Description Usage Arguments Details Value References See Also Examples
Estimation and model selection for latent class analysis and latent class regression model for clustering multivariate categorical data. The best model is automatically selected using BIC.
1  fitLCA(Y, G = 1:3, X = NULL, ctrlLCA = controlLCA())

Y 
A dataframe with (response) categorical variables. The categorical variables used to fit the latent class analysis model are converted to 
G 
An integer vector specifying the numbers of latent classes for which the BIC is to be calculated. 
X 
A vector or dataframe of concomitant covariates used to predict the classmembership probability. If supplied, the number of observations of 
ctrlLCA 
A list of control parameters for the EM algorithm used to fit the model. 
The function is a simple wrapper around the function poLCA
in the homonymous package and returns less information about the estimated model. The selection of the number of latent classes is performed automatically by means of the Bayesian information criterion (BIC).
When included, covariates are used to predict the probability of class membership. In this case the model is termed as "latent class regression", or, alternatively "concomitantvariable latent class analysis". See poLCA
for details.
An object of class 'fitLCA'
providing the optimal latent class model selected by BIC.
The ouptut is a list containing:
G 
The best number of latent classes according to BIC. 
parameters 
A list with the following components:

coeff 
Multinomial logit coefficient estimates on the covariates (when provided). 
loglik 
Value of the maximized Loglikelihood. 
BIC 
All BIC values computed for the range of values of G provided. 
bic 
The optimal BIC value. 
npar 
Number of estimated parameters. 
resDf 
Number of residual degrees of freedom. 
z 
A matrix whose 
class 
Classification corresponding to the maximum a posteriori of matrix 
iter 
Number of iterations. 
Linzer, D. A. and Lewis, J. B. (2011). poLCA: An R package for polytomous variable latent class analysis. Journal of Statistical Software 42 129.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  data(gss82, package = "poLCA")
maxG(gss82, 1:7) # not all latent class models can be fitted
fit < fitLCA(gss82, G = 1:4)
## Not run:
# diminish tolerance and increase number of replicates
fit2 < fitLCA(gss82, G = 1:4, ctrlLCA = controlLCA(tol = 1e06, nrep = 10))
## End(Not run)
# the example with a single covariate as in ?poLCA
data(election, package = "poLCA")
elec < election[, cbind("MORALG", "CARESG", "KNOWG", "LEADG", "DISHONG", "INTELG",
"MORALB", "CARESB", "KNOWB", "LEADB", "DISHONB", "INTELB")]
party < election$PARTY
fit < fitLCA(elec, G = 3, X = party)
pidmat < cbind(1, 1:7)
exb < exp(pidmat %*% fit$coeff)
matplot(1:7, ( cbind(1, exb)/(1 + rowSums(exb)) ),
ylim = c(0,1), type = "l",
main = "Party ID as a predictor of candidate affinity class",
xlab = "Party ID: strong Democratic (1) to strong Republican (7)",
ylab = "Probability of latent class membership",
lwd = 2 , col = 1)

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