plot.biasliu {liureg}

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 d.

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 d.

...

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 d 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 d.

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)