liu {liureg} | R Documentation |
Liu Regression and Estimator
Description
Fits a linear Liu regression model after scaling regressors and returns an object of class "liu" (by calling liuest
function), designed to be used in plotting method, testing of Liu coefficients and for computation of different Liu related statistics. The Liu biasing parameter d
can be a scalar or a vector. This new biased estimator was first proposed by Liu (1993) <doi:10.1080/03610929308831027>.
Usage
liu(formula, data, d = 1, scaling=c("centered", "sc", "scaled"), ...)
liuest(formula, data, d=1, scaling=c("centered", "sc", "scaled"), ...)
## Default S3 method:
liu(formula, data, d = 1, scaling=c("centered", "sc", "scaled"), ...)
## S3 method for class 'liu'
coef(object, ...)
## S3 method for class 'liu'
print(x, digits = max(5,getOption("digits") - 5), ...)
## S3 method for class 'liu'
fitted(object, ...)
Arguments
formula |
Standard R formula expression, that is, a symbolic representation of the model to be fitted and has form |
data |
An optional data frame containing the variables in the model. If not found in data, the variables are taken from |
d |
Liu biasing parameter (may be a vector). |
scaling |
The method to be used to scale the predictors. The scaling option |
object |
A liu object, typically generated by a call to |
x |
An object of class |
digits |
Minimum number of significant digits to be used. |
... |
Additional arguments to be passed to or from other methods. |
Details
liu
or liuest
function fits in Liu regression after scaling the regressors and centering the response. The liu
is default a function that calls liuest
for computation of Liu coefficients and returns an object of class "liu" designed to be used in plotting method, testing of Liu coefficients and for computation of different Liu related statistics. If intercept is present in the model, its coefficient is not penalized. However, intercept is estimated from the relation y=\overline{y}-\beta \overline{X}
. print.liu
tries to be smart about formatting of Liu coefficients.
Value
liu
function returns an object of class "liu" after calling list of named objects from liuest
function:
coef |
A named vector of fitted coefficients. |
call |
The matched call. |
Inter |
Was an intercept included? |
scaling |
The scaling method used. |
mf |
Actual data used. |
y |
The centered response variable. |
xs |
The scaled matrix of predictors. |
xm |
The vector of means of the predictors. |
terms |
The |
xscale |
Square root of sum of squared deviation from mean regarding the scaling option used in |
lfit |
The fitted value of Liu regression for given biasing parameter |
d |
The Liu regression biasing parameter |
Note
The function at the current form cannot handle missing values. The user has to take prior action with missing values before using this function.
Author(s)
Muhammad Imdad Ullah, Muhammad Aslam
References
Akdeniz, F. and Kaciranlar, S. (1995). On the Almost Unbiased Generalized Liu Estimators and Unbiased Estimation of the Bias and MSE. Communications in Statistics-Theory and Methods, 24, 1789–1897. http://doi.org/10.1080/03610929508831585.
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.
Liu, K. (1993). A new Class of Biased Estimate in Linear Regression. Journal of Statistical Planning and Inference, 141, 189–196. http://doi.org/10.1080/03610929308831027.
See Also
Liu model fitting liu
, Liu residuals residuals.liu
, Liu PRESS press.liu
, Testing of Liu Coefficients summary.liu
Examples
data(Hald)
mod<-liu(y~., data = as.data.frame(Hald), d = seq(0, 0.1, 0.01), scaling = "centered")
## Scaled Coefficients
mod$coef
## Re-Scaled Coefficients
coef(mod)
## Liu fitted values
fitted(mod)
## Liu predited values
predict(mod)
## Liu Residuals
residuals(mod)
## Liu trace
plot(mod)
## Liu Var-Cov matrix
vcov(mod)
## Liu biasing parameters by researchers
dest(mod)
## Liu related statistics
lstats(mod)
## list of objects from liuest function
liuest(y~., data = as.data.frame(Hald), d = seq(0, 0.1, 0.01), scaling = "centered")