tests/testthat/test-tdeffectvariation_covtest.R
In cdriveraus/ctsem: Continuous Time Structural Equation Modelling
if(identical(Sys.getenv("NOT_CRAN"), "true")& .Machine$sizeof.pointer != 4){
library(ctsem)
library(testthat)
cores=2
context("randomEffects")
test_that("randomEffectsTDPREDEFFECT", {
nsubjects <- 1000
ntimes <- 20
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
effect <- rnorm(nsubjects, 5-baseline/3, 0.5)
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(c(1,0),1,2),
DRIFT= c(-1,1,
0,-.5),
T0MEANS = c(t0m[i],0),
DIFFUSION=c(0.5,0,0,1e-6),
MANIFESTVAR = 0.5,
T0VAR = c(0,0,0,0),
TDPREDMEANS = matrix(c(rep(0,9),1,rep(0,ntimes-10))),
TDPREDEFFECT = matrix(c(0,effect[i]),2),
MANIFESTMEANS = baseline[i]))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = 1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
LAMBDA=matrix(c(1,0),1,2),
DRIFT= c('drift',1,
0,-0.5),
T0MEANS = c('t0m',0),
T0VAR = diag(1e-3,2),
DIFFUSION=c('diffusion',0,0,0),
TDPREDEFFECT = matrix(c(0,'tdpredeffect|param|TRUE')))
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,0,0,0),1,4),
DRIFT= c('drift',1,0,0,
0,-0.5,0,0,
0,0,-1e-6,0,
0,0,0,-1e-6),
DIFFUSION=c('diffusion',0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0),
T0MEANS = c('t0m',0,'mm','tdpredeffect'),
TDPREDEFFECT = matrix(c(0,'state[4]',0,0)),
MANIFESTMEANS='state[3]')
m2$T0VAR[2,]=m2$T0VAR[,2]=0
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores)
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,derrind=1)
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
plot(subjpars[,1],baseline)
abline(0,1)
plot(subjpars[,2],t0m)
abline(0,1)
plot(subjpars[,3],effect)
abline(0,1)
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#sd checks
dfsd=data.frame(trueSample=c(sd(baseline),sd(t0m),sd(effect)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,]))[c(3,1,4)],
s$popsd[c('mm_Y1','t0m','tdpredeffect'),]) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfsd$f2est,dfsd$X50.,tol=1e-2)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsd$mean,dfsd$subjPars,tol=.2)
#test sd of ctsem between subjects setup vs true sample sd -- why is t0means sd overestimated?
testthat::expect_equivalent(dfsd[,'trueSample'],
dfsd[,'X50.'],tol=1e-1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,4,4]))) #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$popsd[,3]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#corr checks
dfcorr <- data.frame(trueSample=cor(cbind(t0m,baseline,effect))[lower.tri(diag(3))], #true sample cor,
subjPars=cor(subjpars[,c('t0m','mm_Y1','tdpredeffect')])[lower.tri(diag(3))],
f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,-2,-2])[lower.tri(diag(3))],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=1e-2)
#test corr of ctsem between subjects setup vs subject specific pars
# testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=1e-1)
})
test_that("randomEffectsLambda", {
set.seed(1)
nsubjects <- 1000
ntimes <- 20
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
effect <- rnorm(nsubjects, 5-baseline/3, 0.5)
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(c(1,effect[i]),1,2),
DRIFT= c(-1,0,
0,-.5),
T0MEANS = c(t0m[i],0),
DIFFUSION=c(0.5,0,0,1e-6),
MANIFESTVAR = 0.5,
T0VAR = c(0,0,0,0),
TDPREDMEANS = matrix(c(rep(0,9),1,rep(0,ntimes-10))),
TDPREDEFFECT = matrix(c(0,1),2),
MANIFESTMEANS = baseline[i]))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = 1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
LAMBDA=matrix(c(1,'tdpredeffect|param|TRUE'),1,2),
DRIFT= c('drift',0,
0,-0.5),
T0MEANS = c('t0m',0),
T0VAR = diag(1e-3,2),
DIFFUSION=c('diffusion',0,0,0),
TDPREDEFFECT = matrix(c(0,1)))
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,'state[4]',0,0),1,4),
DRIFT= c('drift',0,0,0,
0,-0.5,0,0,
0,0,-1e-6,0,
0,0,0,-1e-6),
DIFFUSION=c('diffusion',0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0),
T0MEANS = c('t0m',0,'mm','tdpredeffect'),
TDPREDEFFECT = matrix(c(0,1,0,0)),
MANIFESTMEANS='state[3]')
m2$T0VAR[2,]=m2$T0VAR[,2]=0
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores)
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,derrind=1)
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#sd checks
dfsd=data.frame(trueSample=c(sd(baseline),sd(t0m),sd(effect)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,]))[c(3,1,4)],
s$popsd[c('mm_Y1','t0m','tdpredeffect'),]) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfsd$f2est,dfsd$X50.,tol=1e-2)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsd$mean,dfsd$subjPars,tol=.2)
#test sd of ctsem between subjects setup vs true sample sd -- why is t0means sd overestimated?
