R: The 'emmGrid' class

emmGrid-class {emmeans} | R Documentation

## 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.

### 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.

### Methods

All methods for these objects are S3 methods except for `show`. They include `[.emmGrid`, `as.glht.emmGrid`, `as.mcmc.emmGrid`, `as.mcmc.list.emmGrid`, `cld.emmGrid`, `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`

[Package emmeans version 1.1.3 Index]