R: Predictions from a submodel (after projection)

pred-projection {projpred}

R Documentation

Predictions from a submodel (after projection)

Description

After the projection of the reference model onto a submodel, proj_linpred() gives the linear predictor (possibly transformed to response scale) for all projected draws of such a submodel. proj_predict() draws from the predictive distribution of such a submodel. If the projection has not been performed, both functions also perform the projection. Both functions can also handle multiple submodels at once (if the input object is of class vsel).

Usage

proj_linpred(
  object,
  newdata = NULL,
  offsetnew = NULL,
  weightsnew = NULL,
  filter_nterms = NULL,
  transform = FALSE,
  integrated = FALSE,
  .seed = sample.int(.Machine$integer.max, 1),
  ...
)

proj_predict(
  object,
  newdata = NULL,
  offsetnew = NULL,
  weightsnew = NULL,
  filter_nterms = NULL,
  nresample_clusters = 1000,
  .seed = sample.int(.Machine$integer.max, 1),
  ...
)

Arguments

`object`	An object returned by `project()` or an object that can be passed to argument `object` of `project()`.
`newdata`	Passed to argument `newdata` of the reference model's `extract_model_data` function (see `init_refmodel()`). Provides the predictor (and possibly also the response) data for the new (or old) observations. May also be `NULL` (see argument `extract_model_data` of `init_refmodel()`). If not `NULL`, any `NA`s will trigger an error.
`offsetnew`	Passed to argument `orhs` of the reference model's `extract_model_data` function (see `init_refmodel()`). Used to get the offsets for the new (or old) observations.
`weightsnew`	Passed to argument `wrhs` of the reference model's `extract_model_data` function (see `init_refmodel()`). Used to get the weights for the new (or old) observations.
`filter_nterms`	Only applies if `object` is an object returned by `project()`. In that case, `filter_nterms` can be used to filter `object` for only those elements (submodels) with a number of solution terms in `filter_nterms`. Therefore, needs to be a numeric vector or `NULL`. If `NULL`, use all submodels.
`transform`	For `proj_linpred()` only. A single logical value indicating whether the linear predictor should be transformed to response scale using the inverse-link function (`TRUE`) or not (`FALSE`).
`integrated`	For `proj_linpred()` only. A single logical value indicating whether the output should be averaged across the projected posterior draws (`TRUE`) or not (`FALSE`).
`.seed`	Pseudorandom number generation (PRNG) seed by which the same results can be obtained again if needed. If `NULL`, no seed is set and therefore, the results are not reproducible. See `set.seed()` for details. Here, this seed is used for drawing new group-level effects in case of a multilevel submodel (however, not yet in case of a GAMM) and for drawing from the predictive distribution of the submodel(s) in case of `proj_predict()`. If a clustered projection was performed, then in `proj_predict()`, `.seed` is also used for drawing from the set of the projected clusters of posterior draws (see argument `nresample_clusters`).
`...`	Arguments passed to `project()` if `object` is not already an object returned by `project()`.
`nresample_clusters`	For `proj_predict()` with clustered projection only. Number of draws to return from the predictive distribution of the submodel. Not to be confused with argument `nclusters` of `project()`: `nresample_clusters` gives the number of draws (with replacement) from the set of clustered posterior draws after projection (with this set being determined by argument `nclusters` of `project()`).

Value

Let S_prj denote the number of (possibly clustered) projected posterior draws (short: the number of projected draws) and N the number of observations. Then, if the prediction is done for one submodel only (i.e., length(nterms) == 1 || !is.null(solution_terms) in the call to project()):

proj_linpred() returns a list with elements pred (predictions) and lpd (log predictive densities). Both elements are S_prj x N matrices.
proj_predict() returns an S_prj x N matrix of predictions where S_prj denotes nresample_clusters in case of clustered projection.

If the prediction is done for more than one submodel, the output from above is returned for each submodel, giving a named list with one element for each submodel (the names of this list being the numbers of solutions terms of the submodels when counting the intercept, too).

Examples

if (requireNamespace("rstanarm", quietly = TRUE)) {
  # Data:
  dat_gauss <- data.frame(y = df_gaussian$y, df_gaussian$x)

  # The "stanreg" fit which will be used as the reference model (with small
  # values for `chains` and `iter`, but only for technical reasons in this
  # example; this is not recommended in general):
  fit <- rstanarm::stan_glm(
    y ~ X1 + X2 + X3 + X4 + X5, family = gaussian(), data = dat_gauss,
    QR = TRUE, chains = 2, iter = 500, refresh = 0, seed = 9876
  )

  # Projection onto an arbitrary combination of predictor terms (with a small
  # value for `nclusters`, but only for the sake of speed in this example;
  # this is not recommended in general):
  prj <- project(fit, solution_terms = c("X1", "X3", "X5"), nclusters = 10,
                 seed = 9182)

  # Predictions (at the training points) from the submodel onto which the
  # reference model was projected:
  prjl <- proj_linpred(prj)
  prjp <- proj_predict(prj, .seed = 7364)
}

[Package projpred version 2.1.2 Index]