predict.spa: Inductive Predict Procedure For SPA

In spa: Implements The Sequential Predictions Algorithm

Description

This implements the inductive prediction for an spa object. The impact of the observation(s) on the existing labeled and unlabeled data is ignored.

For transductive predicton use update function.

Usage

## S3 method for class 'spa'
predict(object,xnew,gnew,type=c("vector","probs","both"),...)

Arguments

object	an existing object of type spa
xnew	the new xdata to predict, of diminsion ngXdim(object[2]) (only applicable if object was called with x data)
gnew	the new graph links. The diminsion on this matrix is ngX dim(object)[1] with ng as the number of observations to predict.
type	the type of prediction to return.
...	additional arguments passed into the function.

Author(s)

Mark Culp

References

M. Culp (2011). spa: A Semi-Supervised R Package for Semi-Parametric Graph-Based Estimation. Journal of Statistical Software, 40(10), 1-29. URL http://www.jstatsoft.org/v40/i10/.

Examples

## Use simulated example (two moon) and generate a contour plot of the border
set.seed(100)
dat=spa.sim(type="moon") 

L=which(!is.na(dat$y))
U=which(is.na(dat$y))
Dij=as.matrix(daisy(dat[,-1]))

##Use spa to train with a supervised/transductive kernel smoother
gsup<-spa(dat$y[L],graph=Dij[L,L],control=spa.control(gcv="lGCV"))
gsemi<-spa(dat$y,graph=Dij,control=spa.control(gcv="aGCV"))

##Use predict to define a grid for contour plot
gsize=100
a1<-seq(min(dat$x1),max(dat$x1),length=gsize)
a2<-seq(min(dat$x2),max(dat$x2),length=gsize)

zsup=matrix(0,gsize,gsize)
for(i in 1:gsize){
  val=sqrt(t(sapply(1:gsize,function(j)(dat$x1[L]-a1[i])^2+(dat$x2[L]-a2[j])^2)))
  zsup[i,]=predict(gsup,gnew=val)  ##supervised prediction with k-smoother
}
zsemi=matrix(0,gsize,gsize)
for(i in 1:gsize){
  val=sqrt(t(sapply(1:gsize,function(j)(dat$x1-a1[i])^2+(dat$x2-a2[j])^2)))
  zsemi[i,]=predict(gsemi,gnew=val) ##inductive prediction with transductive rule
}

## Plot results
gr=c(2,4)
plot(0,1,cex=1.5,xlab="x1",ylab="x2",type="n",
     xlim=c(-0.5,6.5),ylim=c(-6.5,3.5))
gr=gray(c(.7,.9))
points(dat$x1,dat$x2,pch=16,col=8,cex=1)
points(dat$x1[L],dat$x2[L],pch=16,col=gr[dat$y[L]+1],cex=2)
points(dat$x1[L],dat$x2[L],pch=1,col=1,cex=2)
contour(a1,a2,zsup, levels=.5,add=TRUE,lty=1,method="edge",drawlabels=FALSE,lwd=2,col=gray(0.6))
contour(a1,a2,zsemi,levels=.5,add=TRUE,lty=1,method="edge",drawlabels=FALSE,lwd=2,col=1)
exp1=expression(hat(Y)*"="*hat(f)(G[X]))
exp2=expression(Y[L]*"="*f(X[L])+epsilon)
legend(4.25,par()$usr[4],c(exp1,exp2),cex=1.24,fill=gray(c(0,0.6)),bg="white")
legend(4.25,par()$usr[4],c(exp1,exp2),cex=1.24,fill=gray(c(0,0.6)))

title(switch(attr(dat,"type"),moon="Two Moon Simulated Data",supervised="Supervised Border Simulated set"))
title("\n\nInductive Prediction with a Transductive Model",cex.main=1.1)
box()

spa documentation built on May 30, 2017, 1:27 a.m.