This function fits a linear mixed-effects model under heavy-tailed errors using the formulation described in Pinheiro et al. (2001).
heavyLme(fixed, random, groups, data, family = Student(df = 4),
subset, na.action, control)
a two-sided linear formula object describing the fixed-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators.
a one-sided formula of the form ~x1+...+xn specifying the model for the random effects.
a one-sided formula for specifying the grouping variable.
an optional data frame containing the variables named in fixed
, random
and group
.
By default the variables are taken from the environment from which heavy.lme is called.
a description of the error distribution to be used in the model. By default the Student-t distribution with 4 degrees of freedom is considered.
an optional expression indicating the subset of the rows of data that should be used in the fitting process.
a function that indicates what should happen when the data contain NAs.
a list of control values for the estimation algorithm to replace the default
values returned by the function heavy.control
.
An object of class heavyLme
representing the linear mixed-effects model fit. Generic function print
and summary
, show the results of the fit.
The following components must be included in a legitimate heavyLme
object.
an object representing a list of mixed-effects model components.
a list containing an image of the heavyLme
call that produced the object.
the heavy.family
object used, with the estimated shape parameters (if requested).
final estimate of the fixed effects.
final estimate of the scale parameters associated to the random effects.
final scale estimate of the random error.
the log-likelihood at convergence.
the number of iterations used in the iterative algorithm.
a matrix with the estimated random effects.
estimated weights corresponding to the assumed heavy-tailed distribution.
estimated squared Mahalanobis distances.
a data frame with the "marginal"
and "conditional"
fitted values as columns.
a data frame with the "marginal"
and "conditional"
residuals as columns.
Pinheiro, J.C., Liu, C., and Wu, Y.N. (2001). Efficient algorithms for robust estimation in linear mixed-effects models using the multivariate t distribution. Journal of Computational and Graphical Statistics 10, 249--276.
# NOT RUN {
data(dental)
fit <- heavyLme(distance ~ age * Sex, random = ~ age, groups = ~ Subject,
data = dental, family = Student(df = 4))
summary(fit)
# }
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