refund (version 0.1-5)

predict.pffr: Prediction for penalized function-on-function regression

Description

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 function for predict.gam()

Usage

## S3 method for class 'pffr':
predict(object, newdata, reformat = TRUE,
  type = "link", se.fit = FALSE, terms = NULL, ...)

Arguments

object

a fitted pffr-object

newdata

A named list containing the values of the model covariates at which predictions are required. If this is not provided then predictions corresponding to the original data are returned. If newdata is provided then it should contain

reformat

logical, defaults to TRUE. Should predictions be returned in matrix form (default) or in the long vector shape returned by predict.gam()?

type

see predict.gam() for details. Note that type == "lpmatrix" will force reformat to FALSE.

se.fit

see predict.gam()

terms

If type=="terms" or "iterms" then only results for the terms given in this array will be returned. Note that these are the term-labels used in the gam-fit, not those in the original pffr-model specificati

...

additional arguments passed on to predict.gam()

Value

• If 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.

See Also

predict.gam()