else
tailUnscaled <- tbRatio^(1:nTail)
bAreas[(L-nTail+1):(L)] <- tailUnscaled/sum(tailUnscaled)*tailArea
repeat {
e[(L-nTail+2):(L+1)] <- e[L-nTail+1]+(1:(nTail))*tbWidth
bMean <- sum((e[2:(L+1)]+e[1:L])*bAreas)/2
if(bMean>m) break
tbWidth <- tbWidth*2
}
l <- 0
r <- tbWidth
for(i in 1:numIterations) {
tbWidth <- (l+r)/2
e[(L-nTail+2):(L+1)] <- e[L-nTail+1]+(1:(nTail))*tbWidth
bMean <- sum((e[2:(L+1)]+e[1:L])*bAreas)/2
if (bMean<m)
l <- tbWidth
else
r <- tbWidth
}
}
else {
L <- L-1
bAreas <- bCounts[1:L]/tot
e <- e[1:(L+1)]
}
cAreas <- vapply(1:length(bAreas), function(x){sum(bAreas[1:x])}, numeric(1))
bHeights <- bAreas/(e[2:(L+1)]-e[1:L])
stepCDF <- approxfun(e, c(0, cAreas), yleft=0, yright=1, rule=2)
stepPDF <- stepfun(e, c(0, bHeights, 0))
return(list(stepPDF=stepPDF, stepCDF=stepCDF, E=e[L+1], shrinkFactor=shrinkFactor))
}
stepbinsNotail <- function(bEdges, bCounts, m) {
L <- length(bCounts)
tot <- sum(bCounts)
p <- c(1,1-vapply(1:(L-1), function(x){sum(bCounts[1:x])}, numeric(1))/tot)
e <- c(0,bEdges[1:(L-1)],0)
A <- 0.5*sum((e[2:L]-e[1:(L-1)])*(p[2:L]+p[1:(L-1)]))
shrinkFactor <- 1
shrinkMultiplier <- 0.995
while(m<A) {
e <- e*shrinkMultiplier
A <- 0.5*sum((e[2:L]-e[1:(L-1)])*(p[2:L]+p[1:(L-1)]))
shrinkFactor <- shrinkFactor*shrinkMultiplier
}
E <- ifelse(p[L]>0, bEdges[L-1]+2*(m-A)/p[L], bEdges[L-1]*1.001)
e[L+1] <- E
bAreas <- bCounts/tot
cAreas <- vapply(1:length(bAreas), function(x){sum(bAreas[1:x])}, numeric(1))
bHeights <- bAreas/(e[2:(L+1)]-e[1:L])
stepCDF <- approxfun(e, c(0, cAreas), yleft=0, yright=1, rule=2)
stepPDF <- stepfun(e, c(0, bHeights, 0))
return(list(stepPDF=stepPDF, stepCDF=stepCDF, E=E, shrinkFactor=shrinkFactor))
} |
`deg.theta` <-
function(LC,degen.r,degen.x.i,degen.theta,steps, maxchange=1){
Pxji <- array(apply(LC$item.par$delta,2,P.xj, th=degen.theta),
dim=c(steps,length(degen.theta),LC$i.stat$n.i))
degen.x.i <- matrix(! is.na(degen.x.i), nrow=length(degen.r))
der <- d.v(Pxji, degen.r, degen.x.i)
der$d1d2 <- ifelse(abs(der$d1d2) > maxchange, sign(der$d1d2)*maxchange, der$d1d2)
degen.theta <- degen.theta - der$d1d2
degen.theta
} |
listonator<-function(check=TRUE){
blend <- getOption("blend")
homefolder <- getOption("homefolder")
if(!exists("liston") & blend & check){
liston<-readecad(paste0(homefolder,'raw/stations.txt'))
names(liston)<-c('STAID','STANAME','CN','LAT','LON','HGHT')
}else{
if(!exists("liston") & isTRUE(check)){
liston<-listas()
}
}
lat<-apply(as.data.frame(liston$LAT),1,FUN=decimaldegrees)
lon<-apply(as.data.frame(liston$LON),1,FUN=decimaldegrees)
coordinates<-data.frame(lat,lon)
liston$LAT<-lat
liston$LON<-lon
options("liston"=liston)
} |
computeLeafColorIdxs<-function
(
df
) {
leafColorIdxs<-rep(0,df$n)
for (i in seq(along=df$clusters)) {
if (!is.null(df$clusters[[i]]) && length(df$clusters[[i]]$indices)>0) {
members<-computeMemberIndices(df$h,max(df$clusters[[i]]$indices))
leafColorIdxs[members]<-i
}
}
return(leafColorIdxs)
} |
get_city_issues <- function(city=NULL, lat=NULL,long=NULL, status = "open,acknowledged,closed,archived", limit = 100) {
total <- 0
page <- 1
pagelimit <- min(100,limit)
if(length(city)>0 & (length(lat)>0 | length(long)>0)){
lat <- NULL
long <- NULL
warning("Cannot specify both city and lat/long locations. Using city...")
}
if((length(lat)>0 & length(long)<1) | length(lat)<1 & length(long)>0){
stop("Specify valid lat/long pair or city")
}
url <- paste("https://seeclickfix.com/api/v2/issues?", ifelse(length(city)>0,paste("place_url=",city,sep=""),""),ifelse(length(lat)>0,paste("lat=", lat,"&lng=",long,sep=""),""),"&status=",status, "&per_page=",pagelimit,"&page=",page, sep = "")
url <- gsub(" ","%20",x=url)
rawdata <- RCurl::getURL(url)
scf <- jsonlite::fromJSON(txt=rawdata,simplifyDataFrame = T,flatten=F)
issue_id = scf$issues$id
issue_status = scf$issues$status
summary = scf$issues$summary
description = scf$issues$description
rating = scf$issues$rating
lat = scf$issues$lat
lng = scf$issues$lng
issue_address = scf$issues$address
created_at = scf$issues$created_at
acknowledged_at = scf$issues$acknowledged_at
closed_at = scf$issues$closed_at
reopened_at = scf$issues$reopened_at
updated_at = scf$issues$updated_at
shortened_url = scf$issues$shortened_url
video_url = scf$issues$media$video_url
image_full = scf$issues$media$image_full
image_square_100x100 = scf$issues$media$image_square_100x100
representative_image_url = scf$issues$media$representative_image_url
issue_types = scf$issues$point$type
url = scf$issues$url
html_url = scf$issues$html_url
comment_url = scf$issues$comment_url
flag_url = scf$issues$flag_url
close_url = if(length(scf$issues$transitions$close_url)>0){scf$issues$transitions$close_url} else{NA}
open_url = if(length(scf$issues$transitions$open_url)>0){scf$issues$transitions$open_url} else{NA}
reporter_id = scf$issues$reporter$id
reporter_name = scf$issues$reporter$name
reporter_wittytitle = scf$issues$reporter$witty_title
reporter_role = scf$issues$reporter$role
reporter_civicpoints = scf$issues$reporter$civic_points
reporter_avatar_full = scf$issues$reporter$avatar$full
reporter_avatar_square = scf$issues$reporter$avatar$square_100x100
allout <- data.frame(
issue_id,
issue_status,
summary,
description,
rating,
lat,
lng,
issue_address,
created_at,
acknowledged_at,
closed_at,
reopened_at,
updated_at,
shortened_url,
video_url,
image_full,
image_square_100x100,
representative_image_url,
issue_types,
url,
html_url,
comment_url,
flag_url,
close_url,
open_url,
reporter_id,
reporter_name,
reporter_wittytitle,
reporter_role,
reporter_civicpoints,
reporter_avatar_full,
reporter_avatar_square
)
total <- nrow(allout)
limit <- min(limit,scf$metadata$pagination$entries)
while(limit>total){
page <- page+1
if((limit-total)<100){pagelimit <- (limit-total)}
url <- paste("https://seeclickfix.com/api/v2/issues?place_url=", city,"&status=",status, "&per_page=",pagelimit,"&page=",page, sep = "")
rawdata <- readLines(url, warn = F)
scf <- jsonlite::fromJSON(txt=rawdata,simplifyDataFrame = T,flatten=F)
issue_id = scf$issues$id
issue_status = scf$issues$status
summary = scf$issues$summary
description = scf$issues$description
rating = scf$issues$rating
lat = scf$issues$lat
lng = scf$issues$lng
issue_address = scf$issues$address
created_at = scf$issues$created_at
acknowledged_at = scf$issues$acknowledged_at
closed_at = scf$issues$closed_at
reopened_at = scf$issues$reopened_at
updated_at = scf$issues$updated_at
shortened_url = scf$issues$shortened_url
video_url = scf$issues$media$video_url
image_full = scf$issues$media$image_full
image_square_100x100 = scf$issues$media$image_square_100x100
representative_image_url = scf$issues$media$representative_image_url
issue_types = scf$issues$point$type
url = scf$issues$url
html_url = scf$issues$html_url
comment_url = scf$issues$comment_url
flag_url = scf$issues$flag_url
close_url = if(length(scf$issues$transitions$close_url)>0){scf$issues$transitions$close_url} else{NA}
open_url = if(length(scf$issues$transitions$open_url)>0){scf$issues$transitions$open_url} else{NA}
reporter_id = scf$issues$reporter$id
reporter_name = scf$issues$reporter$name
reporter_wittytitle = scf$issues$reporter$witty_title
reporter_role = scf$issues$reporter$role
reporter_civicpoints = scf$issues$reporter$civic_points
reporter_avatar_full = scf$issues$reporter$avatar$full
reporter_avatar_square = scf$issues$reporter$avatar$square_100x100
holder <- data.frame(
issue_id,
issue_status,
summary,
description,
rating,
lat,
lng,
issue_address,
created_at,
acknowledged_at,
closed_at,
reopened_at,
updated_at,
shortened_url,
video_url,
image_full,
image_square_100x100,
representative_image_url,
issue_types,
url,
html_url,
comment_url,
flag_url,
close_url,
open_url,
reporter_id,
reporter_name,
reporter_wittytitle,
reporter_role,
reporter_civicpoints,
reporter_avatar_full,
reporter_avatar_square
)
allout <- rbind(allout,holder)
total <- nrow(allout)
}
return(allout)
} |
ActivePathways <- function(scores, gmt, background = makeBackground(gmt),
geneset.filter = c(5, 1000), cutoff = 0.1, significant = 0.05,
merge.method = c("Brown", "Fisher"),
correction.method = c("holm", "fdr", "hochberg", "hommel",
"bonferroni", "BH", "BY", "none"),
cytoscape.file.tag = NA, color_palette = NULL, custom_colors = NULL, color_integrated_only = "
merge.method <- match.arg(merge.method)
correction.method <- match.arg(correction.method)
if (!(is.matrix(scores) && is.numeric(scores))) stop("scores must be a numeric matrix")
if (any(is.na(scores))) stop("scores may not contain missing values")
if (any(scores < 0) || any(scores > 1)) stop("All values in scores must be in [0,1]")
if (any(duplicated(rownames(scores)))) stop("Scores matrix contains duplicated genes - rownames must be unique.")
stopifnot(length(cutoff) == 1)
stopifnot(is.numeric(cutoff))
if (cutoff < 0 || cutoff > 1) stop("cutoff must be a value in [0,1]")
stopifnot(length(significant) == 1)
stopifnot(is.numeric(significant))
if (significant < 0 || significant > 1) stop("significant must be a value in [0,1]")
if (!is.GMT(gmt)) gmt <- read.GMT(gmt)
if (length(gmt) == 0) stop("No pathways in gmt made the geneset.filter")
if (!(is.character(background) && is.vector(background))) {
stop("background must be a character vector")
}
if (!is.null(geneset.filter)) {
if (!(is.numeric(geneset.filter) && is.vector(geneset.filter))) {
stop("geneset.filter must be a numeric vector")
}
if (length(geneset.filter) != 2) stop("geneset.filter must be length 2")
if (!is.numeric(geneset.filter)) stop("geneset.filter must be numeric")
if (any(geneset.filter < 0, na.rm=TRUE)) stop("geneset.filter limits must be positive")
}
if (!is.null(custom_colors)){
if(!(is.character(custom_colors) && is.vector(custom_colors))){
stop("colors must be provided as a character vector")
}
if(length(colnames(scores)) != length(custom_colors)) stop("incorrect number of colors is provided")
}
if (!is.null(custom_colors) & !is.null(color_palette)){
stop("Both custom_colors and color_palette are provided. Specify only one of these parameters for node coloring.")
}
if (!is.null(color_palette)){
if (!(color_palette %in% rownames(RColorBrewer::brewer.pal.info))) stop("palette must be from the RColorBrewer package")
}
if(!(is.character(color_integrated_only) && is.vector(color_integrated_only))){
stop("color must be provided as a character vector")
}
if(1 != length(color_integrated_only)) stop("only a single color must be specified")
contribution <- TRUE
if (ncol(scores) == 1) {
contribution <- FALSE
message("Scores matrix contains only one column. Column contributions will not be calculated.")
}
if(!is.null(geneset.filter)) {
orig.length <- length(gmt)
if (!is.na(geneset.filter[1])) {
gmt <- Filter(function(x) length(x$genes) >= geneset.filter[1], gmt)
}
if (!is.na(geneset.filter[2])) {
gmt <- Filter(function(x) length(x$genes) <= geneset.filter[2], gmt)
}
if (length(gmt) == 0) stop("No pathways in gmt made the geneset.filter")
if (length(gmt) < orig.length) {
message(paste(orig.length - length(gmt), "terms were removed from gmt",
"because they did not make the geneset.filter"))
}
}
orig.length <- nrow(scores)
scores <- scores[rownames(scores) %in% background, , drop=FALSE]
if (nrow(scores) == 0) {
stop("scores does not contain any genes in the background")
}
if (nrow(scores) < orig.length) {
message(paste(orig.length - nrow(scores), "rows were removed from scores",
"because they are not found in the background"))
}
merged.scores <- merge_p_values(scores, merge.method)
merged.scores <- merged.scores[merged.scores <= cutoff]
if (length(merged.scores) == 0) stop("No genes made the cutoff")
ordered.scores <- names(merged.scores)[order(merged.scores)]
res <- enrichmentAnalysis(ordered.scores, gmt, background)
adjusted_p <- stats::p.adjust(res$adjusted.p.val, method = correction.method)
res[, "adjusted.p.val" := adjusted_p]
significant.indeces <- which(res$adjusted.p.val <= significant)
if (length(significant.indeces) == 0) {
warning("No significant terms were found.")
return()
}
if (contribution) {
sig.cols <- columnSignificance(scores, gmt, background, cutoff,
significant, correction.method, res$adjusted.p.val)
res <- cbind(res, sig.cols[, -1])
} else {
sig.cols <- NULL
}
if (length(significant.indeces) > 0 & !is.na(cytoscape.file.tag)) {
prepareCytoscape(res[significant.indeces, c("term.id", "term.name", "adjusted.p.val")],
gmt[significant.indeces],
cytoscape.file.tag,
sig.cols[significant.indeces,], color_palette, custom_colors, color_integrated_only)
}
res[significant.indeces]
}
enrichmentAnalysis <- function(genelist, gmt, background) {
dt <- data.table(term.id=names(gmt))
for (i in 1:length(gmt)) {
term <- gmt[[i]]
tmp <- orderedHypergeometric(genelist, background, term$genes)
overlap <- genelist[1:tmp$ind]
overlap <- overlap[overlap %in% term$genes]
if (length(overlap) == 0) overlap <- NA
set(dt, i, 'term.name', term$name)
set(dt, i, 'adjusted.p.val', tmp$p.val)
set(dt, i, 'term.size', length(term$genes))
set(dt, i, 'overlap', list(list(overlap)))
}
dt
}
columnSignificance <- function(scores, gmt, background, cutoff, significant, correction.method, pvals) {
dt <- data.table(term.id=names(gmt), evidence=NA)
for (col in colnames(scores)) {
col.scores <- scores[, col, drop=TRUE]
col.scores <- col.scores[col.scores <= cutoff]
col.scores <- names(col.scores)[order(col.scores)]
res <- enrichmentAnalysis(col.scores, gmt, background)
set(res, i = NULL, "adjusted.p.val", stats::p.adjust(res$adjusted.p.val, correction.method))
set(res, i = which(res$adjusted.p.val > significant), "overlap", list(list(NA)))
set(dt, i=NULL, col, res$overlap)
}
ev_names = colnames(dt[,-1:-2])
set_evidence <- function(x) {
ev <- ev_names[!is.na(dt[x, -1:-2])]
if(length(ev) == 0) {
if (pvals[x] <= significant) {
ev <- 'combined'
} else {
ev <- 'none'
}
}
ev
}
evidence <- lapply(1:nrow(dt), set_evidence)
set(dt, i=NULL, "evidence", evidence)
colnames(dt)[-1:-2] = paste0("Genes_", colnames(dt)[-1:-2])
dt
}
export_as_CSV = function (res, file_name) {
data.table::fwrite(res, file_name)
} |
iucn_getname <- function(name, verbose = TRUE, ...) {
mssg(verbose, "searching Global Names ...")
all_names <- gni_search(sci = name, parse_names = TRUE)
if (NROW(all_names) == 0) {
stop("No names found matching ", name, call. = FALSE)
}
mssg(verbose, "searching IUCN...")
out <- suppressWarnings(iucn_summary(all_names$canonical, ...))
x <- all_names$canonical[!sapply(out, function(x) x$status) %in% NA]
unique(as.character(x))
} |
xtraR <- system.file("xtraR", package="robustlmm")
source(file.path(xtraR, "unitTestBase.R"))
source(file.path(xtraR, "unitTestObjects.R"))
test.s <- function(i) {
cat("test.s for", names(rPDs)[i],"...")
ltest <- function() {
actual <- rPDASs[[i]]$s_e()
expected <- .s(theta = FALSE, pp = rPDASTests[[i]])
stopifnot(all.equal(actual, expected, check.attributes = FALSE,
tolerance = 5e-6))
}
lsetTheta <- function(theta) {
rPDASs[[i]]$setTheta(theta)
rPDASTests[[i]]$setTheta(theta)
}
on.exit({
lsetTheta(getME(fms[[i]], "theta"))
gc()
})
ltest()
testBlocks(fms[[i]], ltest, lsetTheta)
if (i == 4) {
lsetTheta(c(1.46725, 0.5210227, 0.1587546))
ltest()
lsetTheta(c(0.2533617, 0.6969634, 0.5566632 ))
ltest()
}
cat("ok\n")
}
for (i in seq_along(rPDASs)) test.s(i)
test.kappaTau <- function(i) {
cat("test.kappaTau for", names(rPDs)[i],"...")
ltest <- function() {
expected <- rPDASTests[[i]]$kappa_e
actual <- rPDASs[[i]]$kappa_e()
stopifnot(all.equal(actual, expected, check.attributes = FALSE,
tolerance = 5e-6))
expected <- rPDASTests[[i]]$kappa_b
actual <- rPDASs[[i]]$kappa_b()
stopifnot(all.equal(actual, expected, check.attributes = FALSE,
tolerance = 5e-5))
}
ltest()
gc()
cat("ok\n")
}
for (i in seq_along(rPDASs)) test.kappaTau(i)
test.updateMatrices <- function(i) {
cat("test.updateMatrices for", names(rPDs)[i],"...")
ltest <- function() {
expected <- as.matrix(rPDASTests[[i]]$A)
actual <- rPDASs[[i]]$A()
stopifnot(all.equal(actual, expected, check.attributes = FALSE))
expected <- as.matrix(rPDASTests[[i]]$Kt)
actual <- rPDASs[[i]]$Kt()
stopifnot(all.equal(actual, expected, check.attributes = FALSE))
expected <- as.matrix(rPDASTests[[i]]$L)
actual <- rPDASs[[i]]$L()
stopifnot(all.equal(actual, expected, check.attributes = FALSE,
tolerance = 1e-6))
}
lsetTheta <- function(theta) {
rPDASs[[i]]$setTheta(theta)
rPDASTests[[i]]$setTheta(theta)
}
on.exit({
lsetTheta(getME(fms[[i]], "theta"))
gc()
})
ltest()
testBlocks(fms[[i]], ltest, lsetTheta)
if (i == 4) {
lsetTheta(c(1.46725, 0.5210227, 0.1587546))
ltest()
lsetTheta(c(0.2533617, 0.6969634, 0.5566632 ))
ltest()
}
cat("ok\n")
}
for (i in seq_along(rPDASs)) test.updateMatrices(i)
test.tau_e <- function(i) {
cat("test.tau_e for", names(rPDs)[i], "...")
ltest <- function() {
Tau <- with(rPDASTests[[i]], V_e - EDpsi_e * (t(A) + A) + Epsi2_e * tcrossprod(A) +
B() %*% tcrossprod(Epsi_bpsi_bt, B()))
expected <- sqrt(diag(Tau))
actual <- rPDASs[[i]]$tau_e()
stopifnot(all.equal(actual, expected, check.attributes = FALSE,
tolerance = 5e-6))
}
lsetTheta <- function(theta) {
rPDASs[[i]]$setTheta(theta)
rPDASTests[[i]]$setTheta(theta)
}
on.exit({
lsetTheta(getME(fms[[i]], "theta"))
gc()
})
ltest()
testBlocks(fms[[i]], ltest, lsetTheta)
if (i == 4) {
lsetTheta(c(1.46725, 0.5210227, 0.1587546))
ltest()
lsetTheta(c(0.2533617, 0.6969634, 0.5566632 ))
ltest()
}
cat("ok\n")
}
for (i in seq_along(rPDASs)) test.tau_e(i)
test.Tau_b <- function(i) {
cat("test.Tau_b for", names(rPDs)[i], "...")
ltest <- function() {
expected <- as.matrix(rPDASTests[[i]]$Tb())
actual <- as.matrix(rPDASs[[i]]$Tb())
stopifnot(all.equal(actual, expected, check.attributes = FALSE,
tolerance = 5e-5))
}
lsetTheta <- function(theta) {
rPDASs[[i]]$setTheta(theta)
rPDASTests[[i]]$setTheta(theta)
}
on.exit({
lsetTheta(getME(fms[[i]], "theta"))
gc()
})
ltest()
testBlocks(fms[[i]], ltest, lsetTheta)
if (i == 4) {
lsetTheta(c(1.46725, 0.5210227, 0.1587546))
ltest()
lsetTheta(c(0.2533617, 0.6969634, 0.5566632 ))
ltest()
}
cat("ok\n")
}
for (i in seq_along(rPDASs)) test.Tau_b(i) |
fpd <- function (state,phy) {
m<-sum(state)
nspecies<-length(state)
aa<-order(phy$edge[,1],decreasing=T)
phy$edge<-phy$edge[aa,]
phy$edge.length<-phy$edge.length[aa]
anc<-phy$edge[seq(from=1,by=2,length.out=length(phy$edge[,1])/2),1]
des<-matrix(phy$edge[,2],ncol=2,byrow=T)
DES<-matrix(0,phy$Nnode,nspecies)
for (i in 1:phy$Nnode) {
tmp<-des[i,]
offs<-which(anc %in% des[i,])
while (length(offs)>0) {
tmp<-c(tmp,des[offs,])
offs<-which(anc %in% des[offs,])
}
tmp<-tmp[tmp<=nspecies]
DES[i,tmp]<-1
}
DES<-rbind(diag(nspecies),DES)
BL<-phy$edge.length[c(order(phy$edge[,2])[1:nspecies],order(phy$edge[,2],decreasing=T)[-c((nspecies-1):(2*nspecies-2))])]
BL<-c(BL,0)
V<-crossprod(DES,DES*BL)
bmstate<-mvtnorm::rmvnorm(n=1000,sigma=V)
bmthred<-apply(bmstate,1,quantile,m/nspecies)
bmstate<-sweep(bmstate,1,bmthred,'<')
bmstate<-matrix(as.numeric(bmstate),1000,nspecies)
rdstate<-replicate(1000,sample(state,size=nspecies,replace=F))
rdstate<-t(rdstate)
calD <- function (state,anc,des,phy) {
I<-rep(NA,phy$Nnode)
I<-c(state,I)
for (i in 1:phy$Nnode) {
I[anc[i]]<-mean(I[des[i,]])
}
out<-I[des]
out<-abs(out[1:phy$Nnode]-out[(phy$Nnode+1):(2*phy$Nnode)])
sum(out)
}
obsD<-calD(state,anc,des,phy)
bmD<-apply(bmstate,1,calD,anc,des,phy)
rdD<-apply(rdstate,1,calD,anc,des,phy)
(obsD-mean(bmD))/(mean(rdD)-mean(bmD))
} |
structure(list(url = "https://api.scryfall.com/cards/search?q=asdf&unique=cards&order=name&dir=auto&include_extras=false&include_multilingual=false&include_variations=false",
status_code = 404L, headers = structure(list(date = "Wed, 05 Jan 2022 05:17:46 GMT",
`content-type` = "application/json; charset=utf-8", `x-frame-options` = "DENY",
`x-xss-protection` = "1; mode=block", `x-content-type-options` = "nosniff",
`x-download-options` = "noopen", `x-permitted-cross-domain-policies` = "none",
`referrer-policy` = "strict-origin-when-cross-origin",
`access-control-allow-origin` = "*", `access-control-allow-methods` = "GET, POST, DELETE, OPTIONS",
`access-control-allow-headers` = "Accept, Accept-Charset, Accept-Language, Authorization, Cache-Control, Content-Language, Content-Type, DNT, Host, If-Modified-Since, Keep-Alive, Origin, Referer, User-Agent, X-Requested-With",
`access-control-max-age` = "300", `x-robots-tag` = "none",
`cache-control` = "public, max-age=7200", `x-action-cache` = "MISS",
vary = "Accept-Encoding", `content-encoding` = "gzip",
`strict-transport-security` = "max-age=31536000; includeSubDomains; preload",
via = "1.1 vegur", `cf-cache-status` = "HIT", age = "5039",
`expect-ct` = "max-age=604800, report-uri=\"https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct\"",
`report-to` = "{\"endpoints\":[{\"url\":\"https:\\/\\/a.nel.cloudflare.com\\/report\\/v3?s=u%2BUnCP4pW5E702xi3kPH%2FZgBgRZy%2FyOW8hvvWYIPR1q9crsX0MMntgvnmKyI4pWNNsna210iqzUQQJPCFQ9bwCfIQ2CFx0e3OAOWrnFx%2FyPf8VN4bpXxH0Rrre16j3KdSS2PUY7Y5Cal%2Fr4gMwE%3D\"}],\"group\":\"cf-nel\",\"max_age\":604800}",
nel = "{\"success_fraction\":0,\"report_to\":\"cf-nel\",\"max_age\":604800}",
server = "cloudflare", `cf-ray` = "6c8a3dfc28674edd-GRU",
`alt-svc` = "h3=\":443\"; ma=86400, h3-29=\":443\"; ma=86400, h3-28=\":443\"; ma=86400, h3-27=\":443\"; ma=86400"), class = c("insensitive",
"list")), all_headers = list(list(status = 404L, version = "HTTP/2",
headers = structure(list(date = "Wed, 05 Jan 2022 05:17:46 GMT",
`content-type` = "application/json; charset=utf-8",
`x-frame-options` = "DENY", `x-xss-protection` = "1; mode=block",
`x-content-type-options` = "nosniff", `x-download-options` = "noopen",
`x-permitted-cross-domain-policies` = "none", `referrer-policy` = "strict-origin-when-cross-origin",
`access-control-allow-origin` = "*", `access-control-allow-methods` = "GET, POST, DELETE, OPTIONS",
`access-control-allow-headers` = "Accept, Accept-Charset, Accept-Language, Authorization, Cache-Control, Content-Language, Content-Type, DNT, Host, If-Modified-Since, Keep-Alive, Origin, Referer, User-Agent, X-Requested-With",
`access-control-max-age` = "300", `x-robots-tag` = "none",
`cache-control` = "public, max-age=7200", `x-action-cache` = "MISS",
vary = "Accept-Encoding", `content-encoding` = "gzip",
`strict-transport-security` = "max-age=31536000; includeSubDomains; preload",
via = "1.1 vegur", `cf-cache-status` = "HIT", age = "5039",
`expect-ct` = "max-age=604800, report-uri=\"https://report-uri.cloudflare.com/cdn-cgi/beacon/expect-ct\"",
`report-to` = "{\"endpoints\":[{\"url\":\"https:\\/\\/a.nel.cloudflare.com\\/report\\/v3?s=u%2BUnCP4pW5E702xi3kPH%2FZgBgRZy%2FyOW8hvvWYIPR1q9crsX0MMntgvnmKyI4pWNNsna210iqzUQQJPCFQ9bwCfIQ2CFx0e3OAOWrnFx%2FyPf8VN4bpXxH0Rrre16j3KdSS2PUY7Y5Cal%2Fr4gMwE%3D\"}],\"group\":\"cf-nel\",\"max_age\":604800}",
nel = "{\"success_fraction\":0,\"report_to\":\"cf-nel\",\"max_age\":604800}",
server = "cloudflare", `cf-ray` = "6c8a3dfc28674edd-GRU",
`alt-svc` = "h3=\":443\"; ma=86400, h3-29=\":443\"; ma=86400, h3-28=\":443\"; ma=86400, h3-27=\":443\"; ma=86400"), class = c("insensitive",
"list")))), cookies = structure(list(domain = ".api.scryfall.com",
flag = TRUE, path = "/", secure = FALSE, expiration = structure(1641440572, class = c("POSIXct",
"POSIXt")), name = "heroku-session-affinity", value = "REDACTED"), row.names = c(NA,
-1L), class = "data.frame"), content = charToRaw("{\n \"object\": \"error\",\n \"code\": \"not_found\",\n \"status\": 404,\n \"details\": \"Your query didn’t match any cards. Adjust your search terms or refer to the syntax guide at https://scryfall.com/docs/reference\"\n}"),
date = structure(1641359866, class = c("POSIXct", "POSIXt"
), tzone = "GMT"), times = c(redirect = 0, namelookup = 5.4e-05,
connect = 5.4e-05, pretransfer = 0.000165, starttransfer = 0.016477,
total = 0.016545)), class = "response") |
computeDistancesToNearestClusterCenter = function(cluster.centers) {
n = nrow(cluster.centers)
min.distance.idx = numeric(n)
max.distance.idx = numeric(n)
min.distance = numeric(n)
max.distance = numeric(n)
for (i in seq(n)) {
distances = apply(cluster.centers, 1, function(x) {
sqrt(sum((x - cluster.centers[i, ])^2))
})
distances[i] = Inf
min.distance.idx[i] = which.min(distances)
min.distance[i] = min(distances)
distances[i] = 0
max.distance[i] = max(distances)
max.distance.idx[i] = which.max(distances)
}
return(list(
min.distance = min.distance,
min.distance.idx = min.distance.idx,
max.distance = max.distance,
max.distance.idx = max.distance.idx
))
} |
pkg_ref_cache.license <- function(x, ...) {
UseMethod("pkg_ref_cache.license")
}
pkg_ref_cache.license.default <- function(x, ...) {
if ("License" %in% colnames(x$description)) unname(x$description[,"License"])
else NA_character_
}
pkg_ref_cache.license.pkg_cran_remote <- function(x, ...) {
license_xpath <- "//td[.='License:']/following::td[1]"
license_nodes <- xml_find_all(x$web_html, xpath = license_xpath)
xml_text(license_nodes)
}
pkg_ref_cache.license.pkg_bioc_remote <- function(x, ...) {
license_xpath <- "//td[.='License']/following::td[1]"
license_nodes <- xml_find_all(x$web_html, xpath = license_xpath)
xml_text(license_nodes)
} |
ComDim <- function(X, group, algo="eigen", ncompprint=NULL, scale="none", option="uniform", nstart=10, threshold=1e-8, plotgraph=TRUE, axes=c(1,2)){
if (any(is.na(X)))
stop("No NA values are allowed")
if (sum(group) != ncol(X))
stop("The sum of group must be equal to the total number of variables of in all the blocks")
if (!algo %in% c("eigen", "nipals"))
stop("algo must be either eigen or nipals")
if (is.character(scale)) {
if (!scale %in% c("none","sd"))
stop("scale must be either none or sd")
}
else {
if (!is.numeric(scale) | length(scale)!=ncol(X))
stop("Non convenient scaling parameter")
}
if (!option %in% c("none","uniform"))
stop("option must be either none or uniform")
X <- as.matrix(X)
if (is.null(rownames(X)))
rownames(X) <- paste("Ind.", seq_len(nrow(X)), sep="")
if (is.null(colnames(X)))
colnames(X) <- paste("X", seq_len(ncol(X)), sep=".")
if (is.null(names(group)))
names(group) <- paste("Block", 1:length(group), sep=" ")
ntab <- length(group);
nind <- nrow(X)
ncolX <- sum(group)
ncomp <- min(nind-1, ncolX)
if (is.null(ncompprint)) ncompprint=ncomp
if (ncompprint > ncomp) stop(cat("\n ncompprint should be less or equal to", ncomp,".\n"))
J <- rep(1:ntab,group)
critnstart <- matrix(0,nstart,ncomp)
niteration <- matrix(0,nstart,ncomp)
dimnames(critnstart) <- dimnames(niteration) <- list(paste("Iter.",1:nstart,sep=""), paste("Dim.",1:ncomp,sep=""))
optimalcrit <- vector("numeric",length=ncomp)
names(optimalcrit) <- paste("Dim.",1:ncomp,sep="")
contrib <- matrix(0,ntab,ncomp)
dimnames(contrib) <- list(names(group), paste("Dim.",1:ncomp,sep=""))
Trace <- vector("numeric",length=ntab)
criterion <- vector("numeric",length=ntab)
saliences <- LAMBDA <- NNLAMBDA <- matrix(1,ntab,ncomp)
dimnames(saliences) <-dimnames(LAMBDA) <- dimnames(NNLAMBDA) <- list(names(group), paste("Dim.",1:ncomp,sep=""))
T <- matrix(0,nrow=nind,ncol=ncomp)
C <- matrix(0,nrow=nind,ncol=ncomp)
dimnames(T) <- dimnames(C) <- list(rownames(X), paste("Dim.",1:ncomp,sep=""))
T.b <- array(0,dim=c(nind,ncomp,ntab))
dimnames(T.b)<- list(rownames(X), paste("Dim.",1:ncomp,sep=""), names(group))
cor.g.b <- array(0,dim=c(ncomp,ncomp,ntab))
dimnames(cor.g.b) <- list(paste("Dim.",1:ncomp,sep=""), paste("Dim.",1:ncomp,sep=""), names(group))
W.b <- vector("list",length=ntab)
blockcor <- vector("list",length=ntab)
for (j in 1:ntab) {
W.b[[j]] <- blockcor[[j]] <- matrix(0,nrow=group[j],ncol=ncomp)
dimnames(W.b[[j]]) <- dimnames(blockcor[[j]]) <- list(colnames(X[,J==j]), paste("Dim.",1:ncomp,sep=""))
}
Px <- matrix(0,nrow=ncolX,ncol=ncomp)
W <- matrix(0,nrow=ncolX,ncol=ncomp)
Wm <- matrix(0,nrow=ncolX,ncol=ncomp)
dimnames(Px) <- dimnames(W)<- dimnames(Wm) <- list(colnames(X), paste("Dim.",1:ncomp,sep=""))
IT.X <- vector("numeric",length=ntab+1)
explained.X <- matrix(0,nrow=ntab+1,ncol=ncomp)
dimnames(explained.X)<- list(c(names(group),'Global'), paste("Dim.",1:ncomp, sep=""))
cumexplained <- matrix(0,nrow=ncomp,ncol=2)
rownames(cumexplained) <- paste("Dim.",1:ncomp,sep="")
colnames(cumexplained) <- c("%explX", "cum%explX")
tb <- matrix(0,nrow=nind,ncol=ntab)
components <- vector("numeric",length=2)
Xscale <- NULL
Block <- NULL
res <- NULL
Xscale$mean <- apply(X, 2, mean)
X <-scale(X,center=Xscale$mean, scale=FALSE)
if (scale=="none") {
Xscale$scale <-rep(1,times=ncol(X))
} else {
if (scale=="sd") {
sd.tab <- apply(X, 2, function (x) {return(sqrt(sum(x^2)/length(x)))})
temp <- sd.tab < 1e-14
if (any(temp)) {
warning("Variables with null variance not standardized.")
