poLCA.predcell: Predicted cell percentages in a latent class model

In poLCAParallel: Polytomous Variable Latent Class Analysis Parallel

Predicted cell percentages in a latent class model

Description

Calculates the predicted cell percentages from a latent class model, for specified values of the manifest variables.

Usage

poLCA.predcell(lc, y)

Arguments

lc	A model object estimated using the poLCA function.
y	A vector or matrix containing series of responses on the manifest variables in lc.

Details

The parameters estimated by a latent class model can be used to produce a density estimate of the underlying probability mass function across the cells in the multi-way table of manifest variables. This function calculates cell percentages for that density estimate, corresponding to selected sets of responses on the manifest variables, y.

Value

A vector containing cell percentages corresponding to the specified sets of responses y, based on the estimated latent class model lc.

See Also

poLCA

Examples

data(carcinoma)
f <- cbind(A, B, C, D, E, F, G) ~ 1
lca3 <- poLCA(f, carcinoma, nclass = 3) # log-likelihood: -293.705

# Only 20 out of 32 possible response patterns are observed
lca3$predcell

# Produce cell probabilities for one sequence of responses
poLCA.predcell(lc = lca3, y = c(1, 1, 1, 1, 1, 1, 1))

# Estimated probabilities for a cell with zero observations
poLCA.predcell(lc = lca3, y = c(1, 1, 1, 1, 1, 1, 2))

# Cell probabilities for both cells at once; y entered as a matrix
poLCA.predcell(lc = lca3, y = rbind(
  c(1, 1, 1, 1, 1, 1, 1),
  c(1, 1, 1, 1, 1, 1, 2)
))

poLCAParallel documentation built on Feb. 20, 2026, 1:09 a.m.