Extract or Get Generalized Components from a Fitted Mixed Effects Model
Description
Extract (or “get”) “components” – in a
generalized sense – from a fitted mixedeffects model,
i.e., (in this version of the package) from an object of
class "merMod"
.
Usage
getME(object, name, ...)
## S3 method for class '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"),
...)
Arguments
object 
a fitted mixedeffects model of class
"merMod" , i.e., typically the result of
lmer() , glmer() or nlmer() .

name 
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" :fixedeffects model matrix
"Z" :randomeffects model matrix
"Zt" :transpose
of randomeffects model matrix. Note that the structure
of Zt has changed since lme4.0 ; to get a
backwardcompatible structure, use
do.call(Matrix::rBind,getME(.,"Ztlist"))
"Ztlist" :list of components of the transpose of the
randomeffects 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 randomeffects 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
subvectors corresponding to the randomeffects terms,
beginning with 0 and including a final element giving the
number of thetas.
"L" :sparse Cholesky factor of the penalized randomeffects 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 fixedeffects parameters
"RZX" :crossterm 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" :fixedeffects parameter estimates
"beta" :fixedeffects parameter estimates (identical
to the result of fixef , but without names)
"theta" :randomeffects 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 randomeffects 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 offdiagonal elements from T .
"n_rtrms" :number of randomeffects terms
"n_rfacs" :number of distinct randomeffects 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 termwise model matrices, ZtList
"k" :number of random effects terms
"m_i" :numbers of covariance parameters in each term
"m" :total number of covariance parameters, i.e., the
same as dims@nth below.
"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:
 ldL2
twice the log determinant of L
 ldRX2
twice the log determinant of RX
 wrss
weighted residual sum of squares
 ussq
squared length of u
 pwrss
penalized weighted residual sum of squares,
“wrss + ussq”
 drsum
sum of residual deviance (GLMMs only)
 REML
REML criterion at optimum (LMMs fit
by REML only)
 dev
deviance criterion at optimum
(models fit by ML only)
 sigmaML
ML estimate of residual standard deviation
 sigmaREML
REML estimate of residual standard deviation
 tolPwrss
tolerance for declaring convergence in the
penalized iteratively weighted residual sumofsquares (GLMMs only)
The elements of dims are:
 N
number of rows of X
 n
length of y
 p
number of columns of X
 nmp
np
 nth
length of theta
 q
number of columns of Z
 nAGQ
see glmer
 compDev
see glmerControl
 useSc
TRUE if model has a scale parameter
 reTrms
number of random effects terms
 REML
0 indicates the model was fitted by maximum
likelihood, any other positive integer indicates fitting by
restricted maximum likelihood
 GLMM
TRUE if a GLMM
 NLMM
TRUE if an NLMM
"offset" :model offset
"lower" :lower bounds on randomeffects model
parameters (i.e, "theta" parameters). In order to constrain
random effects covariance matrices to be semipositivedefinite,
this vector is equal to 0 for elements of
the theta vector corresponding to diagonal elements of
the Cholesky factor, Inf
otherwise. (getME(.,"lower")==0 can be used as a test to
identify diagonal elements, as in isSingular .)
"devfun" :deviance function (so far only available for LMMs)
"glmer.nb.theta" :negative binomial \theta parameter,
only for glmer.nb .
"ALL" :get all of the above as a list .

... 
currently unused in lme4, potentially further
arguments in methods.

Details
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.
Value
Unspecified, as very much depending on the name
.
See Also
getCall()
. More standard methods for "merMod"
objects, such as ranef
, fixef
,
vcov
, etc.: see methods(class="merMod")
Examples
## shows many methods you should consider *before* using getME():
methods(class = "merMod")
(fm1 < lmer(Reaction ~ Days + (DaysSubject), 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))