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 |
k |
Number of classes used for LCA |
niter |
Number of Iterations |
matchdata |
If |
verbose |
If |
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 |
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
## 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), nrow = 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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