RXlarlso {RXshrink} | R Documentation |
Identify whether least angle regression estimates are generalized ridge shrinkage estimates and generate TRACE displays for estimates that do correspond to ridge shrinkage factors between 0.00 and 0.99.
RXlarlso(form, data, rscale = 1, type = "lar", trace = FALSE, eps = .Machine$double.eps, omdmin = 9.9e-13, ...)
form |
A regression formula [y~x1+x2+...] suitable for use with lm(). |
data |
Data frame containing observations on all variables in the formula. |
rscale |
One of three possible choices (0, 1 or 2) for rescaling of variables as they are being "centered" to remove non-essential ill-conditioning: 0 implies no rescaling; 1 implies divide each variable by its standard error; 2 implies rescale as in option 1 but re-express answers as in option 0. |
type |
One of "lasso", "lar" or "forward.stagewise" for function lars(). Names can be abbreviated to any unique substring. Default in RXlarlso() is "lar". |
trace |
If TRUE, lars() function prints out its progress. |
eps |
The effective zero for lars(). |
omdmin |
Strictly positive minimum allowed value for one-minus-delta (default = 9.9e-013.) |
... |
Optional argument(s) passed on to the lars() function from the lars R-package. |
RXlarlso() calls the Efron/Hastie lars() function to perform Least Angle Regression on X-variables that have been centered and possibly rescaled but which may be (highly) correlated. Maximum likelihood TRACE displays paralleling those of RXridge are also computed and (optionally) plotted.
An output list object of class RXlarlso:
form |
The regression formula specified as the first argument. |
data |
Name of the data.frame object specified as the second argument. |
p |
Number of regression predictor variables. |
n |
Number of complete observations after removal of all missing values. |
r2 |
Numerical value of R-square goodness-of-fit statistic. |
s2 |
Numerical value of the residual mean square estimate of error. |
prinstat |
Listing of principal statistics. |
crlqstat |
Listing of criteria for maximum likelihood selection of path Q-shape. |
qmse |
Numerical value of Q-shape most likely to be optimal. |
qp |
Numerical value of the Q-shape actually used for shrinkage. |
coef |
Matrix of shrinkage-ridge regression coefficient estimates. |
risk |
Matrix of MSE risk estimates for fitted coefficients. |
exev |
Matrix of excess MSE eigenvalues (ordinary least squares minus ridge.) |
infd |
Matrix of direction cosines for the estimated inferior direction, if any. |
spat |
Matrix of shrinkage pattern multiplicative delta factors. |
mlik |
Listing of criteria for maximum likelihood selection of M-extent-of-shrinkage. |
sext |
Listing of summary statistics for all M-extents-of-shrinkage. |
Bob Obenchain <wizbob@att.net>
Breiman L. (1995) Better subset regression using the non-negative garrote. Technometrics 37, 373-384.
Efron B, Hastie T, Johnstone I, Tibshirani R. (2004) Least angle regression. Ann. Statis. 32, 407-499.
Obenchain RL. (2005) Shrinkage Regression: ridge, BLUP, Bayes, spline and Stein. Electronic book-in-progress (200+ pages.) http://members.iquest.net/~softrx/
Obenchain RL. (2009) RXshrink-R.PDF ../R_HOME/library/RXshrink
Tibshirani R. (1996) Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B 58, 267-288.
data(longley2) form <- GNP~GNP.deflator+Unemployed+Armed.Forces+Population+Year+Employed rxlobj <- RXlarlso(form, data=longley2) rxlobj names(rxlobj) plot(rxlobj)