# lmekin

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##### Fit a linear mixed effects model

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

Keywords
models
##### Usage
lmekin(formula, data, weights, subset, na.action, control,
varlist, vfixed, vinit, method = c("ML", "REML"),
x = FALSE, y = FALSE, model=FALSE,
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 coxmeMlist function is used.

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

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

model

if TRUE the model frame 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.

##### Details

Let $A= \sigma^2 B$ be the variance matrix of the random effects where $\sigma^2$ is the residual variance for the model. Internally the routine solves for the parameters of $B$, computing $A$ at the end. The vinit and vfixed parmaters refer to $B$, however.

It is possible to specify certain models in lmekin 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 and plotting.

Much of the underlying model code for specification and manipulation of the random effects is shared with the coxme routine. In fact lmekin was originally written only to provide a test routine for those codes, and no expectation that it would find wider utility.

##### Value

An object of class lmekin.

lmekin.object, coxme

• lmekin
##### Examples
# NOT RUN {
data(ergoStool, package="nlme")  # use a data set from nlme
fit1 <- lmekin(effort ~ Type + (1|Subject), data=ergoStool)

# }
# NOT RUN {
# gives the same result
require(nlme)
fit2 <- lme(effort ~ Type, data=ergoStool, random= ~1|Subject,
method="ML")

# }

Documentation reproduced from package coxme, version 2.2-14, License: LGPL-2

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