GLMMlongitDA function

Discriminant analysis for longitudinal profiles based on fitted GLMM's

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.

Details

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

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

Returns

A list with the following components: - ident: ADD DESCRIPTION

• marg: ADD DESCRIPTION

• cond: ADD DESCRIPTION

• ranef: ADD DESCRIPTION

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.

See Also

GLMM_MCMC, GLMM_longitDA2.

Author(s)

Arnošt Komárek arnost.komarek@mff.cuni.cz

Examples

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