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mixAK (version 0.6)

GLMM_longitDA: 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

info
interval in which the function prints the progress of computation

Value

  • A list with the following components:
  • identADD DESCRIPTION
  • margADD DESCRIPTION
  • condADD DESCRIPTION
  • ranefADD DESCRIPTION

Details

I HOPE TO WRITE A PAPER DESCRIBING ALL THE DETAILS.

References

$\mbox{Kom\'{a}rek, A.}$, 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

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