| bootstrap.lca {e1071} | 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 lca |
nsamples |
Number of bootstrap samples |
lcaiter |
Number of LCA iterations |
verbose |
If TRUE some output is printed during the
computations. |
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 coresponding saturated models |
lratiomean, lratiosd |
Mean and Standard deviation of
lratio |
lratioorg |
Likelihood quotient of the original model and the correspomdiong saturated model |
zratio |
Z-Statistics of lratioorg |
pvalzratio, pvalratio |
P-Values for zratio, computed via normal
distribution and empirical distribution |
chisq |
Pearson's Chisq of the models |
chisqmean, chisqsd |
Mean and Standard deviation of
chisq |
chisqorg |
Pearson's Chisq of the original model |
zchisq |
Z-Statistics of chisqorg |
pvalzchisq, pvalchisq |
P-Values for zchisq, computed via normal
distribution and empirical distribution |
nsamples |
Number of bootstrap samples |
lcaiter |
Number of LCA Iterations |
Andreas Weingessel
Anton K. Formann: ``Die Latent-Class-Analysis'', Beltz Verlag 1984
## Generate a 4-dim. 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),nr=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