# lmekin

##### 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`

.

##### See Also

##### 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*