asreml.object {asreml} | R Documentation |
This (S3) class object contains a fitted linear mixed model from
the asreml()
function. Objects of this class have methods
for the generic functions wald()
, coef()
,
fitted()
, plot()
, predict()
, resid()
,
summary()
and update()
.
A list object with class asreml
; the following components
are included in a valid asreml
object:
The loglikelihood at completion of the asreml
call.
The vector of variance parameter estimates from the fit.
A numeric vector identifying the boundary constraint
applied to each variance parameter at termination. Common values are 1, 3 and 4
for Positive, Unconstrained and Fixed, respectively. The
function vpc.char
can be used to interpret the numeric values as per
summary.asreml
.
A numeric vector identifying the variance parameter types.
Numeric values are used internally and the character codes as used by the own()
variance model can be obtained from the function vpt.char
.
Percentage change in gammas
on the last iteration.
The score vector of length number of random parameters.
A list with three components named
fixed
, random
and sparse
containing the
solutions to the mixed model equations corresponding to the fixed
effects, the E-BLUPs of the random effects, and the solutions
corresponding to the sparse fixed effects, respectively. The
coefficients are labelled by a concatenation of factor name and
level separated by "_"
.
A list with three components named fixed
,
random
and sparse
containing the unscaled variances
of the coefficients. The actual variances are calculated as
vcoeff*object\$sigma2
and returned by the
summary
function.
If predict
is not NULL
, a list
object with components pvals, sed, vcov
and
avsed
. The predictions
component only is returned by
the predict
method for asreml
objects.
A vector containing the fitted values from the model, obtained by transforming the linear predictors by the inverse of the link function.
The linear fit on the link scale.
A single column matrix containing the residuals from the model.
The diagonal elements of the matrix W C^{-1} W^T, the extended hat matrix. This is the linear mixed effects model analogue of X(X^T X)^{-1} X^T for ordinary linear models.
The REML estimate of the scale parameter.
The deviance from the fit.
The residual degrees of freedom,
length(y)-rank(X)
.
The number of working variables.
A vector containing the number of effects for each term.
A vector of incremental sums of squares for (dense) fixed terms.
The inverse average information matrix of the variance
parameters. A Matrix
class object, sub-class
dspMatrix
.
Reflexive generalised inverse of the coefficient
matrix of the mixed model equations relating to the dense fixed
effects (if asreml.options()$Cfixed=TRUE
). A matrix of class
Matrix
, sub-class dspMatrix
.
If asreml.options()$Csparse
is not
NULL
, the non-zero elements of the reflexive generalised
inverse matrix of the coefficient matrix for the sparse stored
model terms nominated in the Csparse
formula. A matrix in
triplet form giving the row, column and non-zero element.
The design matrix as a sparse Matrix
of class
dgCMatrix
if asreml.options(design=TRUE)
.
An image of the asreml
function call.
A numeric matrix recording the convergence sequence for each random component, as well as the log-likelihood, residual variance and residual degrees of freedom.
A character string containing the license
information. The string has embedded new-line characters and is
best formatted through cat()
.
A list object containing the constraints and final
estimates of the variance parameters relating to the random part of
the model. This object may be used as the value of the
G.param
argument to provide initial parameter estimates to
asreml
.
A list object containing the constraints and final
estimates of the variance parameters relating to the error
structure of the model. This object may be used as the value of the
R.param
argument to provide initial parameter estimates to
asreml
.
A list object containing the fixed
,
random
, sparse
and residual
formula arguments
to asreml
.
A character vector of term names appearing in the model.
Regressor scores (marker effects) if nominated in the
mef
list argument.
The model frame with the data as a data.table
object with numerous attributes from the model
specification. Inspect names(attributes(object$mf))
for
details.