lca: 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

Either a data matrix of binary observations or a list of patterns as created by countpattern

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
wProbabilities to belong to each class
pProbabilities of a `1' for each variable in each class
matchingDepending on matchdata either the class membership of each pattern or of each data point
logl, loglsatThe LogLikelihood of the model and of the saturated model
bic, bicsatThe BIC of the model and of the saturated model
chisqPearson's Chisq
lhquotLikelihood quotient of the model and the saturated model
nNumber of data points.
npNumber of free parameters.

References

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

Examples

Run this code

## 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)))

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