fastliu (version 1.0)

infoliu: Information Criteria for Liu Regression

Description

For each value of lambda, infoliu calculates the values of the AIC and BIC model selection criteria. Model selection criteria are based on the degrees of the freedom, \(\texttt{df}=\mathrm{trace}\left(\mathbf{H}_\lambda\right)\) of the Liu regression model where \(\mathbf{H}\) is the hat matrix of Liu regression model.

Usage

infoliu(obj)

Value

infoliu returns the matrix of information criteria for each value of the regularization parameter lambda.

Arguments

obj

A liureg object

Author

Murat Genç

References

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transaction on Automatic Control, 9(6), 716-723. tools:::Rd_expr_doi("10.1109/TAC.1974.1100705").

Liu, K. (1993). A new class of blased estimate in linear regression. Communications in Statistics-Theory and Methods, 22(2), 393-402. tools:::Rd_expr_doi("10.1080/03610929308831027").

Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461-464. tools:::Rd_expr_doi("10.1214/aos/1176344136").

See Also

predict(), summary()

Examples

data("Hitters")
Hitters <- na.omit(Hitters)
X <- model.matrix(Salary ~ ., Hitters)[, -1]
y <- Hitters$Salary
lam <- seq(0, 1, 0.01)
liu.mod <- liureg(X, y, lam)
infoliu(liu.mod)