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# Prepare all matrices for the fit, gradient and hessian of varcov models:
prepare_varcov <- function(x, model){
# New model:
if (model@cpp){
newMod <- updateModel_cpp(x,model,FALSE)
} else {
newMod <- updateModel(x,model,updateMatrices = FALSE)
}
# Number of groups:
nGroup <- nrow(model@sample@groups)
# Total sample:
nTotal <- sum(model@sample@groups$nobs)
# Sample per group:
nPerGroup <- model@sample@groups$nobs
# Form the model matrices:
# mats <- formModelMatrices(newMod)
# Compute implied matrices:
if (model@cpp){
imp <- implied_varcov_cpp(newMod, all = FALSE)
} else {
imp <- implied_varcov(newMod, all = FALSE)
}
# Sample stats:
S <- model@sample@covs
means <- model@sample@means
nVar <- nrow(model@sample@variables)
thresholds <- model@sample@thresholds
# Extra mats:
mMat <- list(
M = Mmatrix(model@parameters)
)
# Fill per group:
groupModels <- list()
for (g in 1:nGroup){
groupModels[[g]] <- c(imp[[g]], mMat, model@extramatrices, model@types) # FIXME: This will lead to extra matrices to be stored?
groupModels[[g]]$S <- S[[g]]
groupModels[[g]]$means <- means[[g]]
groupModels[[g]]$corinput <- model@sample@corinput
groupModels[[g]]$meanstructure <- model@meanstructure
groupModels[[g]]$cpp <- model@cpp
if (is.null( groupModels[[g]]$tau)){
groupModels[[g]]$tau <- matrix(NA,1,nVar)
}
if (length(thresholds) > 0){
groupModels[[g]]$thresholds <- thresholds[[g]]
}
}
# Return
return(list(
nPerGroup = nPerGroup,
nTotal=nTotal,
nGroup=nGroup,
groupModels=groupModels
))
}
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