GLMMMCMCifit: Initial (RE)ML fits for the GLMM_MCMC function

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

Initial (RE)ML fits for the GLMM_MCMC function

Description

This is a help function for GLMM_MCMC function. Besides initial (RE)ML fits, the function created variables derived from the design matrices.

THIS FUNCTION IS NOT TO BE CALLED BY ORDINARY USERS.

Usage

GLMM_MCMCifit(do.init, na.complete,
    y, dist, id, time, x, z, random.intercept,
    xempty, zempty, Rc, Rd, p, p_fi, q, q_ri, lalpha, dimb)

Arguments

do.init	logical value indicating whether initial (RE)ML fits should be done
na.complete	logical value. If TRUE then the function removes rows containing NA's from y, id, x, z whenever there is at least one missing value for arbitrary response. If FALSE then the missing values are removed response by response, i.e., different response variables may have different numbers of observations.
y	see output element y of GLMM_MCMCdata function
dist	see argumentdist of GLMM_MCMC function
id	see output element id of GLMM_MCMCdata function
time	see argument time of GLMM_longitClust
x	see output element x of GLMM_MCMCdata function
z	see output element z of GLMM_MCMCdata function
random.intercept	see output element random.intercept of GLMM_MCMCdata function
xempty	see output element xempty of GLMM_MCMCdata function
zempty	see output element zempty of GLMM_MCMCdata function
Rc	see output element Rc of GLMM_MCMCdata function
Rd	see output element Rd of GLMM_MCMCdata function
p	see output element p of GLMM_MCMCdata function
p_fi	see output element p_fi of GLMM_MCMCdata function
q	see output element q of GLMM_MCMCdata function
q_ri	see output element q_ri of GLMM_MCMCdata function
lalpha	see output element lalpha of GLMM_MCMCdata function
dimb	see output element dimb of GLMM_MCMCdata function

Value

A list with the following components (some of them not included if do.init is FALSE):

Y	a list of length R with observations really used in fitting process (after removal of missing values)
ID	a list of length R with id's corresponding to Y
time	a vector time upon removal of missing values
x	a list resulting from the original argument x after removal of observations with some missing information additionaly, intercept column is added if fixed intercept included in the model
z	a list resulting from the original argument z after removal of observations with some missing information additionaly, intercept column is added if random intercept included in the model
I	number of subjects (grouped observations) in the original data (before removing NA's)
n	a list of length R, each component is a vector or length I (may contain zeros if some cluster disappears for particular response due to NA's)
Cn	vectorized n
sumCn	sum(Cn) = total number of observations
Cy_c	vector with continuous response to be passed to C++, equal to 0 if there is no continuous response
Cy_d	vector with discrete response to be passed to C++, equal to 0 if there is no discrete response
CX	vector containing X matrices (without ones for possible intercept) to be passed to C++, equal to 0 if there are no X matrices
CZ	vector containing Z matrices (without ones for possible intercept) to be passed to C++, equal to 0 if there are no Z matrices
iintcpt	data.frame(Est, SE) with estimated intercepts and their SE, R rows, row equal to (0, 0) if there is no fixed intercept for particular response
ifixef	a list of length R, each component is equal to 0 if there are no fixed effects for particular response, and is equal to data.frame(Est, SE) if there are fixed effects
isigma	vector of length R, equal to 0 for discrete response, equal to estimated residual standard deviation for continuous response
iEranef	a list of length R, each component is equal to 0 if there are no random effects for particular response, and is equal to data.frame(Est, SE) with estimated means of the random effects and their std. errors if there are random effects
iSDranef	a list of length R, each component is equal to 0 if there are no random effects for particular response, and is equal to a vector with estimated standard deviations of the random effects if there are random effects
ib	a list of length R, each component is equal to 0 if there are no random effects for particular response, and a matrix with EB estimates of random effects shifted by their estimated mean if there are random effects
is.intcpt	logical vector of length R
is.fixef	logical vector of length R
is.ranef	logical vector of length R
is.sigma	logical vector of length R
ibMat	matrix with initial values of random effects (EB estimates from (RE)ML fits)
ibMat2	matrix with alternative initial values of random effects
iEranefVec	vector with estimated means of random effects
iSDranefVec	vector with estimated standard deviations of random effects
iSEranefVec	vector with standard errors of estimated means of random effects
ialpha	vector with initial values of alpha's (including fixed intercepts)
ialpha2	vector with alternative initial values of alpha's (including fixed intercepts)
iSEalpha	vector with standard errors of estimated values of fixed effects

Author(s)

Arnošt Komárek [e-mail]

See Also

GLMM_MCMC.

mixAK documentation built on Sept. 17, 2024, 1:06 a.m.