R: Bootstrap Samples of LCA Results

bootstrap.lca {e1071} | R Documentation

## Bootstrap Samples of LCA Results

### Description

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.

### 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

### Author(s)

Andreas Weingessel

### References

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

### See Also

`lca`

### Examples

    ## 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), 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

[Package e1071 version 1.7-6 Index]