> Is it possible to estimate prediction interval using GAM? I looked >
through ?gam,
- The easiest and most general way is by posterior simulation. Here's an
example...
## Prediction interval example for Gamma GAM
library(mgcv)
## simulate some data...
f <- function(x) (0.2 * x^11 * (10 * (1 - x))^6 + 10 *
(10 * x)^3 * (1 - x)^10)/2
x <- runif(200)
fx <- f(x)
Ey <- exp(fx);scale <- .5 ## mean and GLM scale parameter
## Note that `shape' and `scale' in `rgamma' are almost
## opposite terminology to that used with GLM/GAM...
set.seed(8)
y <- rgamma(Ey*0,shape=1/scale,scale=Ey*scale)
## fit smooth model to x, y data...
b <- gam(y~s(x,k=20),family=Gamma(link=log),method="REML")
## extract parameter estiamtes and cov matrix...
beta <- coef(b);Vb <- vcov(b)
## simulate replicate beta vectors from posterior...
Cv <- chol(Vb)
n.rep=10000;nb <- length(beta)
br <- t(Cv) %*% matrix(rnorm(n.rep*nb),nb,n.rep) + beta
## turn these into replicate linear predictors...
xp <- 0:200/200
Xp <- predict(b,newdata=data.frame(x=xp),type="lpmatrix")
lp <- Xp%*%br
fv <- exp(lp) ## ... finally, replicate expected value vectors
## now simulate from Gamma deviates with mean as in fv
## and estimated scale...
yr <- matrix(rgamma(fv*0,shape=1/b$scale,scale=fv*scale),nrow(fv),ncol(fv))
plot(rep(xp,n.rep),yr,pch=".") ## plotting replicates
points(x,y,pch=19,cex=.5) ## and original data
## compute 95% prediction interval...
PI <- apply(yr,1,quantile,prob=c(.025,0.975))
## and plot it...
lines(xp,PI[1,],col=2,lwd=2);lines(xp,PI[2,],col=2,lwd=2)
## Confidence interval for comparison...
pred <- predict(b,newdata=data.frame(x=xp),se=TRUE)
lines(xp,exp(pred$fit),col=3,lwd=2)
u.ci <- exp(pred$fit + 2*pred$se.fit)
l.ci <- exp(pred$fit - 2*pred$se.fit)
lines(xp,u.ci,col=3,lwd=2);lines(xp,l.ci,col=3,lwd=2)
On 19/04/11 10:04, yosuke kimura wrote:
Hello,
Is it possible to estimate prediction interval using GAM? I looked through
?gam, ?predict.gam etc and the mgcv.pdf Simon Wood. I found it can
calculate confidence interval but not clear if I can get it to calculate
prediction interval. I read "Inference for GAMs is difficult and somewhat
contentious." in Kuhnert and Venable An Introduction to R, and wondering why
and if that is the reason that current version of mgcv does not provide
prediction interval.
I am thinking about GAM because ggplot2 uses it to fit the data and it looks
working well. There are many other models and wondering what is can be a
good model to gives prediction interval. I am trying to estimate perimeter
of buildings from projected area of buildings. I have both measurement for
some of data but I know only area for the rest.
Thank you for your help,
Yosuke
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Simon Wood, Mathematical Science, University of Bath BA2 7AY UK
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