bootstrap.lca  R Documentation 
This function draws bootstrap samples from a given LCA model and refits a new LCA model for each sample. The quality of fit of these models is compared to the original model.
bootstrap.lca(l, nsamples=10, lcaiter=30, verbose=FALSE)
l 
An LCA model as created by 
nsamples 
Number of bootstrap samples 
lcaiter 
Number of LCA iterations 
verbose 
If 
From a given LCA model l
, nsamples
bootstrap samples are
drawn. For each sample a new LCA model is fitted. The goodness of fit
for each model is computed via Likelihood Ratio and Pearson's
Chisquare. The values for the fitted models are compared with the values
of the original model l
. By this method it can be tested whether
the data to which l
was originally fitted come from an LCA model.
An object of class bootstrap.lca
is returned, containing
logl, loglsat 
The LogLikelihood of the models and of the corresponding saturated models 
lratio 
Likelihood quotient of the models and the corresponding saturated models 
lratiomean, lratiosd 
Mean and Standard deviation of

lratioorg 
Likelihood quotient of the original model and the corresponding saturated model 
zratio 
ZStatistics of 
pvalzratio, pvalratio 
PValues for 
chisq 
Pearson's Chisq of the models 
chisqmean, chisqsd 
Mean and Standard deviation of

chisqorg 
Pearson's Chisq of the original model 
zchisq 
ZStatistics of 
pvalzchisq, pvalchisq 
PValues for 
nsamples 
Number of bootstrap samples 
lcaiter 
Number of LCA Iterations 
Andreas Weingessel
Anton K. Formann: “Die LatentClassAnalysis”, Beltz Verlag 1984
lca
## Generate a 4dim. sample with 2 latent classes of 500 data points each.
## The probabilities for the 2 classes are given by type1 and type2.
type1 < c(0.8, 0.8, 0.2, 0.2)
type2 < c(0.2, 0.2, 0.8, 0.8)
x < matrix(runif(4000), nrow = 1000)
x[1:500,] < t(t(x[1:500,]) < type1) * 1
x[501:1000,] < t(t(x[501:1000,]) < type2) * 1
l < lca(x, 2, niter=5)
bl < bootstrap.lca(l,nsamples=3,lcaiter=5)
bl
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