mixAK: GLMMlongitDA – R documentation

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GLMMlongitDA

Discriminant analysis for longitudinal profiles based on fitted GLMM's

Description

The idea is that we fit (possibly different) GLMM's for data in training groups using the function GLMM_MCMC and then use the fitted models for discrimination of new observations. For more details we refer to Komárek et al. (2010).

Currently, only continuous responses for which linear mixed models are assumed are allowed.

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

This function complements a paper Komárek et al. (2010).

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, 29(30), 3267–3283.

Examples

### WILL BE ADDED.

mixAK

Multivariate Normal Mixture Models and Mixtures of Generalized Linear Mixed Models Including Model Based Clustering

v5.3

GPL (>= 3)

Authors

Arnošt Komárek <arnost.komarek@mff.cuni.cz>

Initial release

2020-06-02