predict.lfreg: Predict LFREG model

In FREG: Functional Regression Models

Predict LFREG model

Description

Prediction of LFREG model

Usage

## S3 method for class 'lfreg'
predict(object, ..., newdata = NULL, type = c("probabilities", "labels"))

Arguments

object	LFREG model for which predictions are computed
...	additional arguments relevant for the generic method
newdata	an optional argument. Newdata should be organized as a list. The elements of the list are covariates from LFREG model, respectively. No data transformation is needed. Thus, functional covariates are entered in the list newdata in their raw form. The predict.lfreg function will take care of the transformation of such covariates into the functional form of their equivalents from LFREG model.
type	c("probabilities", "labels")

Value

predictions of dependent variable y

Examples

library(fda)
precipitation_data = CanadianWeather$daily[1:334,,"Precipitation.mm"]
annualprec = apply(precipitation_data,2,sum) # without December
y = ifelse(annualprec<mean(annualprec), 0, 1)
y = as.factor(y)
x = CanadianWeather$daily[1:334,,"Temperature.C"]
xbasis = create.fourier.basis(c(1,334),5) # 5 basis functions
xfd = smooth.basis(c(1:334),x,xbasis)$fd
bbasis = create.fourier.basis(c(0,334),5)
betalist = list(bbasis)
formula = y ~ xfd
lfreg.model = lfreg(formula, betalist = betalist)
# Prediction on new data
newdata = list(CanadianWeather$dailyAv[1:365,,1])
# newdata = list(xfd_1, latitude, longitude)
yhat = predict(lfreg.model, newdata = newdata, type = "labels")

FREG documentation built on May 9, 2022, 5:07 p.m.