Extract (or “get”) “components” -- in a
generalized sense -- from a fitted mixed-effects model,
i.e., (in this version of the package) from an object of
class "'>merMod".
getME(object, name, ...)# S3 method for merMod
getME(object,
name = c("X", "Z", "Zt", "Ztlist", "mmList", "y", "mu", "u", "b",
"Gp", "Tp", "L", "Lambda", "Lambdat", "Lind", "Tlist",
"A", "RX", "RZX", "sigma", "flist",
"fixef", "beta", "theta", "ST", "REML", "is_REML",
"n_rtrms", "n_rfacs", "N", "n", "p", "q",
"p_i", "l_i", "q_i", "k", "m_i", "m",
"cnms", "devcomp", "offset", "lower", "devfun", "glmer.nb.theta"),
…)
a character vector specifying the name(s) of
the “component”. If length(name) > 1 or if name
= "ALL", a named list of components will be returned. Possible values are:
"X":fixed-effects model matrix
"Z":random-effects model matrix
"Zt":transpose
of random-effects model matrix. Note that the structure
of Zt has changed since lme4.0; to get a
backward-compatible structure, use
do.call(Matrix::rBind,getME(.,"Ztlist"))
"Ztlist":list of components of the transpose of the random-effects model matrix, separated by individual variance component
"mmList":list of raw model matrices associated with random effects terms
"y":response vector
"mu":conditional mean of the response
"u":conditional mode of the “spherical” random effects variable
"b":conditional mode of the random effects variable
"Gp":groups pointer vector. A pointer to the beginning of each group of random effects corresponding to the random-effects terms, beginning with 0 and including a final element giving the total number of random effects
"Tp":theta pointer vector. A pointer to the beginning of the theta sub-vectors corresponding to the random-effects terms, beginning with 0 and including a final element giving the number of thetas.
"L":sparse Cholesky factor of the penalized random-effects model.
"Lambda":relative covariance factor \(\Lambda\) of the random effects.
"Lambdat":transpose \(\Lambda'\) of \(\Lambda\) above.
"Lind":index vector for inserting elements of \(\theta\) into the nonzeros of \(\Lambda\).
"Tlist":vector of template matrices from which the blocks of \(\Lambda\) are generated.
"A":Scaled sparse model matrix (class
"dgCMatrix") for
the unit, orthogonal random effects, \(U\), equal to
getME(.,"Zt") %*% getME(.,"Lambdat")
"RX":Cholesky factor for the fixed-effects parameters
"RZX":cross-term in the full Cholesky factor
"sigma":residual standard error; note that sigma(object) is preferred.
"flist":a list of the grouping variables (factors) involved in the random effect terms
"fixef":fixed-effects parameter estimates
"beta":fixed-effects parameter estimates (identical
to the result of fixef, but without names)
"theta":random-effects parameter estimates: these are parameterized as the relative Cholesky factors of each random effect term
"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 random-effects term. The unit lower triangular matrix, \(T\), and the diagonal matrix, \(S\), for each term are stored as a single matrix with diagonal elements from \(S\) and off-diagonal elements from \(T\).
"n_rtrms":number of random-effects terms
"n_rfacs":number of distinct random-effects grouping factors
"N":number of rows of X
"n":length of the response vector, y
"p":number of columns of the fixed effects model matrix, X
"q":number of columns of the random effects model matrix, Z
"p_i":numbers of columns of the raw model matrices, mmList
"l_i":numbers of levels of the grouping factors
"q_i":numbers of columns of the term-wise model matrices, ZtList
"k":number of random effects terms
"m_i":numbers of covariance parameters in each term
"m":total number of covariance parameters
"cnms":the “component names”, a list.
"REML":0 indicates the model was fitted by maximum
likelihood, any other positive integer indicates fitting by
restricted maximum likelihood
"is_REML":same as the result of isREML(.)
"devcomp":a list consisting of a named numeric vector,
cmp, and a named integer vector, dims, describing
the fitted model. The elements of cmp are:
twice the log determinant of L
twice the log determinant of RX
weighted residual sum of squares
squared length of u
penalized weighted residual sum of squares, “wrss + ussq”
sum of residual deviance (GLMMs only)
REML criterion at optimum (LMMs fit by REML only)
deviance criterion at optimum (models fit by ML only)
ML estimate of residual standard deviation
REML estimate of residual standard deviation
tolerance for declaring convergence in the penalized iteratively weighted residual sum-of-squares (GLMMs only)
dims are:
number of rows of X
length of y
number of columns of X
n-p
length of theta
number of columns of Z
see glmer
see glmerControl
TRUE if model has a scale parameter
number of random effects terms
0 indicates the model was fitted by maximum
likelihood, any other positive integer indicates fitting by
restricted maximum likelihood
TRUE if a GLMM
TRUE if an NLMM
"offset":model offset
"lower":lower bounds on model parameters (random effects parameters only).
"devfun":deviance function (so far only available for LMMs)
"glmer.nb.theta":negative binomial \(\theta\) parameter,
only for glmer.nb.
%% -- keep at the very end:
"ALL":get all of the above as a list.
currently unused in lme4, potentially further arguments in methods.
Unspecified, as very much depending on the name.
The goal is to provide “everything a user may
want” from a fitted "merMod" object as far
as it is not available by methods, such as
fixef, ranef,
vcov, etc.
getCall(). More standard methods for "merMod"
objects, such as ranef, fixef,
vcov, etc.: see methods(class="merMod")
# NOT RUN {
## shows many methods you should consider *before* using getME():
methods(class = "merMod")
(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
Z <- getME(fm1, "Z")
stopifnot(is(Z, "CsparseMatrix"),
c(180,36) == dim(Z),
all.equal(fixef(fm1), b1 <- getME(fm1, "beta"),
check.attributes=FALSE, tolerance = 0))
## A way to get *all* getME()s :
## internal consistency check ensuring that all work:
parts <- getME(fm1, "ALL")
str(parts, max=2)
stopifnot(identical(Z, parts $ Z),
identical(b1, parts $ beta))
# }
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