GLMM_longitDA {mixAK} | R Documentation

## Discriminant analysis for longitudinal profiles based on fitted GLMM's

### Description

THIS FUNCTION IS BEING DEVELOPED AND ORDINARY USERS ARE NOT RECOMMENDED TO USE IT.

### Usage

    GLMM_longitDA(mod, w.prior, y, id, time, x, z, xz.common=TRUE, info)

### Arguments

`mod` | a list containing models fitted with the `GLMM_MCMC` function. Each component of the list is the GLMM fitted in the training dataset of each cluster.
`w.prior` | a vector with prior cluster weights. The length of this argument must be the same as the length of argument `mod`. Can also be given relatively, e.g., as `c(1, 1)` which means that both prior weights are equal to 1/2.
`y` | vector, matrix or data frame (see argument `y` of `GLMM_MCMC` function) with responses of objects that are to be clustered.
`id` | vector which determines clustered observations (see also argument `y` of `GLMM_MCMC` function).
`time` | vector which gives indeces of observations within clusters. It appears (together with `id`) in the output as identifier of observations
`x` | see `xz.common` below.
`z` | see `xz.common` below.
`xz.common` | a logical value. If `TRUE` then it is assumed that the X and Z matrices are the same for GLMM in each cluster. In that case, arguments `x` and `z` have the same structure as arguments `x` and `z` of `GLMM_MCMC` function. If `FALSE` then X and Z matrices for the GLMM may differ across clusters. In that case, arguments `x` and `z` are both lists of length equal to the number of clusters and each component of lists `x` and `z` has the same structure as arguments `x` and `z` of `GLMM_MCMC` function.
`info` | interval in which the function prints the progress of computation

### Details

I HOPE TO WRITE A PAPER DESCRIBING ALL THE DETAILS.

### Value

A list with the following components:

`ident` | ADD DESCRIPTION
`marg` | ADD DESCRIPTION
`cond` | ADD DESCRIPTION
`ranef` | ADD DESCRIPTION

### Author(s)

Arnošt Komárek arnost.komarek[AT]mff.cuni.cz

### References

Komárek, A., Hansen, B. E., Kuiper, E. M. M., van Buuren, H. R., and Lesaffre, E. (2010). Discriminant analysis using a multivariate linear mixed model with a normal mixture in the random effects distribution. Statistics in Medicine. To appear.

### See Also

`GLMM_MCMC`.

### Examples

    ### WILL BE ADDED.

[Package mixAK version 0.6 Index]