lsmeans (version 2.19)

recover.data: Support functions for creating a reference grid

Description

This documents the methods used to create a ref.grid object from a fitted model.

Usage

recover.data(object, ...)
## S3 method for class 'call':
recover.data(object, trms, na.action, 
    data = NULL, params = NULL, ...)

lsm.basis(object, trms, xlev, grid, ...)

All.vars(expr, retain = c("\\$", "\\[\\[", "\\]\\]"), ...)

Arguments

object

An object returned from a model-fitting function.

trms

The terms component of object

xlev

Named list of levels of factors in the model frame. This should not include levels of factors created in the model itself, e.g., by including a factor call in the model formula.

grid

A data.frame containing predictor values at which predictions are needed.

na.action

Integer vector of indices of observations to ignore; or NULL if none

data

Data frame. Usually, this is NULL. However, if non-null, this is used in place of the reconstructed dataset. It must have all of the predictors used in the model, and any factor levels must match those used in fitting the model.

params

Character vector giving the names of any variables in the model formula that are not predictors. An example would be a variable knots specifying the knots to use in a spline model.

expr

A formula

retain

Character vector of operators to retain (escaped as for gsub)

...

Additional arguments passed to other methods.

Value

• recover.data should return a data.frame containing all the variables in the original data that appear as predictors in the model. Several attributes need to be included as well; see the code for lsmeans:::recover.data.lm. lsm.basis should return a list with the following elements:
• X
  The matrix of linear fcns over grid, having the same number of rows as grid and the number of columns equal to the length of bhat.
• bhat
  The vector of regression coefficients for fixed effects. This should include any NAs that result from rank deficiencies.
• nbasis
  A matrix whose columns form a basis for non-estimable functions of beta, or a 1x1 matrix of NA if there is no rank deficiency.
• V
  The estimated covariance matrix of bhat.
• dffun
  A function of (k, dfargs) that returns the degrees of freedom associated with sum(k * bhat).
• dfargs
  A list containing additional arguments needed for dffun.
• All.vars is an enhancement of all.vars, whereby the operators specified in retain are left intact. Thus, All.vars(foo$y ~ bar[[2]]) returns "foo$y", "bar[[2]]", whereas all.vars returns "foo", "y", "bar"

Optional hooks

Some models may need something other than standard linear estimates and standard errors. If so, custom functions may be pointed to via the items misc$estHook, misc$vcovHook and misc$postGridHook. If just the name of the hook function is provided as a character string, then it is retrieved using get. The estHook function should have arguments (object, do.se, tol, ...) where object is the ref.grid or lsmobj object, do.se is a logical flag for whether to return the standard error, and tol is the tolerance for assessing estimability. It should return a matrix with 3 columns: the estimates, standard errors (NA when do.se==FALSE), and degrees of freedom (NA for asymptotic). The number of rows should be the same as object@linfct. The vcovHook function should have arguments (object, tol, ...) as described. It should return the covariance matrix for the estimates. Finally, postGridHook, if present, is called at the very end of ref.grid; it takes one argument, the constructed object, and should return a suitably modifiedref.grid object.

Details

To create a reference grid, the ref.grid function needs to reconstruct the data used in fitting the model, and then obtain a matrix of linear functions of the regression coefficients for a given grid of predictor values. These tasks are performed by calls to recover.data and lsm.basis respectively. To extend lsmeans's support to additional model types, one need only write S3 methods for these two functions. The existing methods serve as helpful guidance for writing new ones. Most of the work for recover.data can be done by its method for class "call", providing the terms component and na.action data as additional arguments. Writing an lsm.basis method is more involved, but the existing methods (e.g., lsmeans:::lsm.basis.lm) can serve as models. See the ``Value'' section below for details on what it needs to return. Also, certain recover.data and lsm.basis methods are exported from lsmeans, so if your object is based on another model-fitting object, it may be that all that is needed is to call one of these exported methods and perhaps make modifications to the results. Contact the developer if you need others of these exported. If the model has a multivariate response, bhat needs to be flattened into a single vector, and X and V must be constructed consistently. In models where a non-full-rank result is possible (often you can tell by seeing if there is a singular.ok argument in the model-fitting function), summary and predict check the estimability of each prediction, using the nonest.basis function in the estimability package. The models already supported are detailed in models. Some packages may provide additional lsmeans support for its object classes.

See Also

models, ref.grid, ref.grid-class

Examples

require(lsmeans)

    # Fit a 2-factor model with two empty cells
    warpsing.lm <- lm(breaks ~ wool*tension, 
        data = warpbreaks, subset = -(16:40))

    lsmeans:::recover.data.lm(warpsing.lm, data = NULL)
    grid = with(warpbreaks, 
        expand.grid(wool = levels(wool), tension = levels(tension)))
    lsmeans:::lsm.basis.lm(warpsing.lm, delete.response(terms(warpsing.lm)),
        warpsing.lm$xlevels, grid)
# Compare results of all.vars and All.vars
form = log(cows$weight) ~ factor(bulls[[3]]) * herd$breed
all.vars(form)
All.vars(form)

all.vars(form, functions = TRUE) ## same as all.names(form, unique = TRUE)
All.vars(form, functions = TRUE)