bam.update {mgcv} | R Documentation

## Update a strictly additive bam model for new data.

### Description

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.

### Usage

    bam.update(b,data,chunk.size=10000)

### Arguments

`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 `weights` column if the original fit was weighted and a `AR.start` column if `AR.start` was non `NULL` in original fit.
`chunk.size` | size of subsets of data to process in one go when getting fitted values.

### Details

`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).

### Value

An object of class `"gam"` as described in `gamObject`.

### Author(s)

Simon N. Wood simon.wood@r-project.org

### References

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

### See Also

`mgcv-package`, `bam`

### Examples

    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),]
    method <- "ML"

    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,rho=0.7)

    b1 <- bam.update(b,dat1)

    summary(b1);summary(b2);summary(b3)

[Package mgcv version 1.8-12 Index]