lca {e1071}R Documentation

Latent Class Analysis (LCA)


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


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


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.


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.


Andreas Weingessel


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

See Also

countpattern, bootstrap.lca


## 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)
p <- predict(l, x)
table(p, c(rep(1,500),rep(2,500)))

[Package e1071 version 1.5-2 Index]