R: Predicted cell percentages in a latent class model

poLCA.predcell {poLCA} | R Documentation

## 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)))

[Package poLCA version 1.6.0.1 Index]