[1] anova as.function coef confint cooks.distance
[6] deviance df.residual drop1 extractAIC family
[11] fitted fixef formula getData getL
[16] getME hatvalues influence isGLMM isLMM
[21] isNLMM isREML logLik model.frame model.matrix
[26] ngrps nobs plot predict print
[31] profile ranef refit refitML rePCA
[36] residuals rstudent show sigma simulate
[41] summary terms update VarCorr vcov
[46] weights
see '?methods' for accessing help and source code
getME
‘ “everything a user may want” from a fitted "merMod" object as far as it is not available by methods ’
# fixed-effects model matrixgetME(model, "X")# random-effects model matrixgetME(model, "Z")# transpose of random-effects model matrixgetME(model, "Zt")# list of components of the transpose of the random-effects model matrix,# separated by individual variance componentgetME(model, "Ztlist")# list of raw model matrices associated with random effects termsgetME(model, "mmList")# response vectorgetME(model, "y")# conditional mean of the responsegetME(model, "mu")# conditional mode of the “spherical” random effects variablegetME(model, "u")# conditional mode of the random effects variablegetME(model, "b")# 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 effectsgetME(model, "Gp")# 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.getME(model, "Tp")# sparse Cholesky factor of the penalized random-effects model.getME(model, "L")# relative covariance factor Λ of the random effects.getME(model, "Lambda")# transpose Λ′ of Λ above.getME(model, "Lambdat")# index vector for inserting elements of θ into the nonzeros of Λ.getME(model, "Lind")# vector of template matrices from which the blocks of Λ are generated.getME(model, "Tlist",)# Scaled sparse model matrix (class `"dgCMatrix"`) for the unit, orthogonal# random effects, U, equal to `getME(.,"Zt") %*% getME(.,"Lambdat")`getME(model, "A")# Cholesky factor for the fixed-effects parametersgetME(model, "RX")# cross-term in the full Cholesky factorgetME(model, "RZX")# residual standard error; note that `sigma(object)` is preferred.getME(model, "sigma")# a list of the grouping variables (factors) involved in the random effect termsgetME(model, "flist",)# fixed-effects parameter estimatesgetME(model, "fixef")# fixed-effects parameter estimates (identical to the result of `fixef`, but without names)getME(model, "beta")# random-effects parameter estimates: these are parameterized as the relative# Cholesky factors of each random effect termgetME(model, "theta")# 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.getME(model, "ST")# number of random-effects termsgetME(model, "n_rtrms")# number of distinct random-effects grouping factorsgetME(model, "n_rfacs")# number of rows of XgetME(model, "N")# length of the response vector, ygetME(model, "n")# number of columns of the fixed effects model matrix, XgetME(model, "p")# number of columns of the random effects model matrix, ZgetME(model, "q",)# numbers of columns of the raw model matrices, mmListgetME(model, "p_i")# numbers of levels of the grouping factorsgetME(model, "l_i")# numbers of columns of the term-wise model matrices, ZtListgetME(model, "q_i")# number of random effects termsgetME(model, "k")# numbers of covariance parameters in each termgetME(model, "m_i")# total number of covariance parameters, i.e., the same as `dims@nth` below.getME(model, "m",)# the “component names”, a `list`.getME(model, "cnms")# 0 indicates the model was fitted by maximum likelihood, any other positive# integer indicates fitting by restricted maximum likelihoodgetME(model, "REML")# same as the result of `isREML(.)`getME(model, "is_REML")# a list consisting of a named numeric vector, `cmp`, and a named integer# vector, `dims`, describing the fitted model.getME(model, "devcomp")# model offsetgetME(model, "offset")# 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`.)getME(model, "lower")# deviance function (so far only available for LMMs)getME(model, "devfun")# negative binomial θ parameter, only for `glmer.nb`.getME(model, "glmer.nb.theta")