biglm {biglmm} R Documentation

Bounded memory linear regression

Description

`biglm` creates a linear model object that uses only `p^2` memory for `p` variables. It can be updated with more data using `update`. This allows linear regression on data sets larger than memory.

Usage

```biglm(formula, data, weights=NULL, sandwich=FALSE)
## S3 method for class 'biglm'
update(object, moredata,...)
## S3 method for class 'biglm'
vcov(object,...)
## S3 method for class 'biglm'
coef(object,...)
## S3 method for class 'biglm'
summary(object,...)
## S3 method for class 'biglm'
AIC(object,...,k=2)
## S3 method for class 'biglm'
deviance(object,...)
```

Arguments

 `formula` A model formula `weights` A one-sided, single term formula specifying weights `sandwich` `TRUE` to compute the Huber/White sandwich covariance matrix (uses `p^4` memory rather than `p^2`) `object` A `biglm` object `data` Data frame that must contain all variables in `formula` and `weights` `moredata` Additional data to add to the model `...` Additional arguments for future expansion `k` penalty per parameter for AIC

Details

The model formula must not contain any data-dependent terms, as these will not be consistent when updated. Factors are permitted, but the levels of the factor must be the same across all data chunks (empty factor levels are ok). Offsets are allowed (since version 0.8).

Value

An object of class `biglm`

References

Algorithm AS274 Applied Statistics (1992) Vol.41, No. 2

lm

Examples

```data(trees)
ff<-log(Volume)~log(Girth)+log(Height)

chunk1<-trees[1:10,]
chunk2<-trees[11:20,]
chunk3<-trees[21:31,]

a <- biglm(ff,chunk1)
a <- update(a,chunk2)
a <- update(a,chunk3)

summary(a)
deviance(a)
AIC(a)
```

[Package biglmm version 0.9-2 Index]