lca {e1071} R Documentation

## Latent Class Analysis (LCA)

### Description

A latent class analysis with `k` classes is performed on the data given by `x`.

### Usage

```lca(x, k, niter=100, matchdata=FALSE, verbose=FALSE)
```

### Arguments

 `x` Either a data matrix of binary observations or a list of patterns as created by `countpattern` `k` Number of classes used for LCA `niter` Number of Iterations `matchdata` If `TRUE` and `x` is a data matrix, the class membership of every data point is returned, otherwise the class membership of every pattern is returned. `verbose` If `TRUE` some output is printed during the computations.

### Value

An object of class `"lca"` is returned, containing

 `w` Probabilities to belong to each class `p` Probabilities of a `1' for each variable in each class `matching` Depending on `matchdata` either the class membership of each pattern or of each data point `logl, loglsat` The LogLikelihood of the model and of the saturated model `bic, bicsat` The BIC of the model and of the saturated model `chisq` Pearson's Chisq `lhquot` Likelihood quotient of the model and the saturated model `n` Number of data points. `np` Number of free parameters.

### Author(s)

Andreas Weingessel

### References

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

`countpattern`, `bootstrap.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)
print(l)
summary(l)
p <- predict(l, x)
table(p, c(rep(1,500),rep(2,500)))
```

[Package e1071 version 1.5-2 Index]