Nothing
if(identical(Sys.getenv("NOT_CRAN"), "true")& .Machine$sizeof.pointer != 4){
require(ctsem)
require(testthat)
context("differenttraits")
test_that("time calc", {
set.seed(4)
Tpoints<-10
n.manifest=2
nsubjects=50
n.latent=2
DRIFT=matrix(c(-.3, .2, 0, -0.5), byrow=TRUE, nrow=n.latent, ncol=n.latent)
genm=ctModel(Tpoints=Tpoints,
n.latent=n.latent, n.manifest=n.manifest,
LAMBDA=matrix(c(1, 0,0,1), nrow=n.manifest, ncol=n.latent),
DRIFT=DRIFT,
DIFFUSION=matrix(c(2, 0, 0, 1), byrow=TRUE, nrow=n.latent, ncol=n.latent),
MANIFESTVAR=matrix(c(1, 0,0,.5), nrow=n.manifest, ncol=n.manifest),
TRAITVAR=matrix(c(1,.5,0,.8),n.latent,n.latent))
cd=ctGenerate(ctmodelobj=genm, n.subjects=nsubjects, burnin=51, dtmean=1,
logdtsd=0,wide=TRUE)
wide=cd
long=ctWideToLong(datawide = cd,Tpoints = Tpoints,n.manifest = n.manifest)
long=ctDeintervalise(datalong = long)
long=long[-seq(3,length(long),3),]
wide=ctLongToWide(datalong = long,id='id',time='time',manifestNames= genm$manifestNames)
Tpoints=7 #updated
wide=ctIntervalise(datawide = wide,Tpoints = Tpoints,n.manifest = n.manifest)
mltrait<-ctModel(Tpoints=Tpoints,n.latent=n.latent,n.manifest=n.manifest,
LAMBDA=diag(1,n.manifest),
TRAITVAR='auto')
mmtrait<-ctModel(Tpoints=Tpoints,n.latent=n.latent,n.manifest=n.manifest,
LAMBDA=diag(1,n.manifest),
MANIFESTTRAITVAR='auto')
mptrait<-ctModel(Tpoints=Tpoints,n.latent=4,n.manifest=n.manifest,
LAMBDA=matrix(c(1,0, 0,1, 0,0, 0,0),2,4),
DRIFT=matrix(c(
'dr11','dr12',1,0,
'dr21','dr22',0,1,
0,0,.0001,0,
0,0,0,.0001),byrow=TRUE,4,4),
DIFFUSION=matrix(c(
'df11',0,0,0,
'df21','df22',0,0,
0,0,.0001,0,
0,0,0,.0001),byrow=TRUE,4,4),
T0MEANS=matrix(c('t1','t2',0,0),ncol=1))
fmlstrait=ctFit(dat = wide,ctmodelobj = mltrait,retryattempts = 5,stationary='T0TRAITEFFECT')
fmltrait=ctRefineTo(dat = wide,ctmodelobj = mltrait,retryattempts = 5,stationary='')
dfmlstrait=ctFit(dat= wide,ctmodelobj = mltrait,retryattempts = 5,discreteTime=TRUE,stationary='T0TRAITEFFECT')
dfmltrait=ctFit(dat = wide,ctmodelobj = mltrait,retryattempts = 5,discreteTime=TRUE,stationary='')
fmmtrait=ctFit(dat = wide,ctmodelobj = mmtrait,retryattempts = 5)
fmptrait=ctFit(dat = wide,ctmodelobj = mptrait,retryattempts = 5,stationary='')
# summary(fmlstrait)
# summary(fmmtrait)
# summary(fmptrait)
# summary(fmltrait)
#check traits using different fit approaches
expect_equal(rep(0,4),c(fmlstrait$mxobj$DRIFT$values-fmmtrait$mxobj$DRIFT$values),tolerance=1e-2)
expect_equal(rep(0,4),c(fmltrait$mxobj$DRIFT$values-fmptrait$mxobj$DRIFT$values[1:2,1:2]),tolerance=1e-2)
# expect_equal(rep(0,4),c(expm(fmlstrait$mxobj$DRIFT$values)-dfmltrait$mxobj$DRIFT$values),tolerance=1e-2)
#check DRIFT is reasonably estimated
expect_equal(rep(0,4),c(fmltrait$mxobj$DRIFT$values-DRIFT),tolerance=.1)
})
}
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