biglars.fit: Least-Angle, Lasso and Stepwise Regression
In biglars: Scalable Least-Angle Regression and Lasso

Description Usage Arguments Details Value References See Also Examples

Least-angle, lasso and stepwise methods for linear regression.

1 2	biglars.fit(x, y, type = "lasso", removeColumns = TRUE, eps = sqrt(.Machine$double.eps), blockSize = NULL, maxStages = NULL)

`x`	A matrix or `ff` of numeric predictors. The number of columns should not be larger than the number of rows.
`y`	A numeric response vector or 1-column `ff`.
`type`	The type of regression to be performed. The usual choices are lasso lasso method (L1 penalty) lar least-angle regression stepwise forward stepwise regression but see the details section below for other possibilities. Default is `"lasso"`.
`removeColumns`	A logical scalar indicating whether columns with small variance should be removed from consideration as predictors; default ‘TRUE’.
`eps`	Numerical tolerance used for assessment of sign, equality, rank determination, column removal, etc. The default is the square root of `.Machine\$double.eps`, the relative machine precision.
`blockSize`	If `NULL`, the block size is determined using methods of class `ff`. Otherwise the passed value is used.
`maxStages`	The maximum number of stages allowed in the algorithm. This argument applies only to the `lasso` option. The defaults is `2p` for `lasso`, where `p` if the number of predictors.

An intercept is always included in the regression. This function calls other routines to do the core calculations, one of biglars.fit.lasso, biglars.fit.lar, or biglars.fit.stepwise. These functions are associated with Fraley et~al. (2007) and will not be undergoing further development except for things like bug fixes. For ongoing development of least-angle regression, see the glars library.

A list with the following elements:

`coef`	An array of regression coefficients for each stage.
`moves`	Any array describing variables added or removed at each stage.
`RSS`	Residual sum of squares.

B. Efron, T. Hastie, I. Johnstone and R. Tibshirani (2004), "Least Angle Regression" (with discussion), Annals of Statistics 32, 407-499.

C. Fraley and T. Hesterberg (2007), " Least-Angle Regression for Large Datasets", Technical Report, Insightful Corporation.

qrBlockApply

data(diabetes)

larFit <- biglars.fit(diabetes$x, diabetes$y, type = "lar")
larFitBlocked <- biglars.fit(diabetes$x, diabetes$y, type = "lar",
                           blockSize = 50)

lassoFit <- biglars.fit(diabetes$x, diabetes$y, type = "lasso")
lassoFitBlocked <- biglars.fit(diabetes$x, diabetes$y, type = "lasso",
                              blockSize = 34)