# bigglm: Bounded memory linear regression In biglm: Bounded Memory Linear and Generalized Linear Models

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```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,quiet=FALSE,...) ## S3 method for class 'RODBC' bigglm(formula, data, family=gaussian(), tablename, ..., chunksize=5000) ## S4 method for signature 'ANY,DBIConnection' bigglm(formula, data, family=gaussian(), tablename, ..., chunksize=5000) ## S3 method for class 'bigglm' vcov(object,dispersion=NULL, ...) ## S3 method for class 'bigglm' deviance(object,...) ## S3 method for class 'bigglm' family(object,...) ## S3 method for class 'bigglm' AIC(object,...,k=2) ```

## 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 `tablename` For the `SQLiteConnection` method, the name of a SQL table, or a string specifying a join or nested select `k` penalty per parameter for AIC `quiet` When `FALSE`, warn if the fit did not converge `...` Additional arguments

## Details

The `data` argument may be a function, a data frame, or a `SQLiteConnection` or `RODBC` connection object.

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. When`reset=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). Offsets are allowed (since version 0.8).

The `SQLiteConnection` and `RODBC` methods loads only the variables needed for the model, not the whole table. The code in the `SQLiteConnection` method should work for other `DBI` connections, but I do not have any of these to check it with.

## Value

An object of class `bigglm`

## References

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

## See Also

`biglm`, glm

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40``` ```data(trees) ff<-log(Volume)~log(Girth)+log(Height) a <- bigglm(ff,data=trees, chunksize=10, sandwich=TRUE) summary(a) gg<-log(Volume)~log(Girth)+log(Height)+offset(2*log(Girth)+log(Height)) b <- bigglm(gg,data=trees, chunksize=10, sandwich=TRUE) summary(b) ## 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) ```

biglm documentation built on Nov. 27, 2020, 5:08 p.m.