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 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 |

### 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),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

[Package

*e1071* version 1.5-2

Index]