emmGrid classThe emmGrid class encapsulates linear functions of regression
parameters, defined over a grid of predictors. This includes reference
grids and grids of marginal means thereof (aka estimated marginal means).
Objects of class `emmGrid` may be used independently of the underlying model
object. Instances are created primarily by ref_grid and
emmeans, and several related functions.
model.infolist. Contains the elements call (the call that
produced the model), terms (its terms object), and
xlev (factor-level information)
roleslist. Contains at least the elements predictors,
responses, and multresp. Each is a character vector of names
of these variables.
griddata.frame. Contains the combinations of the variables that define
the reference grid. In addition, there is an auxiliary column named
".wgt." holding the observed frequencies or weights for each factor
combination (excluding covariates). If the model has one or more
offset() calls, there is an another auxiliary column named
".offset.". Auxiliary columns are not considered part of the
reference grid. (However, any variables included in offset calls
are in the reference grid.)
levelslist. Each entry is a character vector with the distinct levels
of each variable in the reference grid. Note that grid is obtained
by applying the function expand.grid to this list
matlevslist. Like levels but has the levels of any matrices in
the original dataset. Matrix columns are always concatenated and treated as
a single variable for purposes of the reference grid
linfctmatrix. Each row consists of the linear function of the
regression coefficients for predicting its corresponding element of the
reference grid. The rows of this matrix go in one-to-one correspondence
with the rows of grid, and the columns with elements of bhat.
bhatnumeric. The regression coefficients. If there is a multivariate
response, the matrix of coefficients is flattened to a single vector, and
linfct and V redefined appropriately. Important: bhat
must include any NA values produced as a result of
collinearity in the predictors. These are taken care of later in the
estimability check.
nbasismatrix. The basis for the non-estimable functions of the
regression coefficients. Every EMM will correspond to a linear combination
of rows of linfct, and that result must be orthogonal to all the
columns of nbasis in order to be estimable. If everything is
estimable, nbasis should be a 1 x 1 matrix of NA.
Vmatrix. The symmetric variance-covariance matrix of bhat
dffunfunction having two arguments. dffun(k, dfargs) should
return the degrees of freedom for the linear function sum(k*bhat),
or NA if unavailable
dfargslist. Used to hold any additional information needed by
dffun.
misclist. Additional information used by methods. These include at
least the following: estName (the label for the estimates of linear
functions), and the default values of infer, level, and
adjust to be used in the summary.emmGrid method. Elements in
this slot may be modified if desired using the update.emmGrid
method.
post.betamatrix. A sample from the posterior distribution of the
regression coefficients, if MCMC methods were used; or a 1 x 1 matrix of
NA otherwise. When it is non-trivial, the as.mcmc.emmGrid
method returns post.beta %*% t(linfct), which is a sample from the
posterior distribution of the EMMs.
All methods for these objects are S3 methods except for show.
They include [.emmGrid, as.glht.emmGrid,
as.mcmc.emmGrid, as.mcmc.list.emmGrid (see coda),
cld.emmGrid (see multcomp),
coef.emmGrid, confint.emmGrid,
contrast.emmGrid, pairs.emmGrid,
plot.emmGrid, predict.emmGrid, print.emmGrid,
rbind.emmGrid, show.emmGrid, str.emmGrid,
summary.emmGrid, test.emmGrid,
update.emmGrid, vcov.emmGrid, and
xtable.emmGrid