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.
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,...)
formula |
A model formula |
weights |
A one-sided, single term formula specifying weights |
sandwich |
|
object |
A |
data |
Data frame that must contain all variables in
|
moredata |
Additional data to add to the model |
... |
Additional arguments for future expansion |
k |
penalty per parameter for AIC |
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).
An object of class biglm
Algorithm AS274 Applied Statistics (1992) Vol.41, No. 2
lm
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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.