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 newdata<predict.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 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()