coxme (version 2.2-1)

lmekin: Fit a linear mixed effects model

Description

The lmekin function fits a linear mixed effects model, with random effects specified in the same structure as in the coxme function.

Usage

lmekin(formula, data, weights, subset, na.action, control,
varlist, vfixed, vinit, method = c("ML", "REML"),
sparse = c(1, 0), x = FALSE, y = TRUE,
random, fixed, variance, ...)

Arguments

formula

a two-sided formula with the response as the left hand side of a ~ operator and the fixed and random effects on the right.

data

an optional data frame containing the variables named in the formula.

subset, weights, na.action

further model specifications arguments as in lm; see there for details.

control

optional list of control options. See coxme.control for details.

varlist

the variance family to be used for each random term. If there are multiple terms it will be a list of variance functions. The default is coxmeFull. Alternatively it can be a list of matrices, in which case the coxmeMlis

vfixed

optional named list or vector used to fix the value of one or more of the variance terms at a constant.

vinit

optional named list or vector giving suggested starting values for the variance.

method

fit using either maximum likelihood or restricted maximum likelihood

sparse

rule for deciding sparsity of a random effect. See the main documentation for discussion of the issue.

x

if TRUE the X matrix (fixed effects) is included in the output object

y

if TRUE the y variable is included in the output object

fixed, random, variance

In an earlier version of lmekin the fixed and random effects were separate arguments. These arguments are included for backwards compatability, but are depreciated. The variance argument is a depreciated alias for vfixed.

...

any other arguments are passed forward to coxme.control.

Value

• An object of class lmekin.

Details

This routine was originally written as a check for the coxme routine; it uses the same code to process input arguments and form the random effects, comparison of its output with lme helped validate those operations. It is possible to specify some models in this framwork that can not be fit with lme, in particular models with familial genetic effects, i.e., a kinship matrix, and hence the name of the routine. Using user-specified variance functions an even wider range of models is possible.

For simple models the specification of the random effects follows the same form as the lmer function. For any model which can be fit by both lmekin and lmer, the latter routine would normally be prefered due to a much wider selection of post-fit tools for residuals, prediction, plotting, etc.

See Also

lmekin.object, coxme

Examples

fit1 <- lme(effort ~ Type, data=ergoStool, random= ~1|Subject,
            method="ML")
  fit2 <- lmekin(effort ~ Type + (1|Subject), data=ergoStool)