select.grpreg {grpreg} | R Documentation |
Selects a point along the regularization path of a fitted grpreg object according to the AIC, BIC, or GCV criteria.
select(obj,...) ## S3 method for class 'grpreg': select(obj,method="BIC",df="default",...)
obj |
A fitted grpreg object. |
method |
The criteria by which to select the regularization
parameter. One of "AIC" , "BIC" , or "GCV" ; default is "BIC" . |
df |
How should effective model parameters be calculated? One
of: "active" , which counts the number of nonzero
coefficients; or "default" , which uses the calculated
df returned by grpreg . Default is
"default" . |
... |
For S3 method compatibility. |
The criteria are defined as follows, where
L
is the loss function
(usually, -loglik
) and
n
is the sample size:
AIC = 2*L + 2*df
BIC = 2*L + log(n)*df
GCV= 2*L/((1-df/n)^2)
A list containing:
lambda |
The selected value of the regularization parameter,
lambda . |
beta |
The vector of coefficients at the chosen value of
lambda . |
df |
The effective number of model parameters at the chosen value
of lambda . |
IC |
A vector of the calculated model selection criteria for each point on the regularization path. |
Patrick Breheny <patrick-breheny@uiowa.edu>
Breheny, P. and Huang, J. (2008) Penalized Methods for Bi-level variable selection. Tech report No. 393, Department of Statistics and Actuarial Science, University of Iowa.http://www.stat.uiowa.edu/techrep/tr393.pdf
grpreg
data(birthwt.grpreg) Data <- list(y=birthwt.grpreg$low, X=as.matrix(birthwt.grpreg[,c(-1,-2)]), family="binomial", group=c(1,1,1,2,2,2,3,3,4,5,5,6,7,8,8,8)) fit <- grpreg(Data,"gMCP") select(fit) select(fit,method="AIC",df="active") plot(fit$lambda,select(fit)$IC,type="l")