FREG (version 1.1)

predict.lfreg: Predict LFREG model

Description

Prediction of LFREG model

Usage

# S3 method for 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

# NOT RUN {
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")

# }