bigglm {biglm}R Documentation

Bounded memory linear regression

Description

bigglm creates a generalized linear model object that uses only p^2 memory for p variables.

Usage

bigglm(formula, data, family=gaussian(),...)
## S3 method for class 'data.frame':
bigglm(formula, data,...,chunksize=5000)
## S3 method for class 'function':
bigglm(formula, data, family=gaussian(),
     weights=NULL, sandwich=FALSE, maxit=8, tolerance=1e-7,
     start=NULL,...)
## S3 method for class 'bigglm':
vcov(object,dispersion=NULL, ...)

Arguments

formula A model formula
data See Details below. Method dispatch is on this argument
family A glm family object
chunksize Size of chunks for processng the data frame
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)
maxit Maximum number of Fisher scoring iterations
tolerance Tolerance for change in coefficient (as multiple of standard error)
start Optional starting values for coefficients. If NULL, maxit should be at least 2 as some quantities will not be computed on the first iteration
object A bigglm object
dispersion Dispersion parameter, or NULL to estimate
... Additional arguments

Details

The data argument may be a function or a data frame.

When it is a function the function must take a single argument reset. When this argument is FALSE it returns a data frame with the next chunk of data or NULL if no more data are available. Whenreset=TRUE it indicates that the data should be reread from the beginning by subsequent calls. The chunks need not be the same size or in the same order when the data are reread, but the same data must be provided in total. The bigglm.data.frame method gives an example of how such a function might be written, another is in the Examples below.

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).

Value

An object of class biglm

References

Algorithm AS274 Applied Statistics (1992) Vol.41, No. 2

See Also

biglm, glm

Examples

data(trees)
ff<-log(Volume)~log(Girth)+log(Height)
a <- bigglm(ff,data=trees, chunksize=10, sandwich=TRUE)
summary(a)

## Not run: 
## requires internet access
make.data<-function(urlname, chunksize,...){
      conn<-NULL
     function(reset=FALSE){
     if(reset){
       if(!is.null(conn)) close(conn)
       conn<<-url(urlname,open="r")
     } else{
       rval<-read.table(conn, nrows=chunksize,...)
       if (nrow(rval)==0) {
            close(conn)
            conn<<-NULL
            rval<-NULL
       }
       return(rval)
     }
  }
}

airpoll<-make.data("http://faculty.washington.edu/tlumley/NO2.dat",
        chunksize=150,
        col.names=c("logno2","logcars","temp","windsp",
                    "tempgrad","winddir","hour","day"))

b<-bigglm(exp(logno2)~logcars+temp+windsp,
         data=airpoll, family=Gamma(log),
         start=c(2,0,0,0),maxit=10)
summary(b)         
## End(Not run)

[Package biglm version 0.4 Index]