Takes a fitted
pffr-object produced by
pffr() and produces
predictions given a new set of values for the model covariates or the original
values used for the model fit. Predictions can be accompanied by standard errors,
based on the posterior distribution of the model coefficients. This is a wrapper
# S3 method for pffr predict(object, newdata, reformat = TRUE, type = "link", se.fit = FALSE, ...)
A named list (or a
data.frame) containing the values of the
model covariates at which predictions are required.
newdata is provided then predictions corresponding to the original data
are returned. If
newdata is provided then it must contain all the variables needed
for prediction, in the format supplied to
pffr, i.e., functional predictors must be
supplied as matrices with each row corresponding to one observed function.
See Details for more on index variables and prediction for models fit on
irregular or sparse data.
logical, defaults to TRUE. Should predictions be returned in matrix form (default) or
in the long vector shape returned by
predict.gam() for details.
type == "lpmatrix" will force
reformat to FALSE.
additional arguments passed on to
type == "lpmatrix", the design matrix for the supplied covariate values in long format.
se == TRUE, a list with entries
se.fit containing fits and standard errors, respectively.
type == "terms" or
"iterms" each of these lists is a list of matrices of the same dimension as the response for
containing the linear predictor and its se for each term.
Index variables (i.e., evaluation points) for the functional covariates are reused
from the fitted model object and cannot be supplied with
Prediction is always for the entire index range of the responses as defined
in the original fit. If the original fit was performed on sparse or irregular,
non-gridded response data supplied via
newdata was supplied, this function will
simply return fitted values for the original evaluation points of the response (in list form).
If the original fit was performed on sparse or irregular data and
supplied, the function will return predictions on the grid of evaluation points given in