Estimate an auxiliary model based on a latent classification by means of mixture modeling (see mx_mixture).

The auxiliary model is treated as a multi-group model. All cases are used in all groups, but they are weighted by group-specific BCH weights as described in Bolck, Croon, & Hagenaars, 2004.

BCH(x, model, data, ...)

## Arguments

x

An object for which a method exists.

model

An object that can be converted to an OpenMx model using as_ram.

data

A data.frame on which the auxiliary model can be evaluated.

...

further arguments to be passed to or from other methods.

An MxModel.

## References

Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 3–27. <doi:10.2307/25791751>

## Examples

dat <- data.frame(x = iris$Petal.Length) mixmod <- mx_profiles(dat, classes = 2) #> Running mix2 with 4 parameters #> Running mix2 with 4 parameters res <- BCH(mixmod, "y ~ 1", data = data.frame(y = iris$Sepal.Length))
#> Running aux with 4 parameters