# Frequency tables of predicted cell counts from latent class analysis

### Description

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

### Usage

1 | ```
poLCA.table(formula, condition, lc)
``` |

### Arguments

`formula` |
A formula expression of the form |

`condition` |
A list containing the values of the manifest variables to hold fixed when creating the table specified by the |

`lc` |
A model object previously estimated using the |

### 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

1 2 3 4 5 6 7 8 9 10 | ```
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)
``` |

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.