R: Prediction matrix for soap film smooth

Predict.matrix.soap.film {mgcv} | R Documentation

## Prediction matrix for soap film smooth

### Description

Creates a prediction matrix for a soap film smooth object, mapping the coefficients of the smooth to the linear predictor component for the smooth. This is the `Predict.matrix` method function required by `gam`.

### Usage

    ## S3 method for class 'soap.film'
    Predict.matrix(object,data)
    ## S3 method for class 'sw'
    Predict.matrix(object,data)
    ## S3 method for class 'sf'
    Predict.matrix(object,data)

### Arguments

`object` | A class `"soap.film"`, `"sf"` or `"sw"` object.
`data` | A list list or data frame containing the arguments of the smooth at which predictions are required.

### Details

The smooth object will be largely what is returned from `smooth.construct.so.smooth.spec`, although elements `X` and `S` are not needed, and need not be present, of course.

### Value

A matrix. This may have an `"offset"` attribute corresponding to the contribution from any known boundary conditions on the smooth.

### Author(s)

Simon N. Wood s.wood@bath.ac.uk

### References

http://www.maths.bris.ac.uk/~sw15190/

### See Also

`smooth.construct.so.smooth.spec`

### Examples

    ## This is a lower level example. The basis and
    ## penalties are obtained explicitly
    ## and `magic' is used as the fitting routine...

    require(mgcv)
    set.seed(66)

    ## create a boundary...
    fsb <- list(fs.boundary())

    ## create some internal knots...
    knots <- data.frame(x=rep(seq(-.5,3,by=.5),4),
                        y=rep(c(-.6,-.3,.3,.6),rep(8,4)))

    ## Simulate some fitting data, inside boundary...
    n<-1000
    x <- runif(n)*5-1;y<-runif(n)*2-1
    z <- fs.test(x,y,b=1)
    ind <- inSide(fsb,x,y) ## remove outsiders
    z <- z[ind];x <- x[ind]; y <- y[ind]
    n <- length(z)
    z <- z + rnorm(n)*.3 ## add noise

    ## plot boundary with knot and data locations
    plot(fsb[[1]]$x,fsb[[1]]$y,type="l");points(knots$x,knots$y,pch=20,col=2)
    points(x,y,pch=".",col=3);

    ## set up the basis and penalties...
    sob <- smooth.construct2(s(x,y,bs="so",k=40,xt=list(bnd=fsb,nmax=100)),
                  data=data.frame(x=x,y=y),knots=knots)
    ## ... model matrix is element `X' of sob, penalties matrices
    ## are in list element `S'.

    ## fit using `magic'
    um <- magic(z,sob$X,sp=c(-1,-1),sob$S,off=c(1,1))
    beta <- um$b

    ## produce plots...
    par(mfrow=c(2,2),mar=c(4,4,1,1))
    m<-100;n<-50
    xm <- seq(-1,3.5,length=m);yn<-seq(-1,1,length=n)
    xx <- rep(xm,n);yy<-rep(yn,rep(m,n))

    ## plot truth...
    tru <- matrix(fs.test(xx,yy),m,n) ## truth
    image(xm,yn,tru,col=heat.colors(100),xlab="x",ylab="y")
    lines(fsb[[1]]$x,fsb[[1]]$y,lwd=3)
    contour(xm,yn,tru,levels=seq(-5,5,by=.25),add=TRUE)

    ## Plot soap, by first predicting on a fine grid...

    ## First get prediction matrix...
    X <- Predict.matrix2(sob,data=list(x=xx,y=yy))

    ## Now the predictions...
    fv <- X%*%beta

    ## Plot the estimated function...
    image(xm,yn,matrix(fv,m,n),col=heat.colors(100),xlab="x",ylab="y")
    lines(fsb[[1]]$x,fsb[[1]]$y,lwd=3)
    points(x,y,pch=".")
    contour(xm,yn,matrix(fv,m,n),levels=seq(-5,5,by=.25),add=TRUE)

    ## Plot TPRS...
    b <- gam(z~s(x,y,k=100))
    fv.gam <- predict(b,newdata=data.frame(x=xx,y=yy))
    names(sob$sd$bnd[[1]]) <- c("xx","yy","d")
    ind <- inSide(sob$sd$bnd,xx,yy)
    fv.gam[!ind]<-NA
    image(xm,yn,matrix(fv.gam,m,n),col=heat.colors(100),xlab="x",ylab="y")
    lines(fsb[[1]]$x,fsb[[1]]$y,lwd=3)
    points(x,y,pch=".")
    contour(xm,yn,matrix(fv.gam,m,n),levels=seq(-5,5,by=.25),add=TRUE)

[Package mgcv version 1.8-17 Index]