Nothing
if(identical(Sys.getenv("NOT_CRAN"), "true")& .Machine$sizeof.pointer != 4){
library(ctsemOMX)
library(testthat)
context("knownFits")
#anomauth
test_that("anomauth", {
data(AnomAuth)
AnomAuthmodel<-ctModel(LAMBDA=matrix(c(1, 0, 0, 1), nrow=2, ncol=2),
n.latent=2,n.manifest=2,
MANIFESTVAR=diag(0,2),
Tpoints=5)
ll=Inf
counter=0
while(counter < 10 && (ll > 23415.99 || ll < 23415.0)){
counter=counter+1
AnomAuthfit<-ctFit(AnomAuth, AnomAuthmodel)
ll=AnomAuthfit$mxobj$output$Minus2LogLikelihood
}
expect_equal(23415.929,ll)
})
# #anomauth with trait asymptotic vs standard param comparisons ##started failing due to optimizer instability
# test_that("AnomAuth_traitasymptoticcheck", {
#
# data(AnomAuth)
# AnomAuthmodel<-ctModel(LAMBDA=matrix(c(1, 0, 0, 1), nrow=2, ncol=2),
# n.latent=2,n.manifest=2,
# MANIFESTVAR=diag(0,2),
# TRAITVAR='auto',
# CINT=matrix(0,nrow=2),
# T0MEANS=matrix(0,nrow=2),
# MANIFESTMEANS=matrix(c('m1','m2'),nrow=2),
# Tpoints=5)
#
# AnomAuthfit2<-ctFit(AnomAuth, AnomAuthmodel,asymptotes=TRUE, verbose=0,retryattempts=3)
# AnomAuthfit1<-ctFit(AnomAuth, AnomAuthmodel,asymptotes=FALSE, verbose=0,retryattempts=3)
#
#
# expect_equal(AnomAuthfit2$mxobj$output$Minus2LogLikelihood,AnomAuthfit1$mxobj$output$Minus2LogLikelihood)
#
# summ1<-summary(AnomAuthfit1,verbose=TRUE)
# summ2<-summary(AnomAuthfit2,verbose=TRUE)
#
# expect_equal(summ1$ctparameters[,1:2],summ2$ctparameters[,1:2],tolerance = .001)
#
# })
#
# test_that("oscillator", {
# data("Oscillating")
#
# inits <- c(-39.5, -.5, .1, 1, 0, 1, 0.05, .9)
# names(inits) <- c("crosseffect","autoeffect", "diffusion",
# "T0var11", "T0var21", "T0var22","m1", "m2")
#
# oscillatingm <- ctModel(n.latent = 2, n.manifest = 1, Tpoints = 11,
# MANIFESTVAR = matrix(c(0), nrow = 1, ncol = 1),
# LAMBDA = matrix(c(1, 0), nrow = 1, ncol = 2),
# T0MEANS = matrix(c('m1', 'm2'), nrow = 2, ncol = 1),
# T0VAR = matrix(c("T0var11", "T0var21", 0, "T0var22"), nrow = 2, ncol = 2),
# DRIFT = matrix(c(1e-5, "crosseffect", 1, "autoeffect"), nrow = 2, ncol = 2),
# CINT = matrix(0, ncol = 1, nrow = 2),
# DIFFUSION = matrix(c(0, 0, 0, "diffusion"), nrow = 2, ncol = 2))#,
# # startValues = inits)
#
# oscillatingf <- ctFit(Oscillating, oscillatingm, carefulFit = FALSE,retryattempts = 0)
#
# expect_equal(-3461.936,oscillatingf$mxobj$output$Minus2LogLikelihood,tolerance=.001)
#
#
# })
}
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