summary.slos: Summary Method for SLoS Objects

In PFLR: Estimating Penalized Functional Linear Regression

Summary Method for SLoS Objects

Description

Summarizes values of an object of class "slos".

Usage

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

Arguments

object	An object of class "slos".
...	Not applicable

Value

Prints five number summary of beta values, delta, Optgamma, and Optlambda.

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)

summary(slosfit)

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