testthat::expect_equivalent(dfsd[,'trueSample'],
dfsd[,'X50.'],tol=1e-1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,4,4]))) #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$popsd[,2]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#corr checks
dfcorr <- data.frame(trueSample=cor(cbind(baseline,t0m,effect))[lower.tri(diag(3))][c(3,1,2)], #true sample cor,
subjPars=cor(subjpars[,c('t0m','tdpredeffect','mm_Y1')])[lower.tri(diag(3))],
f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,-2,-2])[lower.tri(diag(3))][c(2,1,3)],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=1e-2)
#test corr of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=1e-1)
})
test_that("randomEffectsDRIFT", {
set.seed(1)
nsubjects <- 400
ntimes <- 50
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
raweffect <- rnorm(nsubjects, baseline/2, 0.5)
effect <- -log1p(exp(-raweffect))
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(1),
DRIFT= c(effect[i]),
T0MEANS = c(t0m[i]),
DIFFUSION=c(0.5),
MANIFESTVAR = 0.5,
T0VAR = c(0),
CINT = c(baseline[i]),MANIFESTMEANS=0))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = 1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
CINT='cint',MANIFESTMEANS=0,
LAMBDA=matrix(1),DRIFT='drift|-log1p_exp(-param)|TRUE')
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,0,0),ncol=3),
DRIFT= c('-log1p_exp(-state[2])',0,0,
0,-1e-6,0,
0,0,-1e-6),
DIFFUSION=c('diffusion',0,0,
0,0,0,
0,0,0),
T0MEANS = c('t0m','drift','cint'),
CINT=c('state[3]',0,0),MANIFESTMEANS=0)
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores,optimcontrol = list(stochastic=F,carefulfit=F))
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,c('T0m_eta1','drift','cint')] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,derrind=1,optimcontrol = list(stochastic=F,carefulfit=F))
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
mean(effect)
plot(subjpars[,'cint'],baseline)
abline(0,1)
plot(subjpars[,'drift'],effect)
abline(0,1)
plot(subjpars[,'T0m_eta1'],t0m)
abline(0,1)
plot(subjpars[,c('cint','drift')])
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#loglik checks
testthat::expect_equivalent(s$loglik,s2$loglik,tol=1e-2)
#sd checks (f2 differences expected)
dfsd=data.frame(trueSample=c(sd(t0m),sd(effect),sd(baseline)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
s$popsd[c('T0m_eta1','drift','cint'),]) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f$stanfit$transformedparsfull$rawpopsd*c(10,1,10),tol=1e-2)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsd$mean,dfsd$subjPars,tol=.1)
#test sd of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfsd[,'trueSample'],
dfsd[,'X50.'],tol=1e-1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,2,2]))) #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$rawpopsd[,2]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#corr checks
dfcorr <-
data.frame(trueSample=cor(cbind(t0m,raweffect,baseline))[lower.tri(diag(3))], #true (raw) sample cor,
subjPars=cor(subjpars)[lower.tri(diag(3))],
f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,,])[lower.tri(diag(3))],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=1e-2)
#test corr of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd -- why is t0means sd overestimated?
