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 Kom<U+00E1>rek et al. (2010).
Currently, only continuous responses for which linear mixed models are assumed are allowed.
GLMM_longitDA(mod, w.prior, y, id, time, x, z, xz.common=TRUE, info)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.
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.
vector, matrix or data frame (see argument y of
GLMM_MCMC function) with responses of objects that are
to be clustered.
vector which determines clustered observations (see also
argument y of GLMM_MCMC function).
vector which gives indeces of observations within
clusters. It appears (together with id) in the output as
identifier of observations
see xz.common below.
see xz.common below.
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.
interval in which the function prints the progress of computation
A list with the following components:
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This function complements a paper Kom<U+00E1>rek et al. (2010).
Kom<U+00E1>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, 29(30), 3267--3283.
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