sd.tab[temp] <- 1
}
X <- sweep(X, 2, sd.tab, "/")
Xscale$scale <-sd.tab
} else {
X <- sweep(X, 2, scale, "/")
Xscale$scale <-scale
}
}
if (option=="uniform") {
inertia <- sapply(1:ntab, function(j) inertie(X[, J==j]))
w.tab <- rep(sqrt(inertia) , times = group )
X <- sweep(X, 2, w.tab, "/")
Xscale$scale<- Xscale$scale*w.tab
}
IT.X[1:ntab] <- sapply(1:ntab, function(j) {inertie(X[,J==j]) })
IT.X[ntab+1] <- sum(IT.X[1:ntab])
X00=X
X <- lapply(seq_len(ntab),function(j) {as.matrix(X[,J==j])})
Trace <- sapply(seq_len(ntab), function(j) {sum(diag(tcrossprod(X[[j]])))})
X0=X
Itot <- 0
for (comp in 1:ncomp) {
if (algo=="eigen"){
critt <- 0
deltacrit <- 1
while(deltacrit>threshold) {
P <- matrix(0,nrow=nind,ncol=nind)
for(j in 1:ntab) {
P <- P + LAMBDA[j,comp]*tcrossprod(X[[j]])
}
reseig <- eigen(P)
t <- reseig$vectors[,1]
T[,comp]<- t
optimalcrit[comp] <- reseig$values[1]
LAMBDA[,comp] <- matrix(sapply(seq_len(ntab),function(j){t(t)%*%tcrossprod(X[[j]])%*%t}),nrow=ntab,byrow=FALSE)
LAMBDA[,comp] <- normv(LAMBDA[,comp])
criterion <- reseig$values[1]
deltacrit <- criterion- critt
critt <- criterion
}
} else {
critt.opt <- 0
for (i in seq_len(nstart)) {
index <- sample(1:ncolX,1)
t <- X00[,index]
t <- normv(abs(t))
critt <- 0
deltacrit <- 1
iNIPALS <- 0
while(deltacrit>threshold) {
tb <- matrix(unlist(lapply(seq_len(ntab),function(j){tcrossprod(X[[j]])%*%t})),nrow=nind,byrow=FALSE)
LAMBDA[,comp] <- matrix(sapply(seq_len(ntab),function(j){t(t)%*%tb[,j]}),nrow=ntab,byrow=FALSE)
LAMBDA[,comp] <- normv(LAMBDA[,comp])
t <- rowSums(t(t(tb)*LAMBDA[,comp]))
t <- normv(t)
criterion <- sum(sapply(seq_len(ntab),function(j){LAMBDA[j,comp]*t(t)%*%tb[,j]}))
deltacrit <- criterion- critt
critt <- criterion
iNIPALS <- iNIPALS + 1
}
if (critt > critt.opt) {
T[,comp] <- t
critt.opt <- critt
}
critnstart[i,comp] <- critt
niteration[i,comp] <- iNIPALS
}
optimalcrit[comp] <- max(critnstart[,comp])
}
for(j in 1:ntab) {
W.b[[j]][,comp] <- t(X[[j]])%*%T[,comp]
T.b[,comp,j] <- X[[j]]%*%W.b[[j]][,comp]
}
LAMBDA[,comp] <- matrix(sapply(seq_len(ntab),function(j){t(T[,comp] )%*%T.b[,comp,j]}),nrow=ntab,byrow=FALSE)
NNLAMBDA[,comp] <- LAMBDA[,comp]
LAMBDA[,comp] <- normv(LAMBDA[,comp])
Px[,comp] <- unlist(sapply(1:ntab, function(j){W.b[[j]][,comp]}))
W[,comp] <- unlist(sapply(1:ntab,function(j){LAMBDA[j,comp]*W.b[[j]][,comp]}))
W[,comp] <- normv(W[,comp])
X.exp <- lapply(X,function(Xj) {tcrossprod(T[,comp]) %*% Xj})
X0.exp <- lapply(X0,function(Xj){tcrossprod(T[,comp]) %*% Xj})
explained.X[1:ntab,comp] <- sapply(X0.exp,function(x) {sum(x^2)})
explained.X[ntab+1,comp] <- sum(explained.X[1:ntab,comp])
X <- lapply(seq_len(ntab),function(j) {X[[j]]-X.exp[[j]]})
}
explained.X <- sweep(explained.X[,1:ncomp,drop=FALSE] ,1,IT.X,"/")
cumexplained[,1] <- explained.X[ntab+1,1:ncomp]
cumexplained[,2] <- cumsum(cumexplained[,1])
contrib <- t(t(NNLAMBDA)/colSums(NNLAMBDA))
Wm <- W %*% solve(t(Px)%*%W,tol=1e-150)
if (ncomp==1) {
LambdaMoyen<-apply(NNLAMBDA^2,2,sum)
C=T*LambdaMoyen
}
else {
LambdaMoyen<-apply(NNLAMBDA^2,2,sum)
C=T%*%sqrt(diag(LambdaMoyen))
}
globalcor <- cor(X00, C)
for(j in 1:ntab) {
cor.g.b[,,j] <- cor(T, T.b[,,j])
blockcor[[j]] <- cor(X0[[j]],T.b[,1:ncompprint,j])
if (is.null(rownames(blockcor[[j]]))) rownames(blockcor[[j]]) <- names(group[j])
}
res$components <- c(ncomp=ncomp, ncompprint=ncompprint)
res$optimalcrit <- optimalcrit[1:ncompprint]
res$saliences <- round(LAMBDA[,1:ncompprint,drop=FALSE]^2,2)
res$T <- T[,1:ncompprint,drop=FALSE]
res$C <- C[,1:ncompprint,drop=FALSE]
res$explained.X <- round(100*explained.X[1:ntab,1:ncompprint],2)
res$cumexplained <- round(100*cumexplained[1:ncompprint,],2)
res$contrib <- round(100*contrib[1:ntab,1:ncompprint],2)
res$globalcor <- globalcor[,1:ncompprint]
res$cor.g.b <- cor.g.b[1:ncompprint,1:ncompprint,]
Block$T.b <- T.b[,1:ncompprint,]
Block$blockcor <- blockcor
res$Block <- Block
res$Xscale <- Xscale
res$call <- match.call()
class(res) <- c("ComDim","list")
if (plotgraph) {
plot.ComDim(res,graphtype="saliences", axes=axes)
plot.ComDim(res,graphtype="globalscores", axes=axes)
plot.ComDim(res,graphtype="globalcor", axes=axes)
plot.ComDim(res,graphtype="contrib", axes=axes)
}
return(res)
} |
getDefaultSystemOptions<-function()
{
return(options())
}
removeFunctionVariablesFromRAM<-function(){
print.and.log('cleaning workspace...','info')
if(exists("reference.data",envir = .QC))
if(is(.QC$reference.data, "SQLiteConnection"))
RSQLite::dbDisconnect(.QC$reference.data)
rm(.QC)
invisible(gc())
if(.QC$verbose)
{
cat('\n=============================================', fill = TRUE)
cat('============= FINISHED QC ===================', fill = TRUE)
cat('=============================================', fill = TRUE)
}
}
resetDefaultSystemOptions<-function(user.options)
{
options(user.options)
} |
context("Test fastNaiveBayes")
test_that("fastNaiveBayes wraps", {
real_probs <- matrix(c(
0.9535044256,
0.9999633454,
0.0009301696,
0.1435557348,
0.0009301696,
1 - 0.9535044256,
1 - 0.9999633454,
1 - 0.0009301696,
1 - 0.1435557348,
1 - 0.0009301696
), nrow = 5, ncol = 2)
y <- as.factor(c("Ham", "Ham", "Spam", "Spam", "Spam"))
x1 <- matrix(c(2, 3, 2, 4, 3), nrow = 5, ncol = 1)
colnames(x1) <- c("wo")
x2 <- matrix(c(1, 0, 1, 0, 1), nrow = 5, ncol = 1)
colnames(x2) <- c("no")
x3 <- matrix(c(2.8, 2.7, 3.0, 2.9, 3.0), nrow = 5, ncol = 1)
colnames(x3) <- c("go")
x <- cbind(x1, x2, x3)
col_names <- c("wo", "no", "go")
colnames(x) <- col_names
mixed_mod <- fnb.train(x, y, laplace = 0, sparse = FALSE)
fastNaiveBayesMod <- fastNaiveBayes(x, y, laplace = 0, sparse = FALSE)
expect_equal(mixed_mod, fastNaiveBayesMod)
}) |
context('basic bridge sampling behavior normal Rcpp parallel')
test_that("bridge sampler matches anlytical value normal example", {
testthat::skip_on_cran()
testthat::skip_on_travis()
library(mvtnorm)
if(require(RcppEigen)) {
x <- rmvnorm(1e4, mean = rep(0, 2), sigma = diag(2))
colnames(x) <- c("x1", "x2")
lb <- rep(-Inf, 2)
ub <- rep(Inf, 2)
names(lb) <- names(ub) <- colnames(x)
Rcpp::sourceCpp(file = "unnormalized_normal_density.cpp")
Rcpp::sourceCpp(file = "unnormalized_normal_density.cpp", env = .GlobalEnv)
bridge_normal <- bridge_sampler(samples = x, log_posterior = "log_densityRcpp",
data = NULL, lb = lb, ub = ub,
method = "normal", packages = "RcppEigen",
rcppFile = "unnormalized_normal_density.cpp",
cores = 2, silent = TRUE)
bridge_warp3 <- bridge_sampler(samples = x, log_posterior = "log_densityRcpp",
data = NULL, lb = lb, ub = ub,
method = "warp3", packages = "RcppEigen",
rcppFile = "unnormalized_normal_density.cpp",
cores = 2, silent = TRUE)
expect_equal(bridge_normal$logml, expected = log(2*pi), tolerance = 0.01)
expect_equal(bridge_warp3$logml, expected = log(2*pi), tolerance = 0.01)
mu <- c(1, 2)
x <- rmvnorm(1e4, mean = mu, sigma = diag(2))
colnames(x) <- c("x1", "x2")
lb <- rep(-Inf, 2)
ub <- rep(Inf, 2)
names(lb) <- names(ub) <- colnames(x)
Rcpp::sourceCpp(file = "unnormalized_normal_density_mu.cpp")
Rcpp::sourceCpp(file = "unnormalized_normal_density_mu.cpp", env = .GlobalEnv)
bridge_normal_dots <- bridge_sampler(samples = x, log_posterior = "log_densityRcpp_mu",
mu, data = NULL, lb = lb, ub = ub,
method = "normal", packages = "RcppEigen",
rcppFile = "unnormalized_normal_density_mu.cpp",
cores = 2, silent = TRUE)
bridge_warp3_dots <- bridge_sampler(samples = x, log_posterior = "log_densityRcpp_mu",
mu, data = NULL, lb = lb, ub = ub,
method = "warp3", packages = "RcppEigen",
rcppFile = "unnormalized_normal_density_mu.cpp",
cores = 2, silent = TRUE)
expect_equal(bridge_normal_dots$logml, expected = log(2*pi), tolerance = 0.01)
expect_equal(bridge_warp3_dots$logml, expected = log(2*pi), tolerance = 0.01)
}
}) |
LRstats <- function(object, ...) {
UseMethod("LRstats")
}
LRstats.glmlist <- function(object, ..., saturated = NULL, sortby=NULL)
{
ns <- sapply(object, function(x) length(x$residuals))
if (any(ns != ns[1L]))
stop("models were not all fitted to the same size of dataset")
nmodels <- length(object)
if (nmodels == 1)
return(LRstats.default(object[[1L]], saturated=saturated))
rval <- lapply(object, LRstats.default, saturated=saturated)
rval <- do.call(rbind, rval)
if (!is.null(sortby)) {
rval <- rval[order(rval[,sortby], decreasing=TRUE),]
}
rval
}
LRstats.loglmlist <- function(object, ..., saturated = NULL, sortby=NULL)
{
ns <- sapply(object, function(x) length(x$residuals))
if (any(ns != ns[1L]))
stop("models were not all fitted to the same size of dataset")
nmodels <- length(object)
if (nmodels == 1)
return(LRstats.default(object[[1L]], saturated=saturated))
rval <- lapply(object, LRstats.default, saturated=saturated)
rval <- do.call(rbind, rval)
if (!is.null(sortby)) {
rval <- rval[order(rval[,sortby], decreasing=TRUE),]
}
rval
}
LRstats.default <- function(object, ..., saturated = NULL, sortby=NULL)
{
logLik0 <- if("stats4" %in% loadedNamespaces()) stats4::logLik else logLik
nobs0 <- function(x, ...) {
nobs1 <- if("stats4" %in% loadedNamespaces()) stats4::nobs else nobs
nobs2 <- function(x, ...) NROW(residuals(x, ...))
rval <- try(nobs1(x, ...), silent = TRUE)
if(inherits(rval, "try-error") | is.null(rval)) rval <- nobs2(x, ...)
return(rval)
}
dof <- function(x) {
if (inherits(x, "loglm")) {
rval <- x$df
} else {
rval <- try(x$df.residual, silent=TRUE)
}
if (inherits(rval, "try-error") || is.null(rval)) stop(paste("Can't determine residual df for a", class(x), "object"))
rval
}
objects <- list(object, ...)
nmodels <- length(objects)
ns <- sapply(objects, nobs0)
if(any(ns != ns[1L])) stop("models were not all fitted to the same size of dataset")
ll <- lapply(objects, logLik0)
par <- as.numeric(sapply(ll, function(x) attr(x, "df")))
df <- as.numeric(sapply(objects, function(x) dof(x)))
ll <- sapply(ll, as.numeric)
if(is.null(saturated)) {
dev <- try(sapply(objects, deviance), silent = TRUE)
if(inherits(dev, "try-error") || any(sapply(dev, is.null))) {
saturated <- 0
} else {
saturated <- ll + dev/2
}
}
rval <- matrix(rep(NA, 5 * nmodels), ncol = 5)
colnames(rval) <- c("AIC", "BIC", "LR Chisq", "Df", "Pr(>Chisq)")
rownames(rval) <- as.character(sapply(match.call(), deparse)[-1L])[1:nmodels]
rval[,1] <- -2 * ll + 2 * par
rval[,2] <- -2 * ll + log(ns) * par
rval[,3] <- -2 * (ll - saturated)
rval[,4] <- df
rval[,5] <- pchisq(rval[,3], df, lower.tail = FALSE)
if (!is.null(sortby)) {
rval <- rval[order(rval[,sortby], decreasing=TRUE),]
}
structure(as.data.frame(rval), heading = "Likelihood summary table:", class = c("anova", "data.frame"))
} |
round_df <- function(x, digits) {
numeric_columns <- sapply(x, class) == 'numeric'
x[numeric_columns] <- round(x[numeric_columns], digits)
x
} |
siarproportionbygroupplot <-
function (siardata, siarversion = 0, probs = c(95, 75, 50), xlabels = NULL,
grp = NULL, type = "boxes", clr = gray((9:1)/10), scl = 1,
xspc = 0.5, prn = FALSE, leg = FALSE)
{
if (siardata$SHOULDRUN == FALSE && siardata$GRAPHSONLY ==
FALSE) {
cat("You must load in some data first (via option 1) in order to use \n")
cat("this feature of the program. \n")
cat("Press <Enter> to continue")
readline()
invisible()
return(NULL)
}
if (length(siardata$output) == 0) {
cat("No output found - check that you have run the SIAR model. \n \n")
return(NULL)
}
cat("Plot of proportions by group \n")
cat("Producing plot..... \n \n")
groupnames <- as.character(1:siardata$numgroups)
if (is.null(grp)) {
cat("Enter the group number you wish to plot \n")
cat("The choices are:\n")
title <- "The available options are:"
choose2 <- menu(groupnames)
}
else {
choose2 <- grp
}
groupseq <- seq(1, siardata$numsources, by = 1)
shift <- siardata$numsources + siardata$numiso
usepars <- siardata$output[, ((choose2-1)*(shift) + 1) : ((choose2-1)*(shift) + shift - siardata$numiso)]
newgraphwindow()
if (siardata$TITLE != "SIAR data") {
plot(1, 1, xlab = "Source", ylab = "Proportion", main = paste(siardata$TITLE,
" by group: ", groupnames[choose2], sep = ""),
xlim = c(min(groupseq) - xspc, max(groupseq) + xspc),
ylim = c(0, 1), type = "n", xaxt = "n")
if (is.null(xlabels)) {
axis(side = 1, at = min(groupseq):max(groupseq),
labels = (as.character(siardata$sources[,1])))
}
else {
axis(side = 1, at = min(groupseq):max(groupseq),
labels = (xlabels))
}
}
else {
plot(1, 1, xlab = "Source", ylab = "Proportion", main = paste("Proportions by group: ",
groupnames[choose2], sep = ""), xlim = c(min(groupseq) -
xspc, max(groupseq) + xspc), ylim = c(0, 1), type = "n",
xaxt = "n")
if (is.null(xlabels)) {
axis(side = 1, at = min(groupseq):max(groupseq),
labels = (as.character(siardata$sources[,1])))
}
else {
axis(side = 1, at = min(groupseq):max(groupseq),
labels = (xlabels))
}
}
if (siarversion > 0)
mtext(paste("siar v", siarversion), side = 1, line = 4,
adj = 1, cex = 0.6)
clrs <- rep(clr, 5)
for (j in 1:ncol(usepars)) {
temp <- hdr(usepars[, j], probs, h = bw.nrd0(usepars[,
j]))$hdr
line_widths <- seq(2, 20, by = 4) * scl
bwd <- c(0.1, 0.15, 0.2, 0.25, 0.3) * scl
if (prn == TRUE) {
cat(paste("Probability values for Group", j, "\n"))
}
for (k in 1:length(probs)) {
temp2 <- temp[k, ]
if (type == "boxes") {
polygon(c(groupseq[j] - bwd[k], groupseq[j] -
bwd[k], groupseq[j] + bwd[k], groupseq[j] +
bwd[k]), c(max(min(temp2[!is.na(temp2)]), 0),
min(max(temp2[!is.na(temp2)]), 1), min(max(temp2[!is.na(temp2)]),
1), max(min(temp2[!is.na(temp2)]), 0)), col = clrs[k])
}
if (type == "lines") {
lines(c(groupseq[j], groupseq[j]), c(max(min(temp2[!is.na(temp2)]),
0), min(max(temp2[!is.na(temp2)]), 1)), lwd = line_widths[k],
lend = 2)
}
if (prn == TRUE) {
cat(paste("\t", probs[k], "% lower =", format(max(min(temp2[!is.na(temp2)]),
0), digits = 2, scientific = FALSE), "upper =",
format(min(max(temp2[!is.na(temp2)]), 1), digits = 2,
scientific = FALSE), "\n"))
}
}
}
if (leg == TRUE) {
if (type == "lines") {
legnames <- character(length = length(probs))
for (i in 1:length(probs)) {
legnames[i] <- paste(probs[i], "%", sep = "")
}
legend(mean(c(min(groupseq), max(groupseq))), 1.02,
legend = legnames, lwd = c(2, 6, 10), ncol = length(probs),
xjust = 0.5, text.width = strwidth(legnames)/2,
bty = "n")
}
if (type == "boxes") {
print("Legends not yet supported for box style graph. Use type=lines with leg=TRUE instead.")
}
}
cat("Please maximise this graph before saving or printing. \n")
cat("Press <Enter> to continue")
readline()
invisible()
} |
test_that("plotYieldObservedVsModel works", {
local_edition(3)
params <- NS_params
expect_message(
expect_error(plotYieldObservedVsModel(params),
"You have not provided values"))
species_params(params)$yield_observed <-
c(0.8, 61, 12, 35, 1.6, 20, 10, 7.6, 135, 60, 30, 78)
params <- calibrateYield(params)
expect_message(dummy <- plotYieldObservedVsModel(params, return_data = T))
expect_equal(dummy$observed, species_params(params)$yield_observed[4:12])
expect_error(plotYieldObservedVsModel(params, species = rep(F, 12)),
"No species selected, please fix.")
params2 = params
species_params(params2)$yield_observed[c(1, 7, 10)] = NA
expect_message(dummy <- plotYieldObservedVsModel(params2, return_data = T))
expect_equal(as.character(dummy$species),
species_params(params)$species[c(4,5,6,8,9,11,12)])
expect_equal(dummy$observed,
species_params(params2)$yield_observed[c(4,5,6,8,9,11,12)])
expect_message(dummy <- plotYieldObservedVsModel(params2, return_data = T,
show_unobserved = TRUE))
expect_equal(as.character(dummy$species),
species_params(params)$species[4:12])
sp_select = c(4, 7, 10, 11, 12)
dummy <- plotYieldObservedVsModel(params, species = sp_select,
return_data = T)
expect_equal(nrow(dummy), length(sp_select))
expect_equal(dummy$observed,
species_params(params)$yield_observed[sp_select])
expect_message(p <- plotYieldObservedVsModel(params))
expect_true(is.ggplot(p))
expect_identical(p$labels$x, "observed yield [g/year]")
expect_identical(p$labels$y, "model yield [g/year]")
vdiffr::expect_doppelganger("plotYieldObservedVsModel", p)
}) |
covariance_commonControl <- function (aDataFrame,
control_ID,
X_t,
SD_t,
N_t,
X_c,
SD_c,
N_c,
metric = "RR") {
controlList <- split(aDataFrame, as.factor(aDataFrame[, control_ID]))
listV <- list(); dataAlignedWithV <- data.frame();
for(i in 1:length(controlList)) {
dataAlignedWithV <- rbind(dataAlignedWithV, controlList[[i]])
if(metric == "RR") {
covar <- (controlList[[i]][, SD_c] ^ 2) / (controlList[[i]][, N_c] * (controlList[[i]][, X_c] ^ 2))
var <- covar + (controlList[[i]][, SD_t] ^ 2) / (controlList[[i]][, N_t] * (controlList[[i]][, X_t] ^ 2))
}
V <- matrix(covar, nrow = length(var), ncol = length(var))
diag(V) <- var
listV <- unlist(list(listV, list(V)), recursive = FALSE)
}
V <- as.matrix(bdiag(listV))
return(list(V, dataAlignedWithV))
} |
mice_imputation_input_data_frame <- function(x, y, vname, trafo=NULL)
{
res <- mice_imputation_smcfcs_clean_input(x=x, y=y, state_data=NULL,
sm=NULL, vname=vname, trafo=trafo)
return(res)
} |
logt.error <-
function(parameters, values, probabilities, weights, degreesfreedom){
sum(weights * (pt((log(values) - parameters[1]) / exp(parameters[2]), degreesfreedom) - probabilities)^2)
} |
"_PACKAGE"
dt_vars <- c(
"Elapsed", "ElapsedAccum", "Km", "Vmax", "actual", "avg_decay_rate", "bestModRF", "channel",
"channels", "country", "cpa_total", "decay_accumulated", "decomp.rssd", "decompAbsScaled",
"decomp_perc", "decomp_perc_prev", "depVarHat", "dep_var", "ds", "dsMonthStart", "dsWeekStart",
"duration", "effect_share", "effect_share_refresh", "error_dis", "exposure", "halflife",
"holiday", "i.effect_share_refresh", "i.robynPareto", "i.solID", "i.spend_share_refresh",
"id", "initResponseUnit", "initResponseUnitTotal", "initSpendUnit", "iterNG", "iterPar",
"iterations", "label", "liftAbs", "liftEndDate", "liftMedia", "liftStart", "liftStartDate",
"mape", "mape.qt10", "mape_lift", "mean_response", "mean_spend", "mean_spend_scaled",
"models", "next_unit_response", "nrmse", "optmResponseUnit", "optmResponseUnitTotal",
"optmResponseUnitTotalLift", "optmSpendUnit", "optmSpendUnitTotalDelta", "param",
"perc", "percentage", "pos", "predicted", "refreshStatus", "response", "rn", "robynPareto",
"roi", "roi_mean", "roi_total", "rsq_lm", "rsq_nls", "rsq_train", "s0", "scale_shape_halflife",
"season", "shape", "solID", "spend", "spend_share", "spend_share_refresh", "sid",
"theta", "theta_halflife", "total_spend", "trend", "trial", "type", "value", "variable",
"weekday", "x", "xDecompAgg", "xDecompMeanNon0", "xDecompMeanNon0Perc",
"xDecompMeanNon0PercRF", "xDecompMeanNon0RF", "xDecompPerc", "xDecompPercRF", "y", "yhat",
"respN","iteration","variables","iter_bin", "thetas", "cut_time", "exposure_vars", "OutputModels",
"exposure_pred"
)
if (getRversion() >= "2.15.1") {
globalVariables(c(".", dt_vars))
} |
hlp_to_py_mat <- function(x) {
z <- paste0("[", apply(x, 1, paste0, collapse = ", "), "]")
z <- paste0("Matrix([", paste0(z, collapse = ", "), "])")
return(z)
}
hlp_to_py_vec <- function(x) {
z <- paste0("Matrix([", paste0(x, collapse = ", "), "])")
return(z)
}
hlp_to_py_scalar <- function(x) {
z <- as.character(x)
return(z)
}
as_py_string <- function(x) {
if (is.matrix(x)) {
return(hlp_to_py_mat(x))
}
if (is.vector(x) && length(x) == 1L) {
return(hlp_to_py_scalar(x))
}
return(hlp_to_py_vec(x))
}
as_sym <- function(x,
declare_symbols = TRUE) {
ensure_sympy()
if (is.expression(x)) {
x <- as.character(x)
}
varnames_exclude <- c("S", "sqrt", "log", "I", "exp", "sin", "cos", "Matrix", "Function")
if (declare_symbols) {
xele <- as.vector(x)
m <- gregexpr(pattern = PATTERN_PYHTON_VARIABLE,
text = xele)
varnames <- regmatches(x = xele, m = m, invert = FALSE)
varnames <- unique(unlist(varnames[unlist(lapply(varnames, length)) > 0]))
varnames <- setdiff(varnames, varnames_exclude)
for (varname in varnames) {
cmd <- paste0(varname, " = symbols('", varname, "')")
reticulate::py_run_string(cmd, convert = FALSE)
}
}
if (is.character(x) && length(x) == 1L && grepl("^\\[\\[", x)) {
x <- paste0("Matrix(", r_strings_to_python(x), ")")
y <- eval_to_symbol(x)
return(y)
}
cmd <- as_py_string(x)
y <- eval_to_symbol(cmd)
return(y)
} |
"seattlepets" |
lambda_j_Exp <- function(tau, time, event){
k <- length(tau)
aj <- rep(NA, k + 1)
tau <- c(0, tau, Inf)
hess.diag <- aj
for (j in 2:(k + 2)){
Xtj <- sum(event[time <= tau[j]])
Xtj1 <- sum(event[time <= tau[j - 1]])
diff <- Xtj - Xtj1
nenner <- sum((pmin(time, tau[j]) - tau[j - 1]) * (time > tau[j - 1]))
aj[j - 1] <- diff / nenner
hess.diag[j - 1] <- - diff / aj[j - 1] ^ 2
}
res <- list("aj" = aj, "hess.diag" = hess.diag)
return(res)
} |
require('SoilR')
Result<-setClass(
"Result",
slots=c(
noException="list",
notRunning="list",
tested="list"
),
prototype=list(
noException=list(),
notRunning=list(),
tested=list()
)
)
test.check.pass=function(){
X <- lsf.str('package:SoilR')
has_pass <-function(x){"pass" %in% names(formals(x))}
ind=as.vector(sapply(X,has_pass))
Xpass=X[ind]
passCaller <- function(c,l){
cl=as.list(c)
name=cl[[1]]
print(cl)
if(class(try(eval(c),silent=T))!="try-error"){
l@noException<- append(l@noException,name)
}
c2 <- as.call(append(cl,expression(pass=TRUE)))
if(class(try(eval(c2),silent=T))=="try-error"){
l@notRunning=append(l@notRunning,name)
}
l@tested=append(l@tested,name)
return(l)
}
l=Result()
load("../../data/C14Atm_NH.rda")
load("../../data/HarvardForest14CO2.rda")
years=seq(1801,2010,by=0.5)
l=passCaller(call("GaudinskiModel14",t=years,ks=c(kr=1/3,koi=1/1.5,koeal=1/4,koeah=1/80,kA1=1/3,kA2=1/75,kM=1/110),inputFc=C14Atm_NH),l)
t_start=0
t_end=10
tn=50
timestep=(t_end-t_start)/tn
t=seq(t_start,t_end,timestep)
t_fault=seq(t_start-10,t_end,timestep)
n=3
At=new("BoundLinDecompOp",
t_start,
t_end,
function(t0){
matrix(nrow=n,ncol=n,byrow=TRUE,
c(-0.2, 0, 0,
0 , -0.3, 0,
0, 0, -0.4)
)
}
)
c0=c(0.5, 0.5, 0.5)
inputFluxes=TimeMap.new(
t_start,
t_end,
function(t0){matrix(nrow=n,ncol=1,c(0.0,0,0))}
)
l=passCaller(call("GeneralModel",t_fault,At,c0,inputFluxes),l)
t_start=1960
t_end=2010
tn=220
timestep=(t_end-t_start)/tn
t=seq(t_start,t_end,timestep)
t_fault=seq(t_start-10,t_end,timestep)
n=3
At=new(Class="BoundLinDecompOp",
t_start,
t_end,
function(t0){
matrix(nrow=n,ncol=n,byrow=TRUE,
c(-1, 0.1, 0,
0.5 , -0.4, 0,
0, 0.2, -0.1)
)
}
)
c0=c(100, 100, 100)
F0=ConstFc(c(0,10,10),"Delta14C")
inputFluxes=new(
"TimeMap",
t_start,
t_end,
function(t0){matrix(nrow=n,ncol=1,c(10,10,10))}
)
load("../../data/C14Atm_NH.rda")
Fc=BoundFc(C14Atm_NH,format="Delta14C")
th=5730
k=log(0.5)/th
l=passCaller(call("Model_14",t=t_fault,A=At,ivList=c0,initialValF=F0,inputFluxes=inputFluxes,inputFc=Fc,c14DecayRate=k),l)
t_start=1960
t_end=2010
tn=220
timestep=(t_end-t_start)/tn
t=seq(t_start,t_end,timestep)
t_fault=seq(t_start-10,t_end,timestep)
n=3
At=new(Class="BoundLinDecompOp",
t_start,
t_end,
function(t0){
matrix(nrow=n,ncol=n,byrow=TRUE,
c(-1, 0.1, 0,
0.5 , -0.4, 0,
0, 0.2, -0.1)
)
}
)
c0=c(100, 100, 100)
F0=ConstFc(c(0,10,10),"Delta14C")
inputFluxes=new(
"TimeMap",
t_start,
t_end,
function(t0){matrix(nrow=n,ncol=1,c(10,10,10))}
)
load("../../data/C14Atm_NH.rda")
Fc=BoundFc(C14Atm_NH,format="Delta14C")
th=5730
k=log(0.5)/th
l=passCaller(call("GeneralModel_14",t=t_fault,A=At,ivList=c0,initialValF=F0,inputFluxes=inputFluxes,inputFc=Fc,di=k),l)
t_start=0
t_end=10
tn=3e1
timestep=(t_end-t_start)/tn
t=seq(t_start,t_end,timestep)
t_fault=seq(t_start-10,t_end,timestep)
nr=2
alpha=list()
alpha[["1_to_2"]]=function(C,t){
1
}
k1=3/5
k2=3/5
f=function(C,t){
N=matrix(
nrow=nr,
ncol=nr,
c(
k1, 0,
0 , k2
)
)
return(N%*%C)
}
fac=2e2
inputrates=new("TimeMap",t_start,t_end,function(t){return(matrix(
nrow=nr,
rep(
c(
2*fac, 0*fac
),
length(t)
)
))})
c0= c( fac, 0 )
A=new("TransportDecompositionOperator",t_start,t_end,nr,alpha,f)
l=passCaller(call("GeneralNlModel",t_fault,A,c0,inputrates),l)
times=seq(0,20,by=0.1)
ks=c(k1=-0.8,k2=0.00605)
l=passCaller(call("ICBMModel",t=times, ks=ks,h=0.125, r=1, c0=c(0.3,4.11), In=0.19+0.095),l)
t_start=0
t_end=10
tn=50
timestep=(t_end-t_start)/tn
t=seq(t_start,t_end,timestep)
t_fault=seq(t_start-10,t_end,timestep)
In=data.frame(t,rep(100,length(t)))
t=seq(t_start,t_end,timestep)
k=0.8
C0=100
l=passCaller(call("OnepModel",t_fault,k,C0,In),l)
load("../../data/C14Atm_NH.rda")
years=seq(1901,2009,by=0.5)
years_fault=seq(1901,2019,by=0.5)
LitterInput=700
l=passCaller(call("OnepModel14",t=years_fault,k=1/10,C0=500, F0=0,In=LitterInput, inputFc=C14Atm_NH),l)
t_start=0
t_end=10
tn=50
timestep=(t_end-t_start)/tn
t=seq(t_start,t_end,timestep)
k=TimeMap.new(t_start,t_end,function(times){c(-0.5,-0.2,-0.3)})
c0=c(1, 2, 3)
inputrates=TimeMap.new(
t_start+10,
t_end,
function(t){matrix(nrow=3,ncol=1,c(1,1,1))}
)
l=passCaller(call("ParallelModel",t,k,c0,inputrates),l)
t=0:500
In=data.frame(t,rep(1.7,length(t)))
FYM=data.frame(t,rep(1.7,length(t)))
t_faulty=0:600
l=passCaller(call("RothCModel",t_faulty,In=In,FYM=FYM),l)
t_start=0
t_end=10
tn=50
timestep=(t_end-t_start)/tn
t=seq(t_start,t_end,timestep)
t_fault=seq(t_start-10,t_end,timestep)
ks=c(k1=0.8,k2=0.4,k3=0.2)
C0=c(C10=100,C20=150, C30=50)
In = 60
Temp=rep(15,length(t))
TempEffect=data.frame(t,fT.Daycent1(Temp))
l=passCaller(call("ThreepFeedbackModel",t=t_fault,ks=ks,a21=0.5,a12=0.1,a32=0.2,a23=0.1,C0=C0,In=In,xi=TempEffect),l)
l=passCaller(call("SeriesLinearModel",t=t_fault,ki=ks,m.pools=3,Tij=c(0.5,0.2),C0=C0,In=In,xi=TempEffect),l)
load("../../data/C14Atm_NH.rda")
years=seq(1901,2009,by=0.5)
years_fault=seq(1901,2019,by=0.5)
LitterInput=700
l=passCaller(
call("ThreepFeedbackModel14",t=years_fault,ks=c(k1=1/2.8, k2=1/35, k3=1/100),C0=c(200,5000,500), F0_Delta14C=c(0,0,0),In=LitterInput, a21=0.1,a12=0.01,a32=0.005,a23=0.001,inputFc=C14Atm_NH),
l
)
l=passCaller( call("SeriesLinearModel14",t=years_fault,ki=c(k1=1/2.8, k2=1/35, k3=1/100),C0=c(200,5000,500),m.pools=3, F0_Delta14C=c(0,0,0),In=LitterInput, Tij=c(0.5,0.1),inputFc=C14Atm_NH), l)
t_start=0
t_end=10
tn=50
timestep=(t_end-t_start)/tn
t=seq(t_start,t_end,timestep)
t_fault=seq(t_start-10,t_end,timestep)
In=data.frame(t,rep(1.7,length(t)))
l=passCaller(call("ThreepParallelModel",t=t_fault,ks=c(k1=0.5,k2=0.2,k3=0.1),C0=c(c10=100, c20=150,c30=50),In=In,gam1=0.7,gam2=0.1,xi=0.5),l)
load("../../data/C14Atm_NH.rda")
years=seq(1901,2009,by=0.5)
years_fault=seq(1901,2019,by=0.5)
LitterInput=700
l=passCaller(call("ThreepParallelModel14",t=years_fault,ks=c(k1=1/2.8, k2=1/35, k3=1/100),C0=c(200,5000,500), F0_Delta14C=c(0,0,0),In=LitterInput, gam1=0.7, gam2=0.1, inputFc=C14Atm_NH,lag=2),l)
load("../../data/C14Atm_NH.rda")
years=seq(1901,2009,by=0.5)
years_fault=seq(1901,2019,by=0.5)
LitterInput=700
l=passCaller( call("ThreepFeedbackModel14",t=years_fault,ks=c(k1=1/2.8, k2=1/35, k3=1/100),C0=c(200,5000,500), F0_Delta14C=c(0,0,0),In=LitterInput, a21=0.1,a12=0.01,a32=0.005,a23=0.001,inputFc=C14Atm_NH), l)
t_start=0
t_end=10
tn=50
timestep=(t_end-t_start)/tn
t=seq(t_start,t_end,timestep)
t_fault=seq(t_start-10,t_end,timestep)
ks=c(k1=0.8,k2=0.4,k3=0.2)
C0=c(C10=100,C20=150, C30=50)
In=data.frame(t,rep(50,length(t)))
l=passCaller(call("ThreepSeriesModel",t=t_fault,ks=ks,a21=0.5,a32=0.2,C0=C0,In=In,xi=fT.Q10(15)),l)
load("../../data/C14Atm_NH.rda")
years=seq(1901,2009,by=0.5)
years_fault=seq(1901,2019,by=0.5)
LitterInput=700
l=passCaller( call("ThreepSeriesModel14",t=years_fault,ks=c(k1=1/2.8, k2=1/35, k3=1/100),C0=c(200,5000,500), F0_Delta14C=c(0,0,0),In=LitterInput, a21=0.1, a32=0.01,inputFc=C14Atm_NH), l)
times=seq(0,20,by=0.1)
times_fault=seq(0,30,by=0.1)
ks=c(k1=0.8,k2=0.00605)
C0=c(C10=5,C20=5)
Temp=rnorm(times,15,2)
WC=runif(times,10,20)
TempEffect=data.frame(times,fT=fT.Daycent1(Temp))
MoistEffect=data.frame(times, fW=fW.Daycent2(WC)[2])
Inmean=1
InSin=data.frame(times,Inmean+0.5*sin(times*pi*2))
l=passCaller(call("TwopParallelModel",t=times_fault,ks=ks,C0=C0,In=InSin,gam=0.9, xi=(fT.Daycent1(15)*fW.Demeter(15))),l)
l=passCaller(call("TwopSeriesModel",t=times_fault,ks=ks,a21=0.2*ks[1],C0=C0,In=InSin, xi=(fT.Daycent1(15)*fW.Demeter(15))),l)
l=passCaller(call("TwopFeedbackModel",t=times_fault,ks=ks,a21=0.2*ks[1],a12=0.5*ks[2],C0=C0, In=InSin,xi=MoistEffect),l)
load("../../data/C14Atm_NH.rda")
years=seq(1901,2009,by=0.5)
years_fault=seq(1901,2019,by=0.5)
LitterInput=data.frame(years,rep(700,length(years)))
l=passCaller(call("TwopFeedbackModel14",t=years_fault,ks=c(k1=1/2.8, k2=1/35),C0=c(200,5000), F0_Delta14C=c(0,0),In=LitterInput, a21=0.1,a12=0.01,inputFc=C14Atm_NH),l)
load("../../data/C14Atm_NH.rda")
years=seq(1901,2009,by=0.5)
years_fault=seq(1901,2019,by=0.5)
LitterInput=data.frame(years,rep(700,length(years)))
l=passCaller(call("TwopParallelModel14",t=years_fault,ks=c(k1=1/2.8, k2=1/35),C0=c(200,5000), F0_Delta14C=c(0,0),In=LitterInput, gam=0.7,inputFc=C14Atm_NH,lag=2),l)
load("../../data/C14Atm_NH.rda")
years=seq(1901,2009,by=0.5)
years_fault=seq(1901,2019,by=0.5)
LitterInput=data.frame(years,rep(700,length(years)))
l=passCaller(call("TwopSeriesModel14",t=years_fault,ks=c(k1=1/2.8, k2=1/35),C0=c(200,5000), F0_Delta14C=c(0,0),In=LitterInput, a21=0.1,inputFc=C14Atm_NH),l)
years=seq(0,50,0.1)
years_fault=seq(0,60,0.1)
C0=rep(100,7)
xi=data.frame(years,rep(1,length(years)))
l=passCaller(call("YassoModel",t=years_fault,C0=C0,xi=xi),l)
years=seq(0,50,0.1)
years_fault=seq(0,60,0.1)
C0=rep(100,5)
LitterInput=data.frame(years,rep(0,length(years)))
l=passCaller(call("Yasso07Model",t=years_fault,C0=C0,In=LitterInput),l)
years=seq(0,50,0.1)
years_fault=seq(0,60,0.1)
n=5
C0=rep(100,n)
