getME: Extract or Get Generalized Components from a Fitted Mixed...
In lme4: Linear Mixed-Effects Models using 'Eigen' and S4

Description Usage Arguments Details Value See Also Examples

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 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"),
      ...)

object

a fitted mixed-effects 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":

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, 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 sum-of-squares (GLMMs only)

The elements of dims are:

N: number of rows of X
n: length of y
p: number of columns of X
nmp: n-p
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 random-effects model parameters (i.e, "theta" parameters). In order to constrain random effects covariance matrices to be semi-positive-definite, 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 θ parameter, only for glmer.nb.

"ALL":: get all of the above as a list.

...

currently unused in lme4, potentially further arguments in methods.

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.

Unspecified, as very much depending on the name.

getCall(). More standard methods for "merMod" objects, such as ranef, fixef, vcov, etc.: see methods(class="merMod")

## 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))

Loading required package: Matrix
 [1] VarCorr      anova        as.function  coef         confint     
 [6] deviance     df.residual  drop1        extractAIC   family      
[11] fitted       fixef        formula      getL         getME       
[16] hatvalues    isGLMM       isLMM        isNLMM       isREML      
[21] logLik       model.frame  model.matrix ngrps        nobs        
[26] plot         predict      print        profile      ranef       
[31] refit        refitML      residuals    show         sigma       
[36] simulate     summary      terms        update       vcov        
[41] weights     
see '?methods' for accessing help and source code
Linear mixed model fit by REML ['lmerMod']
Formula: Reaction ~ Days + (Days | Subject)
   Data: sleepstudy
REML criterion at convergence: 1743.628
Random effects:
 Groups   Name        Std.Dev. Corr
 Subject  (Intercept) 24.740       
          Days         5.922   0.07
 Residual             25.592       
Number of obs: 180, groups:  Subject, 18
Fixed Effects:
(Intercept)         Days  
     251.41        10.47  
List of 45
 $ X             : num [1:180, 1:2] 1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "dimnames")=List of 2
  ..- attr(*, "assign")= int [1:2] 0 1
  ..- attr(*, "msgScaleX")= chr(0) 
 $ Z             :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
 $ Zt            :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
 $ Ztlist        :List of 2
  ..$ Subject.(Intercept):Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
  ..$ Subject.Days       :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
 $ mmList        :List of 1
  ..$ Days | Subject: num [1:180, 1:2] 1 1 1 1 1 1 1 1 1 1 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. ..- attr(*, "assign")= int [1:2] 0 1
 $ y             : num [1:180] 250 259 251 321 357 ...
 $ mu            : num [1:180] 254 273 293 313 332 ...
 $ u             : num [1:36] 2.34 39.68 -41.79 -34.58 -40.3 ...
 $ b             :Formal class 'dgeMatrix' [package "Matrix"] with 4 slots
 $ Gp            : int [1:2] 0 36
 $ Tp            : Named num [1:2] 0 3
  ..- attr(*, "names")= chr [1:2] "beg__" "Subject"
 $ L             :Formal class 'dCHMsimpl' [package "Matrix"] with 10 slots
 $ Lambda        :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
 $ Lambdat       :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
 $ Lind          : int [1:54] 1 2 3 1 2 3 1 2 3 1 ...
 $ Tlist         :List of 1
  ..$ Subject: num [1:2, 1:2] 0.9667 0.0152 0 0.2309
 $ A             :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
 $ RX            : num [1:2, 1:2] 3.79 0 2.3 16.56
  ..- attr(*, "dimnames")=List of 2
 $ RZX           : num [1:36, 1:2] 3.022 0.269 3.022 0.269 3.022 ...
  ..- attr(*, "dimnames")=List of 2
 $ sigma         : num 25.6
 $ flist         :List of 1
  ..$ Subject: Factor w/ 18 levels "308","309","310",..: 1 1 1 1 1 1 1 1 1 1 ...
  ..- attr(*, "assign")= int 1
 $ fixef         : Named num [1:2] 251.4 10.5
  ..- attr(*, "names")= chr [1:2] "(Intercept)" "Days"
 $ beta          : num [1:2] 251.4 10.5
 $ theta         : Named num [1:3] 0.9667 0.0152 0.2309
  ..- attr(*, "names")= chr [1:3] "Subject.(Intercept)" "Subject.Days.(Intercept)" "Subject.Days"
 $ ST            :List of 1
  ..$ Subject: num [1:2, 1:2] 0.9667 0.0157 0 0.2309
 $ REML          : int 2
 $ is_REML       : logi TRUE
 $ n_rtrms       : int 1
 $ n_rfacs       : int 1
 $ N             : Named int 180
  ..- attr(*, "names")= chr "N"
 $ n             : Named int 180
  ..- attr(*, "names")= chr "n"
 $ p             : Named int 2
  ..- attr(*, "names")= chr "p"
 $ q             : Named int 36
  ..- attr(*, "names")= chr "q"
 $ p_i           : Named int 2
  ..- attr(*, "names")= chr "Days | Subject"
 $ l_i           : Named int 18
  ..- attr(*, "names")= chr "Subject"
 $ q_i           : Named int 36
  ..- attr(*, "names")= chr "Days | Subject"
 $ k             : int 1
 $ m_i           : Named num 3
  ..- attr(*, "names")= chr "Days | Subject"
 $ m             : int 3
 $ cnms          :List of 1
  ..$ Subject: chr [1:2] "(Intercept)" "Days"
 $ devcomp       :List of 2
  ..$ cmp : Named num [1:10] 7.60e+01 8.28 9.89e+04 1.77e+04 1.17e+05 ...
  .. ..- attr(*, "names")= chr [1:10] "ldL2" "ldRX2" "wrss" "ussq" ...
  ..$ dims: Named int [1:12] 180 180 2 178 36 3 1 1 0 2 ...
  .. ..- attr(*, "names")= chr [1:12] "N" "n" "p" "nmp" ...
 $ offset        : num [1:180] 0 0 0 0 0 0 0 0 0 0 ...
 $ lower         : num [1:3] 0 -Inf 0
 $ devfun        :function (theta)  
 $ glmer.nb.theta: logi NA