# lca

##### Latent Class Analysis (LCA)

A latent class analysis with `k`

classes is performed on the data
given by `x`

.

- Keywords
- multivariate, cluster

##### 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

Probabilities to belong to each class

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

Depending on `matchdata`

either the class
membership of each pattern or of each data point

The LogLikelihood of the model and of the saturated model

The BIC of the model and of the saturated model

Pearson's Chisq

Likelihood quotient of the model and the saturated model

Number of data points.

Number of free parameters.

##### References

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

##### See Also

##### Examples

```
# NOT RUN {
## 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)))
# }
```

*Documentation reproduced from package e1071, version 1.7-3, License: GPL-2 | GPL-3*