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
function for predict.gam().## S3 method for class 'pffr':
predict(object, newdata, reformat = TRUE, type = "link",
se.fit = FALSE, ...)pffr-objectdata.frame) containing the values of the
model covariates at which predictions are required.
If no newdata is provided then predictions corresponding to the original data
are returned. If newdatapredict.gam()?predict.gam() for details.
Note that type == "lpmatrix" will force reformat to FALSE.predict.gam()predict.gam()type == "lpmatrix", the design matrix for the supplied covariate values in long format.
If se == TRUE, a list with entries fit and se.fit containing fits and standard errors, respectively.
If type == "terms" or "iterms" each of these lists is a list of matrices of the same dimension as the response for newdata
containing the linear predictor and its se for each term.newdata.
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 pffr's ydata-argument
and no 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 newdata was
supplied, the function will return predictions on the grid of evaluation points given in
object$pffr$yind.predict.gam()