Predictions or log posterior predictive densities from a reference model

This is the predict() method for refmodel objects (returned by get_refmodel() or init_refmodel()). It offers three types of output which are all based on the reference model and new (or old) observations: Either the linear predictor on link scale, the linear predictor transformed to response scale, or the log posterior predictive density.

# S3 method for refmodel
predict(
  object,
  newdata = NULL,
  ynew = NULL,
  offsetnew = NULL,
  weightsnew = NULL,
  type = "response",
  ...
)

Arguments

object: An object of class refmodel (returned by get_refmodel() or init_refmodel()).
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 for using the original dataset. If not NULL, any NAs will trigger an error.
ynew: If not NULL, then this needs to be a vector of new (or old) response values. See also section "Value" below. In case of (i) the augmented-data projection or (ii) the latent projection with type = "response" and object$family$cats being not NULL, ynew is internally coerced to a factor (using as.factor()). The levels of this factor have to be a subset of object$family$cats (see extend_family()'s arguments augdat_y_unqs and latent_y_unqs, respectively).
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.
type: Usually only relevant if is.null(ynew), but for the latent projection, this also affects the !is.null(ynew) case (see below). The scale on which the predictions are returned, either "link" or "response" (see predict.glm() but note that predict.refmodel() does not adhere to the typical R convention of a default prediction on link scale). For both scales, the predictions are averaged across the posterior draws. In case of the latent projection, argument type is similar in spirit to argument resp_oscale from other functions: If (i) is.null(ynew), then argument type affects the predictions as described above. In that case, note that type = "link" yields the linear predictors without any modifications that may be due to the original response distribution (e.g., for a brms::cumulative() model, the ordered thresholds are not taken into account). If (ii) !is.null(ynew), then argument type also affects the scale of the log posterior predictive densities (type = "response" for the original response scale, type = "link" for the latent Gaussian scale).
...: Currently ignored.

Value

In the following, $N$, $C_{\mathrm{cat}}$, and $C_{\mathrm{lat}}$ from help topic refmodel-init-get are used. Furthermore, let $C$ denote either $C_{\mathrm{cat}}$ (if type = "response") or $C_{\mathrm{lat}}$ (if type = "link"). Then, if is.null(ynew), the returned object contains the reference model's predictions (with the scale depending on argument type) as:

a length-$N$ vector in case of (i) the traditional projection, (ii) the latent projection with type = "link", or (iii) the latent projection with type = "response" and object$family$cats being NULL;
an $N \times C$ matrix in case of (i) the augmented-data projection or (ii) the latent projection with type = "response" and object$family$cats being not NULL.

If !is.null(ynew), the returned object is a length-$N$ vector of log posterior predictive densities evaluated at ynew.

Details

Argument weightsnew is only relevant if !is.null(ynew).

In case of a multilevel reference model, group-level effects for new group levels are drawn randomly from a (multivariate) Gaussian distribution. When setting projpred.mlvl_pred_new to TRUE, all group levels from newdata (even those that already exist in the original dataset) are treated as new group levels (if is.null(newdata), all group levels from the original dataset are considered as new group levels in that case).