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=1e-1)
})
test_that("randomEffectsDIFFUSION", {
set.seed(1)
nsubjects <- 400
ntimes <- 50
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
raweffect <- rnorm(nsubjects,-baseline/3, .1)
effect <- log1p(exp(raweffect))
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(1),
DRIFT= -1,
T0MEANS = c(t0m[i]),
DIFFUSION=effect[i],
MANIFESTVAR = 0.5,
T0VAR = c(0),
CINT = baseline[i],
MANIFESTMEANS=0))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = .1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
T0MEANS='t0m|param',
MANIFESTVAR=.5,
MANIFESTMEANS=0,CINT='cint|param',
LAMBDA=matrix(1),DIFFUSION='diffusion|log1p_exp(param)|TRUE')
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,0,0),ncol=3),
DRIFT= c('drift',0,0,
0,-1e-12,0,
0,0,-1e-12),
DIFFUSION=c('log1p_exp(state[2])',0,0,
0,0,0,
0,0,0),
MANIFESTVAR=.5,
T0MEANS = c('t0m|param','diffusion|param','cint|param'),
CINT=c('state[3]',0,0),
MANIFESTMEANS=0)
m2$T0VAR[diag(3)==1] <- paste0(m2$T0VAR[diag(3)==1] ,'|log1p_exp(2*param-1)')
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores,
optimcontrol = list(stochastic=F,carefulfit=F))#,init=f$stanfit$rawest)
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,c('t0m','diffusion','cint')] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,derrind=1,optimcontrol = list(stochastic=F,carefulfit=F))
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
mean(effect)
plot(subjpars[,'cint'],baseline)
abline(0,1)
plot(subjpars[,'diffusion'],effect)
abline(0,1)
plot(subjpars[,'t0m'],t0m)
abline(0,1)
plot(subjpars[,c('diffusion','cint')])
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#loglik checks
testthat::expect_equivalent(s$loglik,s2$loglik,tol=1e-2)
#sd checks
dfsd=data.frame(trueSample=c(sd(t0m),sd(raweffect),sd(baseline)), #sample sd
# subjPars=sqrt(diag(cov(subjpars)))[c(2,3,1)], #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f1est=c(f$stanfit$transformedparsfull$rawpopsd)) #population estimate
dfsdtf=data.frame(trueSample=c(sd(t0m),sd(effect),sd(baseline)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
s$popsd) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f$stanfit$transformedparsfull$rawpopsd,tol=.05)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsdtf$X50.,dfsdtf$subjPars,tol=.1)
#test sd of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfsd[,'trueSample'],dfsd[,'f1est'],tol=.1)
testthat::expect_equivalent(dfsdtf$X50.,dfsdtf[,'trueSample'],tol=.1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,2,2])),type='l') #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$rawpopsd[,2]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#rawcorr checks
dfcorr <- data.frame(trueSample=cor(cbind(t0m,raweffect,baseline))[lower.tri(diag(3))], #true sample cor,
# subjPars=cor(subjpars[,c(3,1,2)])[lower.tri(diag(3))],
# f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,,])[lower.tri(diag(3))],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=.05)
