| FLXMRlmer {flexmix} | R Documentation |
This is a driver which allows fitting of mixtures of linear models with random effects.
FLXMRlmm(formula = . ~ ., random, lm.fit = c("lm.wfit",
"smooth.spline"), varFix = c(Random = FALSE, Residual =
FALSE), ...)
FLXMRlmer(formula = . ~ ., random, weighted = FALSE,
control = list(), eps = .Machine$double.eps)
formula |
A formula which is interpreted relative to the formula
specified in the call to |
random |
A formula for specifying the random effects. |
weighted |
A logical indicating if the model can estimate weighted ML. |
control |
A list of control parameters. See
|
eps |
Observations with a component-specific posterior smaller
than |
lm.fit |
A character string indicating if the coefficients should
be fitted using either a linear model or the function
|
varFix |
Named logical vector of length 2 indicating if the variance of the random effects and the residuals are fixed over the components. |
... |
Additional arguments to be passed to |
FLXMRlmm allows only one random effect. FLXMRlmer allows
an arbitrary number of random effects if weighted=FALSE; a
certain structure of the model matrix of the random effects has to be
given for weighted ML estimation, i.e. where weighted=TRUE.
Returns an object of class FLXMRlmer and FLXMRlmm.
For FLXMRlmer the weighted ML estimation is only correct if the
covariate matrix of the random effects is the same for each
observation. By default non-weighted ML estimation is made. If this is
changed the condition on the covariate matrix of the random effects is
checked.
Bettina Gruen
id <- rep(1:100, each = 10)
x <- rep(1:10, 100)
sample <- data.frame(y = rep(rnorm(unique(id)/2, 0, c(5, 2)), each = 10) +
rnorm(length(id), rep(c(3, 8), each = 10)) +
rep(c(0, 3), each = 10) * x,
x = x,
id = factor(id))
fitted <- flexmix(.~.|id, k = 2, model = FLXMRlmm(y ~ x, random = ~ 1),
data = sample)
parameters(fitted)
xyplot(y ~ x | clusters(fitted), groups = id, data = sample, type = "l")