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.
GLMM_longitDA(mod, w.prior, y, id, time, x, z, xz.common=TRUE, info)GLMM_MCMC function. Each component of the list is the
GLMM fitted in the training dataset of each cluster.
mod.
Can also be given relatively, e.g., as c(1, 1) which means
that both prior weights are equal to 1/2.
y of
GLMM_MCMC function) with responses of objects that are
to be clustered.
y of GLMM_MCMC function).
id) in the output as
identifier of observationsxz.common below.xz.common below. 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.
GLMM_MCMC, GLMM_longitDA2.