LitterInput<-BoundInFluxes(data.frame(years,rep(0,length(years))))
op<-ConstLinDecompOp(diag(rep(-1,n)))
l<-passCaller(call("Model",A=op,t=years_fault,ivList=C0,inputFluxes=LitterInput),l)
st=FALSE
if (length(setdiff(Xpass,l@tested))!=0){
cat(
paste("not all functions using the pass argument have been tested. \nThe untested functions are:\n",
paste(setdiff(Xpass,l@tested),collapse=", "),
"\n\n"
)
)
st<-TRUE
}
if (length(l@noException)>0){
cat(
paste(
"Not all functions produce the required exception for wrong input if called without the pass argument.\nThe funtions are:\n",
paste(l@noException,collapse=", "),
"\n\n"
)
)
st<-TRUE
}
if (length(l@notRunning)>0){
cat(
paste("Not all functions can be made run with corrupted input by calling them with the pass argument.\nThe funtions are:\n",
paste(unlist(l@notRunning),collapse=", "),
"\n\n"
)
)
st<-TRUE
}
if (st){stop()}
} |
context('simple_slopes function')
library(nlme)
library(lme4)
get_coef <- function(model, row, digits=3) {
return(round(coef(model)[row, 1], digits))
}
get_coef.lme <- function(model, row, digits=3) {
return(round(model$tTable[row, 1], digits))
}
test_that('lm with 2 continuous int. works', {
set.seed(123)
x1 <- rnorm(100)
set.seed(234)
x2 <- rnorm(100)
set.seed(345)
y <- x1 * x2 + rnorm(100)
data <- data.frame(x1, x2, y)
model <- lm(y ~ x1 * x2, data)
slopes <- simple_slopes(model)
model_x1_m1 <- summary(lm(y ~ I((x1 - mean(x1)) + sd(x1)) * x2, data))
expect_equal(round(slopes[1, 'Test Estimate'], 3),
get_coef(model_x1_m1, 'x2'))
model_x1_0 <- summary(lm(y ~ I(x1 - mean(x1)) * x2, data))
expect_equal(round(slopes[2, 'Test Estimate'], 3),
get_coef(model_x1_0, 'x2'))
model_x1_p1 <- summary(lm(y ~ I((x1 - mean(x1)) - sd(x1)) * x2, data))
expect_equal(round(slopes[3, 'Test Estimate'], 3),
get_coef(model_x1_p1, 'x2'))
model_x2_m1 <- summary(lm(y ~ x1 * I((x2 - mean(x2)) + sd(x2)), data))
expect_equal(round(slopes[4, 'Test Estimate'], 3),
get_coef(model_x2_m1, 'x1'))
model_x2_0 <- summary(lm(y ~ x1 * I(x2 - mean(x2)), data))
expect_equal(round(slopes[5, 'Test Estimate'], 3),
get_coef(model_x2_0, 'x1'))
model_x2_p1 <- summary(lm(y ~ x1 * I((x2 - mean(x2)) - sd(x2)), data))
expect_equal(round(slopes[6, 'Test Estimate'], 3),
get_coef(model_x2_p1, 'x1'))
})
test_that('lm with continuous x 2-level categorical int. works', {
set.seed(123)
x1 <- rnorm(100)
x2 <- c(rep(0, 50), rep(1, 50))
set.seed(345)
y <- x1 * x2 + rnorm(100)
x2 <- factor(x2)
data <- data.frame(x1, x2, y)
model <- lm(y ~ x1 * x2, data)
slopes <- simple_slopes(model)
model_x1_m1 <- summary(lm(y ~ I((x1 - mean(x1)) + sd(x1)) * x2, data))
expect_equal(round(slopes[1, 'Test Estimate'], 3),
get_coef(model_x1_m1, 'x21'))
model_x1_0 <- summary(lm(y ~ I(x1 - mean(x1)) * x2, data))
expect_equal(round(slopes[2, 'Test Estimate'], 3),
get_coef(model_x1_0, 'x21'))
model_x1_p1 <- summary(lm(y ~ I((x1 - mean(x1)) - sd(x1)) * x2, data))
expect_equal(round(slopes[3, 'Test Estimate'], 3),
get_coef(model_x1_p1, 'x21'))
model_x2_0 <- summary(lm(y ~ x1 * x2, data))
expect_equal(round(slopes[4, 'Test Estimate'], 3),
get_coef(model_x2_0, 'x1'))
contrasts(data$x2) <- c(1, 0)
model_x2_1 <- summary(lm(y ~ x1 * x2, data))
expect_equal(round(slopes[5, 'Test Estimate'], 3),
get_coef(model_x2_1, 'x1'))
})
test_that('lm with continuous x 3-level categorical int. works', {
set.seed(123)
x1 <- rnorm(150)
x2 <- c(rep(0, 50), rep(1, 50), rep(2, 50))
set.seed(345)
y <- x1 * x2 + rnorm(150)
x2 <- factor(x2)
data <- data.frame(x1, x2, y)
model <- lm(y ~ x1 * x2, data)
slopes <- simple_slopes(model)
model_x1_m1 <- summary(lm(y ~ I((x1 - mean(x1)) + sd(x1)) * x2, data))
expect_equal(round(slopes[1, 'Test Estimate'], 3),
get_coef(model_x1_m1, 'x21'))
expect_equal(round(slopes[2, 'Test Estimate'], 3),
get_coef(model_x1_m1, 'x22'))
model_x1_0 <- summary(lm(y ~ I(x1 - mean(x1)) * x2, data))
expect_equal(round(slopes[3, 'Test Estimate'], 3),
get_coef(model_x1_0, 'x21'))
expect_equal(round(slopes[4, 'Test Estimate'], 3),
get_coef(model_x1_0, 'x22'))
model_x1_p1 <- summary(lm(y ~ I((x1 - mean(x1)) - sd(x1)) * x2, data))
expect_equal(round(slopes[5, 'Test Estimate'], 3),
get_coef(model_x1_p1, 'x21'))
expect_equal(round(slopes[6, 'Test Estimate'], 3),
get_coef(model_x1_p1, 'x22'))
model_x2_0 <- summary(lm(y ~ x1 * x2, data))
expect_equal(round(slopes[7, 'Test Estimate'], 3),
get_coef(model_x2_0, 'x1'))
contrasts(data$x2) <- matrix(c(1, 0, 0, 0, 0, 1), nrow=3)
model_x2_1 <- summary(lm(y ~ x1 * x2, data))
expect_equal(round(slopes[8, 'Test Estimate'], 3),
get_coef(model_x2_1, 'x1'))
contrasts(data$x2) <- matrix(c(1, 0, 0, 0, 1, 0), nrow=3)
model_x2_1 <- summary(lm(y ~ x1 * x2, data))
expect_equal(round(slopes[9, 'Test Estimate'], 3),
get_coef(model_x2_1, 'x1'))
})
test_that('lm with 3 continuous int. works', {
get_model <- function(data, points, test) {
if (is.na(points[1])) {
model <- lm(y ~ x1 * I(x2 - points[2]) * I(x3 - points[3]), data)
} else if (is.na(points[2])) {
model <- lm(y ~ I(x1 - points[1]) * x2 * I(x3 - points[3]), data)
} else {
model <- lm(y ~ I(x1 - points[1]) * I(x2 - points[2]) * x3, data)
}
return(get_coef(summary(model), test))
}
set.seed(123)
x1 <- rnorm(100)
set.seed(234)
x2 <- rnorm(100)
set.seed(345)
x3 <- rnorm(100)
set.seed(456)
y <- x1 * x2 * x3 + rnorm(100)
data <- data.frame(x1, x2, x3, y)
model <- lm(y ~ x1 * x2 * x3, data)
slopes <- simple_slopes(model)
pts <- list(
x1=c(mean(x1) - sd(x1), mean(x1), mean(x1) + sd(x1)),
x2=c(mean(x2) - sd(x2), mean(x2), mean(x2) + sd(x2)),
x3=c(mean(x3) - sd(x3), mean(x3), mean(x3) + sd(x3))
)
expect_equal(round(slopes[1, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][1], pts[['x2']][1], NA), 'x3'))
expect_equal(round(slopes[2, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][2], pts[['x2']][1], NA), 'x3'))
expect_equal(round(slopes[3, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][3], pts[['x2']][1], NA), 'x3'))
expect_equal(round(slopes[4, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][1], pts[['x2']][2], NA), 'x3'))
expect_equal(round(slopes[5, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][2], pts[['x2']][2], NA), 'x3'))
expect_equal(round(slopes[6, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][3], pts[['x2']][2], NA), 'x3'))
expect_equal(round(slopes[7, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][1], pts[['x2']][3], NA), 'x3'))
expect_equal(round(slopes[8, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][2], pts[['x2']][3], NA), 'x3'))
expect_equal(round(slopes[9, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][3], pts[['x2']][3], NA), 'x3'))
expect_equal(round(slopes[10, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][1], NA, pts[['x3']][1]), 'x2'))
expect_equal(round(slopes[11, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][2], NA, pts[['x3']][1]), 'x2'))
expect_equal(round(slopes[12, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][3], NA, pts[['x3']][1]), 'x2'))
expect_equal(round(slopes[13, 'Test Estimate'], 3),
get_model(data, c(NA, pts[['x2']][1], pts[['x3']][1]), 'x1'))
expect_equal(round(slopes[14, 'Test Estimate'], 3),
get_model(data, c(NA, pts[['x2']][2], pts[['x3']][1]), 'x1'))
expect_equal(round(slopes[15, 'Test Estimate'], 3),
get_model(data, c(NA, pts[['x2']][3], pts[['x3']][1]), 'x1'))
expect_equal(round(slopes[16, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][1], NA, pts[['x3']][2]), 'x2'))
expect_equal(round(slopes[17, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][2], NA, pts[['x3']][2]), 'x2'))
expect_equal(round(slopes[18, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][3], NA, pts[['x3']][2]), 'x2'))
expect_equal(round(slopes[19, 'Test Estimate'], 3),
get_model(data, c(NA, pts[['x2']][1], pts[['x3']][2]), 'x1'))
expect_equal(round(slopes[20, 'Test Estimate'], 3),
get_model(data, c(NA, pts[['x2']][2], pts[['x3']][2]), 'x1'))
expect_equal(round(slopes[21, 'Test Estimate'], 3),
get_model(data, c(NA, pts[['x2']][3], pts[['x3']][2]), 'x1'))
expect_equal(round(slopes[22, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][1], NA, pts[['x3']][3]), 'x2'))
expect_equal(round(slopes[23, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][2], NA, pts[['x3']][3]), 'x2'))
expect_equal(round(slopes[24, 'Test Estimate'], 3),
get_model(data, c(pts[['x1']][3], NA, pts[['x3']][3]), 'x2'))
expect_equal(round(slopes[25, 'Test Estimate'], 3),
get_model(data, c(NA, pts[['x2']][1], pts[['x3']][3]), 'x1'))
expect_equal(round(slopes[26, 'Test Estimate'], 3),
get_model(data, c(NA, pts[['x2']][2], pts[['x3']][3]), 'x1'))
expect_equal(round(slopes[27, 'Test Estimate'], 3),
get_model(data, c(NA, pts[['x2']][3], pts[['x3']][3]), 'x1'))
})
test_that('binomial glm with interaction works', {
set.seed(123)
x1 <- rnorm(100)
set.seed(234)
x2 <- rnorm(100)
set.seed(345)
rand <- rnorm(100)
y <- as.numeric((x1 > mean(x1) | x2 > mean(x2)) & rand > mean(rand))
data <- data.frame(x1, x2, y)
model <- glm(y ~ x1 * x2, family='binomial', data)
slopes <- simple_slopes(model)
model_x1_m1 <- summary(glm(y ~ I((x1 - mean(x1)) + sd(x1)) * x2,
family='binomial', data))
expect_equal(round(slopes[1, 'Test Estimate'], 3),
get_coef(model_x1_m1, 'x2'))
model_x1_0 <- summary(glm(y ~ I(x1 - mean(x1)) * x2,
family='binomial', data))
expect_equal(round(slopes[2, 'Test Estimate'], 3),
get_coef(model_x1_0, 'x2'))
model_x1_p1 <- summary(glm(y ~ I((x1 - mean(x1)) - sd(x1)) * x2,
family='binomial', data))
expect_equal(round(slopes[3, 'Test Estimate'], 3),
get_coef(model_x1_p1, 'x2'))
model_x2_m1 <- summary(glm(y ~ x1 * I((x2 - mean(x2)) + sd(x2)),
family='binomial', data))
expect_equal(round(slopes[4, 'Test Estimate'], 3),
get_coef(model_x2_m1, 'x1'))
model_x2_0 <- summary(glm(y ~ x1 * I(x2 - mean(x2)),
family='binomial', data))
expect_equal(round(slopes[5, 'Test Estimate'], 3),
get_coef(model_x2_0, 'x1'))
model_x2_p1 <- summary(glm(y ~ x1 * I((x2 - mean(x2)) - sd(x2)),
family='binomial', data))
expect_equal(round(slopes[6, 'Test Estimate'], 3),
get_coef(model_x2_p1, 'x1'))
})
test_that('lme with interaction works', {
set.seed(123)
pre_treat <- rnorm(50)
set.seed(234)
post_treat <- 2 + rnorm(50)
set.seed(345)
pre_control <- rnorm(50)
set.seed(456)
post_control <- rnorm(50)
dv <- c(pre_treat, post_treat, pre_control, post_control)
pre_post <- factor(rep(c(rep(0, 50), rep(1, 50)), 2))
condition <- factor(c(rep(0, 100), rep(1, 100)))
id <- c(rep(1:50, 2), rep(51:100, 2))
data <- data.frame(id, condition, pre_post, dv)
model <- lme(dv ~ condition * pre_post, random=~1|id, data)
slopes <- simple_slopes(model)
contrasts(data$condition) <- c(0, 1)
model_c_0 <- summary(lme(dv ~ condition * pre_post, random=~1|id, data))
expect_equal(round(slopes[1, 'Test Estimate'], 3),
get_coef.lme(model_c_0, 'pre_post1'))
contrasts(data$condition) <- c(1, 0)
model_c_1 <- summary(lme(dv ~ condition * pre_post, random=~1|id, data))
expect_equal(round(slopes[2, 'Test Estimate'], 3),
get_coef.lme(model_c_1, 'pre_post1'))
contrasts(data$condition) <- c(0, 1)
contrasts(data$pre_post) <- c(0, 1)
model_p_0 <- summary(lme(dv ~ condition * pre_post, random=~1|id, data))
expect_equal(round(slopes[3, 'Test Estimate'], 3),
get_coef.lme(model_p_0, 'condition1'))
contrasts(data$pre_post) <- c(1, 0)
model_p_1 <- summary(lme(dv ~ condition * pre_post, random=~1|id, data))
expect_equal(round(slopes[4, 'Test Estimate'], 3),
get_coef.lme(model_p_1, 'condition1'))
})
test_that('lmer with interaction works', {
set.seed(123)
pre_treat <- rnorm(50)
set.seed(234)
post_treat <- 2 + rnorm(50)
set.seed(345)
pre_control <- rnorm(50)
set.seed(456)
post_control <- rnorm(50)
dv <- c(pre_treat, post_treat, pre_control, post_control)
pre_post <- factor(rep(c(rep(0, 50), rep(1, 50)), 2))
condition <- factor(c(rep(0, 100), rep(1, 100)))
id <- c(rep(1:50, 2), rep(51:100, 2))
data <- data.frame(id, condition, pre_post, dv)
model <- lmer(dv ~ condition * pre_post + (1|id), data)
slopes <- simple_slopes(model)
contrasts(data$condition) <- c(0, 1)
model_c_0 <- summary(lmer(dv ~ condition * pre_post + (1|id), data))
expect_equal(round(slopes[1, 'Test Estimate'], 3),
get_coef(model_c_0, 'pre_post1'))
contrasts(data$condition) <- c(1, 0)
model_c_1 <- summary(lmer(dv ~ condition * pre_post + (1|id), data))
expect_equal(round(slopes[2, 'Test Estimate'], 3),
get_coef(model_c_1, 'pre_post1'))
contrasts(data$condition) <- c(0, 1)
contrasts(data$pre_post) <- c(0, 1)
model_p_0 <- summary(lmer(dv ~ condition * pre_post + (1|id), data))
expect_equal(round(slopes[3, 'Test Estimate'], 3),
get_coef(model_p_0, 'condition1'))
contrasts(data$pre_post) <- c(1, 0)
model_p_1 <- summary(lmer(dv ~ condition * pre_post + (1|id), data))
expect_equal(round(slopes[4, 'Test Estimate'], 3),
get_coef(model_p_1, 'condition1'))
})
test_that('character vector works', {
set.seed(123)
x1 <- rnorm(100)
x2 <- c(rep("first", 50), rep("second", 50))
set.seed(345)
y <- x1 * (as.numeric(factor(x2))-1) + rnorm(100)
data <- data.frame(x1, x2, y)
model <- lm(y ~ x1 * x2, data)
slopes <- simple_slopes(model)
model_x1_m1 <- summary(lm(y ~ I((x1 - mean(x1)) + sd(x1)) * x2, data))
expect_equal(round(slopes[1, 'Test Estimate'], 3),
get_coef(model_x1_m1, 'x2second'))
model_x1_0 <- summary(lm(y ~ I(x1 - mean(x1)) * x2, data))
expect_equal(round(slopes[2, 'Test Estimate'], 3),
get_coef(model_x1_0, 'x2second'))
model_x1_p1 <- summary(lm(y ~ I((x1 - mean(x1)) - sd(x1)) * x2, data))
expect_equal(round(slopes[3, 'Test Estimate'], 3),
get_coef(model_x1_p1, 'x2second'))
model_x2_0 <- summary(lm(y ~ x1 * x2, data))
expect_equal(round(slopes[4, 'Test Estimate'], 3),
get_coef(model_x2_0, 'x1'))
data$x2 <- factor(data$x2)
contrasts(data$x2) <- c(1, 0)
model_x2_1 <- summary(lm(y ~ x1 * x2, data))
expect_equal(round(slopes[5, 'Test Estimate'], 3),
get_coef(model_x2_1, 'x1'))
}) |
censloop_em<-function(meanmodel, theta.old,beta.old, p.old, x.0, X,censor.ind, mean.intercept, maxit, eps){
n<-length(X)
R2<-list()
Z20<-list()
Z21<-list()
Y2<-rep(NA, times=n)
Ca<- rep(NULL, times=n)
conv<-FALSE
it<-NULL
p.old[p.old < 1e-10] <- 1e-10
p.old[p.old > 1e10] <- 1e10
theta.new<-theta.old
z=sqrt(1/p.old)
c_1<-X*z
c_1[censor.ind==-1] <- NA
c_2<-X*z
c_2[censor.ind==1]<-NA
beta.old<-NULL
outmean<-mean.models(c_1, c_2, z,n, meanmodel,mean.intercept, beta.old, maxit, eps)
mean.fit<-outmean$mean.fit
beta.old<-outmean$beta.new
beta.new<-beta.old
for (q in 1:maxit) {
it<-q
X0<-X-mean.fit
Y2[censor.ind==0]<-(theta.old[1]+(theta.old[1]**2/p.old)*(X0**2/p.old - 1))[censor.ind==0]
Ca[censor.ind==-1 & round(stats::pnorm((X0)/sqrt(p.old)), digits=5)!=0]<-((stats::pnorm((X0)/sqrt(p.old)))/(-stats::dnorm((X0)/sqrt(p.old))))[censor.ind==-1 & round(stats::pnorm((X0)/sqrt(p.old)), digits=5)!=0]
Ca[censor.ind==-1 & round(stats::pnorm((X0)/sqrt(p.old)), digits=5)==0]<- 1/((X0)/sqrt(p.old))[censor.ind==-1 & round(stats::pnorm((X0)/sqrt(p.old)), digits=5)==0]
Y2[censor.ind==-1]<-(theta.old[1]+(theta.old[1]**2/p.old)*((X0/sqrt(p.old))/Ca))[censor.ind==-1]
Ca[censor.ind==1 & round(stats::pnorm((X0)/sqrt(p.old)), digits=5)!=0]<-((1-stats::pnorm((X0)/sqrt(p.old)))/(stats::dnorm((X0)/sqrt(p.old))))[censor.ind==1 & round(stats::pnorm((X0)/sqrt(p.old)), digits=5)!=0]
Ca[censor.ind==1 & round(stats::pnorm((X0)/sqrt(p.old)), digits=5)==0]<- 1/((X0)/sqrt(p.old))[censor.ind==1 & round(stats::pnorm((X0)/sqrt(p.old)), digits=5)==0]
Y2[censor.ind==1]<- (theta.old[1]+(theta.old[1]**2/p.old)*((X0/sqrt(p.old))/Ca))[censor.ind==1]
theta.new[1]<-mean(Y2)
if (!is.null(x.0)){
X00<-X0[censor.ind==0]
X01<-X0[censor.ind!=0]
x00<-x.0[censor.ind==0,]
x01<-x.0[censor.ind!=0,]
if (length(theta.old)<3){
x00<-as.matrix(x00)
x01<-as.matrix(x01)
}
pold0<-p.old[censor.ind==0]
pold1<-p.old[censor.ind!=0]
Ca1<-Ca[censor.ind!=0]
Z20<-sapply(1:ncol(x00),function(x) x00[,x]*theta.old[x+1]+((theta.old[x+1]*x00[,x])**2/pold0)*(X00**2/pold0-1))
Z21<-sapply(1:ncol(x01),function(x) x01[,x]*theta.old[x+1]+((theta.old[x+1]*x01[,x])**2/pold1)*(X01/sqrt(pold1))/Ca1)
zz<-rbind(Z20, Z21)
xx<-rbind(x00, x01)
theta.new[-1]<-sapply(1:ncol(x.0), function(x) mean(zz[,x]/xx[,x],na.rm=TRUE ))
}
z=1/sqrt(p.old)
z[z > 1e10] <- 1e10
c_1<-X*z
c_1[censor.ind==-1]<-NA
c_2<-X*z
c_2[censor.ind==1]<-NA
outmean<-tryCatch({
mean.models(c_1, c_2, z,n, meanmodel,mean.intercept,beta.old, maxit, eps)
},error=function(cond){
message("Mean model (survreg) failed to converge at maxit=", maxit, ". Attempting more iterations.")
return(NULL)
},warning=function(cond){
message("Mean model (survreg) failed to converge at maxit=", maxit, ". Attempting more iterations.")
})
if (is.null(outmean)==TRUE || any(is.nan(outmean$beta.new))){
outmean<-tryCatch({
mean.models(c_1, c_2, z,n, meanmodel,mean.intercept,beta.old, maxit*100, eps)
},error=function(cond){
message("Mean model (survreg) still failed to converge at maxit=", maxit, "*100. Review initial estimates.")
return(NULL)
},warning=function(cond){
message("Mean model (survreg) still failed to converge at maxit=", maxit, "*100. Review initial estimates.")
})
}
if (is.null(outmean)==TRUE){
break}
beta.new<-outmean$beta.new
mean.fit<-outmean$mean.fit
if (all(is.finite(c(beta.new, theta.new)))==TRUE){
reldiff<-sqrt(sum((c(theta.new,beta.new)-c(theta.old,beta.old))**2)/sum(c(beta.old,theta.old))**2)
}else {
reldiff<-NaN
print("Estimates are not finite. Please review initial estimates.")
print(c("Mean:",beta.new))
print(c("Variance",theta.new))
break
}
theta.old<-theta.new
beta.old<-beta.new
p.old<-rep(theta.old[1], n)
if (!is.null(x.0)){
p.old<-rowSums(cbind(rep(theta.new[1], n),sapply(1:ncol(x.0), function(x) theta.new[x+1]*x.0[,x])))
}
if (sum(p.old<0)>0){
print("Estimates for variance/scale have gone negative. Please review initial estimates.")
break
}
if (is.finite(reldiff)==TRUE){
if (reldiff<eps){
conv<-TRUE
break
}
}
}
if (is.null(outmean)==TRUE){
list(conv=FALSE, reldiff=NA, it=it, mean=beta.old, theta.new=theta.old, fittedmean=NULL, p.old=p.old)
}else{
list(conv=conv, reldiff=reldiff, it=it, mean=beta.new, theta.new=theta.new, fittedmean=mean.fit, p.old=p.old)
}
}
mean.models<-function(c_1, c_2, z,n, meanmodel,mean.intercept,beta.old, maxit, eps){
if (is.null(meanmodel)==TRUE){
mean.fit<-rep(0,n)
beta.new<-NULL
}else if (is.data.frame(meanmodel)==TRUE){
if (mean.intercept==TRUE){
meanmodel2<-as.data.frame(apply(meanmodel, 2, function(x) x*z))
l<-survival::survreg(survival::Surv(c_1, c_2, type="interval2")~ -1+z+ . , data=meanmodel2, dist = "gaussian", maxiter=maxit, rel.tolerance=eps, init=beta.old)
beta.new<-l$coeff
mean.fit<-l$linear.predictor/z
}else if (mean.intercept==FALSE){
meanmodel2<-as.data.frame(apply(meanmodel, 2, function(x) x*z))
l<-survival::survreg(survival::Surv(c_1, c_2, type="interval2")~ -1+., data=meanmodel2, dist = "gaussian", maxiter=maxit, rel.tolerance=eps, init=beta.old)
beta.new<-l$coeff
mean.fit<-l$linear.predictor/z
}
}else if (meanmodel[1]==FALSE){
l<-survival::survreg(survival::Surv(c_1, c_2, type="interval2")~ -1+z, dist = "gaussian", maxiter=maxit, rel.tolerance=eps)
beta.new<-l$coeff
mean.fit<-l$linear.predictor/z
}
return(list(mean.fit=mean.fit, beta.new=beta.new))
} |
check_pkg_dependencies <- function(pkg_root = NULL,
on_success = NULL,
on_fail = stop,
reader = readLines) {
if (!exists("startsWith", mode = "function")) {
stop("`startsWith` not present, possibly due to insufficient R version.\n\t",
"Current:\t", getRversion(), "\n\t",
"Required:\t", "3.3.0", ".")
}
if (!requireNamespace("rcheology", quietly = TRUE)) {
stop("package:rcheology not installed, but required. ",
"Run `install.packages('rcheology')` and try again.")
}
if (is.null(pkg_root)) {
if (file.exists("DESCRIPTION")) {
pkg_root <- "."
} else {
}
} else {
if (length(pkg_root) > 1L) {
return(sapply(pkg_root,
check_pkg_dependencies,
on_success = on_success,
on_fail = on_fail,
reader = reader))
}
if (!file.exists(file.path(pkg_root, "DESCRIPTION"))) {
return(on_fail("Unable to find DESCRIPTION"))
}
}
strSplitter <- function(x) {
if (!is.character(x)) {
warning("strSplitter provided with non-character object.")
return("")
}
unlist(strsplit(x, split = "(?<=\\()\\b", perl = TRUE))
}
rFunsDefined <-
lapply(dir(file.path(pkg_root, "R"),
pattern = "\\.R",
full.names = TRUE),
reader) %>%
lapply(trimws) %>%
lapply(function(x) x[!startsWith(x, "
lapply(function(x) {
sub("^([A-Za-z0-9_\\.]+)\\s*([=]|<-)\\s*function\\(.*$",
"\\1",
grep("^([A-Za-z0-9_\\.]+)\\s*([=]|<-)\\s*function\\(.*$",
x,
perl = FALSE,
value = TRUE))
}) %>%
unlist %>%
unique
RfunsUsed <-
lapply(dir(file.path(pkg_root, "R"),
pattern = "\\.R$",
full.names = TRUE),
function(x) {
setdiff(all.names(parse(x)),
all.vars(parse(x)))
}) %>%
unlist %>%
unique
stated_R_dep <-
desc::desc_get_deps(file = pkg_root) %>%
as.data.table %>%
.[type == "Depends" & package == "R"] %>%
.[["version"]]
if (length(stated_R_dep)) {
if (length(stated_R_dep) != 1L) {
warning("Multiple stated R dependencies, using first...")
stated_R_dep <- stated_R_dep[1]
}
} else {
stated_R_dep <- ">= 1.0.0"
}
stated_R_dep <- strsplit(stated_R_dep, split = " ")[[1L]]
R_dep_comparator <- stated_R_dep[1]
stated_R_dep <- stated_R_dep[2]
Rversion <- NULL
base_funs_used_by_version_introduced <-
rcheology::rcheology %>%
as.data.table %>%
.[package == "base"] %>%
unique(by = "name") %>%
.[name %chin% RfunsUsed] %>%
setkey(Rversion)
base_funs_introduced_later_than_stated_dep <-
switch(R_dep_comparator,
"<" = base_funs_used_by_version_introduced[Rversion <= stated_R_dep],
"<=" = base_funs_used_by_version_introduced[Rversion < stated_R_dep],
">=" = base_funs_used_by_version_introduced[Rversion > stated_R_dep],
">" = base_funs_used_by_version_introduced[Rversion >= stated_R_dep],
stop("Unexpected R dependency: ", stated_R_dep, "."))
if (nrow(base_funs_introduced_later_than_stated_dep)) {
cat(basename(pkg_root), ":\n")
print(base_funs_introduced_later_than_stated_dep)
return(on_fail("Base function not respecting dependency:\n\t",
base_funs_introduced_later_than_stated_dep[1][[1L]], "\t",
base_funs_introduced_later_than_stated_dep[1][[2L]]))
}
on_success
}
skip_only_on_cran <- function() {
if (identical(Sys.getenv("NOT_CRAN"), "true") ||
identical(Sys.getenv("TRAVIS"), "true") ||
identical(Sys.getenv("APPVEYOR"), "True") ||
nzchar(Sys.getenv("CODECOV_TOKEN"))) {
return(invisible(TRUE))
}
testthat::skip("On CRAN")
} |
makeOMLEstimationProcedure = function(
type,
data.splits.url = NA_character_,
data.splits = NULL,
parameters = NULL) {
assertString(type)
assertString(data.splits.url, na.ok = TRUE)
if (!is.null(data.splits))
assertDataFrame(data.splits)
if (!is.null(parameters))
assertList(parameters, names = "named")
makeS3Obj("OMLEstimationProcedure",
type = type,
data.splits.url = data.splits.url,
data.splits = data.splits,
parameters = parameters
)
}
print.OMLEstimationProcedure = function(x, ...) {
catf("\nEstimation Method :: %s", x$type)
catf("\tParameters:")
for (i in seq_along(x$parameters)) {
if (!is.na(x$parameters[[i]]) && x$parameters[[i]] != "")
catf("\t\t%s = %s", names(x$parameters)[i], x$parameters[[i]])
}
} |
update_yml <- function(template_in = NULL,
template_out = NULL) {
if (is.null(template_in)) {
tic_ymls <- list.files(usethis::proj_path(".github/workflows"),
pattern = "^tic*", full.names = TRUE
)
if (length(tic_ymls) > 0) {
ghactions <- tic_ymls
} else {
if (file.exists(usethis::proj_path(".github/workflows", "main.yml"))) {
ghactions <- usethis::proj_path(".github/workflows", "main.yml")
} else {
ghactions <- NULL
}
}
if (file.exists(usethis::proj_path(".circleci/", "config.yml"))) {
circle <- usethis::proj_path(".circleci", "config.yml")
} else {
circle <- NULL
}
providers <- c(ghactions, circle)
} else {
providers <- template_in
}
for (instance in providers) {
instance_txt <- readLines(instance)
if (is.null(template_out)) {
template_out <- instance
}
rev_date_local <- tryCatch(as.character(gsub(
".*(\\d{4}-\\d{2}-\\d{2}).*", "\\1",
instance_txt[2]
)),
error = function(cond) {
return(NA)
}
)
if (is.na(rev_date_local)) {
cli::cli_alert_warning("{.file {basename(instance)}} does not (yet)
contain a (valid) {.pkg tic} revision date (format: YYYY-MM-DD).
If {.file {basename(instance)}} is managed by {.pkg tic}, please update
the template manually one last time or manually add a revision date
into the template as the first line of your template.
Otherwise ignore this message.",
wrap = TRUE
)
cli::cli_alert("Skipping {.file {basename(instance)}}")
template_out <- NULL
next
}
tmpl_type <- stringr::str_split(instance_txt[1], "template: ",
simplify = TRUE
)[, 2]
ci_provider <- stringr::str_extract_all(instance_txt[1],
"(?<=tic ).+(?= template)",
simplify = TRUE
)[1, 1]
tmpl_latest <- switch(ci_provider,
"GitHub Actions" = use_ghactions_yml(tmpl_type,
write = FALSE,
quiet = TRUE
),
"Circle CI" = use_circle_yml(tmpl_type, write = FALSE, quiet = TRUE)
)
rev_date_latest <- as.character(gsub(
".*(\\d{4}-\\d{2}-\\d{2}).*", "\\1",
tmpl_latest[2]
), quiet = TRUE)
if (!rev_date_latest > rev_date_local) {
cli::cli_alert_info(
"{.file {basename(instance)}}: You already have the latest version of
the {ci_provider} template (rev_date_latest).",
wrap = TRUE
)
next
} else {
cli::cli_alert("Updating {ci_provider} template
{.file {basename(instance)}} from version
{.field {rev_date_local}} to version {.field {rev_date_latest}}.", wrap = TRUE)
}
tmpl_latest <- switch(ci_provider,
"GitHub Actions" = update_ghactions_yml(instance_txt, tmpl_latest),
"Circle CI" = update_circle_yml(instance_txt, tmpl_latest)
)
cli::cli_alert_info("Writing {.file {template_out}}.")
cli::cli_par()
cli::cli_end()
writeLines(tmpl_latest, template_out)
template_out <- NULL
}
}
update_ghactions_yml <- function(tmpl_local, tmpl_latest) {
custom_matrix_matrix_name <- stringr::str_which(
tmpl_local,
"
)
if (length(custom_matrix_matrix_name) > 0) {
cli::cli_alert_info("Found {.val {length(custom_matrix_matrix_name)}}
custom matrix name variable{?s}.", wrap = TRUE)
matrix_name_index_latest <- stringr::str_which(
tmpl_latest,
"name: \\$\\{\\{ matrix"
)
custom_matrix_matrix_name_list <- purrr::map(custom_matrix_matrix_name, ~ {
tmpl_local[.x:(.x + 1)]
})
for (i in seq_along(custom_matrix_matrix_name_list)) {
tmpl_latest <- replace(
tmpl_latest,
c(matrix_name_index_latest:(matrix_name_index_latest + 1)),
custom_matrix_matrix_name_list[[i]]
)
tmpl_latest <- append(tmpl_latest, "",
after = matrix_name_index_latest + 1
)
}
}
custom_matrix_env_vars <- stringr::str_which(
tmpl_local,
"
)
if (length(custom_matrix_env_vars) > 0) {
cli::cli_alert_info("Found {.val {length(custom_matrix_env_vars)}} custom
matrix env variable{?s}.", wrap = TRUE)
matrix_env_var_index_latest <- stringr::str_which(
tmpl_latest,
"config:"
) + 1
custom_matrix_env_var_list <- purrr::map(custom_matrix_env_vars, ~ {
tmpl_local[.x:(.x + 1)]
})
for (i in seq_along(custom_matrix_env_var_list)) {
tmpl_latest <- append(tmpl_latest,
custom_matrix_env_var_list[[i]],
after = matrix_env_var_index_latest
)
}
}
custom_env_vars <- stringr::str_which(tmpl_local, "
if (length(custom_env_vars) > 0) {
cli::cli_alert_info("Found {.val {length(custom_env_vars)}} custom env
variable{?s}.", wrap = TRUE)
env_var_index_latest <- stringr::str_which(tmpl_latest, "env:")
custom_env_var_list <- purrr::map(custom_env_vars, ~ {
tmpl_local[.x:(.x + 1)]
})
for (i in seq_along(custom_env_var_list)) {
tmpl_latest <- append(tmpl_latest,
custom_env_var_list[[i]],
after = env_var_index_latest
)
}
tmpl_latest <- account_for_dup_env_vars(
custom_env_var_list,
env_var_index_latest,
tmpl_latest
)
}
custom_blocks_start <- stringr::str_which(
tmpl_local,
'name: "\\[Custom block'
)
if (length(custom_blocks_start > 0)) {
cli::cli_alert_info("Found {.val {length(custom_blocks_start)}} custom user
block{?s}.", wrap = TRUE)
stringr::str_which(tmpl_local, "^\\s*$")
custom_blocks_list <- purrr::map(custom_blocks_start, ~ {
block_end <- purrr::keep(
stringr::str_which(tmpl_local, "^\\s*$"),
function(y) y > .x
)[1] - 1
append(tmpl_local[.x:block_end], "")
})
tmpl_blocks_names <- purrr::map_chr(custom_blocks_start, ~ {
row_inds_prev_temp_block <- tail(purrr::keep(
stringr::str_which(tmpl_local, "- name"),
function(y) y < .x
), n = 1)
purrr::map_chr(row_inds_prev_temp_block, ~
stringr::str_extract(tmpl_local[.x], "-.*"))
})
tmpl_blocks_names <- purrr::map_chr(custom_blocks_start, ~ {
row_inds_prev_temp_block <- tail(purrr::keep(
stringr::str_which(tmpl_local, "- name: (?!\"\\[Custom)"),
function(y) y < .x
), n = 1)
purrr::map_chr(row_inds_prev_temp_block, ~
stringr::str_extract(tmpl_local[.x], "-.*"))
})
tmpl_blocks_names_fallback <- purrr::map_chr(custom_blocks_start, ~ {
row_inds_prev_temp_block <- tail(purrr::keep(
stringr::str_which(tmpl_local, "- name: (?!\"\\[Custom)"),
function(y) y < .x
), n = 2)[1]
purrr::map_chr(row_inds_prev_temp_block, ~
stringr::str_extract(tmpl_local[.x], "-.*"))
})
for (i in rev(seq_along(tmpl_blocks_names))) {
in_tmpl_latest <- purrr::map_lgl(tmpl_blocks_names[i], function(index) {
any(stringr::str_detect(tmpl_latest, stringr::fixed(index)))
})
if (!in_tmpl_latest) {
tmpl_blocks_names[i] <- tmpl_blocks_names_fallback[i]
}
in_tmpl_latest <- purrr::map_lgl(tmpl_blocks_names[i], function(index) {
any(stringr::str_detect(tmpl_latest, stringr::fixed(index)))
})
if (!in_tmpl_latest) {
cli::cli_par()
cli::cli_end()
cli::cli_alert_danger("Not enough unique anchor points between your
local {.pkg tic} template and the newest upstream version could be
found.
