# biglm

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##### Bounded memory linear regression

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.

Keywords
regression
##### Usage
biglm(formula, data, weights=NULL, sandwich=FALSE)
# S3 method for biglm
update(object, moredata,...)
# S3 method for biglm
vcov(object,...)
# S3 method for biglm
coef(object,...)
# S3 method for biglm
summary(object,...)
# S3 method for biglm
AIC(object,...,k=2)
# S3 method for 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

##### Aliases
• biglm
• update.biglm
• coef.biglm
• vcov.biglm
• print.biglm
• summary.biglm
• print.summary.biglm
• AIC.biglm
• deviance.biglm
##### Examples
# NOT RUN {
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)
# }

Documentation reproduced from package biglm, version 0.9-1, License: GPL

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