| mer-class {lme4} | R Documentation |
The mer class represents linear or generalized
linear or nonlinear mixed-effects models. It incorporates
sparse model matrices for the random effects and corresponding sparse
Cholesky factors. The summary.mer class represents the summary
of these objects.
## Methods with "surprising" arguments ## S4 method for signature 'mer': deviance(object, REML = NULL, ...) ## S4 method for signature 'mer': expand(x, sparse = TRUE, ...) ## S4 method for signature 'mer': logLik(object, REML = NULL, ...) ## S4 method for signature 'mer': print(x, digits, correlation, symbolic.cor, signif.stars, ...)
object |
object of class mer. |
REML |
logical indicating if REML should be used. A value of
NULL, the default, or NA indicates that the REML values
should be returned if the model was fit by REML, otherwise the ML values. |
x |
object of class mer to expand. |
sparse |
logical scalar indicating if the sparse form of the
expanded T and S matrices should be returned. |
digits |
number of digits to use when printing tables of
parameter estimates. Defaults to max(3, getOption("digits") -
3). |
correlation |
logical - should the correlation matrix of the
fixed-effects parameter estimates be printed? Defaults to TRUE. |
symbolic.cor |
logical - should a symbolic form of the
correlation matrix be printed instead of the numeric form? Defaults
to FALSE. |
signif.stars |
logical - should the ‘significance stars’
be printed as part of the table of fixed-effects parameter
estimates? Defaults to getOption("show.signif.stars"). |
... |
potential further arguments passed to methods. |
Objects can be created by calls of the
form new("mer", ...) or, more commonly, via the
lmer, glmer or nlmer
functions.
The class "mer" represents a linear or generalized linear or
nonlinear or generalized nonlinear mixed model and contains the slots:
env:"environment")
created for the evaluation of the nonlinear model function. Not
used except by nlmer models.nlmodel:"call". Not used except by nlmer
models.frame:"data.frame").call:"call").flist:X:nlmer fitted model this matrix has n * s rows
where n is the number of observations and s is the
number of parameters in the nonlinear model.Zt:"dgCMatrix").pWt:offset:y:"numeric").Gp:Gp are 0-based indices of
the first element from each random-effects term. Thus the first
element is always 0. The last element is the total length of the
random effects vector.dims:ST:V:"matrix") of
the nonlinear model function. Not used except by
nlmer models.A:"dgCMatrix") for
the the unit, orthogonal random effects, U.Cm:"dgCMatrix") for the
unit, orthogonal random effects, U. Not used except by
nlmer models.Cx:"x" slot in the weighted sparse model
matrix (class "dgCMatrix")
for the unit, orthogonal random effects, U, in generalized
linear mixed models. For these models the matrices A and
C have the same sparsity pattern and only the "x"
slot of C needs to be stored.L:"dCHMfactor") where P
is the fill-reducing permutation calculated from the pattern of
nonzeros in A.deviance:"ML" element)
and "REML" criteria and various components. The
"ldL2" element is twice the logarithm of the determinant of
the Cholesky factor in the L slot. The "usqr"
component is the value of the random-effects quadratic form.fixef:ranef:u:eta:mu:muEta:var:glm family.resid:sqrtrWt slot (when its length is >0).sqrtXWt:sqrtrWt:RZX:"matrix") to
L RZX = ST'Z'X = AX.RX:"matrix")
of the downdated X'X.
The "summary.mer" class contains the "mer",
class and has additional slots,
methTitle:logLik:logLik(object).ngrps:flist slot.sigma:coefs:vcov:vcov(object).REmat:AICtab:signature(x = "mer"): Extract variance and
correlation components. See VarCorrsignature(object = "mer"): returns the sequential
decomposition of the contributions of fixed-effects terms or, for
multiple arguments, model comparison statistics. See
anova.signature(object = "mer"): returns an object
similar to the ranef method but incorporating the
fixed-effects parameters, thereby forming a table of linear model
coefficients (the columns) by level of the grouping factor (the rows).signature(from = "mer", to = "dtCMatrix"):
returns the L slot as a "dtCMatrix"
(column-oriented, sparse, triangular matrix) object.signature(object = "mer"): returns the
deviance of the fitted model, or the “REML
deviance” (i.e. negative twice the REML criterion), according to
the REML argument. See the arguments section above for a description
of the REML argument.signature(object = "mer"):
returns a list of terms in the expansion of the ST slot.
If sparse is TRUE, the default, the elements of the
list are the numeric scalar "sigma", the REML or ML
estimate of the standard deviation in the model, and three sparse
matrices: "P", the permutation matrix, "S", the
diagonal scale matrix and "T", the lower triangular matrix
determining correlations. When sparse is FALSE each
element of the list is the expansions of the corresponding element
of the ST slot into a list of S, the diagonal
matrix, and T, the (dense) unit lower triangular matrix.
signature(object = "mer"):
returns the fitted conditional means of the responses. See
fitted. The napredict function is
called to align the result with the original data if the model was
fit with na.action = na.exclude.
signature(object = "mer"):
returns the estimates of the fixed-effects parameters. See
fixef.
signature(x = "mer"):
returns the model formula. See formula.
signature(object = "mer"):
returns the log-likelihood or the REML criterion, according to the
optional REML argument (see the arguments section above),
of the fitted model. See also logLik.
signature(object = "mer"):
Create a Markov chain Monte Carlo sample from a posterior
distribution of the model's parameters. See
mcmcsamp for details.
signature(formula = "mer"): returns the
model frame (the frame slot).signature(object = "mer"): returns the
model matrix for the fixed-effects parameters (the X
slot).signature(x = "mer"): print information about
the fitted model. See the arguments section above for a description
of optional arguments.signature(object = "mer"): returns the
conditional modes of the random effects. See ranef.signature(object = "mer", newresp = "numeric"):
Update the response vector only and refit the model. See
refit.signature(object = "mer"): returns the (raw)
residuals. This method calls napredict. See the
above description of the fitted method for details. See
also resid.signature(object = "mer"): Another name
for the resid method.signature(object = "mer"): Same as the
print method without the optional arguments.signature(object = "mer"): simulate
nsim (defaults to 1) responses from the theoretical
distribution corresponding to the fitted model. The refit
method is particularly useful in combination with this method.
See also simulate.signature(x = "mer"): Extract the
terms object for the fixed-effects terms in the
model formula.signature(object = "mer"): see
update on how to update fitted models.signature(object = "mer"): Calculate
variance-covariance matrix of the fixed effect terms,
see also vcov.signature(data = "mer"): Evaluate an R expression
in an environment constructed from the frame slot.
lmer(), glmer() and nlmer(),
which produce these objects.
VarCorr for extracting the variance and
correlation components of the random-effects terms.
(fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject),
data = sleepstudy))
print(fm2, digits = 10, corr = FALSE) # more precision; no corr.matrix
logLik(fm2)
(V2 <- vcov(fm2))
terms(fm2)
str(model.matrix(fm2))
str(model.frame(fm2))
str(resid(fm2))
VarCorr(fm2)
ee <- expand(fm2)
op <- options(digits = 3)
tcrossprod(ee$sigma * ee$P %*% ee$T %*% ee$S)
options(op)
## Not run:
## Simulate 'Reaction' according to the fitted model:
dim(ss <- simulate(fm2, nsim = 200, seed = 101)) ## -> 180 x 200
## End(Not run)