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

## Description

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

## Usage

 ```1 2``` ```## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```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 June 3, 2021, 5:07 p.m.