Description Usage Arguments Details Value Author(s) References See Also Examples
The summary
method for class "liu" for scalar or vector biasing parameter d.
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object |
An "liu" object, typically generated by a call to |
x |
An object of class |
signif.stars |
logical: if |
digits |
The number of significant digits to use when printing. |
... |
Not presently used in this implementation. |
print.summary.liu
tries to be smart about formatting the coefficients, standard errors etc. and additionally gives 'significance stars' if signif.stars
is TRUE
.
The function summary
computes and returns a list of summary statistics of the fitted linear Liu regression model for scalar or vector value biasing parameter d given as argument in liu
function.
coefficients |
A p * 5 matrix with columns for the scaled estimated, descaled estimated coefficients, scaled standard error, scaled t-statistics, and corresponding p-value (two-tailed). The Intercept term is computed by the relation \hat{β}_{0d}=ybar-∑_{j=1}^p(Xbar_j \hat{β}_{jd}). The standard error of intercept term is computed as, SE(\hat{β}_{0d})=√{Var(ybar)+Xbar_j^2 diag[Cov(\hat{β}_{d})]}. |
stats |
Liu related statistics of R-squared, adjusted R-squared, F-statistics for testing of coefficients, AIC and BIC values for given biasing parameter d. |
rmse1 |
Minimum MSE value for given biasing parameter d. |
rmse2 |
Value of d at which MSE is minimum. |
Muhammad Imdad Ullah, Muhammad Aslam
Aslam, M. (2014). Using Heteroscedasticity-Consistent Standard Errors for the Linear Regression Model with Correlated Regressors. Communication in Statistics-Simulation and Computation, 43, 2353–2373. http://doi.org/10.1080/03610918.2012.750354.
Cule, E. and De lorio, M. (2012). A semi-Automatic method to guide the choice of ridge parameter in ridge regression. arXiv:1205.0686v1 [stat.AP]. https://arxiv.org/abs/1205.0686v1.
Halawa, A. And El-Bassiouni, M. (2000). Tests of Regression Coefficients Under Ridge Regression Models. Journal of Statistical Computation and Simulation, 65, 341–356. https://www.tandfonline.com/doi/abs/10.1080/00949650008812006.
Hastie, T. and Tibshirani, R. (1990). Generalized Additive Models. Chapman & Hall.
Imdad, M. U. (2017). Addressing Linear Regression Models with Correlated Regressors: Some Package Development in R (Doctoral Thesis, Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan).
Imdadullah, M., Aslam, M., and Altaf, S. (2017). liureg: A comprehensive R Package for the Liu Estimation of Linear Regression Model with Collinear Regressors. The R Journal, 9 (2), 232–247.
The Liu model fitting liu
, Liu residual residuals
, Liu predicted value predict
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