biglm: Bounded memory linear regression

In biglm: Bounded Memory Linear and Generalized Linear Models

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

See Also

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)

biglm documentation built on June 22, 2024, 9:43 a.m.