JOPS (version 0.1.19)

predict.ps2dnormal: Predict function for ps2DNormal

Description

Prediction function which returns linear predictions at arbitrary (x, y) data locations (using ps2DNormal with class ps2dnormal).

Usage

# S3 method for ps2dnormal
predict(object, ..., XY)

Value

pred

the estimated mean at (x, y) locations, in XY.

Arguments

object

an object using ps2DNormal.

...

other parameters.

XY

a matrix of arbitrary (x, y) locations for desired prediction.

Author

Paul Eilers and Brian Marx

References

Eilers, P.H.C., Marx, B.D., and Durban, M. (2015). Twenty years of P-splines, SORT, 39(2): 149-186.

Eilers, P.H.C. and Marx, B.D. (2021). Practical Smoothing, The Joys of P-splines. Cambridge University Press.

Examples

library(SemiPar)
library(fields)
library(spam)
library(JOPS)

# Get the data
data(ethanol)
x <- ethanol$C
y <- ethanol$E
z <- ethanol$NOx

# Set parameters for domain
xlo <- 7
xhi <- 19
ylo <- 0.5
yhi <- 1.25

# Set P-spline parameters, fit and compute surface
xpars <- c(xlo, xhi, 10, 3, 0.01, 1)
ypars <- c(ylo, yhi, 10, 3, 0.1, 1)
Pars1 <- rbind(xpars, ypars)
fit <- ps2DNormal(cbind(x, y, z), Pars = Pars1)
predict(fit, XY = cbind(x, y)[1:5, ])