Please update manually and try again next time.
If this error persists, your local {.pkg tic} template is too different
compared to the upstream template {.pkg tic} to support automatic
updating.", wrap = TRUE)
stopc("Not enough valid anchors points found between local and upstream template.")
}
tmpl_latest_index <- purrr::map_int(
tmpl_blocks_names[i],
function(index) {
stringr::str_which(tmpl_latest, stringr::fixed(index))
}
)
tmpl_latest_insert_index <- purrr::map_int(
tmpl_latest_index,
function(insert_index) {
purrr::keep(
stringr::str_which(tmpl_latest, "^\\s*$"),
function(x) x > insert_index
)[1]
}
)
tmpl_latest <- append(tmpl_latest, custom_blocks_list[[i]],
after = tmpl_latest_insert_index
)
}
}
custom_header <- stringr::str_which(
tmpl_local,
"
)
if (length(custom_header) > 0) {
cli::cli_alert_info("Found a custom header entry. Will use it
instead of the header in the {.pkg tic} upstream template.",
wrap = TRUE
)
custom_header_local <- stringr::str_which(
tmpl_local,
"env:"
)
custom_header_latest <- stringr::str_which(
tmpl_latest,
"env:"
)
rev_date_latest <- as.Date(gsub(
".*(\\d{4}-\\d{2}-\\d{2}).*", "\\1",
tmpl_latest[2]
), quiet = TRUE)
header_local <- tmpl_local[1:custom_header_local]
tmpl_latest <- tmpl_latest[-(1:custom_header_latest)]
tmpl_latest <- append(tmpl_latest, header_local, after = 0)
tmpl_latest[2] <- sprintf("
}
return(tmpl_latest)
}
update_circle_yml <- function(tmpl_local, tmpl_latest) {
custom_env_vars <- stringr::str_which(tmpl_local, "
if (length(custom_env_vars) > 0) {
cli::cli_alert_info("Found {.val {length(custom_env_vars)}} custom env
variable{?s}.", wrap = TRUE)
for (release in c(
"
"
)) {
env_var_index_latest <- stringr::str_which(tmpl_latest, release) + 1
block_end <- purrr::keep(
stringr::str_which(tmpl_latest, "^\\s*$"), ~
.x > env_var_index_latest
)[1] - 1
custom_env_var_list <- purrr::map(custom_env_vars, ~ {
tmpl_local[.x:(.x + 1)]
})
env_var_index_local <- stringr::str_which(tmpl_local, release) + 1
block_end_local <- purrr::keep(
stringr::str_which(tmpl_local, "^\\s*$"), ~
.x > env_var_index_local
)[1] - 1
sub_custom_env_var_list <- custom_env_var_list[(custom_env_vars >
env_var_index_local) & (custom_env_vars < block_end_local)]
if (length(sub_custom_env_var_list) > 0) {
for (i in sub_custom_env_var_list) {
tmpl_latest <- append(tmpl_latest, i, env_var_index_latest)
}
}
}
tmpl_latest <- account_for_dup_env_vars(
custom_env_var_list,
env_var_index_latest,
tmpl_latest
)
}
custom_blocks_start <- stringr::str_which(
tmpl_local,
'name: "\\[Custom block'
) - 1
if (length(custom_blocks_start > 0)) {
cli::cli_alert_info("Found {.val {length(custom_blocks_start)}} custom user
block{?s}.", wrap = TRUE)
stringr::str_which(tmpl_local, "^\\s*$")
custom_blocks_list <- purrr::map(custom_blocks_start, ~ {
block_end <- purrr::keep(
stringr::str_which(tmpl_local, "^\\s*$"),
function(y) y > .x
)[1] - 1
append(tmpl_local[.x:block_end], "")
})
tmpl_blocks_names <- purrr::map_chr(custom_blocks_start, ~ {
row_inds_prev_temp_block <- tail(purrr::keep(
stringr::str_which(tmpl_local, "- run:"),
function(y) y < .x
), n = 1) + 1
purrr::map_chr(row_inds_prev_temp_block, ~
stringr::str_extract(tmpl_local[.x], "name:.*"))
})
for (i in seq_along(tmpl_blocks_names)) {
tmpl_latest_index <- purrr::map_int(
tmpl_blocks_names[i],
function(index) {
stringr::str_which(tmpl_latest, stringr::fixed(index))
}
)
tmpl_latest_insert_index <- purrr::map_int(
tmpl_latest_index,
function(insert_index) {
purrr::keep(
stringr::str_which(tmpl_latest, "^\\s*$"),
function(x) x > insert_index
)[1]
}
)
tmpl_latest <- append(tmpl_latest, custom_blocks_list[[i]],
after = tmpl_latest_insert_index
)
}
}
return(tmpl_latest)
}
use_update_tic <- function() {
tmpl <- readLines(system.file("templates/update-tic.yml", package = "tic"))
writeLines(tmpl, con = ".github/workflows/update-tic.yml")
cli::cli_alert("Added new file:")
data <- data.frame(
stringsAsFactors = FALSE,
package = c(
basename(getwd()), ".github", "workflows", "update-tic.yml"
),
dependencies = I(list(
".github", "workflows", "update-tic.yml", character(0)
))
)
print(tree(data, root = basename(getwd())))
cli::cli_alert_info("Note that you need to add a secret with 'workflow' scopes
named {.var TIC_UPDATE} to your repo to make this automation work.
You can use {.code tic::gha_add_secret(<secret>, 'TIC_UPDATE')} for this.",
wrap = TRUE
)
} |
Var_approx <- function(y, pik, n, method, ...){
method <- match.arg(method,
c("Hajek1",
"Hajek2",
"HartleyRao1",
"HartleyRao2",
"FixedPoint")
)
N <- length(y)
if( length(n)>1 ){
stop("Argument 'n' should be a scalar!")
}else if( !is.numeric(n) | n != as.integer(n) ){
stop( "Argument 'n' must be an integer number")
}
if( !(class(y) %in% c("numeric", "integer")) ){
stop( "The argument 'y' should be a numeric vector!")
}else if( N < 2 ){
stop( "The 'y' vector is too short!" )
}else if( any(y %in% c(NA, NaN, Inf)) ){
stop( "The 'y' vector contains invalid values (NA, NaN, Inf)" )
}
if( any(y<0) ){
message( "Some 'y' values are negative, continuing anyway...")
}
if( !identical( class(pik), "numeric" ) ){
stop( "The argument 'pik' should be a numeric vector!")
}else if( !identical(N, length(pik) ) ){
stop( "The 'pik' vector must have same length as 'y'!" )
}else if( any(pik<0) | any(pik>1) ){
stop( "Some values of the 'pik' vector are outside the interval [0, 1]")
}else if( any(pik %in% c(NA, NaN, Inf)) ){
stop( "The 'pik' vector contains invalid values (NA, NaN, Inf)" )
}else if( !all.equal( sum(pik), n) ) stop("the sum of 'pik' values should be equal to 'n' ")
if( identical(method, 'Hajek1') ){
bk <- pik * (1-pik) * N / (N-1)
ys <- pik * sum( bk*y/pik ) / sum(bk)
V <- sum( bk*(y-ys)^2 / (pik**2) )
}else if( identical(method, 'Hajek2') ){
d <- pik*(1-pik)
ak <- n * (1-pik) / sum( d )
yt <- sum( y*ak )
V <- sum( d * (y/pik - yt/n)^2 )
}else if( identical(method, 'HartleyRao1') ){
p2 <- pik**2
sp2 <- sum(p2)
Y <- sum(y)
sdif <- (y/pik - Y/n)**2
V <- sum( pik * ( 1 - (n-1)*pik/n ) * sdif )
V <- V - (n-1)/(n**2) * sum( (2*pik^3 - p2*sp2/2) * sdif )
V <- V + 2*(n-1)/(n**3) * ( sum(pik*y) - Y/n * sp2 )^2
}else if( identical(method, 'HartleyRao2') ){
Y <- sum(y)
sdif <- (y/pik - Y/n)**2
V <- sum( pik * (1 - (n-1)/n * pik) * sdif )
}else if( identical(method, 'FixedPoint') ){
argList <- list(...)
ifelse( is.null(argList$eps), eps <- 1e-05, eps <- argList$eps )
ifelse( is.null(argList$maxIter), maxIter <- 1000, maxIter <- argList$maxIter )
d <- pik*(1-pik)
necessaryCondition <- all( d/sum(d) < 0.5 )
if(necessaryCondition){
b0 <- d * N/(N-1)
iter <- 1
err <- Inf
while( iter<maxIter & err>eps ){
bk <- b0**2 / sum(b0) + d
err <- max( abs(bk - b0) )
b0 <- bk
iter <- iter + 1
}
if(err > eps) stop("Did not reach convergence")
}else{
bk <- N*d / ( (N-1) * sum(d) )
bk <- d * (bk + 1)
}
ys <- pik * sum( bk*y/pik ) / sum(bk)
V <- sum( bk*(y-ys)^2 / (pik**2) )
}
return(V)
} |
test_that("runTests works", {
calls <- list()
wd <- NULL
filesToError <- "runner2.R"
sourceStub <- function(...){
calls[[length(calls)+1]] <<- list(...)
wd <<- getwd()
if(list(...)[[1]] %in% filesToError){
stop("I was told to throw an error")
}
NULL
}
loadCalls <- list()
loadSupportStub <- function(...){
loadCalls[[length(calls)+1]] <<- list(...)
NULL
}
runTestsSpy <- rewire(runTests, sourceUTF8 = sourceStub, loadSupport=loadSupportStub)
res <- suppressMessages(
runTestsSpy(test_path("../test-helpers/app1-standard"), assert = FALSE)
)
expect_length(calls, 2)
expect_match(calls[[1]][[1]], "runner1\\.R$", perl=TRUE)
expect_match(calls[[2]][[1]], "runner2\\.R$", perl=TRUE)
env1 <- calls[[1]]$envir
env2 <- calls[[2]]$envir
expect_identical(parent.env(env1), parent.env(env2))
expect_true(!identical(env1, env2))
expect_equal(normalizePath(wd), normalizePath(
file.path(test_path("../test-helpers/app1-standard"), "tests")))
expect_equal(all(res$pass), FALSE)
expect_length(res$file, 2)
expect_equal(basename(res$file[1]), "runner1.R")
expect_equal(res[2,]$result[[1]]$message, "I was told to throw an error")
expect_s3_class(res, "shiny_runtests")
expect_length(loadCalls, 0)
filesToError <- character(0)
calls <- list()
res <- runTestsSpy(test_path("../test-helpers/app1-standard"))
expect_equal(all(res$pass), TRUE)
expect_equal(basename(res$file), c("runner1.R", "runner2.R"))
expect_length(calls, 2)
expect_match(calls[[1]][[1]], "runner1\\.R", perl=TRUE)
expect_match(calls[[2]][[1]], "runner2\\.R", perl=TRUE)
})
test_that("calls out to shinytest when appropriate", {
is_legacy_shinytest_val <- TRUE
is_legacy_shinytest_dir_stub <- function(...){
is_legacy_shinytest_val
}
runTestsSpy <- rewire(runTests, is_legacy_shinytest_dir = is_legacy_shinytest_dir_stub)
expect_error(
runTestsSpy(test_path("../test-helpers/app1-standard"), assert = FALSE),
"not supported"
)
is_legacy_shinytest_val <- FALSE
res <- runTestsSpy(test_path("../test-helpers/app1-standard"))
expect_s3_class(res, "shiny_runtests")
})
test_that("runTests filters", {
calls <- list()
sourceStub <- function(...){
calls[[length(calls)+1]] <<- list(...)
NULL
}
runTestsSpy <- rewire(runTests, sourceUTF8 = sourceStub)
runTestsSpy(test_path("../test-helpers/app1-standard"))
expect_length(calls, 2)
calls <- list()
runTestsSpy(test_path("../test-helpers/app1-standard"), filter="runner1")
expect_length(calls, 1)
calls <- list()
expect_error(runTestsSpy(test_path("../test-helpers/app1-standard"), filter="i don't exist"), "matched the given filter")
})
test_that("runTests handles the absence of tests", {
expect_error(runTests(test_path("../test-helpers/app2-nested")), "No tests directory found")
expect_message(res <- runTests(test_path("../test-helpers/app6-empty-tests")), "No test runners found in")
expect_equal(res$file, character(0))
expect_equal(res$pass, logical(0))
expect_equal(res$result, I(list()))
expect_s3_class(res, "shiny_runtests")
})
test_that("runTests runs as expected without rewiring", {
appDir <- test_path(file.path("..", "test-helpers", "app1-standard"))
df <- testthat::expect_output(
print(runTests(appDir = appDir, assert = FALSE)),
"Shiny App Test Results\\n\\* Success\\n - app1-standard/tests/runner1\\.R\\n - app1-standard/tests/runner2\\.R"
)
expect_equal(df, data.frame(
file = file.path(appDir, "tests", c("runner1.R", "runner2.R")),
pass = c(TRUE, TRUE),
result = I(list(1, NULL)),
stringsAsFactors = FALSE
), ignore_attr = TRUE)
expect_s3_class(df, "shiny_runtests")
})
test_that("app template works with runTests", {
suppressWarnings(testthat::skip_if_not_installed("shinytest", "1.3.1.9000"))
testthat::skip_if(!shinytest::dependenciesInstalled(), "shinytest dependencies not installed. Call `shinytest::installDependencies()`")
make_combos <- function(...) {
args <- list(...)
combo_dt <- do.call(expand.grid, args)
lapply(apply(combo_dt, 1, unlist), unname)
}
combos <- unique(unlist(
recursive = FALSE,
list(
"all",
make_combos("app", list(NULL, "module"), "shinytest"),
make_combos("app", list(NULL, "module"), list(NULL, "rdir"), list(NULL, "testthat"))
)
))
for (combo in combos) {
random_folder <- paste0("shinyAppTemplate-", paste0(combo, collapse = "_"))
tempTemplateDir <- file.path(tempfile(), random_folder)
suppressMessages(shinyAppTemplate(tempTemplateDir, combo))
on.exit(unlink(tempTemplateDir, recursive = TRUE), add = TRUE)
if (any(c("all", "shinytest", "testthat") %in% combo)) {
suppressMessages(capture.output({
out <- runTests(tempTemplateDir, assert = FALSE)
}))
expect_snapshot(out)
} else {
expect_error(
suppressMessages(runTests(tempTemplateDir))
)
}
}
}) |
as_series <- function(x, x_class, y_class, sheetname = "sheet1" ){
dataset <- x$data_series
w_x <- which( names(dataset) %in% x$x )
x_serie_range <- cell_limits(ul = c(2, w_x),
lr = c(nrow(dataset)+1, w_x),
sheet = sheetname)
x_serie_range <- as.range(x_serie_range, fo = "A1", strict = TRUE, sheet = TRUE)
x_serie <- update(x_class, region = x_serie_range, values = dataset[[x$x]])
label_columns <- get_label_names(x)
series <- list()
w_y_values <- which(names(dataset) %in% get_series_names(x))
w_l_values <- which(names(dataset) %in% label_columns)
for( w_y_index in seq_along(w_y_values)){
w_y <- w_y_values[w_y_index]
w_l <- w_l_values[w_y_index]
y_colname <- names(dataset)[w_y]
l_colname <- names(dataset)[w_l]
serie_name_range <- ra_ref(row_ref = 1, col_ref = w_y, sheet = sheetname)
serie_name_range <- to_string(serie_name_range, fo = "A1")
serie_name <- str_ref(values = y_colname, region = serie_name_range)
y_serie_range <- cell_limits(ul = c(2, w_y), lr = c(nrow(dataset)+1, w_y), sheet = sheetname)
y_serie_range <- as.range(y_serie_range, fo = "A1", strict = TRUE, sheet = TRUE)
y_serie <- update(y_class, region = y_serie_range, values = dataset[[y_colname]])
if(length(label_columns) > 0 ){
label_serie_range <- cell_limits(ul = c(2, w_l), lr = c(nrow(dataset)+1, w_l), sheet = sheetname)
label_serie_range <- as.range(label_serie_range, fo = "A1", strict = TRUE, sheet = TRUE)
label_serie <- label_ref(values = dataset[[l_colname]], region = label_serie_range)
} else label_serie <- NULL
ser <- list( idx = length(series), order = length(series),
tx = serie_name,
x = x_serie, y = y_serie, label = label_serie,
stroke = x$series_settings$colour[y_colname],
fill = x$series_settings$fill[y_colname],
symbol = x$series_settings$symbol[y_colname],
line_style = x$series_settings$line_style[y_colname],
size = x$series_settings$size[y_colname],
line_width = x$series_settings$line_width[y_colname],
labels_fp = x$series_settings$labels_fp[[y_colname]],
smooth = x$series_settings$smooth[y_colname]
)
series <- append(series, list(ser) )
}
series
} |
gaussian.marg <- function(link = "identity" ) {
fm <- gaussian( substitute( link ) )
ans <- list()
ans$start <- function(y, x, z, offset) {
if( !is.null(z) )
offset <- list( as.vector(offset$mean), as.vector(offset$precision) )
eps <- sqrt(.Machine$double.eps)
m <- glm.fit( x , y, offset=offset$mean, family=fm )
sigma <- max( 10*eps, sd( residuals(m) ) )
lambda <- c( coef(m), rep.int( 0, NCOL(z) ) )
lambda[ NCOL(x)+1 ] <- ifelse( is.null(z), sigma, log(sigma) )
if( is.null(z) ){
names( lambda ) <- c( dimnames( as.matrix(x) )[[ 2L ]], "sigma" )
attr( lambda, "lower" ) <- c( rep( -Inf, NCOL(x) ), eps )
}
else
names( lambda ) <- c( paste("mean", dimnames( as.matrix(x) )[[ 2L ]], sep="."),
paste("dispersion", dimnames( as.matrix(z) )[[ 2L ]], sep=".") )
lambda
}
ans$npar <- function(x, z) ifelse( !is.null(z), NCOL(x)+NCOL(z), NCOL(x)+1 )
ans$dp <- function(y, x, z, offset, lambda) {
nb <- length(lambda)
mu <- fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
if( is.null(z) )
sd <- lambda[ nb ]
else
sd <- exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
cbind( dnorm( y , mu , sd ) , pnorm( y , mu , sd ) )
}
ans$q <- function(p, x, z, offset, lambda) {
nb <- length(lambda)
mu <- fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
if( is.null(z) )
sd <- lambda[ nb ]
else
sd <- exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
qnorm( p , mu , sd )
}
ans$fitted.val <- function(x, z, offset, lambda){
fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
}
ans$type <- "numeric"
class(ans) <- c( "marginal.gcmr")
ans
}
binomial.marg <- function(link = "logit") {
fm <- binomial( substitute( link ) )
ans <- list()
sizes <- 1
ans$start <- function(y, x, z, offset) {
if(NCOL(y)==1) {
y <- as.factor(y)
y <- y!=levels(y)[1L]
y <- cbind(y, 1-y)
}
sizes <<- y[,1]+y[,2]
lambda <- coef( glm.fit( x, y, offset=offset$mean, family=fm ) )
names(lambda) <- dimnames( as.matrix(x) )[[2L]]
lambda
}
ans$npar <- function(x, z) NCOL(x)
ans$dp <- function(y, x, z, offset, lambda) {
if(NCOL(y)==1){
y <- as.factor(y)
y <- y!=levels(y)[1L]
y <- cbind(y, 1-y)
}
mu <- fm$linkinv( x %*% lambda + offset$mean )
cbind(dbinom( y[,1], sizes, mu ) ,
pbinom( y[,1], sizes, mu ) )
}
ans$q <- function(p, x, z, offset, lambda) {
mu <- fm$linkinv( x %*% lambda + offset$mean )
q <- qbinom( p, sizes, mu )
cbind( q, sizes-q )
}
ans$fitted.val <- function(x, z, offset, lambda){
fm$linkinv( x %*% lambda + offset$mean )
}
ans$type <- "integer"
class(ans) <- c( "marginal.gcmr")
ans
}
poisson.marg <- function(link = "log") {
fm <- poisson( substitute( link ) )
ans <- list()
ans$start <- function(y, x, z, offset) {
lambda <- coef( glm.fit( x , y, offset=offset$mean, family=fm ) )
names(lambda) <- dimnames( as.matrix(x) )[[ 2L ]]
lambda
}
ans$npar <- function(x, z) NCOL(x)
ans$dp <- function(y, x, z, offset, lambda) {
mu <- fm$linkinv( x %*% lambda + offset$mean )
cbind( dpois( y , mu ) , ppois( y , mu ) )
}
ans$q <- function(p, x, z, offset, lambda) {
mu <- fm$linkinv( x %*% lambda + offset$mean )
qpois( p , mu )
}
ans$fitted.val <- function(x, z, offset, lambda){
fm$linkinv( x %*% lambda + offset$mean )
}
ans$type <- "integer"
class(ans) <- c( "marginal.gcmr")
ans
}
negbin.marg <- function(link = "log" ) {
fm <- poisson( substitute( link ) )
ans <- list()
ans$start <- function(y, x, z, offset) {
if( !is.null(z) )
offset <- list( as.vector(offset$mean), as.vector(offset$precision) )
eps <- sqrt(.Machine$double.eps)
m <- glm.fit( x , y, offset=offset$mean, family=fm )
mu <- fitted(m)
kappa <- max( 10*eps , mean( ( (y-mu)^2-mu )/mu^2 ) )
lambda <- c( coef(m), rep.int( 0, NCOL(z) ) )
lambda[ NCOL(x)+1 ] <- ifelse( is.null(z), kappa, log(kappa) )
if( is.null(z) ){
names( lambda ) <- c( dimnames( as.matrix(x) )[[ 2L ]], "dispersion" )
attr( lambda, "lower" ) <- c( rep(-Inf, NCOL(x) ), eps )
}
else
names( lambda ) <- c( paste("mean", dimnames( as.matrix(x) )[[ 2L ]], sep="."),
paste("dispersion", dimnames( as.matrix(z) )[[ 2L ]], sep=".") )
lambda
}
ans$npar <- function(x, z) ifelse( !is.null(z), NCOL(x)+NCOL(z), NCOL(x)+1 )
ans$dp <- function(y, x, z, offset, lambda) {
nb <- length(lambda)
mu <- fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
if( is.null(z) )
size <- 1 / lambda[ nb ]
else
size <- 1 / exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
cbind( dnbinom( y, mu=mu, size=size) , pnbinom( y, mu=mu, size=size) )
}
ans$q <- function(p, x, z, offset, lambda) {
nb <- length(lambda)
mu <- fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
if( is.null(z) )
size <- 1 / lambda[ nb ]
else
size <- 1 / exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
qnbinom( p, mu=mu, size=size)
}
ans$fitted.val <- function(x, z, offset, lambda){
fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
}
ans$type <- "integer"
class(ans) <- c( "marginal.gcmr")
ans
}
gs.marg <- gaussian.marg
bn.marg <- binomial.marg
ps.marg <- poisson.marg
nb.marg <- negbin.marg
weibull.marg <- function(link = "log"){
fm <- Gamma( substitute( link ) )
ans <- list()
ans$start <- function(y, x, z, offset) {
if( !is.null(z) )
offset <- list( as.vector(offset$mean), as.vector(offset$precision) )
eps <- sqrt(.Machine$double.eps)
m <- glm.fit(x , y, offset=offset$mean, family=fm)
shape <- max( 10*eps, 1.2/sqrt( mean( log( y/fitted(m) )^2) ) )
lambda <- c( coef(m), rep.int( 0, NCOL(z) ) )
lambda[ NCOL(x)+1 ] <- ifelse( is.null(z), shape, log(shape) )
if( is.null(z) ){
names( lambda ) <- c( dimnames( as.matrix(x) )[[ 2L ]], "shape" )
attr( lambda, "lower" ) <- c( rep( -Inf, NCOL(x) ), eps )
}
else
names( lambda ) <- c( paste("scale", dimnames( as.matrix(x) )[[ 2L ]], sep="."),
paste("shape", dimnames( as.matrix(z) )[[ 2L ]], sep=".") )
lambda
}
ans$npar <- function(x, z) ifelse( !is.null(z), NCOL(x)+NCOL(z), NCOL(x)+1 )
ans$dp <- function(y, x, z, offset, lambda){
nb <- length(lambda)
scale <- fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
if( is.null(z) )
shape <- lambda[ nb ]
else
shape <- exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
cbind(dweibull(y, shape=shape, scale=scale) ,
pweibull(y, shape=shape, scale=scale) )
}
ans$q <- function(p, x, z, offset, lambda){
nb <- length(lambda)
scale <- fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
if( is.null(z) )
shape <- lambda[ nb ]
else
shape <- exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
qweibull(p, shape=shape, scale=scale)
}
ans$fitted.val <- function(x, z, offset, lambda){
fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
}
ans$type <- "numeric"
class(ans) <- c( "marginal.gcmr")
ans
}
Gamma.marg <- function(link = "inverse"){
fm <- Gamma( substitute( link ) )
ans <- list()
ans$start <- function(y, x, z, offset) {
if( !is.null(z) )
offset <- list( as.vector(offset$mean), as.vector(offset$precision) )
eps <- sqrt(.Machine$double.eps)
m <- glm.fit(x , y, offset=offset$mean, family=fm)
disp <- sum( residuals(m, "pearson")^2 )/( NROW(y)-NCOL(x) )
shape <- max( 10*eps, 1/disp )
lambda <- c( coef(m), rep.int( 0, NCOL(z) ) )
lambda[ NCOL(x)+1 ] <- ifelse( is.null(z), shape, log(shape) )
if( is.null(z) ){
names( lambda ) <- c( dimnames( as.matrix(x) )[[ 2L ]], "shape" )
attr( lambda, "lower" ) <- c( rep( -Inf, NCOL(x) ), eps )
}
else
names( lambda ) <- c( paste("mean", dimnames( as.matrix(x) )[[ 2L ]], sep="."),
paste("shape", dimnames( as.matrix(z) )[[ 2L ]], sep=".") )
lambda
}
ans$npar <- function(x, z) ifelse( !is.null(z), NCOL(x)+NCOL(z), NCOL(x)+1 )
ans$dp <- function(y, x, z, offset, lambda){
nb <- length(lambda)
mu <- fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
if( is.null(z) )
shape <- lambda[ nb ]
else
shape <- exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
cbind(dgamma(y, shape=shape, rate=shape/mu) ,
pgamma(y, shape=shape, rate=shape/mu) )
}
ans$q <- function(p, x, z, offset, lambda){
nb <- length(lambda)
mu <- fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
if( is.null(z) )
shape <- lambda[ nb ]
else
shape <- exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
qgamma(p, shape=shape, rate=shape/mu)
}
ans$fitted.val <- function(x, z, offset, lambda){
fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
}
ans$type <- "numeric"
class(ans) <- c( "marginal.gcmr")
ans
}
beta.marg <- function(link = "logit"){
fm <- binomial( substitute( link ) )
ans <- list()
ans$start <- function(y, x, z, offset) {
if( !is.null(z) )
offset <- list( as.vector(offset$mean), as.vector(offset$precision) )
m <- betareg.fit(x=x, y=as.vector(y), z=z, offset=offset, link=link )
lambda <- unlist( coef(m) )
if( is.null(z) ){
pos <- NCOL(x)+1
lambda[pos] <- exp( lambda[pos] )
names(lambda)[pos] <- "dispersion"
attr(lambda, "lower") <- c( rep( -Inf, NCOL(x) ), sqrt(.Machine$double.eps) )
}
lambda
}
ans$npar <- function(x, z) ifelse(!is.null(z), NCOL(x)+NCOL(z), NCOL(x)+1)
ans$dp <- function(y, x, z, offset, lambda) {
nb <- length(lambda)
mu <- fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
if( is.null(z) )
phi <- lambda[ nb ]
else
phi <- exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
shape1 <- phi*mu
shape2 <- phi*(1-mu)
cbind( dbeta(as.vector(y), shape1, shape2), pbeta(as.vector(y), shape1, shape2) )
}
ans$q <- function(p, x, z, offset, lambda) {
nb <- length(lambda)
mu <- fm$linkinv( x %*% lambda[1:NCOL(x)] + offset$mean )
if( is.null(z) )
phi <- lambda[ nb ]
else
phi <- exp( z %*% lambda[ ( NCOL(x)+1 ):nb ] + offset$precision )
shape1 <- phi*mu
shape2 <- phi*(1-mu)
qbeta(p, shape1, shape2)
}
ans$fitted.val <- function(x, z, offset, lambda){
fm$linkinv( x %*% lambda[ 1:NCOL(x) ] + offset$mean )
}
ans$type <- "numeric"
class(ans) <- c("marginal.gcmr")
ans
}
|
"_PACKAGE"
NULL
NULL
matrixpls <- function(S, model, W.model = NULL, weightFun = weightFun.pls,
parameterEstim = parameterEstim.separate, weightSign = NULL,
..., validateInput = TRUE, standardize = TRUE) {
nativeModel <- parseModelToNativeFormat(model)
lvNames <- colnames(nativeModel$inner)
if(any(duplicated(lvNames)))
stop(paste("Each composite must have a unique name. The names contain duplicates:",
lvNames))
if(! identical(lvNames, colnames(nativeModel$reflective)) ||
! identical(lvNames,rownames(nativeModel$formative))){
print(nativeModel)
stop("Names of composites are inconsistent between inner, reflective, and formative models.")
}
if(! identical(rownames(nativeModel$reflective), colnames(nativeModel$formative))){
print(nativeModel)
stop("Names of observed variables are inconsistent between reflective and formative models.")
}
if(! identical(colnames(S), rownames(S))) stop("S must have identical row and column names.")
if(! matrixcalc::is.symmetric.matrix(S)) stop("S must be symmetric to be a valid covariance matrix.")
if(! matrixcalc::is.positive.semi.definite(S)) stop("S must be positive semi-definite to be a valid covariance matrix.")
if(! identical(colnames(S), colnames(nativeModel$formative))){
d <- setdiff(colnames(nativeModel$formative), colnames(S))
if(length(d)>0) stop(paste("Variable(s)",paste(d,collapse=", "),"are not included in S."))
S <- S[colnames(nativeModel$formative), colnames(nativeModel$formative)]
}
if(is.null(W.model)) W.model <- defaultWeightModelWithModel(nativeModel)
if(! identical(colnames(W.model), colnames(S))) stop("Column names of W.model do not match the variable names")
if(! identical(rownames(W.model), lvNames)) stop("Row names of W.model do not match the latent variable names")
if(standardize){
S <- stats::cov2cor(S)
S[lower.tri(S)] <- t(S)[lower.tri(S)]
}
W <- weightFun(S, W.model = W.model,
model = nativeModel, parameterEstim = parameterEstim.separate,
..., validateInput = validateInput, standardize = standardize)
if(!is.null(weightSign)) W <- weightSign(W, S)
estimates <- parameterEstim(S, model, W, ...)
indices <- W.model!=0
WVect <- W[indices]
names(WVect) <- paste(rownames(nativeModel$formative)[row(W)[indices]],"=+",colnames(nativeModel$formative)[col(W)[indices]], sep="")
ret <- c(estimates,WVect)
allAttributes <- c(attributes(W),attributes(estimates))
for(a in setdiff(names(allAttributes), c("dim", "dimnames", "class", "names"))){
attr(ret,a) <- allAttributes[[a]]
attr(W,a) <- NULL
}
class(W) <- "numeric"
attr(ret,"W") <- W
attr(ret,"model") <- nativeModel
attr(ret,"call") <- match.call()
class(ret) <-("matrixpls")
return(ret)
}
print.matrixpls <- function(x, ...){
cat("\n matrixpls parameter estimates\n")
indices <- ! grepl("=+", names(x), fixed=TRUE)
toPrint <- x[indices]
estimates <- as.matrix(toPrint)
colnames(estimates)[1] <- "Est."
se <- attr(x,"se")
boot.out <- attr(x,"boot.out")
if(! is.null(boot.out)){
estimates <- cbind(estimates, apply(boot.out$t[,indices],2,stats::sd))
colnames(estimates)[2] <- "SE"
}
else if(! is.null(se)){
estimates <- cbind(estimates,se)
colnames(estimates)[2] <- "SE"
}
print(estimates, ...)
if(! is.null(boot.out)){
cat("\n Standard errors based on",boot.out$R,"bootstrap replications\n")
}
W <- attr(x,"W")
attr(W,"iterations") <- attr(x,"iterations")
attr(W,"converged") <- attr(x,"converged")
class(W) <- "matrixplsweights"
print(W, ...)
}
summary.matrixpls <- function(object, ...){
ret <- list(estimates = object,
effects = effects(object),
r2 = r2(object),
residuals = stats::residuals(object),
gof = gof(object),
cr = cr(object),
ave = ave(object),
htmt = htmt(object),
cei = cei(object))
class(ret) <-("matrixplssummary")
return(ret)
}
print.matrixplssummary <- function(x, ...){
for(element in x){
print(element, ...)
}
} |
expected <- eval(parse(text="FALSE"));
test(id=0, code={
argv <- eval(parse(text="list(structure(list(weight = c(4.17, 5.58), group = structure(c(1L, 1L), .Label = c(\"Ctl\", \"Trt\"), class = \"factor\")), .Names = c(\"weight\", \"group\"), row.names = 1:2, class = \"data.frame\"))"));
do.call(`is.array`, argv);
}, o=expected); |
quantileForecast.fitMOScsg0 <-
function(fit, ensembleData, quantiles = 0.5, dates = NULL, ...)
{
M <- matchEnsembleMembers(fit,ensembleData)
nForecasts <- ensembleSize(ensembleData)
if (!all(M == 1:nForecasts)) ensembleData <- ensembleData[,M]
M <- apply(ensembleForecasts(ensembleData), 1, function(z) all(is.na(z)))
ensembleData <- ensembleData[!M,]
nObs <- nrow(ensembleData)
if (!is.null(dates)) warning("dates ignored")
Quants <- matrix(NA, nObs, length(quantiles))
dimnames(Quants) <- list(ensembleObsLabels(ensembleData),as.character(quantiles))
Mu <- rep(NA,nObs)
Sig.sq <- rep(NA,nObs)
ensembleData <- ensembleForecasts(ensembleData)
x <- c(fit$a,fit$B)
A <- cbind(rep(1,nObs),ensembleData)
Q <- fit$q
S.sq <- apply(ensembleData,1,mean)
Mu <- A%*%x
Sig.sq <- rep(fit$c,nObs) + rep(fit$d,nObs)*S.sq
Shp <- Mu^2/Sig.sq
Scl <- Sig.sq/Mu
for (i in 1:length(quantiles))
{zero.prec <- pgamma(Q,shape=Shp,scale=Scl)>=quantiles[i]
Quants[is.na(zero.prec),i]<-NA
Quants[(!is.na(zero.prec))&zero.prec,i]<-0
Quants[(!is.na(zero.prec))&(!zero.prec),i] <- qgamma(quantiles[i], shape=Shp[(!is.na(zero.prec))&(!zero.prec)], scale=Scl[(!is.na(zero.prec))&(!zero.prec)])-fit$q}
Quants
} |
computeSpacingPredictors <- function(data, KCs) {
if (!("CF..reltime." %in% colnames(data))) {
data$CF..reltime. <- practiceTime(data)
}
if (!("CF..Time." %in% colnames(data))) {
data$CF..Time. <- data$CF..reltime.
