rlars: Robust least angle regression In robustHD: Robust Methods for High-Dimensional Data

Description

Robustly sequence candidate predictors according to their predictive content and find the optimal model along the sequence.

Usage

 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 rlars(x, ...) ## S3 method for class 'formula' rlars(formula, data, ...) ## Default S3 method: rlars( x, y, sMax = NA, centerFun = median, scaleFun = mad, winsorize = FALSE, const = 2, prob = 0.95, fit = TRUE, s = c(0, sMax), regFun = lmrob, regArgs = list(), crit = c("BIC", "PE"), splits = foldControl(), cost = rtmspe, costArgs = list(), selectBest = c("hastie", "min"), seFactor = 1, ncores = 1, cl = NULL, seed = NULL, model = TRUE, tol = .Machine\$double.eps^0.5, ... )

Value

If fit is FALSE, an integer vector containing the indices of the sequenced predictors.

Else if crit is "PE", an object of class "perrySeqModel" (inheriting from class "perrySelect", see perrySelect). It contains information on the prediction error criterion, and includes the final model as component finalModel.

Otherwise an object of class "rlars" (inheriting from class "seqModel") with the following components:

active

an integer vector containing the indices of the sequenced predictors.

s

an integer vector containing the steps for which submodels along the sequence have been computed.

coefficients

a numeric matrix in which each column contains the regression coefficients of the corresponding submodel along the sequence.

fitted.values

a numeric matrix in which each column contains the fitted values of the corresponding submodel along the sequence.

residuals

a numeric matrix in which each column contains the residuals of the corresponding submodel along the sequence.

df

an integer vector containing the degrees of freedom of the submodels along the sequence (i.e., the number of estimated coefficients).

robust

a logical indicating whether a robust fit was computed (TRUE for "rlars" models).

scale

a numeric vector giving the robust residual scale estimates for the submodels along the sequence.

crit

an object of class "bicSelect" containing the BIC values and indicating the final model (only returned if argument crit is "BIC" and argument s indicates more than one step along the sequence).

muX

a numeric vector containing the center estimates of the predictors.

sigmaX

a numeric vector containing the scale estimates of the predictors.

muY

numeric; the center estimate of the response.

sigmaY

numeric; the scale estimate of the response.

x

the matrix of candidate predictors (if model is TRUE).

y

the response (if model is TRUE).

w

a numeric vector giving the data cleaning weights (if winsorize is TRUE).

call

the matched function call.

Author(s)

Andreas Alfons, based on code by Jafar A. Khan, Stefan Van Aelst and Ruben H. Zamar

References

Khan, J.A., Van Aelst, S. and Zamar, R.H. (2007) Robust linear model selection based on least angle regression. Journal of the American Statistical Association, 102(480), 1289–1299.