lca {e1071} | R Documentation |

## Latent Class Analysis (LCA)

### Description

A latent class analysis with `k`

classes is performed on the data
given by `x`

.

### Usage

lca(x, k, niter=100, matchdata=FALSE, verbose=FALSE)

### Arguments

`x` |
Either a data matrix of binary observations or a list of
patterns as created by `countpattern` |

`k` |
Number of classes used for LCA |

`niter` |
Number of Iterations |

`matchdata` |
If `TRUE` and `x` is a data matrix, the class
membership of every data point is returned, otherwise the class
membership of every pattern is returned. |

`verbose` |
If `TRUE` some output is printed during the
computations. |

### Value

An object of class `"lca"`

is returned, containing

`w` |
Probabilities to belong to each class |

`p` |
Probabilities of a `1' for each variable in each class |

`matching` |
Depending on `matchdata` either the class
membership of each pattern or of each data point |

`logl, loglsat` |
The LogLikelihood of the model and of the
saturated model |

`bic, bicsat` |
The BIC of the model and of the
saturated model |

`chisq` |
Pearson's Chisq |

`lhquot` |
Likelihood quotient of the model and the saturated
model |

`n` |
Number of data points. |

`np` |
Number of free parameters. |

### Author(s)

Andreas Weingessel

### References

Anton K. Formann: ``Die Latent-Class-Analysis'', Beltz
Verlag 1984

### See Also

`countpattern`

,
`bootstrap.lca`

### Examples

## Generate a 4-dim. sample with 2 latent classes of 500 data points each.
## The probabilities for the 2 classes are given by type1 and type2.
type1 <- c(0.8,0.8,0.2,0.2)
type2 <- c(0.2,0.2,0.8,0.8)
x <- matrix(runif(4000),nr=1000)
x[1:500,] <- t(t(x[1:500,])<type1)*1
x[501:1000,] <- t(t(x[501:1000,])<type2)*1
l <- lca(x, 2, niter=5)
print(l)
summary(l)
p <- predict(l, x)
table(p, c(rep(1,500),rep(2,500)))

[Package

*e1071* version 1.5-2

Index]