statliu {fastliu} | R Documentation |
Liu Regression Statistics
Description
statliu
computes the statistics related to the Liu regression.
Usage
statliu(obj)
Arguments
obj |
An object of class |
Details
EDF (Liu, 1993; Hastie et al., 2009) | Effective degrees of freedom, for each where is the number of the observations in the design matrix and is the hat matrix of Liu regression at . |
sigma2 | Computed from the Liu regression for each . |
VAR | Variance from the Liu regression for each . |
BIAS2 | Squared-bias from the Liu regression for each . |
MSE | Mean squared error (MSE) from the Liu regression for each . |
FVal | F-statistics value from the Liu regression for each . |
GCV | Generalized cross-validation (GCV) from the Liu regression for each . The GCV is computed by where is the residual sum of squares and is the trace of the hat matrix at corresponding value of from Liu regression. |
R2 | R-squared from the Liu regression for each . |
AdjR2 | Adjusted R-squared from the Liu regression for each . |
Value
The return object is the statistics related to the Liu regression.
Author(s)
Murat Genç
References
Liu, K. (1993). A new class of blased estimate in linear regression. Communications in Statistics-Theory and Methods, 22(2), 393-402. doi:10.1080/03610929308831027.
Hastie, T., Tibshirani, R., Friedman, J. H., Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.
See Also
liureg()
, summary()
, pressliu()
, residuals()
Examples
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)
stats <- statliu(liu.mod)
print(stats)
[Package fastliu version 1.0 Index]