# predict.ps2dnormal: Predict function for 'ps2DNormal' In JOPS: Practical Smoothing with P-Splines

 predict.ps2dnormal R Documentation

## 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, ])
``````

JOPS documentation built on Sept. 8, 2023, 5:42 p.m.