}
for (i in KCs) {
data$index <- paste(eval(parse(text = paste("data$", i, sep = ""))), data$Anon.Student.Id, sep = "")
eval(parse(text = paste("data$", i, "spacing <- componentspacing(data,data$index,data$CF..Time.)", sep = "")))
eval(parse(text = paste("data$", i, "relspacing <- componentspacing(data,data$index,data$CF..reltime.)", sep = "")))
eval(parse(text = paste("data$", i, "prev <- componentprev(data,data$index,data$CF..ansbin.)", sep = "")))
data$index <- paste(eval(parse(text = paste("data$", i, sep = ""))), data$Anon.Student.Id, sep = "")
eval(parse(text = paste("data$", i, "meanspacing <- meanspacingf(data,data$index,data$", i, "spacing)", sep = "")))
eval(parse(text = paste("data$", i, "relmeanspacing <- meanspacingf(data,data$index,data$", i, "spacing)", sep = "")))
data$index <- paste(eval(parse(text = paste("data$", i, sep = ""))), data$Anon.Student.Id, sep = "")
eval(parse(text = paste("data$", i, "spacinglagged <- laggedspacingf(data,data$index,data$", i, "spacing)", sep = "")))
}
return(data)
}
LKT <- function(data,
components,
features,
fixedpars = NA,
seedpars = NA,
covariates = NA,
dualfit = FALSE,
interc = FALSE,
cv=FALSE,
elastic = FALSE,
verbose = TRUE,
epsilon = 1e-4,
cost = 512,
type = 0, maketimes = FALSE, bias = 0) {
if (maketimes) {
if (!("CF..reltime." %in% colnames(data))) {
data$CF..reltime. <- practiceTime(data)
}
if (!("CF..Time." %in% colnames(data))) {
data$CF..Time. <- data$CF..reltime.
}
}
if (!("Outcome" %in% colnames(data))) {
data$Outcome <- ifelse(data$CF..ansbin. == 1, "CORRECT", "INCORRECT")
}
if (!("CF..ansbin." %in% colnames(data))) {
data$CF..ansbin. <- ifelse(data$Outcome == "CORRECT", 1, 0)
}
equation <- "CF..ansbin.~ "
e <- new.env()
e$data <- data
e$fixedpars <- fixedpars
e$seedpars <- seedpars
e$counter <- 0
e$flag <- FALSE
modelfun <- function(seedparameters) {
k <- 0
optimparcount <- 1
fixedparcount <- 1
m <- 1
if (interc == TRUE) {
eq <- "1"
} else {
eq <- "0"
}
e$counter <- e$counter + 1
for (i in features) {
k <- k + 1
if (gsub("[$@]", "", i) %in% c(
"powafm", "recency", "recencysuc", "recencyfail", "errordec", "propdec", "propdec2",
"logitdec", "base", "expdecafm", "expdecsuc", "expdecfail", "dashafm", "dashsuc", "dashfail",
"base2", "base4", "basesuc", "basefail", "logit", "base2suc", "base2fail", "ppe",
"base5suc", "base5fail", "clogitdec", "crecency"
)) {
if (is.na(e$fixedpars[m])) {
para <- seedparameters[optimparcount]
e$flag <- TRUE
optimparcount <- optimparcount + 1
}
else {
if (e$fixedpars[m] >= 1 & e$fixedpars[m] %% 1 == 0) {
para <- seedparameters[e$fixedpars[m]]
} else {
para <- e$fixedpars[m]
}
}
m <- m + 1
}
if (gsub("[$]", "", i) %in% c("base2", "base4", "base2suc", "base2fail", "ppe", "base5suc", "base5fail")) {
if (is.na(e$fixedpars[m])) {
parb <- seedparameters[optimparcount]
optimparcount <- optimparcount + 1
}
else {
if (e$fixedpars[m] >= 1 & e$fixedpars[m] %% 1 == 0) {
parb <- seedparameters[e$fixedpars[m]]
} else {
parb <- e$fixedpars[m]
}
}
m <- m + 1
}
if (gsub("[$]", "", i) %in% c("base4", "ppe", "base5suc", "base5fail")) {
if (is.na(e$fixedpars[m])) {
parc <- seedparameters[optimparcount]
optimparcount <- optimparcount + 1
}
else {
if (e$fixedpars[m] >= 1 & e$fixedpars[m] %% 1 == 0) {
parc <- seedparameters[e$fixedpars[m]]
} else {
parc <- e$fixedpars[m]
}
}
m <- m + 1
}
if (gsub("[$]", "", i) %in% c("base4", "ppe", "base5suc", "base5fail")) {
if (is.na(e$fixedpars[m])) {
pard <- seedparameters[optimparcount]
optimparcount <- optimparcount + 1
}
else {
if (e$fixedpars[m] >= 1 & e$fixedpars[m] %% 1 == 0) {
pard <- seedparameters[e$fixedpars[m]]
} else {
pard <- e$fixedpars[m]
}
}
m <- m + 1
}
if (gsub("[$]", "", i) %in% c("base5suc", "base5fail")) {
if (is.na(e$fixedpars[m])) {
pare <- seedparameters[optimparcount]
optimparcount <- optimparcount + 1
}
else {
if (e$fixedpars[m] >= 1 & e$fixedpars[m] %% 1 == 0) {
pare <- seedparameters[e$fixedpars[m]]
} else {
pare <- e$fixedpars[m]
}
}
m <- m + 1
}
if (e$flag == TRUE | e$counter < 2) {
if (length(grep("%", components[k]))) {
KCs <- strsplit(components[k], "%")
e$data$index <- paste(eval(parse(text = paste("e$data$", KCs[[1]][1], sep = ""))), e$data$Anon.Student.Id, sep = "")
e$data$indexcomp <- paste(eval(parse(text = paste("e$data$", KCs[[1]][1], sep = ""))), sep = "")
e$data$cor <- as.numeric(paste(eval(parse(text = paste("countOutcomeGen(e$data,e$data$index,\"CORRECT\",e$data$", KCs[[1]][2], ",\"", KCs[[1]][3], "\")", sep = "")))))
e$data$icor <- as.numeric(paste(eval(parse(text = paste("countOutcomeGen(e$data,e$data$index,\"INCORRECT\",e$data$", KCs[[1]][2], ",\"", KCs[[1]][3], "\")", sep = "")))))
}
else
if (length(grep("\\?", components[k]))) {
KCs <- strsplit(components[k], "\\?")
e$data$indexcomp <- NULL
e$data$cor <- as.numeric(paste(eval(parse(text = paste("countOutcomeOther(e$data,e$data$Anon.Student.Id,\"CORRECT\",e$data$", KCs[[1]][3], ",\"", KCs[[1]][4], "\",e$data$", KCs[[1]][1], ",\"", KCs[[1]][2], "\")", sep = "")))))
e$data$icor <- as.numeric(paste(eval(parse(text = paste("countOutcomeOther(e$data,e$data$Anon.Student.Id,\"INCORRECT\",e$data$", KCs[[1]][3], ",\"", KCs[[1]][4], "\",e$data$", KCs[[1]][1], ",\"", KCs[[1]][2], "\")", sep = "")))))
}
else
if (length(grep("__", components[k]))) {
if (!(i %in% c("clogitdec"))) {
e$data$cor <- countOutcome(e$data, e$data$index, "CORRECT")
e$data$icor <- countOutcome(e$data, e$data$index, "INCORRECT")
}
}
else {
Anon.Student.Id<-index<-indexcomp<-NULL
vec <- eval(parse(text = paste0("e$data$", components[k])))
e$data[, index := do.call(paste0, list(vec, Anon.Student.Id))]
e$data[, indexcomp := vec]
if (!(i %in% c("numer", "intercept"))) {
e$data$cor <- countOutcome(e$data, e$data$index, "CORRECT")
e$data$icor <- countOutcome(e$data, e$data$index, "INCORRECT")
}
}
}
if (e$flag == TRUE | e$counter < 2) {
e$flag <- FALSE
if (right(i, 1) == "@") {
eval(parse(text = paste("e$data$", components[k],
"<-computefeatures(e$data,i,para,parb,e$data$index,e$data$indexcomp,
parc,pard,pare,components[k])",
sep = ""
)))
} else {
eval(parse(text = paste("e$data$", gsub("\\$", "", i), gsub("[%]", "", components[k]),
"<-computefeatures(e$data,i,para,parb,e$data$index,e$data$indexcomp,
parc,pard,pare,components[k])",
sep = "")))
}
}
if (verbose) {
cat(paste(
i, components[k], if (exists("para")) {
para
},
if (exists("parb")) {
parb
}, if (exists("parc")) {
parc
},
if (exists("pard")) {
pard
}, if (exists("pare")) {
pare
}, "\n"
))
}
if (exists("para")) {
rm(para)
}
if (exists("parb")) {
rm(parb)
}
if (exists("parc")) {
rm(parc)
}
if (exists("pard")) {
rm(pard)
}
if (exists("pare")) {
rm(pare)
}
if (right(i, 1) == "$") {
cleanfeat <- gsub("\\$", "", i)
if (is.na(covariates[k])) {
eval(parse(text = paste("eq<-paste(cleanfeat,components[k],\":e$data$\",components[k],
\"+\",eq,sep=\"\")")))
}
else {
eval(parse(text = paste("eq<-paste(cleanfeat,components[k],\":e$data$\",components[k]
,\":\",covariates[k]
,\"+\",eq,sep=\"\")")))
}
}
else if (right(i, 1) == "@") {
eval(parse(text = paste("eq<-paste(\"(1|\",components[k],\")+\",eq,sep=\"\")")))
}
else {
if (is.na(covariates[k])) {
eval(parse(text = paste("eq<-paste(i,gsub('[%]','',components[k]),\"+\",eq,sep=\"\")")))
}
else {
eval(parse(text = paste("eq<-paste(i,gsub('[%]','',components[k]),\":\",covariates[k],\"+\",eq,sep=\"\")")))
}
}
}
if (verbose) {
cat(paste(eq, "\n"))
}
e$form <- as.formula(paste(equation, eq, sep = ""))
if (any(grep("[@]", features)) & dualfit == FALSE) {
temp <- glmer(e$form, data = e$data, family = binomial(logit))
fitstat <- logLik(temp)
} else {
if (elastic == "glmnet") {
temp <- glmnet(e$form, data = e$data, family = "binomial")
plot(temp, xvar = "lambda", label = TRUE)
print(temp)
} else
if (elastic == "cv.glmnet") {
temp <- cv.glmnet(e$form, data = e$data, family = "binomial")
plot(temp)
print(temp)
print(coef(temp, s = "lambda.min"))
} else
if (elastic == "cva.glmnet") {
temp <- cva.glmnet(e$form, data = e$data, family = "binomial")
plot(temp)
print(temp)
} else {
predictset <- sparse.model.matrix(e$form, e$data)
predictset.csc <- new("matrix.csc",
ra = predictset@x,
ja = predictset@i + 1L,
ia = predictset@p + 1L,
dimension = predictset@Dim
)
predictset.csr <- as.matrix.csr(predictset.csc)
predictset2 <- predictset.csr
temp <- LiblineaR(predictset2, e$data$CF..ansbin.,
bias = bias,
cost = cost, epsilon = epsilon, type = type
)
if(temp$ClassNames[1]==0){temp$W=temp$W*(-1)}
modelvs <- data.frame(temp$W)
colnames(modelvs) <- colnames(predictset)
e$modelvs <- t(modelvs)
colnames(e$modelvs) <- "coefficient"
e$data$pred <- pmin(pmax(predict(temp, predictset2, proba = TRUE)$probabilities[, 1],.00001),.99999)
if(cv==TRUE){
cv_rmse<-rep(0,length(unique(e$data$fold)))
cv_mcfad<-rep(0,length(unique(e$data$fold)))
for(i in 1:length(unique(e$data$fold))){
idx1 = which(e$data$fold!=i)
e1_tmp = e$data[idx1,]
predictsetf1=slice(t(predictset),idx1)
predictsetf1=t(predictsetf1)
predictsetf1.csc <- new("matrix.csc", ra = predictsetf1@x,
ja = predictsetf1@i + 1L,
ia = predictsetf1@p + 1L,
dimension = predictsetf1@Dim)
predictsetf1.csr <- as.matrix.csr(predictsetf1.csc)
idx2 = which(e$data$fold==i)
e2_tmp = e$data[idx2,]
predictsetf2=slice(t(predictset),idx2)
predictsetf2=t(predictsetf2)
predictsetf2.csc <- new("matrix.csc", ra = predictsetf2@x,
ja = predictsetf2@i + 1L,
ia = predictsetf2@p + 1L,
dimension = predictsetf2@Dim)
predictsetf2.csr <- as.matrix.csr(predictsetf2.csc)
tempTr<-LiblineaR(predictsetf1.csr,e1_tmp$CF..ansbin.,bias=0,
cost=512,epsilon=.0001,type=0)
if(tempTr$ClassNames[1]==0){tempTr$W=tempTr$W*(-1)}
pred3<-predict(tempTr,predictsetf2.csr,proba=TRUE)$probabilities[,1]
e1_ansbin <-e1_tmp$CF..ansbin.
e2_ansbin <-e2_tmp$CF..ansbin.
cv_fitstat<- sum(log(ifelse(e2_ansbin==1,pred3,1-pred3)))
cv_nullmodel<-glm(as.formula(paste("CF..ansbin.~ 1",sep="")),data=e2_tmp,family=binomial(logit))
cv_nullfit<-logLik(cv_nullmodel)
cv_mcfad[i]= round(1-cv_fitstat/cv_nullfit,6)
cv_rmse[i] = sqrt(mean((e2_tmp$CF..ansbin.-pred3)^2))
}
e$cv_res = data.frame("rmse" = cv_rmse,"mcfad" = cv_mcfad)
}else{e$cv_res = data.frame("rmse" = rep(NA,5),"mcfad" = rep(NA,5))}
fitstat <- sum(log(ifelse(e$data$CF..ansbin. == 1, e$data$pred, 1 - e$data$pred)))
}
}
if (dualfit == TRUE && elastic == FALSE) {
rt.pred <- exp(-qlogis(e$data$pred[which(e$data$CF..ansbin. == 1)]))
outVals <- boxplot(e$data$Duration..sec., plot = FALSE)$out
outVals <- which(e$data$Duration..sec. %in% outVals)
e$data$Duration..sec. <- as.numeric(e$data$Duration..sec.)
if (length(outVals) > 0) {
e$data$Duration..sec.[outVals] <- quantile(e$data$Duration..sec., .95)
}
the.rt <- e$data$Duration..sec.[which(e$data$CF..ansbin. == 1)]
e$lm.rt <- lm(the.rt ~ as.numeric(rt.pred))
fitstat2 <- cor(the.rt, predict(e$lm.rt, type = "response"))^2
if (verbose) {
cat(paste("R2 (cor squared) latency: ", fitstat2, "\n", sep = ""))
}
}
e$temp <- temp
if (elastic == FALSE) {
e$nullmodel <- glm(as.formula(paste("CF..ansbin.~ 1", sep = "")), data = e$data, family = binomial(logit))
e$nullfit <- logLik(e$nullmodel)
e$loglike <- fitstat
e$mcfad <- round(1 - fitstat[1] / e$nullfit[1], 6)
if (verbose) {
cat(paste("McFadden's R2 logistic:", e$mcfad, "\n"))
cat(paste("LogLike logistic:", round(fitstat, 8), "\n"))
}
if (length(seedparameters) > 0 & verbose) {
cat(paste("step par values ="))
cat(seedparameters, sep = ",")
cat(paste("\n\n"))
}
-fitstat[1]
}
else {
NULL
}
}
parlength <-
sum("powafm" == gsub("[$]", "", features)) +
sum("recency" == gsub("[$]", "", features)) +
sum("crecency" == gsub("[$]", "", features)) +
sum("recencysuc" == gsub("[$]", "", features)) +
sum("recencyfail" == gsub("[$]", "", features)) +
sum("logit" == gsub("[$]", "", features)) +
sum("errordec" == gsub("[$]", "", features)) +
sum("propdec" == gsub("[$]", "", features)) +
sum("propdec2" == gsub("[$]", "", features)) +
sum("logitdec" == gsub("[$]", "", features)) +
sum("clogitdec" == gsub("[$]", "", features)) +
sum("base" == gsub("[$]", "", features)) +
sum("expdecafm" == gsub("[$]", "", features)) +
sum("expdecsuc" == gsub("[$]", "", features)) +
sum("expdecfail" == gsub("[$]", "", features)) +
sum("base2" == gsub("[$]", "", features)) * 2 +
sum("base4" == gsub("[$]", "", features)) * 4 +
sum("ppe" == gsub("[$]", "", features)) * 4 +
sum("basefail" == gsub("[$]", "", features)) +
sum("basesuc" == gsub("[$]", "", features)) +
sum("base2suc" == gsub("[$]", "", features)) * 2 +
sum("base2fail" == gsub("[$]", "", features)) * 2 +
sum("dashafm" == gsub("[$]", "", features)) +
sum("dashsuc" == gsub("[$]", "", features)) +
sum("dashfail" == gsub("[$]", "", features)) +
sum("base5suc" == gsub("[$]", "", features)) * 5 +
sum("base5fail" == gsub("[$]", "", features)) * 5 -
sum(!is.na(e$fixedpars))
seeds <- e$seedpars[is.na(e$fixedpars)]
seeds[is.na(seeds)] <- .5
if (parlength > 0) {
optimizedpars <- optim(seeds, modelfun, method = c("L-BFGS-B"), lower = 0.00001, upper = .99999, control = list(maxit = 100))
} else
{
modelfun(numeric(0))
}
if (dualfit == TRUE && elastic == FALSE) {
failureLatency <- mean(e$data$Duration..sec.[which(e$data$CF..ansbin. == 0)])
Scalar <- coef(e$lm.rt)[2]
Intercept <- coef(e$lm.rt)[1]
if (verbose) {
cat(paste("Failure latency: ", failureLatency, "\n"))
cat(paste("Latency Scalar: ", Scalar, "\n",
"Latency Intercept: ", Intercept, "\n",
sep = ""
))
}
}
results <- list(
"model" = e$temp,
"coefs" = e$modelvs,
"r2" = e$mcfad,
"prediction" = if ("pred" %in% colnames(e$data)) {
e$data$pred
},
"nullmodel" = e$nullmodel,
"latencymodel" = if (dualfit == TRUE) {
list(e$lm.rt, failureLatency)
},
"optimizedpars" = if (exists("optimizedpars")) {
optimizedpars
},
"studentRMSE" = if ("pred" %in% colnames(e$data)) {
aggregate((e$data$pred - e$data$CF..ansbin.)^2,
by = list(e$data$Anon.Student.Id), FUN = mean
)
},
"newdata" = e$data,
"cv_res" = e$cv_res,
"loglike" = e$loglike
)
results$studentRMSE[,2]<-sqrt(results$studentRMSE[,2])
return(results)
}
computefeatures <- function(data, feat, par1, par2, index, index2, par3, par4, par5, fcomp) {
mn<-Anon.Student.Id<-temptemp<-icor<-CF..ansbin.<-NULL
feat <- gsub("[$@]", "", feat)
if (feat == "intercept") {
return(as.character(index2))
}
if (feat == "numer") {
temp <- eval(parse(text = paste("data$", fcomp, sep = "")))
return(temp)
}
if (feat == "clineafm") {
data$temp <- 0
data$div <- 0
for (m in strsplit(fcomp, "__")[[1]]) {
data[, mn := do.call(paste0, list(eval(parse(text = paste("data$", m, sep = "")))))]
data[, index := do.call(paste, list(mn, Anon.Student.Id, sep = "-"))]
data$cor <- countOutcome(data, data$index, "CORRECT")
data$icor <- countOutcome(data, data$index, "INCORRECT")
data[, temptemp := cor + icor, by = index]
data[, temp := temp + temptemp * as.numeric(mn)]
data$div <- data$div + as.numeric(data$mn)
}
data$temp <- fifelse(data$div != 0, data$temp / data$div, 0)
return(data$temp)
}
if (feat == "clogafm") {
data$temp <- 0
data$div <- 0
for (m in strsplit(fcomp, "__")[[1]]) {
data[, mn := do.call(paste0, list(eval(parse(text = paste("data$", m, sep = "")))))]
data[, index := do.call(paste, list(mn, Anon.Student.Id, sep = "-"))]
data$cor <- countOutcome(data, data$index, "CORRECT")
data$icor <- countOutcome(data, data$index, "INCORRECT")
data[, temptemp := log(1 + icor + cor), by = index]
data[, temp := temp + temptemp * as.numeric(mn)]
data$div <- data$div + as.numeric(data$mn)
}
data$temp <- fifelse(data$div != 0, data$temp / data$div, 0)
return(data$temp)
}
if (feat == "lineafm") {
return((data$cor + data$icor))
}
if (feat == "logafm") {
return(log(1 + data$cor + data$icor))
}
if (feat == "powafm") {
return((data$cor + data$icor)^par1)
}
if (feat == "crecency") {
data$temp <- 0
data$div <- 0
for (m in strsplit(fcomp, "_")[[1]]) {
eval(parse(text = paste("data$rec <- data$", m, "spacing", sep = "")))
data$temp <-
data$temp + ifelse(data$rec == 0, 0, data$rec^-par1) * eval(parse(
text =
paste("data$", m, sep = "")
))
data$div <- data$div + eval(parse(text = paste("data$", m, sep = "")))
}
data$temp <- ifelse(data$div != 0, data$temp / data$div, 0)
return(data$temp)
}
if (feat == "recency") {
eval(parse(text = paste("data$rec <- data$", fcomp, "spacing", sep = "")))
return(ifelse(data$rec == 0, 0, data$rec^-par1))
}
if (feat == "expdecafm") {
return(ave(rep(1, length(data$CF..ansbin.)), index, FUN = function(x) slideexpdec(x, par1)))
}
if (feat == "base") {
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$CF..age. <- data$CF..Time. - data$mintime
return(log(1 + data$cor + data$icor) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1)))
}
if (feat == "base2") {
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$minreltime <- ave(data$CF..reltime., index, FUN = min)
data$CF..trueage. <- data$CF..Time. - data$mintime
data$CF..intage. <- data$CF..reltime. - data$minreltime
data$CF..age. <- (data$CF..trueage. - data$CF..intage.) * par2 + data$CF..intage.
return(log(1 + data$cor + data$icor) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1)))
}
if (feat == "base4") {
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$minreltime <- ave(data$CF..reltime., index, FUN = min)
data$CF..trueage. <- data$CF..Time. - data$mintime
data$CF..intage. <- data$CF..reltime. - data$minreltime
data$CF..age. <- (data$CF..trueage. - data$CF..intage.) * par2 + data$CF..intage.
eval(parse(text = paste("data$meanspace <- data$", fcomp, "meanspacing", sep = "")))
eval(parse(text = paste("data$meanspacerel <- data$", fcomp, "relmeanspacing", sep = "")))
data$meanspace2 <- par2 * (data$meanspace - data$meanspacerel) + data$meanspacerel
return(ifelse(data$meanspace <= 0,
par4 * log(1 + data$cor + data$icor) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1)),
data$meanspace2^par3 * log(1 + data$cor + data$icor) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1))
))
}
if (feat == "ppe") {
data$Nc <- (data$cor + data$icor)^par1
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$Tn <- data$CF..Time. - data$mintime
eval(parse(text = paste("data$space <- data$", fcomp, "spacinglagged", sep = "")))
data$space <- ifelse(data$space == 0, 0, 1 / log(data$space + exp(1)))
data$space <- ave(data$space, index, FUN = function(x) cumsum(x))
data$space <- ifelse((data$cor + data$icor) <= 1, 0, data$space / (data$cor + data$icor - 1))
data$tw <- ave(data$Tn, index, FUN = function(x) slideppetw(x, par4))
return(data$Nc * data$tw^-(par2 + par3 * data$space))
}
if (feat == "base5suc") {
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$minreltime <- ave(data$CF..reltime., index, FUN = min)
data$CF..trueage. <- data$CF..Time. - data$mintime
data$CF..intage. <- data$CF..reltime. - data$minreltime
data$CF..age. <- (data$CF..trueage. - data$CF..intage.) * par2 + data$CF..intage.
eval(parse(text = paste("data$meanspace <- data$", fcomp, "meanspacing", sep = "")))
eval(parse(text = paste("data$meanspacerel <- data$", fcomp, "relmeanspacing", sep = "")))
data$meanspace2 <- par2 * (data$meanspace - data$meanspacerel) + (data$meanspacerel)
return(ifelse(data$meanspace <= 0,
par4 * 10 * (log((par5 * 10) + data$cor)) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1)),
data$meanspace2^par3 * (log((par5 * 10) + data$cor)) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1))
))
}
if (feat == "base5fail") {
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$minreltime <- ave(data$CF..reltime., index, FUN = min)
data$CF..trueage. <- data$CF..Time. - data$mintime
data$CF..intage. <- data$CF..reltime. - data$minreltime
data$CF..age. <- (data$CF..trueage. - data$CF..intage.) * par2 + data$CF..intage.
eval(parse(text = paste("data$meanspace <- data$", fcomp, "meanspacing", sep = "")))
eval(parse(text = paste("data$meanspacerel <- data$", fcomp, "relmeanspacing", sep = "")))
data$meanspace2 <- par2 * (data$meanspace - data$meanspacerel) + (data$meanspacerel)
return(ifelse(data$meanspace <= 0,
par4 * 10 * (log((par5 * 10) + data$icor)) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1)),
data$meanspace2^par3 * (log((par5 * 10) + data$icor)) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1))
))
}
if (feat == "dashafm") {
data$x <- ave(data$CF..Time., index, FUN = function(x) countOutcomeDash(x, par1))
return(log(1 + data$x))
}
if (feat == "dashsuc") {
dataV <- data.frame(data$CF..Time., data$Outcome, index)
h <- countOutcomeDashPerf(dataV, "CORRECT", par1)
return(log(1 + h))
}
if (feat == "diffrelcor1") {
return(countRelatedDifficulty1(data, data$index, "CORRECT"))
}
if (feat == "diffrelcor2") {
return(countRelatedDifficulty2(data, data$index, "CORRECT"))
}
if (feat == "diffcor1") {
return(countOutcomeDifficulty1(data, data$index, "CORRECT"))
}
if (feat == "diffcor2") {
return(countOutcomeDifficulty2(data, data$index, "CORRECT"))
}
if (feat == "diffcorComp") {
return(countOutcomeDifficulty1(data, data$index, "CORRECT") - countOutcomeDifficulty2(data, data$index, "CORRECT"))
}
if (feat == "diffincorComp") {
return(countOutcomeDifficulty1(data, data$index, "INCORRECT") - countOutcomeDifficulty2(data, data$index, "INCORRECT"))
}
if (feat == "diffallComp") {
return(countOutcomeDifficultyAll1(data, data$index) - countOutcomeDifficultyAll2(data, data$index))
}
if (feat == "diffincor1") {
return(countOutcomeDifficulty1(data, data$index, "INCORRECT"))
}
if (feat == "diffincor2") {
return(countOutcomeDifficulty2(data, data$index, "INCORRECT"))
}
if (feat == "diffall1") {
return(countOutcomeDifficultyAll1(data, data$index))
}
if (feat == "diffall2") {
return(countOutcomeDifficultyAll2(data, data$index))
}
if (feat == "logsuc") {
return(log(1 + data$cor))
}
if (feat == "linesuc") {
return(data$cor)
}
if (feat == "clogsuc") {
data$temp <- 0
data$div <- 0
for (m in strsplit(fcomp, "__")[[1]]) {
data[, mn := do.call(paste0, list(eval(parse(text = paste("data$", m, sep = "")))))]
data[, index := do.call(paste, list(mn, Anon.Student.Id, sep = "-"))]
data$cor <- countOutcome(data, data$index, "CORRECT")
data[, temptemp := log(1 + cor), by = index]
data[, temp := temp + temptemp * as.numeric(mn)]
data$div <- data$div + as.numeric(data$mn)
}
data$temp <- fifelse(data$div != 0, data$temp / data$div, 0)
return(data$temp)
}
if (feat == "clinesuc") {
data$temp <- 0
data$div <- 0
for (m in strsplit(fcomp, "__")[[1]]) {
data[, mn := do.call(paste0, list(eval(parse(text = paste("data$", m, sep = "")))))]
data[, index := do.call(paste, list(mn, Anon.Student.Id, sep = "-"))]
data$cor <- countOutcome(data, data$index, "CORRECT")
data[, temptemp := cor, by = index]
data[, temp := temp + temptemp * as.numeric(mn)]
data$div <- data$div + as.numeric(data$mn)
}
data$temp <- fifelse(data$div != 0, data$temp / data$div, 0)
return(data$temp)
}
if (feat == "logfail") {
return(log(1 + data$icor))
}
if (feat == "linefail") {
return(data$icor)
}
if (feat == "clogfail") {
data$temp <- 0
data$div <- 0
for (m in strsplit(fcomp, "__")[[1]]) {
data[, mn := do.call(paste0, list(eval(parse(text = paste("data$", m, sep = "")))))]
data[, index := do.call(paste, list(mn, Anon.Student.Id, sep = "-"))]
data$icor <- countOutcome(data, data$index, "INCORRECT")
data[, temptemp := log(1 + icor), by = index]
data[, temp := temp + temptemp * as.numeric(mn)]
data$div <- data$div + as.numeric(data$mn)
}
data$temp <- fifelse(data$div != 0, data$temp / data$div, 0)
return(data$temp)
}
if (feat == "clinefail") {
data$temp <- 0
data$div <- 0
for (m in strsplit(fcomp, "__")[[1]]) {
data[, mn := do.call(paste0, list(eval(parse(text = paste("data$", m, sep = "")))))]
data[, index := do.call(paste, list(mn, Anon.Student.Id, sep = "-"))]
data$icor <- countOutcome(data, data$index, "INCORRECT")
data[, temptemp := icor, by = index]
data[, temp := temp + temptemp * as.numeric(mn)]
data$div <- data$div + as.numeric(data$mn)
}
data$temp <- fifelse(data$div != 0, data$temp / data$div, 0)
return(data$temp)
}
if (feat == "recencyfail") {
eval(parse(text = paste("data$rec <- data$", fcomp, "spacing", sep = "")))
eval(parse(text = paste("data$prev <- data$", fcomp, "prev", sep = "")))
return(ifelse(data$rec == 0, 0, (1 - data$prev) * data$rec^-par1))
}
if (feat == "recencysuc") {
eval(parse(text = paste("data$rec <- data$", fcomp, "spacing", sep = "")))
eval(parse(text = paste("data$prev <- data$", fcomp, "prev", sep = "")))
return(ifelse(data$rec == 0, 0, data$prev * data$rec^-par1))
}
if (feat == "expdecsuc") {
return(ave(data$CF..ansbin., index, FUN = function(x) slideexpdec(x, par1)))
}
if (feat == "expdecfail") {
return(ave(1 - data$CF..ansbin., index, FUN = function(x) slideexpdec(x, par1)))
}
if (feat == "basesuc") {
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$CF..age. <- data$CF..Time. - data$mintime
return(log(1 + data$cor) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1)))
}
if (feat == "basefail") {
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$CF..age. <- data$CF..Time. - data$mintime
return(log(1 + data$icor) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1)))
}
if (feat == "base2fail") {
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$minreltime <- ave(data$CF..reltime., index, FUN = min)
data$CF..trueage. <- data$CF..Time. - data$mintime
data$CF..intage. <- data$CF..reltime. - data$minreltime
data$CF..age. <- (data$CF..trueage. - data$CF..intage.) * par2 + data$CF..intage.
return(log(1 + data$icor) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1)))
}
if (feat == "base2suc") {
data$mintime <- ave(data$CF..Time., index, FUN = min)
data$minreltime <- ave(data$CF..reltime., index, FUN = min)
data$CF..trueage. <- data$CF..Time. - data$mintime
data$CF..intage. <- data$CF..reltime. - data$minreltime
data$CF..age. <- (data$CF..trueage. - data$CF..intage.) * par2 + data$CF..intage.
return(log(1 + data$cor) * ave(data$CF..age., index, FUN = function(x) baselevel(x, par1)))
}
if (feat == "linecomp") {
return((data$cor - data$icor))
}
if (feat == "logit") {
return(log((.1 + par1 * 30 + data$cor) / (.1 + par1 * 30 + data$icor)))
}
if (feat == "errordec") {
return(ave(data$pred_ed - data$CF..ansbin., index, FUN = function(x) slideerrordec(x, par1)))
}
if (feat == "propdec") {
return(ave(data$CF..ansbin., index, FUN = function(x) slidepropdec(x, par1)))
}
if (feat == "propdec2") {
return(ave(data$CF..ansbin., index, FUN = function(x) slidepropdec2(x, par1)))
}
if (feat == "logitdec") {
return(ave(data$CF..ansbin., index, FUN = function(x) slidelogitdec(x, par1)))
}
if (feat == "clogitdec") {
data$temp <- 0
data$div <- 0
for (m in strsplit(fcomp, "__")[[1]]) {
data[, mn := do.call(paste0, list(eval(parse(text = paste("data$", m, sep = "")))))]
data[, index := do.call(paste, list(mn, Anon.Student.Id, sep = "-"))]
data[mn == 1, temptemp := slidelogitdec(CF..ansbin., par1), by = index]
data[is.na(temptemp), temptemp := 0]
data[, temp := temp + temptemp * as.numeric(mn)]
data$div <- data$div + as.numeric(data$mn)
}
data$temp <- fifelse(data$div != 0, data$temp / data$div, 0)
return(data$temp)
}
if (feat == "prop") {
ifelse(is.nan(data$cor / (data$cor + data$icor)), .5, data$cor / (data$cor + data$icor))
}
}
getFeedDur <- function(data, index) {
temp <- rep(0, length(data$CF..ansbin.))
for (i in unique(index)) {
le <- length(data$time_to_answer[index == i])
subtemp <- data$time_since_prior_probe[index == i] - data$time_to_answer_inferred[index == i]
subtemp <- subtemp[2:(le - 1)]
subtemp <- c(subtemp, median(subtemp, na.rm = TRUE))
cutoff <- which(subtemp > 3600)
subtemp[cutoff] <- median(subtemp[-cutoff], na.rm = TRUE)
temp[index == i] <- subtemp
}
return(temp)
}
right <- function(string, char) {
substr(string, nchar(string) - (char - 1), nchar(string))
}
slice <- function(tSparse, index) {
the_slice <- tSparse[,index]
attr(the_slice, "mapping") <- attr(the_slice, "mapping")
return(the_slice)
}
countOutcome <- function(data, index, response) {
temp <- Outcome <- NULL
data[, temp := cumsum(Outcome == response), by = index]
data[Outcome == response, temp := temp - 1, by = index]
data$temp
}
countOutcomeDash <- function(times, scalev) {
l <- length(times)
v1 <- c(rep(0, l))
v2 <- c(rep(0, l))
v1[1] <- 0
v2[1] <- v1[1] + 1
if (l > 1) {
spacings <- times[2:l] - times[1:(l - 1)]
for (i in 2:l) {
v1[i] <- v2[i - 1] * exp(-spacings[i - 1] / (scalev * 86400))
v2[i] <- v1[i] + 1
}
}
return(v1)
}
countOutcomeDashPerf <- function(datav, seeking, scalev) {
temp <- rep(0, length(datav[, 1]))
for (s in unique(datav[, 3])) {
l <- length(datav[, 1][datav[, 3] == s])
v1 <- c(rep(0, l))
v2 <- c(rep(0, l))
r <- as.character(datav[, 2][datav[, 3] == s]) == seeking
v1[1] <- 0
v2[1] <- v1[1] + r[1]
if (l > 1) {
spacings <- as.numeric(datav[, 1][datav[, 3] == s][2:l]) - as.numeric(datav[, 1][datav[, 3] == s][1:(l - 1)])
for (i in 2:l) {
v1[i] <- v2[i - 1] * exp(-spacings[i - 1] / (scalev * 86400))
v2[i] <- v1[i] + r[i]
}
}
temp[datav[, 3] == s] <- v1
}
return(temp)
}
countOutcomeDifficulty1 <- function(data, index, r) {
temp <- data$pred
temp <- ifelse(data$Outcome == r, temp, 0)
data$temp <- ave(temp, index, FUN = function(x) as.numeric(cumsum(x)))
data$temp <- data$temp - temp
data$temp
}
countRelatedDifficulty1 <- function(data, index, r) {
temp <- (data$contran)
temp <- ifelse(data$Outcome == r, temp, 0)
data$temp <- ave(temp, index, FUN = function(x) as.numeric(cumsum(x)))
data$temp <- data$temp - temp
data$temp
}
countRelatedDifficulty2 <- function(data, index, r) {
temp <- (data$contran)^2
temp <- ifelse(data$Outcome == r, temp, 0)
data$temp <- ave(temp, index, FUN = function(x) as.numeric(cumsum(x)))
data$temp <- data$temp - temp
data$temp
}
countOutcomeDifficulty2 <- function(data, index, r) {
temp <- data$pred^2
temp <- ifelse(data$Outcome == r, temp, 0)
data$temp <- ave(temp, index, FUN = function(x) as.numeric(cumsum(x)))
data$temp <- data$temp - temp
data$temp
}
countOutcomeDifficultyAll1 <- function(data, index) {
temp <- data$pred
data$temp <- ave(temp, index, FUN = function(x) as.numeric(cumsum(x)))
data$temp <- data$temp - temp
data$temp
}
countOutcomeDifficultyAll2 <- function(data, index) {
temp <- data$pred^2
data$temp <- ave(temp, index, FUN = function(x) as.numeric(cumsum(x)))
data$temp <- data$temp - temp
data$temp
}
countOutcomeGen <- function(data, index, item, sourcecol, sourc) {
data$tempout <- paste(data$Outcome, sourcecol)
item <- paste(item, sourc)
data$temp <- as.numeric(ave(as.character(data$tempout), index, FUN = function(x) as.numeric(cumsum(tolower(x) == tolower(item)))))
data$temp <- data$temp - as.numeric(tolower(as.character(data$tempout)) == tolower(item))
as.numeric(data$temp)
}
countOutcomeOther <- function(data, index, item, sourcecol, sourc, targetcol, target) {
data$tempout <- paste(data$Outcome, sourcecol)
item <- paste(item, sourc)
targetcol <- as.numeric(targetcol == target)
data$temp <- ave(as.character(data$tempout), index, FUN = function(x) as.numeric(cumsum(tolower(x) == tolower(item))))
data$temp[tolower(as.character(data$tempout)) == tolower(item)] <- as.numeric(data$temp[tolower(as.character(data$tempout)) == tolower(item)]) - 1
as.numeric(data$temp) * targetcol
}
practiceTime <- function(data) {
temp <- rep(0, length(data$CF..ansbin.))
