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e1071 (version 1.3-4)

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

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
  • 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

See Also

countpattern, bootstrap.lca

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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