plot.biasliu {liureg} | R Documentation |
Bias Variance and MSE Trade-off Plot
Description
Trade-off between bias, variance and MSE of the Liu regression against vector or scalar value of biasing parameter .
Usage
## S3 method for class 'biasliu'
plot(x, abline = TRUE, ...)
Arguments
x |
An object of "liu". |
abline |
Horizontal and vertical lines show the minimum value of the Liu MSE at certain value of biasing parameter |
... |
No presently used in this implementation. |
Details
The effect of multicollinearity on the coefficients can be identified using different graphical display. One of them is plot of bias, variance and MSE. Addition of biasing parameter lead to a substantial impact on variance and MSE of Liu regression estimates. Therefore, a trade-off is made between bias and variance to have an acceptable MSE. The
plot.biasliu
can be helpful for selection of optimal value of biasing parameter .
Value
Nothing returned
Author(s)
Muhammad Imdad Ullah, Muhammad Aslam
References
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.
Kalivas, J. H., and Palmer, J. (2014). Characterizing multivariate calibration tradeoff (bias, variance, selectivity, and sensitivity) to select model tuning parameters. Journal of Chemometrics, 28(5), 347–357. https://doi.org/10.1002/cem.2555.
See Also
Liu model fitting liu
, Liu residuals residuals.liu
, Liu PRESS press.liu
, Testing of Liu Coefficients summary.liu
Examples
mod<-liu(y~., as.data.frame(Hald), d = seq(-5, 5, 0.1))
## for indication of vertical line (biasing parameter d) and
## horizontal line (minimum Liu MSE value corresponding to vertical line)
plot.biasliu(mod)
## without horizontal and vertical line
plot.biasliu(mod, abline = FALSE)