information.lmm: Extract The Information From a Linear Mixed Model

In LMMstar: Repeated Measurement Models for Discrete Times

Extract The Information From a Linear Mixed Model

Description

Extract or compute the (expected) second derivative of the log-likelihood of a linear mixed model.

Usage

## S3 method for class 'lmm'
information(
  x,
  effects = NULL,
  data = NULL,
  p = NULL,
  indiv = FALSE,
  type.information = NULL,
  transform.sigma = NULL,
  transform.k = NULL,
  transform.rho = NULL,
  transform.names = TRUE,
  ...
)

Arguments

x	a lmm object.
effects	[character] Should the information relative to all coefficients be output ("all" or "fixed"), or only coefficients relative to the mean ("mean"), or only coefficients relative to the variance and correlation structure ("variance" or "correlation").
data	[data.frame] dataset relative to which the information should be computed. Only relevant if differs from the dataset used to fit the model.
p	[numeric vector] value of the model coefficients at which to evaluate the information. Only relevant if differs from the fitted values.
indiv	[logical] Should the contribution of each cluster to the information be output? Otherwise output the sum of all clusters of the derivatives.
type.information	[character] Should the expected information be computed (i.e. minus the expected second derivative) or the observed inforamtion (i.e. minus the second derivative).
transform.sigma	[character] Transformation used on the variance coefficient for the reference level. One of "none", "log", "square", "logsquare" - see details.
transform.k	[character] Transformation used on the variance coefficients relative to the other levels. One of "none", "log", "square", "logsquare", "sd", "logsd", "var", "logvar" - see details.
transform.rho	[character] Transformation used on the correlation coefficients. One of "none", "atanh", "cov" - see details.
transform.names	[logical] Should the name of the coefficients be updated to reflect the transformation that has been used?
...	Not used. For compatibility with the generic method.

Details

For details about the arguments transform.sigma, transform.k, transform.rho, see the documentation of the coef.lmm function.

Value

When argument indiv is FALSE, a matrix with the value of the infroamtion relative to each pair of coefficient (in rows and columns) and each cluster (in rows). When argument indiv is TRUE, a 3-dimensional array with the value of the information relative to each pair of coefficient (dimension 2 and 3) and each cluster (dimension 1).

LMMstar documentation built on Nov. 9, 2023, 1:06 a.m.