predict.slos: Predict Method for SLoS objects

In PFLR: Estimating Penalized Functional Linear Regression

Predict Method for SLoS objects

Description

Predicted values based on objects of class "slos".

Usage

## S3 method for class 'slos'
predict(object, Xnew, ...)

Arguments

object	An object of class "slos".
Xnew	New covariate matrix for prediction, should be dense, centred.
...	Not applicable

Value

Predicted values.

Examples

library(fda)
betaind = 1
snr  = 2
nsim = 1
n    = 50
p    = 21
Y = array(NA,c(n,nsim))
X = array(NA,c(n,p,nsim))
domain = c(0,1)

M = 20
d = 3
norder   = d+1
nknots   = M+1
knots    = seq(domain[1],domain[2],length.out = nknots)
nbasis   = nknots + norder - 2
basis    = create.bspline.basis(knots,nbasis,norder)
V = eval.penalty(basis,int2Lfd(2))

extra=list(lambda=exp(seq(-18,-12, length.out = 10)),gamma=10^(-8:-6))

for(itersim in 1:nsim)
{
 dat = ngr.data.generator.bsplines(n=n,nknots=64,norder=4,p=p,domain=domain,snr=snr,betaind=betaind)
  Y[,itersim]  = dat$Y
  X[,,itersim] = dat$X
}

slosfit = SLoS(Y=Y[1:n,1],(X[1:n,,1]),M=M,d=d,domain=domain,extra=extra)

predict(slosfit,(X[1:n,,1]))

PFLR documentation built on Sept. 11, 2024, 7:21 p.m.