GPFDA (version 1.1)

fdapred: Predict using result from learning the training data.

Description

Use fuctional data analysis to do the prediction using result from learning training data.

Usage

fdapred(yregfd, sigv2fd, data, TrainData)

Arguments

yregfd

Object from "fdatrain".

sigv2fd

Object from "fdatrain".

data

Test data. Must be a object from "wrap" or a data.table with certain format.

TrainData

Train data. Must be a object from "wrap" or a data.table with certain format.

Value

• A list of
• ypred
  The matrix of predicted value and confidence interval
• rcc
  Rooted mean squared error and correlation of predicted value and test data
• time
  The time scale that the curves lie on
• s2
  covariance structure

References

Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.

Examples

library(GPFDA)

traindata=vector('list',50)
for(i in 1:50) traindata[[i]]=i
n=500
traindata=lapply(traindata,function(i) {
  x=seq(-3,3,len=n)
  y=sin(x^2)-x+0.2*rnorm(n,runif(1,-3,3),runif(1,0.5,3))
  x1=0.5*x^3+exp(x)+rnorm(n,runif(1,-3,3),runif(1,0.5,5))
  x2=cos(x^3)+0.2*rnorm(n,runif(1,-3,3),runif(1,0.5,5))
  mat=cbind(x,x1,x2,y)
  colnames(mat)=c('time','x1','x2','y')
  scale=t(c(2*(mean(y)>0.25)-1,(var(y)>3.6)*2-1,(sd(y)-sd(x)>1.4)*2-1))
  i=list(mat,scale)
})

n=300 #test input
x=seq(-3,3,len=n)
y=sin(x^2)-x+0.2*rnorm(n,runif(1,-3,3),runif(1,0.5,3))
x1=0.5*x^3+exp(x)+rnorm(n,runif(1,-3,3),runif(1,0.5,5))
x2=cos(x^3)+0.2*rnorm(n,runif(1,-3,3),runif(1,0.5,5))
mat=cbind(x,x1,x2,y)
colnames(mat)=c('time','x1','x2','y')
scale=t(c(2*(mean(y)>0.25)-1,(var(y)>3.6)*2-1,(sd(y)-sd(x)>1.4)*2-1))
# testdata[[1]]=vector('list',3)
n=100 # test new points
xt=seq(1,3,len=n)
yt=sin(xt^2)-xt+0.2*rnorm(n,runif(1,-3,3),runif(1,0.5,3))
xt1=0.5*xt^3+exp(xt)+rnorm(n,runif(1,-3,3),runif(1,0.5,5))
xt2=cos(xt^3)+0.2*rnorm(n,runif(1,-3,3),runif(1,0.5,5))
mat_t=cbind(xt,xt1,xt2,yt)
colnames(mat_t)=c('time','xt1','xt2','y')
td=list(mat,scale,mat_t)


trdata=wrap(functional=lapply(traindata,function(i)i[[1]]),
     do.call('rbind',lapply(traindata,function(i)i[[2]])),
     list='traindata',time='time',response='y')
tedata=wrap(functional=td[[1]],scale=td[[2]],testdata=td[[3]],
     list='testdata',time='time',response='y')


# fda_train=fdatrain(trdata)
# fda_result=fdapred(yregfd=fda_train$betaestlist,sigv2fd=fda_train$sigv2fd,tedata,trdata)