latticeDensity (version 1.2.7)

predict.NparRegOut: 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 NparRegOut
predict(object, new_pred = NULL, ...)

Value

Vector of predicted values.

Arguments

object

An object of type NparRegOut returned by createNparReg.

new_pred

if new_pred is left out, predictions are made at the locations of the point pattern. Otherwise, new_pred is a 2-column matrix of locations where you wish to obtain predictions.

...

Aditionally arguments affecting the predictions, of which there are none at this time.

Author

Ronald P. Barry

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.

References

Julie McIntyre, Ronald P. Barry (2018) A Lattice-Based Smoother for Regions with Irregular Boundaries and Holes. Journal of Computational and Graphical Statistics. <doi:10.1080/10618600.2017.1375935>

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(polygon2,type="n")
polygon(polygon2)
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)