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GLMMMCMCifit

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 argument`dist` 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 arnost.komarek[AT]mff.cuni.cz

GLMMMCMCifit

Description

Usage

Arguments

Value

Author(s)

See Also

mixAK

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