for (i in unique(data$Anon.Student.Id)) {
if (length(data$Duration..sec.[data$Anon.Student.Id == i]) > 1) {
temp[data$Anon.Student.Id == i] <-
c(0, cumsum(data$Duration..sec.[data$Anon.Student.Id == i])
[1:(length(cumsum(data$Duration..sec.[data$Anon.Student.Id == i])) - 1)])
}
}
return(temp)
}
componentspacing <- function(data, index, times) {
temp <- rep(0, length(data$CF..ansbin.))
for (i in unique(index)) {
lv <- length(data$CF..ansbin.[index == i])
if (lv > 1) {
temp[index == i] <- c(0, times[index == i][2:(lv)] - times[index == i][1:(lv - 1)])
}
}
return(temp)
}
componentprev <- function(data, index, answers) {
temp <- rep(0, length(data$CF..ansbin.))
for (i in unique(index)) {
lv <- length(data$CF..ansbin.[index == i])
if (lv > 1) {
temp[index == i] <- c(0, answers[index == i][1:(lv - 1)])
}
}
return(temp)
}
meanspacingf <- function(data, index, spacings) {
temp <- rep(0, length(data$CF..ansbin.))
for (i in unique(index)) {
j <- length(temp[index == i])
if (j > 1) {
temp[index == i][2] <- -1
}
if (j == 3) {
temp[index == i][3] <- spacings[index == i][2]
}
if (j > 3) {
temp[index == i][3:j] <- cumsum(spacings[index == i][2:(j - 1)]) / (1:(j - 2))
}
}
return(temp)
}
laggedspacingf <- function(data, index, spacings) {
temp <- rep(0, length(data$CF..ansbin.))
for (i in unique(index)) {
j <- length(temp[index == i])
if (j > 1) {
temp[index == i][2] <- 0
}
if (j >= 3) {
temp[index == i][3:j] <- spacings[index == i][2:(j - 1)]
}
}
return(temp)
}
errordec <- function(v, d) {
w <- length(v)
sum((c(0, v[1:w]) * d^((w):0)) / sum(d^((w + 1):0)))
}
slideerrordec <- function(x, d) {
v <- c(rep(0, length(x)))
for (i in 1:length(x)) {
v[i] <- errordec(x[1:i], d)
}
return(c(0, v[1:length(x) - 1]))
}
expdec <- function(v, d) {
w <- length(v)
sum(v[1:w] * d^((w - 1):0))
}
propdec2 <- function(v, d) {
w <- length(v)
sum((v[1:w] * d^((w - 1):0)) / sum(d^((w + 2):0)))
}
propdec <- function(v, d) {
w <- length(v)
sum((c(1, v[1:w]) * d^((w):0)) / sum(d^((w + 1):0)))
}
logitdec <- function(v, d) {
w <- length(v)
corv <- sum(c(1, v[1:w]) * d^(w:0))
incorv <- sum(c(1, abs(v[1:w] - 1)) * d^(w:0))
log(corv / incorv)
}
slideexpdec <- function(x, d) {
v <- c(rep(0, length(x)))
for (i in 1:length(x)) {
v[i] <- expdec(x[1:i], d)
}
return(c(0, v[1:length(x) - 1]))
}
slidepropdec <- function(x, d) {
v <- c(rep(0, length(x)))
for (i in 1:length(x)) {
v[i] <- propdec(x[1:i], d)
}
return(c(.5, v[1:length(x) - 1]))
}
slidepropdec2 <- function(x, d) {
v <- c(rep(0, length(x)))
for (i in 1:length(x)) {
v[i] <- propdec2(x[1:i], d)
}
return(c(0, v[1:length(x) - 1]))
}
slidelogitdec <- function(x, d) {
v <- c(rep(0, length(x)))
for (i in 1:length(x)) {
v[i] <- logitdec(x[1:i], d)
}
return(c(0, v[1:length(x) - 1]))
}
slidelogitdec <- function(x, d) {
v <- c(rep(0, length(x)))
for (i in 1:length(x)) {
v[i] <- logitdec(x[max(1, i - 60):i], d)
}
return(c(0, v[1:length(x) - 1]))
}
ppew <- function(times, wpar) {
times^-wpar *
(1 / sum(times^-wpar))
}
ppet <- function(times) {
times[length(times)] - times
}
ppetw <- function(x, d) {
v <- length(x)
ppetv <- ppet(x)[1:(v - 1)]
ppewv <- ppew(ppetv, d)
ifelse(is.nan(crossprod(ppewv[1:(v - 1)], ppetv[1:(v - 1)])),
1,
crossprod(ppewv[1:(v - 1)], ppetv[1:(v - 1)])
)
}
slideppetw <- function(x, d) {
v <- c(rep(0, length(x)))
for (i in 1:length(x)) {
v[i] <- ppetw(x[1:i], d)
}
return(c(v[1:length(x)]))
}
baselevel <- function(x, d) {
l <- length(x)
return(c(0, x[2:l]^-d)[1:l])
}
splittimes <- function(times) {
(match(max(rank(diff(times))), rank(diff(times))))
}
smallSet <- function(data, nSub) {
totsub <- length(unique(data$Anon.Student.Id))
datasub <- unique(data$Anon.Student.Id)
smallSub <- datasub[sample(1:totsub)[1:nSub]]
smallIdx <- which(data$Anon.Student.Id %in% smallSub)
smalldata <- data[smallIdx, ]
smalldata <- droplevels(smalldata)
return(smalldata)
}
texteval <- function(stringv) {
eval(parse(text = stringv))
} |
test_that("custom scalar translated correctly", {
local_con(simulate_postgres())
expect_equal(translate_sql(bitwXor(x, 128L)), sql("`x`
expect_equal(translate_sql(log10(x)), sql("LOG(`x`)"))
expect_equal(translate_sql(log(x)), sql("LN(`x`)"))
expect_equal(translate_sql(log(x, 2)), sql("LOG(`x`) / LOG(2.0)"))
expect_equal(translate_sql(cot(x)), sql("1 / TAN(`x`)"))
expect_equal(translate_sql(round(x, digits = 1.1)), sql("ROUND((`x`) :: numeric, 1)"))
expect_equal(translate_sql(grepl("exp", x)), sql("(`x`) ~ ('exp')"))
expect_equal(translate_sql(grepl("exp", x, TRUE)), sql("(`x`) ~* ('exp')"))
expect_equal(translate_sql(substr("test", 2 , 3)), sql("SUBSTR('test', 2, 2)"))
})
test_that("custom stringr functions translated correctly", {
local_con(simulate_postgres())
expect_equal(translate_sql(str_detect(x, y)), sql("`x` ~ `y`"))
expect_equal(translate_sql(str_detect(x, y, negate = TRUE)), sql("!(`x` ~ `y`)"))
expect_equal(translate_sql(str_replace(x, y, z)), sql("REGEXP_REPLACE(`x`, `y`, `z`)"))
expect_equal(translate_sql(str_replace_all(x, y, z)), sql("REGEXP_REPLACE(`x`, `y`, `z`, 'g')"))
expect_equal(translate_sql(str_squish(x)), sql("LTRIM(RTRIM(REGEXP_REPLACE(`x`, '\\s+', ' ', 'g')))"))
expect_equal(translate_sql(str_remove(x, y)), sql("REGEXP_REPLACE(`x`, `y`, '')"))
expect_equal(translate_sql(str_remove_all(x, y)), sql("REGEXP_REPLACE(`x`, `y`, '', 'g')"))
})
test_that("two variable aggregates are translated correctly", {
local_con(simulate_postgres())
expect_equal(translate_sql(cor(x, y), window = FALSE), sql("CORR(`x`, `y`)"))
expect_equal(translate_sql(cor(x, y), window = TRUE), sql("CORR(`x`, `y`) OVER ()"))
})
test_that("pasting translated correctly", {
local_con(simulate_postgres())
expect_equal(translate_sql(paste(x, y), window = FALSE), sql("CONCAT_WS(' ', `x`, `y`)"))
expect_equal(translate_sql(paste0(x, y), window = FALSE), sql("CONCAT_WS('', `x`, `y`)"))
expect_error(translate_sql(paste0(x, collapse = ""), window = FALSE), "`collapse` not supported")
})
test_that("postgres mimics two argument log", {
local_con(simulate_postgres())
expect_equal(translate_sql(log(x)), sql('LN(`x`)'))
expect_equal(translate_sql(log(x, 10)), sql('LOG(`x`) / LOG(10.0)'))
expect_equal(translate_sql(log(x, 10L)), sql('LOG(`x`) / LOG(10)'))
})
test_that("custom lubridate functions translated correctly", {
local_con(simulate_postgres())
expect_equal(translate_sql(yday(x)), sql("EXTRACT(DOY FROM `x`)"))
expect_equal(translate_sql(quarter(x)), sql("EXTRACT(QUARTER FROM `x`)"))
expect_equal(translate_sql(quarter(x, with_year = TRUE)), sql("(EXTRACT(YEAR FROM `x`) || '.' || EXTRACT(QUARTER FROM `x`))"))
expect_error(translate_sql(quarter(x, fiscal_start = 2)))
expect_equal(translate_sql(seconds(x)), sql("CAST('`x` seconds' AS INTERVAL)"))
expect_equal(translate_sql(minutes(x)), sql("CAST('`x` minutes' AS INTERVAL)"))
expect_equal(translate_sql(hours(x)), sql("CAST('`x` hours' AS INTERVAL)"))
expect_equal(translate_sql(days(x)), sql("CAST('`x` days' AS INTERVAL)"))
expect_equal(translate_sql(weeks(x)), sql("CAST('`x` weeks' AS INTERVAL)"))
expect_equal(translate_sql(months(x)), sql("CAST('`x` months' AS INTERVAL)"))
expect_equal(translate_sql(years(x)), sql("CAST('`x` years' AS INTERVAL)"))
expect_equal(translate_sql(floor_date(x, 'month')), sql("DATE_TRUNC('month', `x`)"))
expect_equal(translate_sql(floor_date(x, 'week')), sql("DATE_TRUNC('week', `x`)"))
})
test_that("custom SQL translation", {
lf <- lazy_frame(x = 1, con = simulate_postgres())
expect_snapshot(left_join(lf, lf, by = "x", na_matches = "na"))
})
test_that("can explain", {
db <- copy_to_test("postgres", data.frame(x = 1:3))
expect_snapshot(db %>% mutate(y = x + 1) %>% explain())
})
test_that("can overwrite temp tables", {
src <- src_test("postgres")
copy_to(src, mtcars, "mtcars", overwrite = TRUE)
expect_error(copy_to(src, mtcars, "mtcars", overwrite = TRUE), NA)
}) |
tmap_icons <- function(file, width=48, height=48, keep.asp=TRUE, just=c("center", "center"), as.local=TRUE, ...) {
icon_names <- names(file)
icons <- lapply(file, tmap_one_icon, width=width, height=height, keep.asp=keep.asp, just=just, as.local=as.local, ...)
merge_icons(icons, icon_names)
}
tmap_one_icon <- function(file, width, height, keep.asp, just, as.local, ...) {
args <- list(...)
args$iconUrl <- NULL
pu <- is_path_or_url(file)
if (is.na(pu)) {
stop(file, " is neither a valid path nor url", call.=FALSE)
}
if (!pu) {
tmpfile <- tempfile(fileext=".png")
download.file(file, destfile=tmpfile, mode="wb")
localfile <- tmpfile
} else {
localfile <- file
}
if (!pu && as.local) file <- localfile
if (any(c("iconWidth", "iconHeight") %in% names(args))) keep.asp <- FALSE
if (keep.asp) {
x <- png::readPNG(localfile)
xasp <- dim(x)[2]/dim(x)[1]
iasp <- width/height
if (xasp > iasp) {
height <- floor(width/xasp)
} else {
width <- floor(height*xasp)
}
}
if (!("iconWidth" %in% names(args))) args$iconWidth <- width
if (!("iconHeight" %in% names(args))) args$iconHeight <- height
just <- c(ifelse(is_num_string(just[1]), as.numeric(just[1]), ifelse(just[1]=="left", 1, ifelse(just[1]=="right", 0, .5))),
ifelse(is_num_string(just[2]), as.numeric(just[2]), ifelse(just[2]=="bottom", 1, ifelse(just[2]=="top", 0, .5))))
if (!("iconAnchorX" %in% names(args))) args$iconAnchorX <- round(args$iconWidth * (1-just[1]))
if (!("iconAnchorY" %in% names(args))) args$iconAnchorY <- round(args$iconHeight * just[2])
do.call(leaflet::icons, c(list(iconUrl=file), args))
}
marker_icon <- function() {
file <- system.file("htmlwidgets/lib/leaflet/images/marker-icon.png", package="leaflet")
if (!file.exists(file)) stop("leaflet marker icon not found")
icons(iconUrl = system.file("htmlwidgets/lib/leaflet/images/marker-icon.png", package="leaflet"), iconWidth=25, iconHeight=41, iconAnchorX = 12, iconAnchorY = 41)
}
pngGrob <- function(file, fix.borders=FALSE, n=NULL, height.inch=NULL, target.dpi=NULL) {
if (!requireNamespace("png", quietly = TRUE)) {
stop("png package needed for this function to work. Please install it.",
call. = FALSE)
} else {
pu <- is_path_or_url(file)
if (is.na(pu)) {
stop(file, " is neither a valid path nor url", call.=FALSE)
}
if (!pu) {
tmpfile <- tempfile(fileext=".png")
download.file(file, destfile=tmpfile, mode="wb")
file <- tmpfile
}
x <- png::readPNG(file)
if (fix.borders) {
if (dim(x)[3]==3) {
x <- array(c(x, rep(1, dim(x)[1]*dim(x)[2])), dim = c(dim(x)[1], dim(x)[2], dim(x)[3]+1))
}
x2 <- add_zero_borders_to_3d_array(x, n=n, height.inch=height.inch,target.dpi=target.dpi)
rasterGrob(x2, interpolate=TRUE)
} else {
rasterGrob(x, interpolate=TRUE)
}
}
}
add_zero_borders_to_3d_array <- function(x, perc=NA, n=NULL, height.inch=NULL, target.dpi=NULL) {
dims <- dim(x)
if (is.na(perc)) {
dpi <- dims[2] / height.inch
compress <- dpi/target.dpi
borders <- rep(compress * n, 2)
} else {
borders <- round(dims / 100 * perc)
}
res <- lapply(1:dims[3], function(i) {
rbind(cbind(x[,,i], matrix(0, nrow=nrow(x), ncol=borders[2])), matrix(0, nrow=borders[1], ncol=ncol(x)+borders[2]))
})
array(unlist(res, use.names = FALSE), dim = c(dim(x)[1]+borders[1], dim(x)[2]+borders[2], dim(x)[3]))
}
icon2grob <- function(icon) {
if (!is.list(icon)) stop("icon is not a list")
if (!"iconUrl" %in% names(icon)) stop("iconUrl not defined")
if (length(icon$iconUrl)==1) {
pngGrob(icon$iconUrl)
} else {
lapply(icon$iconUrl, pngGrob)
}
}
grob2icon <- function(grob, grob.dim, just) {
tmp <- tempfile(fileext=".png")
png(filename=tmp, width=grob.dim[3], height=grob.dim[4], bg = "transparent")
grid.draw(grob)
dev.off()
w <- grob.dim[1]
h <- grob.dim[2]
icons(iconUrl = tmp, iconWidth = w, iconHeight = h, iconAnchorX = w * (1-just[1]), iconAnchorY = h * just[2])
}
split_icon <- function(icon) {
ni <- max(vapply(icon, length, integer(1)))
icon_max <- lapply(icon, function(ic) {
rep(ic, length.out=ni)
})
if ("iconNames" %in% names(icon_max)) {
icon_names <- icon_max$iconNames
icon_max$iconNames <- NULL
} else {
icon_names <- NULL
}
res <- lapply(1:ni, function(i) {
lapply(icon_max, function(ic) {
ic[i]
})
})
if (!is.null(icon_names)) names(res) <- icon_names
res
}
merge_icons <- function(icons, icon_names = NULL) {
list_names <- unique(unlist(lapply(icons, names), use.names = FALSE))
names(list_names) <- list_names
res <- lapply(list_names, function(ln) {
unname(sapply(icons, function(ic) {
if (ln %in% names(ic)) {
ic[[ln]][1]
} else NA
}))
})
if (!is.null(icon_names)) res$iconNames <- icon_names
res
}
is_path_or_url <- function(file) {
if (file.exists(file)) {
TRUE
} else {
con.url <- suppressWarnings(try({
u <- url(file, open='rb')
close(u)
}, silent=TRUE))
try.error <- inherits(con.url, "try-error")
if (try.error) NA else FALSE
}
} |
gridp4d <- function (p4d, trait = names(tdata(p4d)), center = TRUE, scale = TRUE,
tree.ladderize = FALSE, tree.type = "phylogram", tree.ratio = NULL,
tree.xlim = NULL, tree.open.angle = 0, tree.open.crown = TRUE,
show.tip = TRUE, tip.labels = NULL, tip.col = "black",
tip.cex = 1, tip.font = 3, tip.adj = 0, cell.col = topo.colors(100),
show.color.scale = TRUE, show.trait = TRUE, trait.labels = NULL,
trait.col = "black", trait.cex = 0.7, trait.font = 1,
trait.bg.col = "grey90", show.box = FALSE, grid.vertical = FALSE,
grid.horizontal = FALSE, grid.col = "grey25", grid.lty = "dashed",
...)
{
plot.phylo4d(x = p4d, trait = trait, center = center, scale = scale,
plot.type = "gridplot", tree.ladderize = tree.ladderize,
tree.type = tree.type, tree.ratio = tree.ratio, tree.xlim = tree.xlim,
tree.open.angle = tree.open.angle, tree.open.crown = tree.open.crown,
show.tip = show.tip, tip.labels = tip.labels, tip.col = tip.col,
tip.cex = tip.cex, tip.font = tip.font, tip.adj = tip.adj,
cell.col = cell.col, show.color.scale = show.color.scale,
show.trait = show.trait, trait.labels = trait.labels,
trait.col = trait.col, trait.cex = trait.cex, trait.font = trait.font,
trait.bg.col = trait.bg.col, show.box = show.box, grid.vertical = grid.vertical,
grid.horizontal = grid.horizontal, grid.col = grid.col,
grid.lty = grid.lty, ...)
} |
BASIX.match <- function(elements, vec){
ids <- .Call("my_match_C",elements,vec, PACKAGE="BASIX")
return(ids)
} |
setClass(
Class = "Tr.label",
contains = "ADEg.Tr"
)
setMethod(
f = "initialize",
signature = "Tr.label",
definition = function(.Object, data = list(dfxyz = NULL, labels = NULL, frame = 0, storeData = TRUE), ...) {
.Object <- callNextMethod(.Object, data = data, ...)
.Object@data$labels <- data$labels
return(.Object)
})
setMethod(
f = "prepare",
signature = "Tr.label",
definition = function(object) {
name_obj <- deparse(substitute(object))
if(object@data$storeData) {
labels <- object@data$labels
df <- object@data$dfxyz
} else {
labels <- eval(object@data$labels, envir = sys.frame(object@data$frame))
df <- eval(object@data$dfxyz, envir = sys.frame(object@data$frame))
}
oldparamadeg <- adegpar()
on.exit(adegpar(oldparamadeg))
adegtot <- adegpar([email protected])
if((is.null([email protected]$plabels$boxes$draw) & adegtot$plabels$optim) || (is.null([email protected]$plabels$boxes$draw) & length(labels) > 1000))
adegtot$plabels$boxes$draw <- FALSE
if([email protected]$addmean) {
default <- list(pch = 20, col = "black", cex = 2)
if(is.list([email protected]$meanpar))
[email protected]$meanpar <- modifyList(default, [email protected]$meanpar, keep.null = TRUE)
else {
if(!is.null([email protected]$meanpar))
stop("meanpar must be a list of graphical parameters (pch, col, cex)", call. = FALSE)
else
[email protected]$meanpar <- default
}
}
if([email protected]$addaxes | [email protected]$addmean) {
default <- list(col = "black", lwd = 1, lty = 1)
if(is.list([email protected]$axespar))
[email protected]$axespar <- modifyList(default, [email protected]$axespar, keep.null = TRUE)
else {
if(!is.null([email protected]$axespar))
stop("axespar must be a list of graphical parameters (lwd, col, lty)", call. = FALSE)
else
[email protected]$axespar <- default
}
default <- list(pch = 20, col = "black", cex = 2)
if(is.list([email protected]$meanpar))
[email protected]$meanpar <- modifyList(default, [email protected]$meanpar, keep.null = TRUE)
else {
if(!is.null([email protected]$meanpar))
stop("meanpar must be a list of graphical parameters (pch, col, cex)", call. = FALSE)
else
[email protected]$meanpar <- default
}
}
[email protected] <- adegtot
callNextMethod()
df <- sweep(df, 1, rowSums(df), "/")
object@stats$coords2d <- .coordtotriangleM(df, mini3 = [email protected]$min3d, maxi3 = [email protected]$max3d)[, 2:3]
assign(name_obj, object, envir = parent.frame())
})
setMethod(
f = "panel",
signature = "Tr.label",
definition = function(object, x, y) {
if(object@data$storeData) {
labels <- object@data$labels
df <- object@data$dfxyz
} else {
labels <- eval(object@data$labels, envir = sys.frame(object@data$frame))
df <- eval(object@data$dfxyz, envir = sys.frame(object@data$frame))
}
if(any([email protected]$ppoints$cex > 0))
panel.points(object@stats$coords2d[, 1], object@stats$coords2d[, 2], pch = [email protected]$ppoints$pch, cex = [email protected]$ppoints$cex, col = [email protected]$ppoints$col, alpha = [email protected]$ppoints$alpha, fill = [email protected]$ppoints$fill)
if(any([email protected]$plabels$cex > 0))
adeg.panel.label(object@stats$coords2d[, 1], object@stats$coords2d[, 2], labels, [email protected]$plabels)
if([email protected]$addmean | [email protected]$addaxes) {
df <- sweep(df, 1, rowSums(df), "/")
mini3 <- [email protected]$min3d
maxi3 <- [email protected]$max3d
m3 <- colMeans(df)
mxy <- .coordtotriangleM(t(as.matrix(m3)), mini3 = mini3, maxi3 = maxi3)[-1]
if([email protected]$addmean) {
axp3 <- rbind(c(m3[1], mini3[2], 1 - m3[1] - mini3[2]),
c(1 - m3[2] -mini3[3], m3[2], mini3[3]),
c(mini3[1], 1 - m3[3] - mini3[1], m3[3]))
axpxyz <- .coordtotriangleM(axp3, mini3 = mini3, maxi3 = maxi3)
apply(axpxyz, 1, FUN = function(x) {
do.call("panel.lines", c(list(x = c(x[2], mxy[1]), y = c(x[3], mxy[2])), [email protected]$axespar))
})
do.call("panel.points", c(list(x = c(mxy[1], axpxyz[, 2]), y = c(mxy[2], axpxyz[, 3])), [email protected]$meanpar))
panel.text(x = axpxyz[, 2], y = axpxyz[, 3], labels = as.character(round(m3, digits = 4)), pos = c(2, 1, 4))
}
if([email protected]$addaxes) {
axx <- dudi.pca(df, scale = FALSE, scannf = FALSE)$c1
cornerp <- [email protected]$cornerp
a1 <- axx[, 1]
x1 <- a1[1] * cornerp$A + a1[2] * cornerp$B + a1[3] * cornerp$C
do.call("panel.segments", c(list(x0 = mxy[1] - x1[1], x1 = mxy[1] + x1[1], y0 = mxy[2] - x1[2], y1 = mxy[2] + x1[2]), [email protected]$axespar))
a2 <- axx[, 2]
x1 <- a2[1] * cornerp$A + a2[2] * cornerp$B + a2[3] * cornerp$C
do.call("panel.segments", c(list(x0 = mxy[1] - x1[1], x1 = mxy[1] + x1[1], y0 = mxy[2] - x1[2], y1 = mxy[2] + x1[2]), [email protected]$axespar))
do.call("panel.points", c(list(x = mxy[1], y = mxy[2]), [email protected]$meanpar))
}
}
})
triangle.label <- function(dfxyz, labels = rownames(dfxyz), adjust = TRUE, min3d = NULL, max3d = NULL, addaxes = FALSE, addmean = FALSE, meanpar = NULL, axespar = NULL,
showposition = TRUE, facets = NULL, plot = TRUE, storeData = TRUE, add = FALSE, pos = -1, ...) {
thecall <- .expand.call(match.call())
sortparameters <- sortparamADEg(...)
if(!is.null(facets)) {
object <- multi.facets.Tr(thecall, samelimits = sortparameters$g.args$samelimits)
}
else {
if(length(sortparameters$rest))
warning(c("Unused parameters: ", paste(unique(names(sortparameters$rest)), " ", sep = "")), call. = FALSE)
g.args <- c(sortparameters$g.args, list(adjust = adjust, min3d = min3d, max3d = max3d, addaxes = addaxes, addmean = addmean, meanpar = meanpar, axespar = axespar))
if(storeData)
tmp_data <- list(dfxyz = dfxyz, labels = labels, frame = sys.nframe() + pos, storeData = storeData)
else
tmp_data <- list(dfxyz = thecall$dfxyz, labels = thecall$labels, frame = sys.nframe() + pos, storeData = storeData)
object <- new(Class = "Tr.label", data = tmp_data, adeg.par = sortparameters$adepar, trellis.par = sortparameters$trellis, g.args = g.args, Call = match.call())
prepare(object)
setlatticecall(object)
if(showposition & add) {
warning("cannot show position and add")
showposition <- FALSE
}
if(showposition)
object <- new(Class = "ADEgS", ADEglist = list("triangle" = object, "positions" = .showpos(object)), positions = rbind(c(0, 0, 1, 1), c(0, 0.7, 0.3, 1)), add = matrix(0, ncol = 2, nrow = 2), Call = match.call())
if(add)
object <- add.ADEg(object)
}
if(!add & plot)
print(object)
invisible(object)
} |
sizelegend<-function(se, am, pch=pch)
{
if(missing(pch)) pch= 1
u = par('usr')
ex = c(u[1]+ .05*(u[2]-u[1]), u[1]+ .2*(u[2]-u[1]))
why = u[3]+.95*(u[4]-u[3])
N = length(se)
rect(u[1], u[3]+.9*(u[4]-u[3]) , u[1]+ .25*(u[2]-u[1]) , u[4], col="white", border=NA, xpd=TRUE)
points(seq(from=ex[1], to=ex[2], length=N), rep(why, length=N), pch=pch, cex=se, xpd=TRUE)
text(seq(from=ex[1], to=ex[2], length=N), rep(why, length=N),labels=am, pos=3, xpd=TRUE)
} |
VERBDESEMANTICIZATION <-
function(agent){
distinctions=world$distinctions; minimalSpecification=world$minimalSpecification; desemanticizationCeiling=world$desemanticizationCeiling; power=world$desemanticizationPower
dims=length(distinctions)
steps=dims-1-minimalSpecification
factor=(desemanticizationCeiling*agent$age-8)/steps^power
steps=round(factor*(0:steps)^power + 8)
verbs=xtabs(~agent$usageHistory$verbs$verb)
verbs=names(verbs[verbs>steps[1]])
for(verb in verbs){
verbTargets=agent$usageHistory$verbs[agent$usageHistory$verbs$verb==verb,grep('^D\\d',names(agent$usageHistory$verbs))]
for(i in 1:ncol(verbTargets)){
values=table(verbTargets[,i])
change=MAX(values, forceChoice=T)
if(sum(values[-change])<(sum(values)/log(sum(values)))){
agent$verbs[agent$verbs$ID==verb, i]=as.numeric(names(values)[change])
graveyard$history[nrow(graveyard$history) + 1,]=c(agent$generation, 'verb meaning changed', 'VERBDESEMANTICIZATION', verb, '', '', '')
} }
if(agent$verbs[agent$verbs$ID==verb,]$semanticWeight>(minimalSpecification/dims)){
if(nrow(verbTargets)>steps[sum(is.na(agent$verbs[agent$verbs$ID==verb,grep('^D\\d',names(agent$verbs))])) + 1]){
actionProfile=agent$verbs[agent$verbs$ID==verb,grep('^D\\d',names(agent$verbs))]
vars=rep(0, ncol(verbTargets))
for(i in 1:length(vars)){vars[i]=sum(actionProfile[,i]!=verbTargets[,i], na.rm=T)}
agent$verbs[agent$verbs$ID==verb, MAX(vars, forceChoice=T)]=NA
agent$verbs[agent$verbs$ID==verb, ]$semanticWeight=(length(grep('^D\\d',names(agent$verbs)))-sum(is.na(agent$verbs[agent$verbs$ID==verb, grep('^D\\d',names(agent$verbs))])))/length(grep('^D\\d',names(agent$verbs)))
extProfile=agent$verbs[agent$verbs$ID==verb, grep('^Ext\\d',names(agent$verbs))]
vars=rep(0, ncol(extProfile))
performerProfiles=agent$nouns[match(agent$collostructions$SV[grep(paste('^',verb, '$',sep=''),agent$collostructions$SV$V),]$S, agent$nouns$ID),grep('^D\\d',names(agent$nouns))]
for(i in 1:length(vars)){vars[i]=sum(extProfile[,i]!=performerProfiles[,i], na.rm=T)}
agent$verbs[agent$verbs$ID==verb, grep('^Ext\\d',names(agent$verbs))[MAX(vars, forceChoice=T)]]=NA
intProfile=agent$verbs[agent$verbs$ID==verb, grep('^Int\\d',names(agent$verbs))]
vars=rep(0, ncol(intProfile))
performerProfiles=agent$nouns[match(agent$collostructions$OV[grep(paste('^',verb, '$',sep=''),agent$collostructions$OV$V),]$O, agent$nouns$ID),grep('^D\\d',names(agent$nouns))]
for(i in 1:length(vars)){vars[i]=sum(intProfile[,i]!=performerProfiles[,i], na.rm=T)}
agent$verbs[agent$verbs$ID==verb, grep('^Int\\d',names(agent$verbs))[MAX(vars, forceChoice=T)]]=NA
graveyard$history[nrow(graveyard$history) + 1,]=c(agent$generation, 'verb meaning dimension removed', 'VERBDESEMANTICIZATION', verb, '', '', '')
} } }
graveyard <<- graveyard
agent
} |
NULL
if(getRversion() >= "2.15.1") {
utils::globalVariables(c(".", "sentence", "bytes", "bytes_sum"))
} |
flex_zones = function(coords, w, k = 10, longlat = FALSE,
cl = NULL, loop = FALSE,
verbose = FALSE, pfreq = 1) {
nn = knn(coords = coords, longlat = longlat, k = k)
N = nrow(coords)
idx = seq_along(nn)
lprimes = log(randtoolbox::get.primes(N))
if (!loop) {
czones = scsg2_cpp(nn, w, idx = idx, nlevel = k, lprimes = lprimes, verbose = verbose)
czones = logical2zones(czones, nn, idx)
return(czones[distinct(czones)])
} else {
czones = list()
pri = randtoolbox::get.primes(N)
czones_id = numeric(0)
for (i in seq_len(N)) {
if (verbose) {
if ((i %% pfreq) == 0) {
message(i, "/", N, ". Starting region ", i,
" at ", Sys.time(), ".")
}
}
izones = scsg2_cpp(nn, w, i, k, lprimes, verbose = FALSE)
izones = logical2zones(izones, nn, idx = i)
izones_id = sapply(izones, function(xi) sum(lprimes[xi]))
dup_id = which(izones_id %in% czones_id)
if (length(dup_id) > 0) {
czones = combine.zones(czones, izones[-dup_id])
czones_id = c(czones_id, izones_id[-dup_id])
} else {
czones = combine.zones(czones, izones)
czones_id = c(czones_id, izones_id)
}
}
return(czones)
}
}
logical2zones = function(czones, nn, idx = seq_along(nn)) {
unlist(lapply(seq_along(czones), function(i) {
lapply(czones[[i]], function(x) {
nn[[idx[i]]][x]
})
}), recursive = FALSE)
} |
extractMandatory <- hutilscpp:::extractMandatory
where_square_bracket_opens <- hutilscpp:::where_square_bracket_opens
library(hutils)
x <- c('a', '', 'b', 'd', '{', 'e', '}', '.', 'b', 'e')
res <- extractMandatory(x, c('b', 'd'), nCommands = 1L)[[1]]
expect_equal(res,
if_else(nzchar(res), x, ''))
xop <- strsplit("qxy[ab]{jys}", split = "")[[1]]
res_op <- extractMandatory(xop, c("q", "x", "y"), nCommands = 1L)[[1]]
expect_true("y" %in% res_op)
x <- c('a', '', 'b', 'd', ' ', '{', 'e', '}', '.')
res <- extractMandatory(x, c('b', 'd'), nCommands = 1L)[[1]]
expect_true(any(nzchar(x)))
x <- strsplit("a \\Def[a [b] c]{df} x", split = "")[[1]]
res <- extractMandatory(x, c("D", "e", "f"), 1L)
expect_false("[" %in% res$support)
expect_true("d" %in% res$support)
x <- strsplit("a \\Def[a [b{q}] c]{df} x", split = "")[[1]]
res <- extractMandatory(x, c("D", "e", "f"), 1L)
expect_false("[" %in% res$support)
expect_true("d" %in% res$support)
x <- strsplit("a \\Def[a [b{q{}}] c]{df} xDe", split = "")[[1]]
res <- extractMandatory(x, c("D", "e", "f"), 1L)
expect_false("[" %in% res$support)
expect_true("d" %in% res$support)
x <- strsplit("a \\Defg[a [b{q{}}] c]{df} \\Def{a} b", split = "")[[1]]
res <- extractMandatory(x, c("D", "e", "f"), 1L)
x <- strsplit("a{b", split = "")[[1]]
res <- extractMandatory(x, c("foo"), 1L)
expect_false(any(nzchar(res$support)))
x <- strsplit("a[{b", split = "")[[1]]
res <- extractMandatory(x, strsplit(c("foo"), split = "")[[1]], 1L)
expect_false(any(nzchar(res$support)))
x <- strsplit("b \\ad[s]", split = "")[[1]]
res <- extractMandatory(x, c("a", "d"), 1L)
expect_false(any(nzchar(res$support)))
x <- strsplit(c("the \\XYZ{cliff \\za{ba} wood.} flighty."), split = "")[[1L]]
res <- extractMandatory(x, strsplit("XYZ", split = "")[[1]], 1L)
expect_true(all(strsplit("cliff \\za{ba} wood.", split = "")[[1L]] %in% res$support))
report.tex <-
if (file.exists("~/AP-2018-retirement/report.tex")) {
"~/AP-2018-retirement/report.tex"
} else {
system.file("extdata", "ap-2018-retirement-report.tex",
package = "hutilscpp")
}
if (file.exists(report.tex)) {
library(TeXCheckR)
library(data.table)
Housing <- tryCatch(read_tex_document(report.tex),
error = function(e) {
out <- 0L
names(out) <- e$m
out
})
if (!is.integer(Housing)) {
Housing_split = unlist(strsplit(Housing, split = ""))
nFootnotes <- (length(grep("\\\\footnote(?![A-Za-z])[^\\{]*\\{", Housing, perl = TRUE)))
footnote <- strsplit("footnote", split = "")[[1L]]
res <- extractMandatory(Housing_split, footnote, nCommands = nFootnotes)
Seq_All <- function(froms, tos) {
unlist(lapply(seq_along(froms), function(i) {
seq.int(from = froms[i], to = tos[i], by = 1L)
}))
}
DT <-
data.table(Ope = res$Openers,
Clo = res$Closers)[, I := .I][, .(Text = paste0(res$support[.BY[[1]]:.BY[[2]]],
collapse = "")),
keyby = c("Ope", "Clo", "I")]
expect_true(any(grepl("For example, see: \\textcites[][]{IndustrySuperAustralia2015inquiryintoeconom",
DT$Text[1:10],
fixed = TRUE)))
}
} |
rhsFormula2list <- function(form){
if (is.character(form)) return(list(form))
if (is.numeric(form)) return(lapply(list(form), "as.character"))
if (is.list(form)) return(lapply(form, "as.character"))
.xxx. <- form[[ length( form ) ]]
form1 <- unlist(strsplit(paste(deparse(.xxx.), collapse="")," *\\+ *"))
form2 <- unlist(lapply(form1, strsplit, " *\\* *| *: *| *\\| *"),
recursive=FALSE)
form2
}
rhsf2list <- rhsFormula2list
rhsf2vec <- function(form){
rhsf2list(form)[[1]]
}
listify_dots <- function(dots){
dots <- lapply(dots, function(a) if (!is.list(a)) list(a) else a)
unlist(dots, recursive=FALSE)
}
list2rhsFormula <- function(form){
if (inherits(form, "formula")) return(form)
as.formula(paste("~",paste(unlist(lapply(form,paste, collapse='*')), collapse="+")),
.GlobalEnv)
}
list2rhsf <- list2rhsFormula
rowmat2list <- rowmat2list__
colmat2list <- colmat2list__
matrix2list <- function(X, byrow=TRUE){
if (byrow) rowmat2list__(X)
else colmat2list__(X)
}
which.arr.index <- function(X){
nr <- nrow(X)
nc <- ncol(X)
rr <- rep.int(1:nr, nc)
cc <- rep(1:nc, each=nr)
cbind(rr[X!=0L], cc[X!=0L])
}
which_matrix_index <- which_matrix_index__
rowSumsPrim <- function(X){
.Call("R_rowSums", X, PACKAGE="gRbase")}
colSumsPrim <- function(X){
.Call("R_colSums", X, PACKAGE="gRbase")}
colwiseProd <- function(v, X){
.Call("R_colwiseProd", v, X, PACKAGE="gRbase")}
lapplyV2I <- function(setlist, item){lapply(setlist, function(elt) match(elt, item))}
lapplyI2V <- function (setlist, item) {lapply(setlist, function(elt) item[elt])}
pairs2num <- function(x, vn, sort=TRUE){
if (!inherits(x, "matrix")){
if (is.null(x))
return(NULL)
if (inherits(x,"list"))
x <- do.call(rbind,x)
else {
if (inherits(x,"character"))
x <- matrix(x,nrow=1)
}
}
dd <- dim(x)
if (dd[1L] == 0){
return(numeric(0))
} else {
if (sort){
i <- x[, 2L]< x[, 1L]
c1 <- i+1L
c2 <- -1L * (i - 1L) + 1L
x <- cbind(
x[cbind(seq_along(c1), c1)],
x[cbind(seq_along(c2), c2)])
}
ans <- match(x, vn)
dim(ans) <- dim(x)
colSumsPrim(t.default(ans) * c(100000, 1))
}
} |
attach(mtcars)
plot(wt, mpg, main="Scatterplot Example",
xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19)
abline(lm(mpg~wt), col="red")
lines(lowess(wt,mpg), col="blue")
library(car)
scatterplot(mpg ~ wt | cyl, data=mtcars,
xlab="Weight of Car", ylab="Miles Per Gallon",
main="Enhanced Scatter Plot",
labels=row.names(mtcars))
pairs(~mpg+disp+drat+wt,data=mtcars,
main="Simple Scatterplot Matrix")
library(lattice)
splom(mtcars[c(1,3,5,6)], groups=cyl, data=mtcars,
panel=panel.superpose,
key=list(title="Three Cylinder Options",
columns=3,
points=list(pch=super.sym$pch[1:3],
col=super.sym$col[1:3]),
text=list(c("4 Cylinder","6 Cylinder","8 Cylinder"))))
library(car)
scatterplot.matrix(~mpg+disp+drat+wt|cyl, data=mtcars,
main="Three Cylinder Options")
library(gclus)
dta <- mtcars[c(1,3,5,6)]
dta.r <- abs(cor(dta))
dta.col <- dmat.color(dta.r)
dta.o <- order.single(dta.r)
cpairs(dta, dta.o, panel.colors=dta.col, gap=.5,
main="Variables Ordered and Colored by Correlation" )
library(hexbin)
x <- rnorm(1000)
y <- rnorm(1000)
bin<-hexbin(x, y, xbins=50)
plot(bin, main="Hexagonal Binning")
pdf("c:/scatterplot.pdf")
x <- rnorm(1000)
y <- rnorm(1000)
plot(x,y, main="PDF Scatterplot Example", col=rgb(0,100,0,50,maxColorValue=255), pch=16)
dev.off()
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt,disp,mpg, main="3D Scatterplot")
library(scatterplot3d)
attach(mtcars)
scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,
type="h", main="3D Scatterplot")
library(scatterplot3d)
attach(mtcars)
s3d <-scatterplot3d(wt,disp,mpg, pch=16, highlight.3d=TRUE,
type="h", main="3D Scatterplot")
fit <- lm(mpg ~ wt+disp)
s3d$plane3d(fit)
library(rgl)
plot3d(wt, disp, mpg, col="red", size=3)
library(Rcmdr)
attach(mtcars)
scatter3d(wt, disp, mpg) |
conditionalTransform <- function(..., data, else_condition = NA, type = NULL,
categories = NULL, formulas = NULL) {
dots <- list(...)
is_formula <- function(x) inherits(x, "formula")
dot_formulas <- Filter(is_formula, dots)
if (length(dot_formulas) > 0) {
if (!is.null(formulas)) {
halt(
"Must not supply conditions in both the ", dQuote("formulas"),
" argument and ", dQuote("...")
