lmekin {coxme} | R Documentation |
The lmekin function fits a linear mixed effects model, with random
effects specified in the same structure as in the coxme
function.
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, ...)
formula |
a two-sided formula with the response as the left hand side of a
|
data |
an optional data frame containing the variables named in the |
subset, weights, na.action |
further model specifications arguments as in |
control |
optional list of control options. See |
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 |
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 |
... |
any other arguments are passed forward to |
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.
An object of class lmekin
.
Terry Therneau
fit1 <- lme(effort ~ Type, data=ergoStool, random= ~1|Subject, method="ML") fit2 <- lmekin(effort ~ Type + (1|Subject), data=ergoStool)