# #test corr of ctsem between subjects setup vs subject specific pars
# testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd -- why is t0means sd overestimated?
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=.2)
})
test_that("randomEffectsMANIFESTVAR", {
set.seed(1)
nsubjects <- 400
ntimes <- 50
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
raweffect <- rnorm(nsubjects,-baseline/5, .3)
effect <- log1p(exp(raweffect))
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(1),
DRIFT= -1,
T0MEANS = c(t0m[i]),
MANIFESTVAR=effect[i],
DIFFUSION = 0.5,
T0VAR = c(0),
CINT = baseline[i],
MANIFESTMEANS=0))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = .1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
T0MEANS='t0m|param',
DIFFUSION=.5,
MANIFESTMEANS=0,CINT='cint|param',
LAMBDA=matrix(1),MANIFESTVAR='errsd|log1p_exp(param)|TRUE')
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,0,0),ncol=3),
DRIFT= c('drift',0,0,
0,-1e-12,0,
0,0,-1e-12),
DIFFUSION=c(.5,0,0,
0,0,0,
0,0,0),
MANIFESTVAR='log1p_exp(state[2])',
T0MEANS = c('t0m|param','errsd|param','cint|param'),
CINT=c('state[3]',0,0),
MANIFESTMEANS=0)
m2$T0VAR[diag(3)==1] <- paste0(m2$T0VAR[diag(3)==1] ,'|log1p_exp(2*param-1)')
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores,
optimcontrol = list(stochastic=F,carefulfit=F))#,init=f$stanfit$rawest)
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,c('t0m','errsd','cint')] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,optimcontrol = list(stochastic=F,carefulfit=F))
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
mean(effect)
plot(subjpars[,'cint'],baseline)
abline(0,1)
plot(subjpars[,'errsd'],effect)
abline(0,1)
plot(subjpars[,'t0m'],t0m)
abline(0,1)
plot(subjpars[,c('errsd','cint')])
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#loglik checks
testthat::expect_equivalent(s$loglik,s2$loglik,tol=1e-2)
#sd checks
dfsd=data.frame(trueSample=c(sd(t0m),sd(raweffect),sd(baseline)), #sample sd
# subjPars=sqrt(diag(cov(subjpars)))[c(2,3,1)], #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f1est=c(f$stanfit$transformedparsfull$rawpopsd)) #population estimate
dfsdtf=data.frame(trueSample=c(sd(t0m),sd(effect),sd(baseline)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
s$popsd) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f$stanfit$transformedparsfull$rawpopsd,tol=.1)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsdtf$X50.,dfsdtf$subjPars,tol=.1)
#test sd of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfsd[,'trueSample'],dfsd[,'f1est'],tol=.1)
testthat::expect_equivalent(dfsdtf$X50.,dfsdtf[,'trueSample'],tol=.1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,2,2])),type='l') #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$rawpopsd[,2]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#rawcorr checks
dfcorr <- data.frame(trueSample=cor(cbind(t0m,raweffect,baseline))[lower.tri(diag(3))], #true sample cor,
# subjPars=cor(subjpars[,c(3,1,2)])[lower.tri(diag(3))],
# f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,,])[lower.tri(diag(3))],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=.1)
# #test corr of ctsem between subjects setup vs subject specific pars
# testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=.2)
})
}
cdriveraus/ctsem documentation built on May 3, 2024, 12:37 p.m.
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if(identical(Sys.getenv("NOT_CRAN"), "true")& .Machine$sizeof.pointer != 4){
library(ctsem)
library(testthat)
cores=2
context("randomEffects")
test_that("randomEffectsTDPREDEFFECT", {
nsubjects <- 1000
ntimes <- 20
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
effect <- rnorm(nsubjects, 5-baseline/3, 0.5)
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(c(1,0),1,2),
DRIFT= c(-1,1,
0,-.5),
T0MEANS = c(t0m[i],0),
DIFFUSION=c(0.5,0,0,1e-6),
MANIFESTVAR = 0.5,
T0VAR = c(0,0,0,0),
TDPREDMEANS = matrix(c(rep(0,9),1,rep(0,ntimes-10))),
TDPREDEFFECT = matrix(c(0,effect[i]),2),
MANIFESTMEANS = baseline[i]))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = 1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
LAMBDA=matrix(c(1,0),1,2),
DRIFT= c('drift',1,
0,-0.5),
T0MEANS = c('t0m',0),
T0VAR = diag(1e-3,2),
DIFFUSION=c('diffusion',0,0,0),
TDPREDEFFECT = matrix(c(0,'tdpredeffect|param|TRUE')))
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,0,0,0),1,4),
DRIFT= c('drift',1,0,0,
0,-0.5,0,0,
0,0,-1e-6,0,
0,0,0,-1e-6),
DIFFUSION=c('diffusion',0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0),
T0MEANS = c('t0m',0,'mm','tdpredeffect'),
TDPREDEFFECT = matrix(c(0,'state[4]',0,0)),
MANIFESTMEANS='state[3]')
m2$T0VAR[2,]=m2$T0VAR[,2]=0
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores)
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,derrind=1)
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
plot(subjpars[,1],baseline)
abline(0,1)
plot(subjpars[,2],t0m)
abline(0,1)
plot(subjpars[,3],effect)
abline(0,1)
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#sd checks
dfsd=data.frame(trueSample=c(sd(baseline),sd(t0m),sd(effect)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,]))[c(3,1,4)],
s$popsd[c('mm_Y1','t0m','tdpredeffect'),]) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfsd$f2est,dfsd$X50.,tol=1e-2)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsd$mean,dfsd$subjPars,tol=.2)
#test sd of ctsem between subjects setup vs true sample sd -- why is t0means sd overestimated?
