# bootstrap.lca

0th

Percentile

##### Bootstrap Samples of LCA Results

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.

Keywords
multivariate
##### Usage
bootstrap.lca(l, nsamples=10, lcaiter=30, verbose=FALSE)
##### Arguments
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.

##### Details

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.

##### Value

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

##### References

Anton K. Formann: Die Latent-Class-Analysis'', Beltz Verlag 1984

lca

##### Aliases
• bootstrap.lca
• print.bootstrap.lca
##### Examples
# NOT RUN {
## 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
# }

Documentation reproduced from package e1071, version 1.7-3, License: GPL-2 | GPL-3

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