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).