testthat::expect_equivalent(dfsd[,'trueSample'],
dfsd[,'X50.'],tol=1e-1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,4,4]))) #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$popsd[,3]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#corr checks
dfcorr <- data.frame(trueSample=cor(cbind(t0m,baseline,effect))[lower.tri(diag(3))], #true sample cor,
subjPars=cor(subjpars[,c('t0m','mm_Y1','tdpredeffect')])[lower.tri(diag(3))],
f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,-2,-2])[lower.tri(diag(3))],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=1e-2)
#test corr of ctsem between subjects setup vs subject specific pars
# testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=1e-1)
})
test_that("randomEffectsLambda", {
set.seed(1)
nsubjects <- 1000
ntimes <- 20
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
effect <- rnorm(nsubjects, 5-baseline/3, 0.5)
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(c(1,effect[i]),1,2),
DRIFT= c(-1,0,
0,-.5),
T0MEANS = c(t0m[i],0),
DIFFUSION=c(0.5,0,0,1e-6),
MANIFESTVAR = 0.5,
T0VAR = c(0,0,0,0),
TDPREDMEANS = matrix(c(rep(0,9),1,rep(0,ntimes-10))),
TDPREDEFFECT = matrix(c(0,1),2),
MANIFESTMEANS = baseline[i]))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = 1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
LAMBDA=matrix(c(1,'tdpredeffect|param|TRUE'),1,2),
DRIFT= c('drift',0,
0,-0.5),
T0MEANS = c('t0m',0),
T0VAR = diag(1e-3,2),
DIFFUSION=c('diffusion',0,0,0),
TDPREDEFFECT = matrix(c(0,1)))
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,'state[4]',0,0),1,4),
DRIFT= c('drift',0,0,0,
0,-0.5,0,0,
0,0,-1e-6,0,
0,0,0,-1e-6),
DIFFUSION=c('diffusion',0,0,0,
0,0,0,0,
0,0,0,0,
0,0,0,0),
T0MEANS = c('t0m',0,'mm','tdpredeffect'),
TDPREDEFFECT = matrix(c(0,1,0,0)),
MANIFESTMEANS='state[3]')
m2$T0VAR[2,]=m2$T0VAR[,2]=0
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores)
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,derrind=1)
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#sd checks
dfsd=data.frame(trueSample=c(sd(baseline),sd(t0m),sd(effect)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,]))[c(3,1,4)],
s$popsd[c('mm_Y1','t0m','tdpredeffect'),]) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfsd$f2est,dfsd$X50.,tol=1e-2)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsd$mean,dfsd$subjPars,tol=.2)
#test sd of ctsem between subjects setup vs true sample sd -- why is t0means sd overestimated?
