poLCA.table: Frequency tables of predicted cell counts from latent class...

In poLCA: Polytomous Variable Latent Class Analysis

Frequency tables of predicted cell counts from latent class analysis

Description

Calculates predicted cell frequencies based on an estimated latent class model.

Usage

 poLCA.table(formula, condition, lc) 

Arguments

formula	A formula expression of the form variable ~ 1 for a one-way frequency distribution, or row ~ column for two way-tables.
condition	A list containing the values of the manifest variables to hold fixed when creating the table specified by the formula argument. Setting this to an empty list, condition=list(), conditions on none of the other manifest variables, producing the marginal frequencies.
lc	A model object previously estimated using the poLCA function.

Details

This function outputs predicted cell counts for user-specified combinations of the manifest variables, based on a latent class model estimated by the poLCA function. The predcell table outputted automatically by poLCA also contains predicted cell frequencies, but only for cells containing at least one observation. In contrast, poLCA.table will calculate predicted cell counts for all cells, including those with zero observations.

Value

A vector or table containing the specified frequency distribution.

See Also

poLCA

Examples

data(gss82)
f <- cbind(PURPOSE,ACCURACY,UNDERSTA,COOPERAT)~1
gss.lc2 <- poLCA(f,gss82,nclass=2)
gss.lc2$predcell

poLCA.table(formula=COOPERAT~1,condition=list(PURPOSE=3,ACCURACY=1,UNDERSTA=2),lc=gss.lc2)

poLCA.table(formula=COOPERAT~UNDERSTA,condition=list(PURPOSE=3,ACCURACY=1),lc=gss.lc2)

poLCA.table(formula=COOPERAT~UNDERSTA,condition=list(),lc=gss.lc2)

poLCA documentation built on April 25, 2022, 5:06 p.m.