)
}
formulas <- dot_formulas
}
var_def <- Filter(Negate(is_formula), dots)
if (length(formulas) == 0) {
halt(
"Conditions must be supplied: ",
"Have you forgotten to supply conditions as formulas in either the ",
dQuote("formulas"), " argument, or through ", dQuote("..."), ""
)
}
if (!missing(type) && !type %in% c("categorical", "text", "numeric")) {
halt(
"Type must be either ", dQuote("categorical"), ", ",
dQuote("text"), ", or ", dQuote("numeric")
)
}
conditional_vals <- makeConditionalValues(formulas, data, else_condition)
if (!missing(type)) {
if (type == "numeric") {
result <- as.numeric(conditional_vals$values)
} else {
result <- as.character(conditional_vals$values)
}
} else {
result <- conditional_vals$values
type <- conditional_vals$type
}
if (type != "categorical" & !is.null(categories)) {
warning(
"Type is not ", dQuote("categorical"), " ignoring ",
dQuote("categories")
)
}
var_def$type <- type
if (type == "categorical") {
if (missing(categories)) {
result <- factor(result)
categories <- Categories(data = categoriesFromLevels(levels(result)))
} else {
if (!is.categories(categories)) {
categories <- Categories(data = categoriesFromLevels(categories))
}
uni_results <- unique(result[!is.na(result)])
results_not_categories <- !uni_results %in% names(categories)
if (any(results_not_categories)) {
halt(
"When specifying categories, all categories in the ",
"results must be included. These categories are in the ",
"results that were not specified in categories: ",
serialPaste(uni_results[results_not_categories])
)
}
result <- factor(result, levels = names(categories))
}
categories <- ensureNoDataCategory(categories)
category_list <- categories
var_def$categories <- category_list
vals <- as.character(result)
vals[is.na(vals)] <- "No Data"
var_def$values <- ids(categories[vals])
} else {
var_def$values <- result
}
class(var_def) <- "VariableDefinition"
return(var_def)
}
makeConditionalValues <- function(formulas, data, else_condition) {
n <- length(formulas)
cases <- vector("list", n)
values <- vector("list", n)
for (i in seq_len(n)) {
formula <- formulas[[i]]
if (length(formula) != 3) {
halt(
"The condition provided must be a proper formula: ",
deparseAndFlatten(formula)
)
}
cases[[i]] <- evalLHS(formula, data)
if (!inherits(cases[[i]], c("logical", "CrunchLogicalExpr"))) {
halt(
"The left-hand side provided must be a logical or a ",
"CrunchLogicalExpr: ", dQuote(LHS_string(formula))
)
}
values[[i]] <- evalRHS(formula, data)
}
ds_refs <- unlist(unique(lapply(c(cases, values), datasetReference)))
if (!missing(data)) {
ds_refs <- unique(c(ds_refs, datasetReference(data)))
}
if (length(ds_refs) > 1) {
halt(
"There must be only one dataset referenced. Did you accidentally ",
"supply more than one?"
)
} else if (length(ds_refs) == 0) {
halt(
"There must be at least one crunch expression in the formulas ",
"specifying cases or use the data argument to specify a dataset."
)
}
n_rows <- nrow(CrunchDataset(crGET(ds_refs)))
case_indices <- lapply(cases, which)
case_indices <- lapply(seq_along(case_indices), function(i) {
setdiff(case_indices[[i]], unlist(case_indices[seq_len(i - 1)]))
})
values_to_fill <- Map(function(ind, var) {
if (inherits(var, c("CrunchVariable", "CrunchExpr"))) {
return(as.vector(var)[ind])
} else {
return(var)
}
}, ind = case_indices, var = values)
pre_collation_types <- vapply(values, class, character(1))
values <- collateValues(values_to_fill, case_indices, else_condition, n_rows)
if (all(pre_collation_types == "factor")) {
type <- "categorical"
} else if (is.numeric(values)) {
type <- "numeric"
} else {
type <- "text"
}
return(list(values = values, type = type))
}
collateValues <- function(values_to_fill, case_indices, else_condition,
n_rows) {
result <- rep(else_condition, n_rows)
for (i in seq_along(case_indices)) {
vals <- values_to_fill[[i]]
if (is.factor(vals)) {
vals <- as.character(vals)
}
result[case_indices[[i]]] <- vals
}
return(result)
} |
reorient_volume <- function( volume, Torig ){
order_index <- round((Torig %*% c(1,2,3,0))[1:3])
volume <- aperm(volume, abs(order_index))
sub <- sprintf(c('%d:1', '1:%d')[(sign(order_index) + 3) / 2], dim(volume))
volume <- eval(parse(text = sprintf('volume[%s]', paste(sub, collapse = ','))))
volume
} |
grenander = function(F, type=c("decreasing", "increasing"))
{
if( !any(class(F) == "ecdf") ) stop("ecdf object required as input!")
type <- match.arg(type)
if (type == "decreasing")
{
ll = gcmlcm(environment(F)$x, environment(F)$y, type="lcm")
}
else
{
l = length(environment(F)$y)
ll = gcmlcm(environment(F)$x, c(0,environment(F)$y[-l]), type="gcm")
}
f.knots = ll$slope.knots
f.knots = c(f.knots, f.knots[length(f.knots)])
g = list(F=F,
x.knots=ll$x.knots,
F.knots=ll$y.knots,
f.knots=f.knots)
class(g) <- "grenander"
return(g)
}
plot.grenander <- function(x, ...)
{
if (x$f.knots[1] > x$f.knots[2])
main = "Grenander Decreasing Density"
else
main = "Grenander Increasing Density"
par(mfrow=c(1,2))
plot(x$x.knots, x$f.knots, type="s", xlab="x", ylab="fn(x)",
main=main, col=4, lwd=2, ...)
plot(x$F, do.points=FALSE)
lines(x$x.knots, x$F.knots, type='l', col=4, lwd=2)
par(mfrow=c(1,1))
} |
sarem2srREmod <-
function (X, y, ind, tind, n, k, t., nT, w, w2, coef0 = rep(0, 4),
hess = FALSE, trace = trace, x.tol = 1.5e-18, rel.tol = 1e-15,
method="nlminb",
...)
{
nam.beta <- dimnames(X)[[2]]
nam.errcomp <- c("phi", "psi", "rho", "lambda")
myparms0 <- coef0
Vmat <- function(rho, t.) {
V1 <- matrix(ncol = t., nrow = t.)
for (i in 1:t.) V1[i, ] <- rho^abs(1:t. - i)
V <- (1/(1 - rho^2)) * V1
}
Vmat.1 <- function(rho, t.) {
if(t.==1) {Vmat.1 <- 1} else {
Vmat.1 <- matrix(0, ncol = t., nrow = t.)
for (i in 2:(t.-1)) Vmat.1[i,i] <- (1-rho^4)/(1-rho^2)
Vmat.1[1,1] <- Vmat.1[t.,t.] <- 1
for (j in 1:(t.-1)) Vmat.1[j+1,j] <- -rho
for (k in 1:(t.-1)) Vmat.1[k,k+1] <- -rho
}
return(Vmat.1)
}
alfa2 <- function(rho) (1 + rho)/(1 - rho)
d2 <- function(rho, t.) alfa2(rho) + t. - 1
Jt <- matrix(1, ncol = t., nrow = t.)
In <- diag(1, n)
det2 <- function(phi, rho, lambda, t., w) (d2(rho, t.) * (1 -
rho)^2 * phi + 1)
invSigma <- function(phirholambda, n, t., w) {
phi <- phirholambda[1]
rho <- phirholambda[2]
lambda <- phirholambda[3]
invVmat <- Vmat.1(rho, t.)
BB <- xprodB(lambda, w)
chi <- phi/(d2(rho, t.)*(1-rho)^2*phi+1)
invSigma <- kronecker((invVmat-chi*(invVmat %*% Jt %*% invVmat)),
BB)
invSigma
}
ll.c <- function(phirholambda, y, X, n, t., w, w2, wy) {
phi <- phirholambda[1]
rho <- phirholambda[2]
lambda <- phirholambda[3]
psi <- phirholambda[4]
sigma.1 <- invSigma(phirholambda, n, t., w2)
Ay <- y - psi * wy
glsres <- GLSstep(X, Ay, sigma.1)
e <- glsres[["ehat"]]
s2e <- glsres[["sigma2"]]
zero <- t.*ldetB(psi, w)
uno <- n/2 * log(1 - rho^2)
due <- -n/2 * log(det2(phi, rho, lambda, t., w2))
tre <- -(n * t.)/2 * log(s2e)
quattro <- (t.) * ldetB(lambda, w2)
cinque <- -1/(2 * s2e) * t(e) %*% sigma.1 %*% e
const <- -(n * t.)/2 * log(2 * pi)
ll.c <- const + zero + uno + due + tre + quattro + cinque
llc <- -ll.c
}
lower.bounds <- c(1e-08, -0.999, -0.999, -0.999)
upper.bounds <- c(1e+09, 0.999, 0.999, 0.999)
cA <- cbind(c(1, rep(0,6)),
c(0,1,-1,rep(0,4)),
c(rep(0,3), 1, -1, rep(0,2)),
c(rep(0,5), 1, -1))
cB <- c(0, rep(1,6))
Wy <- function(y, w, tind) {
wyt <- function(y, w) {
if("listw" %in% class(w)) {
wyt <- lag.listw(w, y)
} else {
wyt <- w %*% y
}
return(wyt)
}
wy<-list()
for (j in 1:length(unique(tind))) {
yT<-y[tind==unique(tind)[j]]
wy[[j]] <- wyt(yT, w)
}
return(unlist(wy))
}
GLSstep <- function(X, y, sigma.1) {
b.hat <- solve(t(X) %*% sigma.1 %*% X,
t(X) %*% sigma.1 %*% y)
ehat <- y - X %*% b.hat
sigma2ehat <- (t(ehat) %*% sigma.1 %*% ehat)/(n * t.)
return(list(betahat=b.hat, ehat=ehat, sigma2=sigma2ehat))
}
wy <- Wy(y, w, tind)
parscale <- 1/max(myparms0, 0.1)
if(method=="nlminb") {
optimum <- nlminb(start = myparms0, objective = ll.c,
gradient = NULL, hessian = NULL,
y = y, X = X, n = n, t. = t., w = w, w2 = w2, wy = wy,
scale = parscale,
control = list(x.tol = x.tol,
rel.tol = rel.tol, trace = trace),
lower = lower.bounds, upper = upper.bounds)
myll <- -optimum$objective
myparms <- optimum$par
myHessian <- fdHess(myparms, function(x) -ll.c(x,
y, X, n, t., w, w2, wy))$Hessian
} else {
maxout<-function(x,a) ifelse(x>a, x, a)
myparms0 <- maxout(myparms0, 0.01)
ll.c2 <- function(phirholambda, y, X, n, t., w, w2, wy) {
-ll.c(phirholambda, y, X, n, t., w, w2, wy)
}
optimum <- maxLik(logLik = ll.c2,
grad = NULL, hess = NULL, start=myparms0,
method = method,
parscale = parscale,
constraints=list(ineqA=cA, ineqB=cB),
y = y, X = X, n = n, t. = t., w = w, w2 = w2, wy = wy)
myll <- optimum$maximum
myparms <- optimum$estimate
myHessian <- optimum$hessian
}
sigma.1 <- invSigma(myparms, n, t., w2)
Ay <- y - myparms[length(myparms)] * wy
beta <- GLSstep(X, Ay, sigma.1)
covB <- as.numeric(beta[[3]]) *
solve(t(X) %*% sigma.1 %*% X)
nvcovpms <- length(nam.errcomp) - 1
covTheta <- try(solve(-myHessian), silent=TRUE)
if(class(covTheta) == "try-error") {
covTheta <- matrix(NA, ncol=nvcovpms+1,
nrow=nvcovpms+1)
warning("Hessian matrix is not invertible")
}
covAR <- covTheta[nvcovpms+1, nvcovpms+1, drop=FALSE]
covPRL <- covTheta[1:nvcovpms, 1:nvcovpms, drop=FALSE]
betas <- as.vector(beta[[1]])
sigma2 <- as.numeric(beta[["sigma2"]])
arcoef <- myparms[which(nam.errcomp=="lambda")]
errcomp <- myparms[which(nam.errcomp!="lambda")]
names(betas) <- nam.beta
names(arcoef) <- "lambda"
names(errcomp) <- nam.errcomp[which(nam.errcomp!="lambda")]
dimnames(covB) <- list(nam.beta, nam.beta)
dimnames(covAR) <- list(names(arcoef), names(arcoef))
dimnames(covPRL) <- list(names(errcomp), names(errcomp))
RES <- list(betas = betas, arcoef=arcoef, errcomp = errcomp,
covB = covB, covAR=covAR, covPRL = covPRL, ll = myll,
sigma2 = sigma2)
return(RES)
} |
library(fsdaR)
set.seed(1234)
n <- 45
a <- 1
b <- 0.8
sig <- 1
seq <- 1:n
y <- a + b * seq + sig * rnorm(n)
y[round(n/2):n] <- y[round(n/2):n] + 10
out <- ltsts(y, plot=TRUE, trace=TRUE)
library(fsdaR)
out <- simulate_ts(100, plot=TRUE)
out1 <- ltsts(out$y, plot=TRUE, trace=TRUE) |
setClass('gchol',
representation(.Data= 'numeric',
Dim = 'integer',
Dimnames = 'list',
rank = 'integer'))
setGeneric('gchol', function(x, tolerance=1e-10) standardGeneric('gchol'),
useAsDefault=FALSE)
as.matrix.gchol <- function(x, ones=TRUE, ...) {
temp <- matrix([email protected], x@Dim[1], dimnames=x@Dimnames, byrow=TRUE)
if (ones) diag(temp) <- 1
temp
}
setAs('gchol', 'matrix', function(from) as.matrix.gchol(from))
setMethod('gchol', signature(x='matrix'),
function(x, tolerance) {
d <- dim(x)
if (d[1] != d[2])
stop("Cholesky decomposition requires a square matrix")
temp <- .C(Cgchol, as.integer(d[1]),
x = as.double(x),
rank= as.double(tolerance))
dnames <- dimnames(x)
if (is.null(dnames)) dnames <- list(NULL, NULL)
new('gchol', .Data= temp$x , Dim=d,
Dimnames= dnames, rank=as.integer(temp$rank))
})
setMethod('diag', signature(x='gchol'),
function(x, nrow, ncol) {
d <- x@Dim[1]
[email protected][ seq(1, length=d, by=d+1)]
})
setMethod('show', 'gchol', function(object) show(as.matrix(object, F)))
setMethod('dim', 'gchol', function(x) x@Dim)
setMethod('dimnames', 'gchol', function(x) x@Dimnames)
setMethod("%*%", signature(x='gchol', y='matrix'),
function(x, y) {
if (!is.numeric(y))
stop("Matrix multiplication is defined only for numeric objects")
dy <- dim(y)
dx <- dim(x)
ldy <- length(dy)
if (ldy!=2) dy <- c(length(y), 1)
if (dx[2] != dy[1])
stop("Number of columns of x should be the same as number of rows of y")
if (any(diag(x) < 0)) stop("gchol matrix does not have a Cholesky repres
entation: no matrix product is possible")
as.matrix(x) %*% (sqrt(diag(x)) * y)
})
setMethod("%*%", signature(x='matrix', y='gchol'),
function(x, y) {
if (!is.numeric(x))
stop("Matrix multiplication is defined only for numeric objects")
dy <- dim(y)
dx <- dim(x)
ldx <- length(dx)
if (ldx!=2) dx <- c(length(x), 1)
if (dx[2] != dy[1])
stop("Number of columns of x should be the same as number of rows of y")
if (any(diag(y) < 0)) stop("gchol matrix does not have a Cholesky repres
entation: no matrix product is possible")
(y %*% as.matrix(x)) * rep(sqrt(diag(y)), each=ncol(y))
})
setMethod('[', "gchol",
function(x, i,j, drop=TRUE) {
if (missing(i) && missing(j)) return(x)
temp <- matrix([email protected], nrow=x@Dim[1], dimnames=x@Dimnames)
if (missing(i)) temp[,j,drop=drop]
else {
if (missing(j)) temp[i,,drop=drop]
else {
temp <- temp[i,j,drop=drop]
if (length(i)==length(j) && length(i)>1 && all(i==j)) {
new("gchol", .Data= as.vector(temp), Dim=dim(temp),
Dimnames=dimnames(temp), rank=sum(diag(temp) !=0))
}
else temp
}
}
})
|
stratEst.simulate.check.coefficients <- function( coefficients , covariate_mat , num_strats , names_strategies ){
if( is.matrix( coefficients ) ){
rows_coefficients <- nrow( coefficients )
cols_coefficients <- ncol( coefficients )
if( rows_coefficients != ncol(covariate_mat) ){
stop("stratEst error: Input object 'coefficients' must have as many rows as there are covariates.");
}
if( cols_coefficients != num_strats ){
stop("stratEst error: Input object 'coefficients' must have as many columns as there are strategies.");
}
if( any( is.na( coefficients ) ) ){
stop("stratEst error: The input object 'coefficients' cannot contain NA values.");
}
colnames_coefficients <- colnames(coefficients)
rownames_coefficients <- rownames(coefficients)
if( is.null(rownames_coefficients) == FALSE ){
num.covariates <- ncol(covariate_mat)
names_covariates <- colnames(covariate_mat)
coefficient_mat <- coefficients
for( v in 1:num.covariates){
if( names_covariates[v] %in% rownames_coefficients ){
coefficient_mat[v,] <- coefficients[names_covariates[v],]
}else{
stop(paste("stratEst error: There is a column named '", names_covariates[v] , "' in 'covariates' but no row with this name in 'coefficients'.",sep=""));
}
}
}else{
stop("stratEst error: The row names of the input object 'coefficients' must correspond to the column names of covariates.");
}
if( is.null(colnames_coefficients) == FALSE ){
for( s in 1:num_strats){
if( names_strategies[s] %in% colnames_coefficients ){
coefficient_mat[,s] <- coefficients[,names_strategies[s]]
}else{
stop(paste("stratEst error: There is a strategy named '", names_strategies[s] , "' but no column with this name in 'coefficients'.",sep=""));
}
}
}else{
stop("stratEst error: The column names of the input object 'coefficients' must correspond to the names of the strategies.");
}
}else{
stop("stratEst error: The input object 'coefficients' has to be a matrix.");
}
return( coefficient_mat )
} |
data(arab);
grp.ids = as.factor(c(1, 1, 1, 2, 2, 2));
x = model.matrix(~grp.ids);
beta0 = c(NA, 0);
fit = nb.glm.test(arab, x, beta0, subset=1:50);
print(str(fit));
subset = order(fit$test.results$HOA$p.values)[1:10];
cbind(fit$data$counts[subset,], fit$test.results$HOA[subset,]);
subset = order(fit$test.results$LR$p.values)[1:10];
cbind(fit$data$counts[subset,], fit$test.results$LR[subset,]); |
ci.mm2 <- function(ind1, ind2, cs = NULL, suffStat) {
dataset <- suffStat$dataset
y <- dataset[, ind1]
x <- dataset[, ind2]
if ( is.null(cs) ) {
if ( is.numeric(y) ) {
mod1 <- lm(y ~., data = data.frame(x) )
a1 <- anova(mod1)
p1 <- a1[1, 5]
} else if ( is.ordered(y) ) {
ds <- data.frame(y = y, x = x)
mod1 <- ordinal.reg(y ~., data = ds)
mod0 <- ordinal.reg(y ~ 1, ds)
t1 <- mod0$devi - mod1$devi
dof <- length( mod1$be ) - length( mod0$be )
p1 <- pchisq(t1, dof, lower.tail = FALSE)
}
if ( is.numeric(x) ) {
mod2 <- lm(x ~., data = data.frame(y) )
a2 <- anova(mod2)
p2 <- a2[1, 5]
} else if ( is.ordered(x) ) {
ds <- data.frame(x = x, y = y)
mod2 <- ordinal.reg(x ~., data = ds)
mod0 <- ordinal.reg(x ~ 1, ds)
t2 <- mod0$devi - mod2$devi
dof <- length( mod2$be ) - length( mod0$be )
p2 <- pchisq(t2, dof, lower.tail = FALSE)
}
} else {
if ( is.numeric(y) ) {
ds <- data.frame(y = y, dataset[, cs], x = x)
mod1 <- lm(y ~., data = ds)
ds0 <- data.frame(y = y, dataset[, cs])
mod0 <- lm(y ~., data = ds0)
a1 <- anova(mod0, mod1)
p1 <- a1[2, 6]
} else if ( is.ordered(y) ) {
ds1 <- data.frame(y = y, dataset[, cs], x = x)
mod1 <- ordinal.reg(y ~., data = ds1)
ds0 <- data.frame(y = y, dataset[, cs])
mod0 <- ordinal.reg(y ~., data = ds0 )
t1 <- mod0$devi - mod1$devi
dof <- length( mod1$be ) - length( mod0$be )
p1 <- pchisq(t1, dof, lower.tail = FALSE)
}
if ( is.numeric(x) ) {
ds <- data.frame(x = x, dataset[, cs], y = y)
mod2 <- lm(x ~., data = ds)
ds0 <- data.frame(x = x, dataset[, cs])
mod0 <- lm(x ~., data = ds0)
a2 <- anova(mod0, mod2)
p2 <- a2[2, 6]
} else if ( is.ordered(x) ) {
ds2 <- data.frame(x = x, dataset[, cs], y = y)
mod2 <- ordinal.reg(x ~., data = ds2)
ds0 <- data.frame(x = x, dataset[, cs])
mod0 <- ordinal.reg(x ~., data = ds0 )
t2 <- mod0$devi - mod2$devi
dof <- length( mod2$be ) - length( mod0$be )
p2 <- pchisq(t2, dof, lower.tail = FALSE)
}
}
min( 2 * min(p1, p2), max(p1, p2) )
} |
context("aqp package environment")
test_that("defaults", {
expect_equal(getOption(".aqp.show.n.cols"), 10)
options(.aqp.show.n.cols = 100)
expect_equal(getOption(".aqp.show.n.cols"), 100)
expect_silent(aqp:::.onLoad("foo","bar"))
}) |
data <- list()
data[[1]] <- as.matrix(read.csv(system.file("extdata",
"dataset1.csv", package = "coca"), row.names = 1))
data[[2]] <- as.matrix(read.csv(system.file("extdata",
"dataset2.csv", package = "coca"), row.names = 1))
data[[3]] <- as.matrix(read.csv(system.file("extdata",
"dataset3.csv", package = "coca"), row.names = 1))
outputBuildMOC <- coca::buildMOC(data, M = 3, K = 5, distances = "cor")
moc <- outputBuildMOC$moc
datasetIndicator <- outputBuildMOC$datasetIndicator
true_labels <- as.matrix(read.csv(system.file("extdata", "cluster_labels.csv",
package = "coca"), row.names = 1))
annotations <- data.frame(true_labels = as.factor(true_labels))
coca::plotMOC(moc, datasetIndicator, annotations = annotations)
true_labels <- as.matrix(read.csv(system.file("extdata", "cluster_labels.csv",
package = "coca"), row.names = 1))
annotations <- data.frame(true_labels = as.factor(true_labels))
datasetNames <- c(rep("A", 5), rep("B", 5), rep("C", 5))
coca::plotMOC(moc, datasetIndicator, datasetNames = datasetNames,
annotations = annotations)
coca <- coca::coca(moc, K = 5)
ari <- mclust::adjustedRandIndex(true_labels, coca$clusterLabels)
ari
annotations$coca <- as.factor(coca$clusterLabels)
coca::plotMOC(moc, datasetIndicator, datasetNames = datasetNames,
annotations = annotations)
coca <- coca::coca(moc, maxK = 10, hclustMethod = "average")
ari <- mclust::adjustedRandIndex(true_labels, coca$clusterLabels)
ari
annotations$coca <- as.factor(coca$clusterLabels)
coca::plotMOC(moc, datasetIndicator, datasetNames = datasetNames,
annotations = annotations) |
aSPUsim2 <- function(Y, X, cov = NULL, model=c("gaussian","binomial"), pow=c(1:8, Inf), n.perm=1000){
model = match.arg(model)
n <- length(Y)
if (is.null(X) && length(X)>0) X=as.matrix(X, ncol=1)
k <- ncol(X)
if (is.null(cov)){
Xg <- XUs <- X
U <- t(Xg) %*% (Y-mean(Y))
yresids <- Y-mean(Y)
yfits <- rep(mean(Y), n)
Xgb <- apply(X, 2, function(x)(x-mean(x)) )
if( model == "binomial" ) {
CovS <- mean(Y)*(1-mean(Y))*(t(Xgb) %*% Xgb)
} else {
CovS <- var(Y)*(t(Xgb) %*% Xgb)
}
} else {
tdat1 <- data.frame(trait=Y, cov)
if(is.null(colnames(cov))) {
colnames(tdat1) = c("trait", paste("cov",1:dim(cov)[2],sep=""))
} else {
colnames(tdat1) = c("trait", colnames(cov))
}
fit1 <- glm(trait~., family = model, data=tdat1)
yfits <- fitted.values(fit1)
yresids <- fit1$residuals
Us <- XUs <- matrix(0, nrow=n, ncol=k)
Xmus = X
for(i in 1:k){
tdat2 <- data.frame(X1=X[,i], cov)
fit2 <- glm(X1~., data=tdat2)
Xmus[,i] <- fitted.values(fit2)
XUs[, i] <- (X[,i] - Xmus[,i])
}
U <- t(XUs) %*% (Y - yfits)
CovS<-matrix(0, nrow=k, ncol=k)
for(i in 1:n)
CovS<-CovS + Us[i,] %*% t(Us[i,])
}
Ts <- rep(0, length(pow))
for(j in 1:length(pow)){
if (pow[j] < Inf)
Ts[j] = sum(U^pow[j]) else Ts[j] = max(abs(U))
}
svd.CovS<-svd(CovS)
CovSsqrt<-svd.CovS$u %*% diag(sqrt(svd.CovS$d))
T0s = matrix(0, nrow=n.perm, ncol=length(pow))
Y0 = Y
for(b in 1:n.perm){
U00<-rnorm(k, 0, 1)
U0<-CovSsqrt %*% U00
for(j in 1:length(pow))
if (pow[j] < Inf)
T0s[b, j] = sum(U0^pow[j]) else T0s[b, j] = max(abs(U0))
}
pPerm0 = rep(NA,length(pow))
for ( j in 1:length(pow))
{
pPerm0[j] = sum(abs(Ts[j])<=abs(T0s[,j])) / n.perm
P0s = ( ( n.perm - rank( abs(T0s[,j]) ) ) + 1 ) / (n.perm )
if (j == 1 ) minp0 = P0s else minp0[which(minp0>P0s)] = P0s[which(minp0>P0s)]
}
Paspu <- (sum(minp0 <= min(pPerm0)) + 1) / (n.perm+1)
pvs <- c(pPerm0, Paspu)
Ts <- c(Ts, min(pPerm0))
names(Ts) <- c(paste("SPU", pow, sep=""), "aSPU")
names(pvs) = names(Ts)
list(Ts = Ts, pvs = pvs)
} |
context("nLTTstat")
test_that("Identical trees have an nLTTstat of zero", {
set.seed(314)
p <- ape::rcoal(10)
set.seed(314)
q <- ape::rcoal(10)
expect_equal(
0.0, nLTTstat(tree1 = p, tree2 = p, distance_method = "abs"),
tolerance = 0.0001
)
expect_equal(
0.0, nLTTstat(tree1 = p, tree2 = p, distance_method = "squ"),
tolerance = 0.0001
)
})
test_that("abs nLTTstat on known tree", {
p <- ape::read.tree(text = "((A:1, B:1):2, C:3);")
q <- ape::read.tree(text = "((A:2, B:2):1, C:3);")
expect_equal(
0.111111,
nLTTstat(tree1 = p, tree2 = q, distance_method = "abs"),
tolerance = 0.0001
)
})
test_that("squ nLTTstat on known tree", {
p <- ape::read.tree(text = "((A:1, B:1):2, C:3);")
q <- ape::read.tree(text = "((A:2, B:2):1, C:3);")
expect_equal(
0.037,
nLTTstat(tree1 = p, tree2 = q, distance_method = "squ"),
tolerance = 0.0001
)
})
test_that("nLTTstat abuse", {
phylo <- ape::rcoal(10)
expect_error(
nLTTstat(tree1 = 42, tree2 = phylo, distance_method = "abs"),
"nLTTstat: tree1 must be of class 'phylo'"
)
expect_error(
nLTTstat(tree1 = phylo, tree2 = 42, distance_method = "abs"),
"nLTTstat: tree2 must be of class 'phylo'"
)
expect_error(
nLTTstat(tree1 = phylo, tree2 = phylo, distance_method = "nonsense"),
"nLTTstat: distance method unknown"
)
}) |
ruv_svdgridplot <-
function (Y.data, Y.space = NULL, rowinfo = NULL, colinfo = NULL,
k = 1:3, Z = 1, left.additions = NULL, right.additions = NULL,
factor.labels = paste("S.V.", k))
{
checks = check.ggplot() & check.gridExtra()
if (checks) {
get_legend = function(myggplot) {
tmp = ggplot_gtable(ggplot_build(myggplot))
leg = which(sapply(tmp$grobs, function(x) x$name) ==
"guide-box")
if (length(leg) != 0)
return(tmp$grobs[[leg]])
else return(ggplot() + theme_void())
}
if (is.data.frame(Y.space))
Y.space = data.matrix(Y.space)
if (is.numeric(Z))
if (length(Z) == 1)
Z = matrix(1, nrow(Y.data), 1)
if (!is.null(Z))
Y.data = residop(Y.data, Z)
if (is.null(Y.space))
Y.space = Y.data
if (!is.list(Y.space)) {
if (!is.null(Z))
Y.space = residop(Y.space, Z)
Y.space = svd(Y.space)
}
d.scaled = Y.space$d/sqrt(sum(Y.space$d^2))
K = length(k)
U = matrix(0, nrow(Y.data), K)
V = matrix(0, ncol(Y.data), K)
D = rep(0, K)
ulim = matrix(0, K, 2)
vlim = matrix(0, K, 2)
for (i in 1:K) {
k1 = floor(abs(k[i]))
k2 = ceiling(abs(k[i]))
a = c(1 - (abs(k[i]) - k1), abs(k[i]) - k1)
a = a/sqrt(sum(a^2))
a[1] = a[1] * sign(k[i])^k1
a[2] = a[2] * sign(k[i])^k2
u = a[1] * Y.space$u[, k1] + a[2] * Y.space$u[, k2]
v = a[1] * Y.space$v[, k1] + a[2] * Y.space$v[, k2]
ulimvect = a[1] * Y.space$d[k1] * Y.space$u[, k1] +
a[2] * Y.space$d[k2] * Y.space$u[, k2]
vlimvect = a[1] * Y.space$d[k1] * Y.space$v[, k1] +
a[2] * Y.space$d[k2] * Y.space$v[, k2]
ulim[i, ] = c(min(ulimvect), max(ulimvect))
vlim[i, ] = c(min(vlimvect), max(vlimvect))
U[, i] = as.vector(Y.data %*% v)
V[, i] = as.vector(t(u) %*% Y.data)
D[i] = sqrt(a[1]^2 * d.scaled[k1]^2 + a[2]^2 * d.scaled[k2]^2)
}
if (!is.null(rowinfo))
rowinfo = data.frame(rowinfo)
if (!is.null(colinfo))
colinfo = data.frame(colinfo)
plots = rep(list(NA), K^2)
for (i in 1:K) for (j in 1:K) {
if (i == j) {
df.rect = data.frame(x = 0, y = 0, xmin = -D[i],
ymin = -D[i], xmax = D[i], ymax = D[i])
thisplot = ggplot() + theme_classic() + theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) + theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank()) + theme(axis.line = element_blank()) +
xlab(factor.labels[i]) + ylab(factor.labels[i]) +
scale_x_continuous(position = "top") + geom_rect(data = df.rect,
aes_string(xmin = "xmin", ymin = "ymin", xmax = "xmax",
ymax = "ymax"), alpha = 0.4) + coord_cartesian(xlim = c(-1,
1), ylim = c(-1, 1))
plots[[(i - 1) * K + j]] = thisplot
}
else if (i > j) {
df = data.frame(x = U[, j], y = U[, i])
if (!is.null(rowinfo))
df = cbind(df, rowinfo)
thisplot = ggplot(data = df, aes_string(x = "x",
y = "y")) + theme_bw() + theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(), axis.title.x = element_blank()) +
theme(axis.text.y = element_blank(), axis.ticks.y = element_blank(),
axis.title.y = element_blank()) + theme(legend.position = "none") +
coord_cartesian(xlim = ulim[j, ], ylim = ulim[i,
]) + geom_point()
if (!is.null(rowinfo) & is.null(left.additions)) {
if (ncol(rowinfo) == 1)
left.additions = list(aes(color = rowinfo),
labs(color = ""))
if (ncol(rowinfo) == 2)
left.additions = list(aes(color = rowinfo[[1]],
shape = rowinfo[[2]]), labs(color = "",
shape = ""))
}
if (!is.null(left.additions))
thisplot = thisplot + left.additions
plots[[(i - 1) * K + j]] = thisplot
}
else {
df = data.frame(x = V[, j], y = V[, i])
if (!is.null(colinfo))
df = cbind(df, colinfo)
thisplot = ggplot(df, aes_string(x = "x", y = "y")) +
theme_bw() + theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(), axis.title.x = element_blank()) +
theme(axis.text.y = element_blank(), axis.ticks.y = element_blank(),
axis.title.y = element_blank()) + theme(legend.position = "none") +
coord_cartesian(xlim = vlim[j, ], ylim = vlim[i,
]) + geom_point()
if (!is.null(colinfo) & is.null(right.additions)) {
if (ncol(colinfo) == 1)
right.additions = list(aes(color = colinfo,
alpha = 0.2), scale_alpha_identity(), labs(color = ""))
if (ncol(colinfo) == 2)
right.additions = list(aes(color = colinfo[[1]],
shape = colinfo[[2]], alpha = 0.2), scale_alpha_identity(),
labs(color = "", shape = ""))
}
if (!is.null(right.additions))
thisplot = thisplot + right.additions
plots[[(i - 1) * K + j]] = thisplot
}
}
layout_matrix = kronecker(t(matrix(1:K^2, K, K)), matrix(1,
3, 3))
if (!is.null(left.additions) | !is.null(right.additions)) {
layout_matrix = rbind(layout_matrix, K^2 + 1)
layout_matrix = cbind(layout_matrix, K^2 + 2)
layout_matrix = cbind(layout_matrix, K^2 + 2)
layout_matrix[K * 3 + 1, ((K) * 3 + 1):ncol(layout_matrix)] = K^2 +
3
layout_matrix[((K) * 3 + 1):nrow(layout_matrix),
K * 3 + (1:2)] = K^2 + 4
plots[[K^2 + 1]] = plots[[K^2 + 2]] = ggplot() +
theme_void()
if (!is.null(left.additions))
plots[[K^2 + 1]] = get_legend(plots[[K + 1]] +
theme(legend.position = "bottom"))
if (!is.null(right.additions))
plots[[K^2 + 2]] = get_legend(plots[[2]] + theme(legend.position = "right"))
}
return(grid.arrange(grobs = plots, layout_matrix = layout_matrix))
}
else return(FALSE)
} |
expected <- eval(parse(text="logical(0)"));
test(id=0, code={
argv <- eval(parse(text="list(NULL, NULL)"));
do.call(`!=`, argv);
}, o=expected); |
col2gray <-
function(col, method="BT.709")
{
method <- match.arg(method, c("BT.709", "BT.601",
"desaturate", "average", "maximum", "minimum",
"red", "green", "blue"))
col <- col2rgb(col) / 255
if (method == "BT.709")
out <- 0.2126*col["red",] + 0.7152*col["green",] + 0.0722*col["blue",]
if (method == "BT.601")
out <- 0.299*col["red",] + 0.587*col["green",] + 0.114*col["blue",]
if (method == "desaturate")
out <- (apply(col, 2, max) + apply(col, 2, min)) / 2
if (method == "average")
out <- colMeans(col)
if (method == "maximum")
out <- apply(col, 2, max)
if (method == "minimum")
out <- apply(col, 2, min)
if (method == "red")
out <- col["red",]
if (method == "green")
out <- col["green",]
if (method == "blue")
out <- col["blue",]
gray(out)
} |
massSTD <- c(0.01, 0.5, 1, 10, 20, 50, 100, 120, 150, 200, 220)
indError <- c(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.2, -0.2)
uncert <- c(0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.4, 0.5)
d <- 0.1
traceability <- 'Set of weights class E2. Certificate number 1473 D-K 17296, 2019-05-10.'
