biglm  R Documentation 
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 onesided, 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 datadependent 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)
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