mkReTrms {lme4} R Documentation

## Make Random Effect Terms: Create Z, Lambda, Lind, etc.

### Description

From the result of findbars applied to a model formula and the evaluation frame fr, create the model matrix Zt, etc, associated with the random-effects terms.

### Usage

  mkReTrms(bars, fr, drop.unused.levels=TRUE,
reorder.terms=TRUE,
reorder.vars=FALSE)
mkNewReTrms(object, newdata, re.form=NULL,
na.action=na.pass,
allow.new.levels=FALSE,
sparse = max(lengths(orig.random.levs)) > 100)


### Arguments

 bars a list of parsed random-effects terms fr a model frame in which to evaluate these terms drop.unused.levels (logical) drop unused factor levels? reorder.terms arrange random effects terms in decreasing order of number of groups (factor levels)? reorder.vars arrange columns of individual random effects terms in alphabetical order? object a fitted merMod object newdata data frame for which to create new RE terms object re.form (formula, NULL, or NA) specify which random effects to condition on when predicting. If NULL, include all random effects; if NA or ~0, include no random effects na.action function determining what should be done with missing values for fixed effects in newdata allow.new.levels (logical) if new levels (or NA values) in newdata are allowed. If FALSE (default), such new values in newdata will trigger an error; if TRUE, then the prediction will use the unconditional (population-level) values for data with previously unobserved levels (or NAs) sparse generate sparse contrast matrices?

### Value

a list with components

 Zt transpose of the sparse model matrix for the random effects theta initial values of the covariance parameters Lind an integer vector of indices determining the mapping of the elements of the theta vector to the "x" slot of Lambdat Gp a vector indexing the association of elements of the conditional mode vector with random-effect terms; if nb is the vector of numbers of conditional modes per term (i.e. number of groups times number of effects per group), Gp is c(0,cumsum(nb)) (and conversely nb is diff(Gp)) lower lower bounds on the covariance parameters Lambdat transpose of the sparse relative covariance factor flist list of grouping factors used in the random-effects terms cnms a list of column names of the random effects according to the grouping factors Ztlist list of components of the transpose of the random-effects model matrix, separated by random-effects term nl names of the terms (in the same order as Zt, i.e. reflecting the reorder.terms argument)

### Note

mkNewReTrms is used in the context of prediction, to generate a new "random effects terms" object from an already fitted model

Other utilities: findbars, mkRespMod, nlformula, nobars, subbars. getME can retrieve these components from a fitted model, although their values and/or forms may be slightly different in the final fitted model from their original values as returned from mkReTrms.

### Examples

data("Pixel", package="nlme")
mform <- pixel ~ day + I(day^2) + (day | Dog) + (1 | Side/Dog)
(bar.f <- findbars(mform)) # list with 3 terms
mf <- model.frame(subbars(mform),data=Pixel)
rt <- mkReTrms(bar.f,mf)
names(rt)


[Package lme4 version 1.1-32 Index]