MT.XPE.204 <- calibCert(balanceID = 'MT XPE 204', serial = 'B403223982', certificate = 5143,
d = d, d.units = 'mg',
indError = data.frame(massSTD, indError, uncert),
indError.units = c('g', 'mg', 'mg'),
rep = data.frame(load = c(0.1, 100, 220), sd = c(0.00, 0.04, 0.03)),
rep.units = c('g', 'mg'),
eccen = c(100, 0.1), eccen.units = c('g', 'mg'),
classSTD = 'E2', traceability = traceability,
Temp = c(17.4, 17.9),
p = c(750.4, 751.0),
h = c(70.5, 71.4),
unitsENV = c('deg.C', 'hPa', '%'),
institution = 'Instituto Nacional de Metrologia de Colombia',
date = '2021-03-18')
usethis::use_data(MT.XPE.204, overwrite = TRUE) |
"x2u"<-
function(x, labels = seq(along = x))
{
if(length(labels) != length(x)) stop(
"Arguments x and labels not of equal length")
rep(labels, x)
} |
perf_pairwise <- function(y, f, group, metric="ndcg", w=NULL, max_rank=0){
func.name <- switch(metric,
conc = "ir_measure_conc",
mrr = "ir_measure_mrr",
map = "ir_measure_map",
ndcg = "ir_measure_ndcg",
stop(paste("Metric",metric,"is not supported"))
)
if (metric == "conc" && all(is.element(y, 0:1))) {
func.name <- "ir_measure_auc"
}
if (max_rank <= 0) {
max_rank <- length(y)+1
}
f <- f + 1E-10 * runif(length(f), min=-0.5, max=0.5)
measure.by.group <- as.matrix(by(list(y, f), INDICES=group, FUN=get(func.name), max_rank=max_rank))
idx <- which((!is.null(measure.by.group)) & measure.by.group >= 0)
if (is.null(w)) {
return (mean(measure.by.group[idx]))
} else {
w.by.group <- tapply(w, group, mean)
return (weighted.mean(measure.by.group[idx], w=w.by.group[idx]))
}
}
ir_measure_conc <- function(y.f, max_rank=0) {
y <- y.f[[1]]
f <- y.f[[2]]
tab <- table(y)
csum <- cumsum(tab)
total.pairs <- sum(tab * (csum - tab))
if (total.pairs == 0) {
return (-1.0)
} else {
return (gbm_conc(y[order(-f)]) / total.pairs)
}
}
ir_measure_auc <- function(y.f, max_rank=0){
y <- y.f[[1]]
f <- y.f[[2]]
num.pos <- sum(y>0)
if (length(f) <= 1 || num.pos == 0 || num.pos == length(f))
{
return (-1.0)
}
else
{
return (gbm_roc_area(obs=y, pred=f))
}
}
ir_measure_mrr <- function(y.f, max_rank) {
y <- y.f[[1]]
f <- y.f[[2]]
num.pos <- sum(y>0)
if (length(f) <= 1 || num.pos == 0 || num.pos == length(f))
{
return (-1.0)
}
ord <- order(f, decreasing=TRUE)
min.idx.pos <- min(which(y[ord]>0))
if (min.idx.pos <= max_rank)
{
return (1.0 / min.idx.pos)
}
else
{
return (0.0)
}
}
ir_measure_map <- function(y.f, max_rank=0) {
y <- y.f[[1]]
f <- y.f[[2]]
ord <- order(f, decreasing=TRUE)
idx.pos <- which(y[ord]>0)
num.pos <- length(idx.pos)
if (length(f) <= 1 || num.pos == 0 || num.pos == length(f))
{
return (-1.0)
}
return (sum((1:length(idx.pos))/idx.pos) / num.pos)
}
ir_measure_ndcg <- function(y.f, max_rank) {
y <- y.f[[1]]
f <- y.f[[2]]
if (length(f) <= 1 || all(diff(y)==0)) return (-1.0)
num.items <- min(length(f), max_rank)
ord <- order(f, decreasing=TRUE)
dcg <- sum(y[ord][1:num.items] / log2(2:(num.items+1)))
ord.max <- order(y, decreasing=TRUE)
dcg.max <- sum(y[ord.max][1:num.items] / log2(2:(num.items+1)))
return (dcg / dcg.max)
} |
ml_lrnm <- function(...){
ml_lvm(..., within_latent = "ggm", between_latent = "ggm", within_residual = "ggm", between_residual = "ggm")
} |
check_sibling_order <- function(data, outcome, pair_identifiers, row) {
data <- data[row,]
outcome1 <- data[, base::paste0(outcome, pair_identifiers[1])]
outcome2 <- data[, base::paste0(outcome, pair_identifiers[2])]
if (outcome1 > outcome2) {
data$order <- "s1"
} else if (outcome1 < outcome2) {
data$order <- "s2"
} else if (outcome1 == outcome2) {
p <- stats::rbinom(1,1,0.5)
if (p) {data$order <- "s1"
}else if (!p) {data$order <- "s2"}
}
return(data)
}
make_mean_diffs <- function(data, id, sex, race, demographics, variable, pair_identifiers, row) {
S1 <- base::paste0(variable, pair_identifiers[1])
S2 <- base::paste0(variable, pair_identifiers[2])
sexS1 <- base::paste0(sex, pair_identifiers[1])
sexS2 <- base::paste0(sex, pair_identifiers[2])
raceS1 <- base::paste0(race, pair_identifiers[1])
raceS2 <- base::paste0(race, pair_identifiers[2])
data <- data[row,]
if (data[, "order"] == "s1") {
diff <- data[[S1]] - data[[S2]]
mean <- base::mean(c(data[[S1]], data[[S2]]))
output <- data.frame(id = data[[id]],
variable_1 = data[[S1]],
variable_2 = data[[S2]],
variable_diff = diff,
variable_mean = mean)
} else if (data[, "order"] == "s2") {
diff <- data[[S2]] - data[[S1]]
mean <- base::mean(c(data[[S1]], data[[S2]]))
output <- data.frame(id = data[[id]],
variable_1 = data[[S2]],
variable_2 = data[[S1]],
variable_diff = diff,
variable_mean = mean)
}
names(output) <- c("id",
paste0(variable, "_1"),
paste0(variable, "_2"),
paste0(variable, "_diff"),
paste0(variable, "_mean"))
if (demographics == "race") {
if (data[, "order"] == "s1") {
output_demographics <- data.frame(race_1 = data[[raceS1]],
race_2 = data[[raceS2]])
} else if (data[, "order"] == "s2") {
output_demographics <- data.frame(race_1 = data[[raceS2]],
race_2 = data[[raceS1]])
}
} else if (demographics == "sex") {
if (data[, "order"] == "s1") {
output_demographics <- data.frame(sex_1 = data[[sexS1]],
sex_2 = data[[sexS2]])
} else if (data[, "order"] == "s2") {
output_demographics <- data.frame(sex_1 = data[[sexS2]],
sex_2 = data[[sexS1]])
}
} else if (demographics == "both") {
if (data[, "order"] == "s1") {
output_demographics <- data.frame(sex_1 = data[[sexS1]],
sex_2 = data[[sexS2]],
race_1 = data[[raceS1]],
race_2 = data[[raceS2]])
} else if (data[, "order"] == "s2") {
output_demographics <- data.frame(sex_1 = data[[sexS2]],
sex_2 = data[[sexS1]],
race_1 = data[[raceS2]],
race_2 = data[[raceS1]])
}
}
if (exists("output_demographics")) {
output <- base::cbind(output, output_demographics)
}
return(output)
}
check_discord_errors <- function(data, id, sex, race, pair_identifiers) {
if (!id %in% base::names(data)) {
stop(paste0("The kinship pair ID \"", id, "\" is not valid. Please check that you have the correct column name."))
}
if (!base::is.null(sex) && base::sum(base::grepl(sex, base::names(data))) == 0) {
stop(paste0("The kinship pair sex identifier \"", sex, "\" is not appropriately defined. Please check that you have the correct column name."))
}
if (!base::is.null(race) && base::sum(base::grepl(race, base::names(data))) == 0) {
stop(paste0("The kinship pair race identifier \"", race, "\" is not appropriately defined. Please check that you have the correct column name."))
}
if (base::sum(base::grepl(pair_identifiers[1], base::names(data))) == 0 | base::sum(base::grepl(pair_identifiers[2], base::names(data))) == 0) {
stop(paste0("Please check that the kinship pair identifiers \"", pair_identifiers[1], "\" and \"", pair_identifiers[2],"\" are valid, i.e. ensure that you have the correct labels for each kin."))
}
if (!base::is.null(sex) & !base::is.null(race) && sex == race) {
stop("Please check that your sex and race variables are not equal.")
}
} |
run_kegg <- function(gene_up,gene_down,geneList=F,pro='test'){
gene_up=unique(gene_up)
gene_down=unique(gene_down)
gene_diff=unique(c(gene_up,gene_down))
kk.up <- enrichKEGG(gene = gene_up,
organism = 'hsa',
pvalueCutoff = 0.9,
qvalueCutoff =0.9)
head(kk.up)[,1:6]
kk=kk.up
dotplot(kk)
kk=DOSE::setReadable(kk, OrgDb='org.Hs.eg.db',keytype='ENTREZID')
write.csv(kk@result,paste0(pro,'_kk.up.csv'))
kk.down <- enrichKEGG(gene = gene_down,
organism = 'hsa',
pvalueCutoff = 0.9,
qvalueCutoff =0.9)
head(kk.down)[,1:6]
kk=kk.down
dotplot(kk)
kk=DOSE::setReadable(kk, OrgDb='org.Hs.eg.db',keytype='ENTREZID')
write.csv(kk@result,paste0(pro,'_kk.down.csv'))
kk.diff <- enrichKEGG(gene = gene_diff,
organism = 'hsa',
pvalueCutoff = 0.05)
head(kk.diff)[,1:6]
kk=kk.diff
dotplot(kk)
kk=DOSE::setReadable(kk, OrgDb='org.Hs.eg.db',keytype='ENTREZID')
write.csv(kk@result,paste0(pro,'_kk.diff.csv'))
kegg_diff_dt <- as.data.frame(kk.diff)
kegg_down_dt <- as.data.frame(kk.down)
kegg_up_dt <- as.data.frame(kk.up)
down_kegg<-kegg_down_dt[kegg_down_dt$pvalue<0.01,];down_kegg$group=-1
up_kegg<-kegg_up_dt[kegg_up_dt$pvalue<0.01,];up_kegg$group=1
g_kegg=kegg_plot(up_kegg,down_kegg)
print(g_kegg)
ggsave(g_kegg,filename = paste0(pro,'_kegg_up_down.png') )
if(geneList){
kk_gse <- gseKEGG(geneList = geneList,
organism = 'hsa',
nPerm = 1000,
minGSSize = 20,
pvalueCutoff = 0.9,
verbose = FALSE)
head(kk_gse)[,1:6]
gseaplot(kk_gse, geneSetID = rownames(kk_gse[1,]))
gseaplot(kk_gse, 'hsa04110',title = 'Cell cycle')
kk=DOSE::setReadable(kk_gse, OrgDb='org.Hs.eg.db',keytype='ENTREZID')
tmp=kk@result
write.csv(kk@result,paste0(pro,'_kegg.gsea.csv'))
down_kegg<-kk_gse[kk_gse$pvalue<0.05 & kk_gse$enrichmentScore < 0,];down_kegg$group=-1
up_kegg<-kk_gse[kk_gse$pvalue<0.05 & kk_gse$enrichmentScore > 0,];up_kegg$group=1
g_kegg=kegg_plot(up_kegg,down_kegg)
print(g_kegg)
ggsave(g_kegg,filename = paste0(pro,'_kegg_gsea.png'))
}
}
run_go <- function(gene_up,gene_down,pro='test'){
gene_up=unique(gene_up)
gene_down=unique(gene_down)
gene_diff=unique(c(gene_up,gene_down))
g_list=list(gene_up=gene_up,
gene_down=gene_down,
gene_diff=gene_diff)
if(T){
go_enrich_results <- lapply( g_list , function(gene) {
lapply( c('BP','MF','CC') , function(ont) {
cat(paste('Now process ',ont ))
ego <- enrichGO(gene = gene,
OrgDb = org.Hs.eg.db,
ont = ont ,
pAdjustMethod = "BH",
pvalueCutoff = 0.99,
qvalueCutoff = 0.99,
readable = TRUE)
print( head(ego) )
return(ego)
})
})
save(go_enrich_results,file =paste0(pro, '_go_enrich_results.Rdata'))
}
load(file=paste0(pro, '_go_enrich_results.Rdata'))
n1= c('gene_up','gene_down','gene_diff')
n2= c('BP','MF','CC')
for (i in 1:3){
for (j in 1:3){
fn=paste0(pro, '_dotplot_',n1[i],'_',n2[j],'.png')
cat(paste0(fn,'\n'))
png(fn,res=150,width = 1080)
print( dotplot(go_enrich_results[[i]][[j]] ))
dev.off()
}
}
}
kegg_plot <- function(up_kegg,down_kegg){
dat=rbind(up_kegg,down_kegg)
colnames(dat)
dat$pvalue = -log10(dat$pvalue)
dat$pvalue=dat$pvalue*dat$group
dat=dat[order(dat$pvalue,decreasing = F),]
g_kegg<- ggplot(dat, aes(x=reorder(Description,order(pvalue, decreasing = F)), y=pvalue, fill=group)) +
geom_bar(stat="identity") +
scale_fill_gradient(low="blue",high="red",guide = FALSE) +
scale_x_discrete(name ="Pathway names") +
scale_y_continuous(name ="log10P-value") +
coord_flip() + theme_bw()+theme(plot.title = element_text(hjust = 0.5))+
ggtitle("Pathway Enrichment")
} |
NULL
load_mbb_pbp <- function(seasons = most_recent_mbb_season(),...,
dbConnection = NULL, tablename = NULL) {
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
dots <- rlang::dots_list(...)
loader <- rds_from_url
if (!is.null(dbConnection) && !is.null(tablename)) in_db <- TRUE else in_db <- FALSE
if(isTRUE(seasons)) seasons <- 2006:most_recent_mbb_season()
stopifnot(is.numeric(seasons),
seasons >= 2006,
seasons <= most_recent_mbb_season())
urls <- paste0("https://raw.githubusercontent.com/saiemgilani/hoopR-data/master/mbb/pbp/rds/play_by_play_",seasons,".rds")
p <- NULL
if (is_installed("progressr")) p <- progressr::progressor(along = seasons)
out <- lapply(urls, progressively(loader, p))
out <- data.table::rbindlist(out, use.names = TRUE, fill = TRUE)
if (in_db) {
DBI::dbWriteTable(dbConnection, tablename, out, append = TRUE)
out <- NULL
} else {
class(out) <- c("tbl_df","tbl","data.table","data.frame")
}
out
}
NULL
load_mbb_team_box <- function(seasons = most_recent_mbb_season(), ...,
dbConnection = NULL, tablename = NULL) {
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
dots <- rlang::dots_list(...)
loader <- rds_from_url
if (!is.null(dbConnection) && !is.null(tablename)) in_db <- TRUE else in_db <- FALSE
if(isTRUE(seasons)) seasons <- 2003:most_recent_mbb_season()
stopifnot(is.numeric(seasons),
seasons >= 2003,
seasons <= most_recent_mbb_season())
urls <- paste0("https://raw.githubusercontent.com/saiemgilani/hoopR-data/master/mbb/team_box/rds/team_box_",seasons,".rds")
p <- NULL
if (is_installed("progressr")) p <- progressr::progressor(along = seasons)
out <- lapply(urls, progressively(loader, p))
out <- data.table::rbindlist(out, use.names = TRUE, fill = TRUE)
if (in_db) {
DBI::dbWriteTable(dbConnection, tablename, out, append = TRUE)
out <- NULL
} else {
class(out) <- c("tbl_df","tbl","data.table","data.frame")
}
out
}
NULL
load_mbb_player_box <- function(seasons = most_recent_mbb_season(), ...,
dbConnection = NULL, tablename = NULL) {
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
dots <- rlang::dots_list(...)
loader <- rds_from_url
if (!is.null(dbConnection) && !is.null(tablename)) in_db <- TRUE else in_db <- FALSE
if(isTRUE(seasons)) seasons <- 2003:most_recent_mbb_season()
stopifnot(is.numeric(seasons),
seasons >= 2003,
seasons <= most_recent_mbb_season())
urls <- paste0("https://raw.githubusercontent.com/saiemgilani/hoopR-data/master/mbb/player_box/rds/player_box_",seasons,".rds")
p <- NULL
if (is_installed("progressr")) p <- progressr::progressor(along = seasons)
out <- lapply(urls, progressively(loader, p))
out <- data.table::rbindlist(out, use.names = TRUE, fill = TRUE)
if (in_db) {
DBI::dbWriteTable(dbConnection, tablename, out, append = TRUE)
out <- NULL
} else {
class(out) <- c("tbl_df","tbl","data.table","data.frame")
}
out
}
NULL
load_mbb_schedule <- function(seasons = most_recent_mbb_season(), ...,
dbConnection = NULL, tablename = NULL) {
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
dots <- rlang::dots_list(...)
loader <- rds_from_url
if (!is.null(dbConnection) && !is.null(tablename)) in_db <- TRUE else in_db <- FALSE
if(isTRUE(seasons)) seasons <- 2002:most_recent_mbb_season()
stopifnot(is.numeric(seasons),
seasons >= 2002,
seasons <= most_recent_mbb_season())
urls <- paste0("https://raw.githubusercontent.com/saiemgilani/hoopR-data/master/mbb/schedules/rds/mbb_schedule_",seasons,".rds")
p <- NULL
if (is_installed("progressr")) p <- progressr::progressor(along = seasons)
out <- lapply(urls, progressively(loader, p))
out <- data.table::rbindlist(out, use.names = TRUE, fill = TRUE)
if (in_db) {
DBI::dbWriteTable(dbConnection, tablename, out, append = TRUE)
out <- NULL
} else {
class(out) <- c("tbl_df","tbl","data.table","data.frame")
}
out
}
load_mbb_games <- function(){
.url <- "https://raw.githubusercontent.com/saiemgilani/hoopR-data/master/mbb/mbb_games_in_data_repo.csv"
dat <- hoopR::csv_from_url(.url)
return (dat)
}
update_mbb_db <- function(dbdir = ".",
dbname = "hoopR_db",
tblname = "hoopR_mbb_pbp",
force_rebuild = FALSE,
db_connection = NULL) {
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
if (!is_installed("DBI") | !is_installed("purrr") |
(!is_installed("RSQLite") & is.null(db_connection))) {
usethis::ui_stop("{my_time()} | Packages {usethis::ui_value('DBI')}, {usethis::ui_value('RSQLite')} and {usethis::ui_value('purrr')} required for database communication. Please install them.")
}
if (any(force_rebuild == "NEW")) {
usethis::ui_stop("{my_time()} | The argument {usethis::ui_value('force_rebuild = NEW')} is only for internal usage!")
}
if (!(is.logical(force_rebuild) | is.numeric(force_rebuild))) {
usethis::ui_stop("{my_time()} | The argument {usethis::ui_value('force_rebuild')} has to be either logical or numeric!")
}
if (!dir.exists(dbdir) & is.null(db_connection)) {
usethis::ui_oops("{my_time()} | Directory {usethis::ui_path(dbdir)} doesn't exist yet. Try creating...")
dir.create(dbdir)
}
if (is.null(db_connection)) {
connection <- DBI::dbConnect(RSQLite::SQLite(), glue::glue("{dbdir}/{dbname}"))
} else {
connection <- db_connection
}
if (!DBI::dbExistsTable(connection, tblname)) {
build_mbb_db(tblname, connection, rebuild = "NEW")
} else if (DBI::dbExistsTable(connection, tblname) & all(force_rebuild != FALSE)) {
build_mbb_db(tblname, connection, rebuild = force_rebuild)
}
user_message("Checking for missing completed games...", "todo")
completed_games <- load_mbb_games() %>%
dplyr::filter(.data$season >= 2006) %>%
dplyr::pull(.data$game_id)
missing <- get_missing_mbb_games(completed_games, connection, tblname)
if(length(missing) > 16) {
build_mbb_db(tblname, connection, show_message = FALSE, rebuild = as.numeric(unique(stringr::str_sub(missing, 1, 4))))
missing <- get_missing_mbb_games(completed_games, connection, tblname)
}
message_completed("Database update completed", in_builder = TRUE)
usethis::ui_info("{my_time()} | Path to your db: {usethis::ui_path(DBI::dbGetInfo(connection)$dbname)}")
if (is.null(db_connection)) DBI::dbDisconnect(connection)
}
build_mbb_db <- function(tblname = "hoopR_mbb_pbp", db_conn, rebuild = FALSE, show_message = TRUE) {
old <- options(list(stringsAsFactors = FALSE, scipen = 999))
on.exit(options(old))
valid_seasons <- load_mbb_games() %>%
dplyr::filter(.data$season >= 2006) %>%
dplyr::group_by(.data$season) %>%
dplyr::summarise() %>%
dplyr::ungroup()
if (all(rebuild == TRUE)) {
usethis::ui_todo("{my_time()} | Purging the complete data table {usethis::ui_value(tblname)} in your connected database...")
DBI::dbRemoveTable(db_conn, tblname)
seasons <- valid_seasons %>% dplyr::pull("season")
usethis::ui_todo("{my_time()} | Starting download of {length(seasons)} seasons between {min(seasons)} and {max(seasons)}...")
} else if (is.numeric(rebuild) & all(rebuild %in% valid_seasons$season)) {
string <- paste0(rebuild, collapse = ", ")
if (show_message){usethis::ui_todo("{my_time()} | Purging {string} season(s) from the data table {usethis::ui_value(tblname)} in your connected database...")}
DBI::dbExecute(db_conn, glue::glue_sql("DELETE FROM {`tblname`} WHERE season IN ({vals*})", vals = rebuild, .con = db_conn))
seasons <- valid_seasons %>% dplyr::filter(.data$season %in% rebuild) %>% dplyr::pull("season")
usethis::ui_todo("{my_time()} | Starting download of the {string} season(s)...")
} else if (all(rebuild == "NEW")) {
usethis::ui_info("{my_time()} | Can't find the data table {usethis::ui_value(tblname)} in your database. Will load the play by play data from scratch.")
seasons <- valid_seasons %>% dplyr::pull("season")
usethis::ui_todo("{my_time()} | Starting download of {length(seasons)} seasons between {min(seasons)} and {max(seasons)}...")
} else {
seasons <- NULL
usethis::ui_oops("{my_time()} | At least one invalid value passed to argument {usethis::ui_code('force_rebuild')}. Please try again with valid input.")
}
if (!is.null(seasons)) {
load_mbb_pbp(seasons, dbConnection = db_conn, tablename = tblname)
}
}
get_missing_mbb_games <- function(completed_games, dbConnection, tablename) {
db_ids <- dplyr::tbl(dbConnection, tablename) %>%
dplyr::select("game_id") %>%
dplyr::distinct() %>%
dplyr::collect() %>%
dplyr::pull("game_id")
need_scrape <- completed_games[!completed_games %in% db_ids]
usethis::ui_info("{my_time()} | You have {length(db_ids)} games and are missing {length(need_scrape)}.")
return(need_scrape)
} |
multinomT <- function(Yp, Xarray, xvec, jacstack,
start=NA, nobsvec, fixed.df = NA)
{
obs <- dim(Yp)[1];
cats <- dim(Yp)[2];
mcats <- cats-1;
nvars <- dim(Xarray)[2];
if (any(xvec[,cats] != 0)) {
stop("(multinomT): invalid specification of Xarray (regressors not allowed for last category");
}
smdim <- dim(jacstack);
stack.index <- matrix(FALSE, smdim[2], smdim[3]);
for (i in 1:smdim[2]) for (j in 1:smdim[3]) {
stack.index[i,j] <- !all(jacstack[,i,j] == 0);
}
if (sum(stack.index) != sum(xvec != 0)) {
print("multinomT: xvec is:"); print(xvec);
print("multinomT: stack.index is:"); print(stack.index);
stop("(multinomT): jacstack structure check failed");
}
kY <- matrix(nrow=obs,ncol=mcats)
indx1 <- Yp==0;
indx1vec <- apply(indx1,1,sum) > 0;
if (sum(indx1) > 0)
{
cat("multinomT: Need to remove 0 in multinomT transformation\n");
}
Yp[indx1] <- .5/nobsvec[indx1vec];
kY <- log(Yp[,1:mcats]/Yp[,cats])
if (is.na(start[1]))
{
mt.obj <- mt.mle(Xarray=Xarray, xvec=xvec, jacstack=jacstack, y=kY,
stack.index=stack.index, fixed.df=fixed.df);
} else {
mt.obj <- mt.mle(Xarray=Xarray, xvec=xvec, jacstack=jacstack, y=kY,
stack.index=stack.index, start=start, fixed.df=fixed.df);
}
tvec <- mnl.xvec.mapping(forward=FALSE,xvec,xvec, mt.obj$dp$beta, cats, nvars);
pred <- mnl.probfunc(Yp, Yp==Yp, Xarray, tvec)
return( list(call=mt.obj$call, logL=mt.obj$logL, deviance=mt.obj$deviance,
par=mt.obj$dp, se=mt.obj$se, optim=mt.obj$optim, pred=pred))
}
mt.mle <- function (Xarray, xvec, jacstack, y, stack.index,
start = NA, freq =NA, fixed.df = NA, trace = FALSE,
method = "BFGS", control = list(maxit = 600,trace=0))
{
nvars <- dim(Xarray)[2];
Diag <- function(x) diag(x, nrow = length(x), ncol = length(x))
y.names <- dimnames(y)[[2]]
y <- as.matrix(y)
if (missing(freq) | is.na(freq)) {
freq <- rep(1, nrow(y))
}
d <- ncol(y)
n <- sum(freq)
m <- sum(xvec == 1) + length(unique(xvec[xvec>1]));
if (is.na(start[1]))
{
cat("mt.mle: I don't know how to generate starting values in the general case\n");
stop();
}
beta <- start$beta
Omega <- start$Omega
if (!is.na(fixed.df)) start$df <- fixed.df;
df <- start$df
Oinv <- solve(Omega, tol=1e-100)
Oinv <- (Oinv + t(Oinv))/2
upper <- chol(Oinv)
D <- diag(upper)
A <- upper/D
D <- D^2
if (d > 1)
{
param <- c(beta, -0.5 * log(D), A[!lower.tri(A, diag = TRUE)])
} else {
param <- c(beta, -0.5 * log(D))
}
if (is.na(fixed.df))
param <- c(param, log(df))
opt <- optim(param, fn = mt.dev, method = method,
control = control, hessian = TRUE,
Xarray=Xarray, xvec=xvec, jacstack=jacstack, y = y,
stack.index = stack.index, nvars = nvars, freq = freq,
trace = trace, fixed.df = fixed.df)
dev <- opt$value
param <- opt$par
if (trace) {
cat("mt.mle: Message from optimization routine:", opt$message,
"\n")
cat("mt.mle: deviance:", dev, "\n")
}
beta <- param[1:m] ;
D <- exp(-2 * param[(m + 1):(m + d)])
if (d > 1) {
A <- diag(d)
A[!lower.tri(A, diag = TRUE)] <-
param[(m + d + 1):(m + d + d * (d - 1)/2)]
i0 <- m + d + d * (d - 1)/2
} else {
i0 <- m + 1
A <- as.matrix(1)
}
if (is.na(fixed.df))
{
df <- exp(param[i0 + 1])
} else {
df <- fixed.df
}
Ainv <- backsolve(A, diag(d))
Omega <- Ainv %*% Diag(1/D) %*% t(Ainv)
dimnames(Omega) <- list(y.names, y.names)
info <- opt$hessian/2
if (all(is.finite(info))) {
qr.info <- qr(info)
info.ok <- (qr.info$rank == length(param))
} else {
info.ok <- FALSE
}
if (info.ok) {
se2 <- diag(solve(qr.info))
if (min(se2) < 0)
{
se <- NA
} else {
se <- sqrt(se2)
se.beta <- se[1:m] ;
se.df <- df * se[i0 + 1]
se <- list(beta = se.beta, df = se.df,
info = info)
}
} else {
se <- NA
}
dp <- list(beta = beta, Omega = Omega, df = df)
list(call = match.call(), logL = -0.5 * dev, deviance = dev,
dp = dp, se = se, optim = opt)
}
mt.dev <- function (param, Xarray, xvec, jacstack, y, stack.index, nvars, freq,
fixed.df = NA, trace = FALSE)
{
Diag <- function(x) diag(x, nrow = length(x), ncol = length(x))
d <- ncol(y)
n <- sum(freq)
m <- sum(xvec == 1) + length(unique(xvec[xvec>1]));
beta <- param[1:m];
D <- exp(-2 * param[(m + 1):(m + d)])
if (d > 1) {
A <- diag(d)
A[!lower.tri(A, diag = TRUE)] <-
param[(m + d + 1):(m + d + d * (d - 1)/2)]
i0 <- m + d + d * (d - 1)/2
} else {
i0 <- m + 1
A <- as.matrix(1)
}
eta <- rep(0,d);
if (is.na(fixed.df))
{
df <- exp(param[i0 + 1])
} else {
df <- fixed.df
}
Oinv <- t(A) %*% Diag(D) %*% A
tvec <- mnl.xvec.mapping(forward=FALSE, xvec, xvec, beta, d+1, nvars);
ylinpred <- y;
if (dim(tvec)[1] == 1) {
for (j in 1:d) {
ylinpred[,j] <- Xarray[,,j] * tvec[,j];
}
}
else {
for (j in 1:d) {
ylinpred[,j] <- Xarray[,,j] %*% tvec[,j];
}
}
u <- y - ylinpred ;
Q <- apply((u %*% Oinv) * u, 1, sum)
L <- as.vector(u %*% eta)
logDet <- sum(log(df * pi/D))
dev <- (n * (2 * lgamma(df/2) + logDet - 2 * lgamma((df +
d)/2)) + (df + d) * sum(freq * log(1 + Q/df)) - 2 * sum(freq *
log(2 * pt(L * sqrt((df + d)/(Q + df)), df + d))))
if (trace)
cat("mt.dev: ", dev, "\n")
dev
}
mt.dev.grad <- function (param, Xarray, xvec, jacstack, y, stack.index, nvars, freq,
fixed.df = NA, trace = FALSE)
{
Diag <- function(x) diag(x, nrow = length(x), ncol = length(x))
d <- ncol(y)
n <- sum(freq)
m <- sum(xvec == 1) + length(unique(xvec[xvec>1]));
nvarsunique <- dim(jacstack)[2];
beta <- param[1:m];
D <- exp(-2 * param[(m + 1):(m + d)])
if (d > 1) {
A <- diag(d)
A[!lower.tri(A, diag = TRUE)] <-
param[(m + d + 1):(m + d + d * (d - 1)/2)]
i0 <- m + d + d * (d - 1)/2
}
else {
i0 <- m + d
A <- as.matrix(1)
}
eta <- rep(0,d);
if (is.na(fixed.df))
df <- exp(param[i0 + 1])
else df <- fixed.df
tA <- t(A)
Oinv <- tA %*% Diag(D) %*% A
tvec <- mnl.xvec.mapping(forward=FALSE, xvec, xvec, beta, d+1, nvars);
ylinpred <- y;
if (dim(tvec)[1] == 1) {
for (j in 1:d) {
ylinpred[,j] <- Xarray[,,j] * tvec[,j];
}
}
else {
for (j in 1:d) {
ylinpred[,j] <- Xarray[,,j] %*% tvec[,j];
}
}
u <- y - ylinpred ;
Q <- as.vector(apply((u %*% Oinv) * u, 1, sum))
L <- as.vector(u %*% eta)
t. <- L * sqrt((df + d)/(Q + df))
dlogft <- -(df + d)/(2 * df * (1 + Q/df))
dt.dQ <- (-0.5) * L * sqrt(df + d)/(Q + df)^1.5
T. <- pt(t., df + d)
dlogT. <- dt(t., df + d)/T.
u.freq <- u * freq
fooA <- matrix(0, nvarsunique, d);
for (j in 1:d) {
fooA[,j] <- t(jacstack[,,j]) %*% (u.freq * (dlogft + dlogT. * dt.dQ));
}
Dbeta <- -2 * fooA %*% Oinv
if (d > 1) {
M <- 2 * (Diag(D) %*% A %*% t(u * dlogft) %*% u.freq +
Diag(D) %*% A %*% t(u * dlogT. * dt.dQ) %*% u.freq)
DA <- M[!lower.tri(M, diag = TRUE)]
}
else DA <- NULL
M <- (A %*% t(u * dlogft) %*% u.freq %*% tA + A %*% t(u *
dlogT. * dt.dQ) %*% u.freq %*% tA)
if (d > 1)
DD <- diag(M) + 0.5 * n/D
else DD <- as.vector(M + 0.5 * n/D)
grad <- (-2) * c(Dbeta[stack.index[,-(d+1)]], DD * (-2 * D), DA)
if (is.na(fixed.df)) {
dlogft.ddf <- 0.5 * (digamma((df + d)/2) - digamma(df/2) -
d/df + (df + d) * Q/((1 + Q/df) * df^2) - log(1 +
Q/df))
eps <- 1e-04
T.eps <- pt(L * sqrt((df + eps + d)/(Q + df + eps)),
df + eps + d)
dlogT.ddf <- (log(T.eps) - log(T.))/eps
Ddf <- sum((dlogft.ddf + dlogT.ddf) * freq)
grad <- c(grad, -2 * Ddf * df)
}
if (trace)
cat("mt.dev.grad: norm is ", sqrt(sum(grad^2)), "\n")
return(grad)
} |
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