testthat::expect_equivalent(dfsd[,'trueSample'],
dfsd[,'X50.'],tol=1e-1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,4,4]))) #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$popsd[,2]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#corr checks
dfcorr <- data.frame(trueSample=cor(cbind(baseline,t0m,effect))[lower.tri(diag(3))][c(3,1,2)], #true sample cor,
subjPars=cor(subjpars[,c('t0m','tdpredeffect','mm_Y1')])[lower.tri(diag(3))],
f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,-2,-2])[lower.tri(diag(3))][c(2,1,3)],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=1e-2)
#test corr of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=1e-1)
})
test_that("randomEffectsDRIFT", {
set.seed(1)
nsubjects <- 400
ntimes <- 50
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
raweffect <- rnorm(nsubjects, baseline/2, 0.5)
effect <- -log1p(exp(-raweffect))
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(1),
DRIFT= c(effect[i]),
T0MEANS = c(t0m[i]),
DIFFUSION=c(0.5),
MANIFESTVAR = 0.5,
T0VAR = c(0),
CINT = c(baseline[i]),MANIFESTMEANS=0))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = 1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
CINT='cint',MANIFESTMEANS=0,
LAMBDA=matrix(1),DRIFT='drift|-log1p_exp(-param)|TRUE')
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,0,0),ncol=3),
DRIFT= c('-log1p_exp(-state[2])',0,0,
0,-1e-6,0,
0,0,-1e-6),
DIFFUSION=c('diffusion',0,0,
0,0,0,
0,0,0),
T0MEANS = c('t0m','drift','cint'),
CINT=c('state[3]',0,0),MANIFESTMEANS=0)
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores,optimcontrol = list(stochastic=F,carefulfit=F))
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,c('T0m_eta1','drift','cint')] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,derrind=1,optimcontrol = list(stochastic=F,carefulfit=F))
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
mean(effect)
plot(subjpars[,'cint'],baseline)
abline(0,1)
plot(subjpars[,'drift'],effect)
abline(0,1)
plot(subjpars[,'T0m_eta1'],t0m)
abline(0,1)
plot(subjpars[,c('cint','drift')])
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#loglik checks
testthat::expect_equivalent(s$loglik,s2$loglik,tol=1e-2)
#sd checks (f2 differences expected)
dfsd=data.frame(trueSample=c(sd(t0m),sd(effect),sd(baseline)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
s$popsd[c('T0m_eta1','drift','cint'),]) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f$stanfit$transformedparsfull$rawpopsd*c(10,1,10),tol=1e-2)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsd$mean,dfsd$subjPars,tol=.1)
#test sd of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfsd[,'trueSample'],
dfsd[,'X50.'],tol=1e-1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,2,2]))) #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$rawpopsd[,2]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#corr checks
dfcorr <-
data.frame(trueSample=cor(cbind(t0m,raweffect,baseline))[lower.tri(diag(3))], #true (raw) sample cor,
subjPars=cor(subjpars)[lower.tri(diag(3))],
f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,,])[lower.tri(diag(3))],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=1e-2)
#test corr of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd -- why is t0means sd overestimated?
