Predictions at data locations from lattice-based non-parametric regression.

Description

Takes as input a NparRegOut object from the function createNparReg. A vector of predicted values is produced corresponding to each location in the data.

Usage

## S3 method for class 'NparRegOut'
predict(object,new.pred=NULL,...)

Arguments

Details

If new.pred is not used as an arguments, this function returns a vector of predictions at each node closest to an observations of the original point process. If you wish to make predictions at arbitrary locations, let new.pred be a 2-column matrix of those locations. Note that all predictions are actually at the nearest node to the desired locations. NOTE: Like all functions in this package, new locations are relocated to the nearest node in the region, even if they are outside the boundary. Thus you should ensure that your locations of interest are inside the boundary and that the density of nodes is high enough that the nearest node is close enough to the location you queried.

Author(s)

Ronald P. Barry rpbarry@alaska.edu

Examples

data(nparExample)
attach(nparExample)
plot.new()
#  Simulate a response variable
index1 = (grid2[,2]<0.8)|(grid2[,1]>0.6)
Z = rep(NA,length(grid2[,1]))
n1 = sum(index1)
n2 = sum(!index1)
Z[index1] = 3*grid2[index1,1] + 4 + rnorm(n1,0,sd=0.4)
Z[!index1] = -2*grid2[!index1,1] + 4 + rnorm(n2,0,sd=0.4)
#
plot(rbind(polygon2,polygon2[1,]),type="l")
points(grid2,pch=19,cex=0.5,xlim=c(-0.1,1))
text(grid2,labels=round(Z,1),pos=4,cex=0.5)
#  Following is the generation of the nonparametric
#  regression prediction surface.
nodeFillingOutput = nodeFilling(poly=polygon2,node.spacing=0.025)
plot(nodeFillingOutput)
formLatticeOutput = formLattice(nodeFillingOutput)
plot(formLatticeOutput)
NparRegOut = createNparReg(formLatticeOutput,Z,PointPattern=grid2,k=2)
plot(NparRegOut)
predict(NparRegOut)

Results