Description Usage Arguments Details Value Author(s) References Examples

View source: R/predict.NparRegOut.R

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

1 2 |

`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. |

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.

Vector of predicted values.

Ronald P. Barry

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>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
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)
``` |

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