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 0.99875-9
Index]