| lca {e1071} | R Documentation |
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
## 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)))