predict.ps2dnormal {JOPS}

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 class 'ps2dnormal'
predict(object, ..., XY)

Arguments

object

an object using ps2DNormal.

...

other parameters.

XY

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

Value

pred

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

Author(s)

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, ])