BayesLCA (version 1.9)

blca: Bayesian Latent Class Analysis with one of several methods

Description

Latent class analysis (LCA) attempts to find G hidden classes in binary data X. blca utilises one of: an EM algorithm, a variational Bayes approximation, Gibbs sampling or boot-strapping techniques to find maximum a posteriori (MAP), standard error and density estimates of the parameters.

Usage

blca(X, G, method = c("em", "gibbs", "boot", "vb"), ...)

Arguments

X

The data matrix. This may take one of several forms, see data.blca.

G

The number of classes to run lca for.

method

The method with which to perform lca on the data. Four methods are currently available, "em", "gibbs", "boot" or "vb". Defaults to "em", with a warning.

…

Additional arguments to be passed on, depending on the method. See additional help files for details.

Value

A list of class "blca" is returned. All methods return the following items:

classprob

The class probabilities.

itemprob

The item probabilities, conditional on class membership.

Z

Estimate of class membership for each unique datapoint.

prior

A list containing the prior values specified for the model.

See additional help files for details.

Details

The function calls to one of blca.em, blca.boot, blca.gibbs, blca.vb, depending on the method specified.

References

Arthur White, Thomas Brendan Murphy (2014). BayesLCA: An R Package for Bayesian Latent Class Analysis." Journal of Statistical Software, 61(13), 1-28. URL: http://www.jstatsoft.org/v61/i13/.

See Also

blca.em, blca.boot, blca.gibbs, blca.vb

Examples

# NOT RUN {
type1 <- c(0.8, 0.8, 0.2, 0.2)
type2 <- c(0.2, 0.2, 0.8, 0.8)
x<- rlca(1000, rbind(type1,type2), c(0.6,0.4))

set.seed(1)
fit <- blca(x, 2) ## EM algorithm used, warning returned
print(fit)	  ## No posterior standard deviations returned
summary(fit)

set.seed(1)
fit2 <- blca(x, 2, method="em", sd=TRUE) ##No warning - same fit
print(fit2) 				 ##Posterior standard deviations returned

set.seed(1)
##Variational Bayes approximation, with priors specified.
fit3 <- blca(x, 2, method="vb", delta=c(5,5), alpha=2, beta=1) 
print(fit3)	##Posterior standard deviations returned also.
par(mfrow=c(3,2))			  
plot(fit3, which=3:4)
par(mfrow=c(1,1))
# }