bootstrap.lca: Bootstrap Samples of LCA Results
In molnplus/DasProjekt-e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

1	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 corresponding saturated models
`lratiomean, lratiosd`	Mean and Standard deviation of `lratio`
`lratioorg`	Likelihood quotient of the original model and the corresponding 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

lca

## 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