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emmeans (version 1.3.2)

emmGrid-class: The emmGrid class

Description

The 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.

Arguments

Slots

model.info

list. Contains the elements call (the call that produced the model), terms (its terms object), and xlev (factor-level information)

roles

list. Contains at least the elements predictors, responses, and multresp. Each is a character vector of names of these variables.

grid

data.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.)

levels

list. 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

matlevs

list. 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

linfct

matrix. 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.

bhat

numeric. 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.

nbasis

matrix. 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.

V

matrix. The symmetric variance-covariance matrix of bhat

dffun

function having two arguments. dffun(k, dfargs) should return the degrees of freedom for the linear function sum(k*bhat), or NA if unavailable

dfargs

list. Used to hold any additional information needed by dffun.

misc

list. 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.beta

matrix. 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.