bam.update {mgcv} | R Documentation |
Gaussian with identity link models fitted by bam
can be efficiently updated as new data becomes available,
by simply updating the QR decomposition on which estimation is based, and re-optimizing the smoothing parameters, starting
from the previous estimates. This routine implements this.
bam.update(b,data,chunk.size=10000)
b |
A |
data |
Extra data to augment the original data used to obtain |
chunk.size |
size of subsets of data to process in one go when getting fitted values. |
bam.update
updates the QR decomposition of the (weighted) model matrix of the GAM represented by b
to take
account of the new data. The orthogonal factor multiplied by the response vector is also updated. Given these updates the model
and smoothing parameters can be re-estimated, as if the whole dataset (original and the new data) had been fitted in one go. The
function will use the same AR1 model for the residuals as that employed in the original model fit (see rho
parameter
of bam
).
Note that there may be small numerical differences in fit between fitting the data all at once, and fitting in stages by updating, if the smoothing bases used have any of their details set with reference to the data (e.g. default knot locations).
An object of class "gam"
as described in gamObject
.
AIC computation does not currently take account of AR model, if used.
Simon N. Wood simon.wood@r-project.org
http://www.maths.bris.ac.uk/~sw15190/
library(mgcv) ## following is not *very* large, for obvious reasons... set.seed(8) n <- 5000 dat <- gamSim(1,n=n,dist="normal",scale=5) dat[c(50,13,3000,3005,3100),]<- NA dat1 <- dat[(n-999):n,] dat0 <- dat[1:(n-1000),] bs <- "ps";k <- 20 method <- "GCV.Cp" b <- bam(y ~ s(x0,bs=bs,k=k)+s(x1,bs=bs,k=k)+s(x2,bs=bs,k=k)+ s(x3,bs=bs,k=k),data=dat0,method=method) b1 <- bam.update(b,dat1) b2 <- bam.update(bam.update(b,dat1[1:500,]),dat1[501:1000,]) b3 <- bam(y ~ s(x0,bs=bs,k=k)+s(x1,bs=bs,k=k)+s(x2,bs=bs,k=k)+ s(x3,bs=bs,k=k),data=dat,method=method) b1;b2;b3 ## example with AR1 errors... e <- rnorm(n) for (i in 2:n) e[i] <- e[i-1]*.7 + e[i] dat$y <- dat$f + e*3 dat[c(50,13,3000,3005,3100),]<- NA dat1 <- dat[(n-999):n,] dat0 <- dat[1:(n-1000),] b <- bam(y ~ s(x0,bs=bs,k=k)+s(x1,bs=bs,k=k)+s(x2,bs=bs,k=k)+ s(x3,bs=bs,k=k),data=dat0,rho=0.7) b1 <- bam.update(b,dat1) summary(b1);summary(b2);summary(b3)