R: Projection Predictive Variable Selection: Get Reference Model

get_refmodel.brmsfit {brms} | R Documentation

## Projection Predictive Variable Selection: Get Reference Model

### Description

The `get_refmodel.brmsfit` method can be used to create the reference model structure which is needed by the projpred package for performing a projection predictive variable selection. This method is called automatically when performing variable selection via `varsel` or `cv_varsel`, so you will rarely need to call it manually yourself.

### Usage

    get_refmodel.brmsfit(object, newdata = NULL, resp = NULL, cvfun = NULL, ...)

### Arguments

`object` | An object of class `brmsfit`.
`newdata` | An optional data.frame for which to evaluate predictions. If `NULL` (default), the original data of the model is used. `NA` values within factors are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding.
`resp` | Optional names of response variables. If specified, predictions are performed only for the specified response variables.
`cvfun` | Optional cross-validation function (see `get_refmodel` for details). If `NULL` (the default), `cvfun` is defined internally based on `kfold.brmsfit`.
`...` | Further arguments passed to `init_refmodel`.

### Details

Note that the `extract_model_data` function used internally by `get_refmodel.brmsfit` ignores arguments `wrhs`, `orhs`, and `extract_y`. This is relevant for `predict.refmodel`, for example.

### Value

A `refmodel` object to be used in conjunction with the projpred package.

### Examples

    ## Not run:
    # fit a simple model
    fit <- brm(count ~ zAge + zBase * Trt,
               data = epilepsy, family = poisson())
    summary(fit)

    # The following code requires the 'projpred' package to be installed:
    library(projpred)

    # perform variable selection without cross-validation
    vs <- varsel(fit)
    summary(vs)
    plot(vs)

    # perform variable selection with cross-validation
    cv_vs <- cv_varsel(fit)
    summary(cv_vs)
    plot(cv_vs)

    ## End(Not run)

[Package brms version 2.16.3 Index]