GLMM_MCMCifit {mixAK} | R Documentation

## 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, lbeta, 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
`lbeta` | see output element `lbeta` 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 clusters 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)
`iEranefVec` | vector with estimated means of random effects
`iSEranefVec` | vector with standard errors of estimated means of random effects
`iSDranefVec` | vector with estimated standard deviations of random effects
`ibeta` | vector with initial values of beta's (including fixed intercepts)

### Author(s)

Arnošt Komárek arnost.komarek[AT]mff.cuni.cz

### See Also

`GLMM_MCMC`.

[Package mixAK version 0.6 Index]