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=1e-1)
})
test_that("randomEffectsDIFFUSION", {
set.seed(1)
nsubjects <- 400
ntimes <- 50
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
raweffect <- rnorm(nsubjects,-baseline/3, .1)
effect <- log1p(exp(raweffect))
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(1),
DRIFT= -1,
T0MEANS = c(t0m[i]),
DIFFUSION=effect[i],
MANIFESTVAR = 0.5,
T0VAR = c(0),
CINT = baseline[i],
MANIFESTMEANS=0))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = .1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
T0MEANS='t0m|param',
MANIFESTVAR=.5,
MANIFESTMEANS=0,CINT='cint|param',
LAMBDA=matrix(1),DIFFUSION='diffusion|log1p_exp(param)|TRUE')
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,0,0),ncol=3),
DRIFT= c('drift',0,0,
0,-1e-12,0,
0,0,-1e-12),
DIFFUSION=c('log1p_exp(state[2])',0,0,
0,0,0,
0,0,0),
MANIFESTVAR=.5,
T0MEANS = c('t0m|param','diffusion|param','cint|param'),
CINT=c('state[3]',0,0),
MANIFESTMEANS=0)
m2$T0VAR[diag(3)==1] <- paste0(m2$T0VAR[diag(3)==1] ,'|log1p_exp(2*param-1)')
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores,
optimcontrol = list(stochastic=F,carefulfit=F))#,init=f$stanfit$rawest)
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,c('t0m','diffusion','cint')] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,derrind=1,optimcontrol = list(stochastic=F,carefulfit=F))
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
mean(effect)
plot(subjpars[,'cint'],baseline)
abline(0,1)
plot(subjpars[,'diffusion'],effect)
abline(0,1)
plot(subjpars[,'t0m'],t0m)
abline(0,1)
plot(subjpars[,c('diffusion','cint')])
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#loglik checks
testthat::expect_equivalent(s$loglik,s2$loglik,tol=1e-2)
#sd checks
dfsd=data.frame(trueSample=c(sd(t0m),sd(raweffect),sd(baseline)), #sample sd
# subjPars=sqrt(diag(cov(subjpars)))[c(2,3,1)], #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f1est=c(f$stanfit$transformedparsfull$rawpopsd)) #population estimate
dfsdtf=data.frame(trueSample=c(sd(t0m),sd(effect),sd(baseline)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
s$popsd) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f$stanfit$transformedparsfull$rawpopsd,tol=.05)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsdtf$X50.,dfsdtf$subjPars,tol=.1)
#test sd of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfsd[,'trueSample'],dfsd[,'f1est'],tol=.1)
testthat::expect_equivalent(dfsdtf$X50.,dfsdtf[,'trueSample'],tol=.1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,2,2])),type='l') #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$rawpopsd[,2]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#rawcorr checks
dfcorr <- data.frame(trueSample=cor(cbind(t0m,raweffect,baseline))[lower.tri(diag(3))], #true sample cor,
# subjPars=cor(subjpars[,c(3,1,2)])[lower.tri(diag(3))],
# f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,,])[lower.tri(diag(3))],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=.05)
# #test corr of ctsem between subjects setup vs subject specific pars
# testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd -- why is t0means sd overestimated?
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=.2)
})
test_that("randomEffectsMANIFESTVAR", {
set.seed(1)
nsubjects <- 400
ntimes <- 50
baseline <- rnorm(nsubjects,2, 2)
t0m <- rnorm(nsubjects,baseline/2,1)
raweffect <- rnorm(nsubjects,-baseline/5, .3)
effect <- log1p(exp(raweffect))
for(i in 1:nsubjects){
gm <- suppressMessages(ctModel(silent=TRUE,Tpoints=ntimes,
LAMBDA=matrix(1),
DRIFT= -1,
T0MEANS = c(t0m[i]),
MANIFESTVAR=effect[i],
DIFFUSION = 0.5,
T0VAR = c(0),
CINT = baseline[i],
MANIFESTMEANS=0))
