bam.update {mgcv} | R Documentation |
Strictly additive models fitted by bam
can be efficiently updated as new data becomes available,
by simply updateing 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 gam object fitted by bam and representing a strictly additive model
(i.e. gaussian errors, identity link). |
data |
Extra data to augment the original data used to obtain b . Must include a column of weights if the
original fit was weighted. |
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.
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 detials set with reference to the data (e.g. default knot locations).
An object of class "gam"
as described in gamObject
.
Simon N. Wood simon.wood@r-project.org
http://www.maths.bath.ac.uk/~sw283/
library(mgcv) ## following is not *very* large, for obvious reasons... set.seed(8) dat <- gamSim(1,n=5000,dist="normal",scale=5) dat[c(50,13,3000,3005,3100),]<- NA dat1 <- dat[4001:5000,] dat0 <- dat[1:4000,] 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 summary(b1);summary(b2);summary(b3)