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 |
REML |
logical indicating if REML should be used. A value of
|
x |
object of class |
sparse |
logical scalar indicating if the sparse form of the
expanded |
digits |
number of digits to use when printing tables of
parameter estimates. Defaults to |
correlation |
logical - should the correlation matrix of the
fixed-effects parameter estimates be printed? Defaults to |
symbolic.cor |
logical - should a symbolic form of the
correlation matrix be printed instead of the numeric form? Defaults
to |
signif.stars |
logical - should the ‘significance stars’
be printed as part of the table of fixed-effects parameter
estimates? Defaults to |
... |
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
:An environment (class "environment"
)
created for the evaluation of the nonlinear model function. Not
used except by nlmer
models.
nlmodel
:The nonlinear model function as an object of
class "call"
. Not used except by nlmer
models.
frame
:The model frame (class "data.frame"
).
call
:The matched call to the function that
created the object. (class "call"
).
flist
:The list of grouping factors for the random effects.
X
:Model matrix for the fixed effects. In an
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
:The transpose of model matrix for the random
effects, stored as a compressed column-oriented sparse matrix (class
"dgCMatrix"
).
pWt
:Numeric prior weights vector. This may be of length zero (0), indicating unit prior weights.
offset
:Numeric offset vector. This may be of length zero (0), indicating no offset.
y
:The response vector (class "numeric"
).
Gp
:Integer vector of group pointers within the random
effects vector. The elements of 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
:A named integer vector of dimensions. Some of the dimensions are n, the number of observations, p, the number of fixed effects, q, the total number of random effects, s, the number of parameters in the nonlinear model function and nt, the number of random-effects terms in the model.
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.
V
:Numeric gradient matrix (class "matrix"
) of
the nonlinear model function. Not used except by
nlmer
models.
A
:Scaled sparse model matrix (class
"dgCMatrix"
) for
the the unit, orthogonal random effects, U.
Cm
:Reduced, weighted sparse model matrix (class
"dgCMatrix"
) for the
unit, orthogonal random effects, U. Not used except by
nlmer
models.
Cx
:The "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
:The sparse lower Cholesky factor of P(AA'+I)P'
(class "dCHMfactor"
) where P
is the fill-reducing permutation calculated from the pattern of
nonzeros in A.
deviance
:Named numeric vector containing the deviance
corresponding to the maximum likelihood (the "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
:Numeric vector of fixed effects.
ranef
:Numeric vector of random effects on the original scale.
u
:Numeric vector of orthogonal, constant variance, random effects.
eta
:The linear predictor at the current values of the parameters and the random effects.
mu
:The means of the responses at the current parameter values.
muEta
:The diagonal of the Jacobian of mu by eta. Has length zero (0) except for generalized mixed models.
var
:The diagonal of the conditional variance of
Y given the random effects, up to prior weights. In
generalized mixed models this is the value of the variance
function for the glm
family.
resid
:The residuals, y-mu, weighted by
the sqrtrWt
slot (when its length is >0).
sqrtXWt
:The square root of the weights applied to the model matrices X and Z. This may be of length zero (0), indicating unit weights.
sqrtrWt
:The square root of the weights applied to the residuals to obtain the weighted residual sum of squares. This may be of length zero (0), indicating unit weights.
RZX
:The dense solution (class "matrix"
) to
L RZX = ST'Z'X = AX.
RX
:The upper Cholesky factor (class "matrix"
)
of the downdated X'X.
The "summary.mer"
class contains the "mer"
,
class and has additional slots,
methTitle
:character string specifying a method title
logLik
:the same as logLik(object)
.
ngrps
:the number of levels per grouping factor in the
flist
slot.
sigma
:the scale factor for the variance-covariance estimates
coefs
:the matrix of estimates, standard errors, etc. for the fixed-effects coefficients
vcov
:the same as vcov(object)
.
REmat
:the formatted Random-Effects matrix
AICtab
:A named vector of values of AIC, BIC, log-likelihood and deviance
signature(x = "mer")
: Extract variance and
correlation components. See VarCorr
signature(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
.
Note that AIC
and BIC
methods automatically work (via 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")
: 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(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.
mcmcsamp
for posterior MCMC sampling of a mer
fit;
simulate-mer
for simulation and parametric bootstrapping
(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)