nlmer-class {lme4}R Documentation

Representation of a Nonlinear Mixed Model

Description

The lmer class is a representaiton of nonlinear mixed model using sparse matrices.

Objects from the Class

Objects can be created by calls of the form new("nlmer", ...) or, more commonly via the nlmer function.

Slots

env:
An environment (of class "environment") created for the evaluation of the nonlinear model function.
model:
The nonlinear model function as an object of class "call".
frame:
The model frame for the model, as an object of class "data.frame".
pnames:
Names of the parameters in the nonlinear model (class "character").
call:
The matched call to the function that created the object. (class "call").
flist:
The list of grouping factors for the random effects.
Xt:
Sparse model matrix (class "dgCMatrix") for the fixed effects.
Zt:
Sparse model matrix (class "dgCMatrix") for the random effects.
y:
The response vector (class "numeric").
weights:
Numeric weights vector. This may be of length zero (0) indicating unit weights.
cnames:
a list of character vectors of column names of the random-effects variance-covariance matrix associated with each grouping factor and the fixed-effects model matrix.
Gp:
integer vector of group pointers within the random effects and the rows of the transposed model matrix in the Zt slot. The elements of Gp are the 0-based index of the first element corresponding to each grouping factor. Thus the first element is always 0. The last element is the total length of the random effects vector (also the total number of rows in the matrix in the Zt slot).
dims:
A named integer vector of dimensions.
ST:
A list of S and T factors in the TSST' Cholesky factorization of the relative variance matrices of the random effects associated with each grouping factor. The unit lower triangular matrix T and the diagonal matrix S are stored as a single matrix whose diagonal elements determine S while the subdiagonal elements determine the non-trivial elements of T.
Vt:
A sparse matrix (of class "dgCMatrix") containing the transpose of V=ZTS.
L:
The Cholesky decomposition (class "CHMfactor") of V'V+I.
mu:
A numeric vector of predicted values from the model with an attibute "gradient", the gradient matrix.
Mt:
The derivative of mu with respect to uvec stored as a sparse matrix (class "dgCMatrix").
deviance:
Named numeric vector of containing the deviance corresponding to the maximum likelihood (ML) and REML criteria and various components.
fixef:
Numeric vector of fixed effects.
ranef:
Numeric vector of random effects on the original scale.
uvec:
Numeric vector of orthogonal, constant variance random effects.

Methods

show
signature(object = "nlmer")
VarCorr
signature(x = "nlmer"): Extract the variances, standard deviations and correlations of the random effects.

See Also

nlmer, lmer

Examples

showClass("nlmer")

[Package lme4 version 0.99875-1 Index]