FLXMRlmer {flexmix}

FlexMix Interface to Linear Mixed Models

Description

This is a driver which allows fitting of mixtures of linear models with random effects.

Usage

FLXMRlmm(formula = . ~ ., random, lm.fit = c("lm.wfit",
          "smooth.spline"), varFix = c(Random = FALSE, Residual =
          FALSE), ...)
FLXMRlmer(formula = . ~ ., random, weighted = TRUE,
          control = list(), eps = .Machine$double.eps)

Arguments

formula

A formula which is interpreted relative to the formula specified in the call to flexmix using update.formula. Default is to use the original flexmix model formula.

random

A formula for specifying the random effects.

weighted

A logical indicating if the model should be estimated with weighted ML.

control

A list of control parameters. See lmer for details.

eps

Observations with a component-specific posterior smaller than eps are omitted in the M-step for this component.

lm.fit

A character string indicating if the coefficients should be fitted using either a linear model or the function smooth.spline

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 smooth.spline.

Details

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.

Value

Returns an object of class FLXMRlmer and FLXMRlmm inheriting from FLXMRglm and FLXMR, respectively.

Warning

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 weighted ML estimation is made and the condition on the covariate matrix of the random effects is checked. If this fails, only estimation with weighted = FALSE is possible which will maximize the classification likelihood.

Author(s)

Bettina Gruen

Examples

id <- rep(1:50, each = 10)
x <- rep(1:10, 50)
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, control = list(tolerance = 10^-3),
                  cluster = rep(rep(1:2, each = 10), 25))
parameters(fitted)

fitted1 <- flexmix(.~.|id, k = 2, model = FLXMRlmer(y ~ x, random = ~ 1),
                  data = sample, control = list(tolerance = 10^-3),
                  cluster = rep(rep(1:2, each = 10), 25))
parameters(fitted1)

fitted2 <- flexmix(.~.|id, k = 2,
                   model = FLXMRlmm(y ~ 0 + x, random = ~ 1,
                     lm.fit = "smooth.spline"),
                  data = sample, control = list(tolerance = 10^-3),
                  cluster = rep(rep(1:2, each = 10), 25))
parameters(fitted2)