robflreg (version 1.3)

predict_fpqr: Prediction for a function-on-function linear quantile regression model based on functional partial quantile regression

Description

This function is used to make prediction for a new set of functional predictors based upon a fitted function-on-function linear quantile regression model in the output of fpqr.

Usage

predict_fpqr(object, xnew)

Value

A matrix of predicted values of the functional response variable for the given set of new functional predictor xnew.

Arguments

object

An output object obtained from fpqr.

xnew

A matricx consisting of the new observations of functional predictor. The argument xnew must have the same length and the same structure as the input x of fpqr.

Author

Muge Mutis, Ufuk Beyaztas, Filiz Karaman, and Han Lin Shang

Examples

# \donttest{
  gpx <- (1:25)/25
  gpy <- (1:30)/30
  data <- fpqr_dgp(n = 10, gpy = gpy, gpx = gpx, err.dist = "normal")
  y <- data$y
  x <- data$x
  data.test <- fpqr_dgp(n = 10, gpy = gpy, gpx = gpx, err.dist = "normal")
  x.test <- data.test$x
  y.test <- data.test$y.true
  fpqr.model.li <- fpqr(y=y, x=x, tau = 0.5, gpx = gpx, gpy = gpy, qc.type = "li")
  predictions <- predict_fpqr(object = fpqr.model.li, xnew = x.test)
# }