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)
"update"(object, moredata,...)
"vcov"(object,...)
"coef"(object,...)
"summary"(object,...)
"AIC"(object,...,k=2)
"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
...
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
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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