d <- suppressMessages(data.frame(ctGenerate(ctmodelobj = gm,n.subjects = 1,burnin = 0,dtmean = .1,logdtsd = 0)))
d$id <- i
if(i==1) dat <- d else dat <- rbind(dat,d)
}
#regular bw effect approach
m <- ctModel(silent=TRUE,type='stanct',
T0MEANS='t0m|param',
DIFFUSION=.5,
MANIFESTMEANS=0,CINT='cint|param',
LAMBDA=matrix(1),MANIFESTVAR='errsd|log1p_exp(param)|TRUE')
#manual bw effects
m2 <- ctModel(silent=TRUE,type='omx',Tpoints=3,
LAMBDA=matrix(c(1,0,0),ncol=3),
DRIFT= c('drift',0,0,
0,-1e-12,0,
0,0,-1e-12),
DIFFUSION=c(.5,0,0,
0,0,0,
0,0,0),
MANIFESTVAR='log1p_exp(state[2])',
T0MEANS = c('t0m|param','errsd|param','cint|param'),
CINT=c('state[3]',0,0),
MANIFESTMEANS=0)
m2$T0VAR[diag(3)==1] <- paste0(m2$T0VAR[diag(3)==1] ,'|log1p_exp(2*param-1)')
m2=ctStanModel(m2,type='stanct')
m2$pars$indvarying=F
f <- ctStanFit(datalong = dat,ctstanmodel = m,cores=cores,
optimcontrol = list(stochastic=F,carefulfit=F))#,init=f$stanfit$rawest)
s=summary(f)
s
subjpars=ctStanSubjectPars(f)[1,,c('t0m','errsd','cint')] #calculate subject specific parameter estimates
f2 <- ctStanFit(datalong = dat,ctstanmodel = m2,cores=cores,optimcontrol = list(stochastic=F,carefulfit=F))
s2=summary(f2)
s2
cp2=ctStanContinuousPars(f2)
# checks ------------------------------------------------------------------
mean(effect)
plot(subjpars[,'cint'],baseline)
abline(0,1)
plot(subjpars[,'errsd'],effect)
abline(0,1)
plot(subjpars[,'t0m'],t0m)
abline(0,1)
plot(subjpars[,c('errsd','cint')])
f$stanfit$transformedparsfull$popsd
log1p_exp(2*f$stanfit$transformedparsfull$rawpopsdbase-1)
#loglik checks
testthat::expect_equivalent(s$loglik,s2$loglik,tol=1e-2)
#sd checks
dfsd=data.frame(trueSample=c(sd(t0m),sd(raweffect),sd(baseline)), #sample sd
# subjPars=sqrt(diag(cov(subjpars)))[c(2,3,1)], #sd of individual effect point estimates
f2est=sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f1est=c(f$stanfit$transformedparsfull$rawpopsd)) #population estimate
dfsdtf=data.frame(trueSample=c(sd(t0m),sd(effect),sd(baseline)), #sample sd
subjPars=sqrt(diag(cov(subjpars))), #sd of individual effect point estimates
s$popsd) #population estimate
#test sd of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(sqrt(diag(f2$stanfit$transformedparsfull$pop_T0cov[1,,])),
f$stanfit$transformedparsfull$rawpopsd,tol=.1)
#test sd of ctsem between subjects setup vs subject specific pars
testthat::expect_equivalent(dfsdtf$X50.,dfsdtf$subjPars,tol=.1)
#test sd of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfsd[,'trueSample'],dfsd[,'f1est'],tol=.1)
testthat::expect_equivalent(dfsdtf$X50.,dfsdtf[,'trueSample'],tol=.1)
plot(density(sqrt(f2$stanfit$transformedpars$pop_T0cov[,2,2])),type='l') #distribution of pop sd estimates
points(density(f$stanfit$transformedpars$rawpopsd[,2]),col=2,type='l') #distribution of pop sd estimates
#cov checks
f$stanfit$transformedparsfull$popcov[1,,]
cov(subjpars)
cov(cbind(baseline,t0m,effect)) #true sample cov
#rawcorr checks
dfcorr <- data.frame(trueSample=cor(cbind(t0m,raweffect,baseline))[lower.tri(diag(3))], #true sample cor,
# subjPars=cor(subjpars[,c(3,1,2)])[lower.tri(diag(3))],
# f1popCovbased=cov2cor(f$stanfit$transformedparsfull$popcov[1,,])[lower.tri(diag(3))],
f2est=cov2cor(f2$stanfit$transformedparsfull$pop_T0cov[1,,])[lower.tri(diag(3))],
s$rawpopcorr )
#test corr of ctsem between subjects setup vs manual specification
testthat::expect_equivalent(dfcorr$f2est,dfcorr$X50.,tol=.1)
# #test corr of ctsem between subjects setup vs subject specific pars
# testthat::expect_equivalent(dfcorr$mean,dfcorr$subjPars,tol=.2)
#test corr of ctsem between subjects setup vs true sample sd
testthat::expect_equivalent(dfcorr[,'trueSample'],
dfcorr[,'X50.'],tol=.2)
})
}
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