Estimation of varies types of estimators in the linear model
To combat multicollinearity several estimators have been introduced. By using this package some of those estimators and corresponding scalar Mean Square Error (MSE) values and Prediction Sum of Square (PRESS) values (Only for some estimators) can be found easily. In addition graphical methods are available to determine the variation of MSE values of those estimators and the variation of PRESS values of some of the estimators.
|GPL-2 | GPL-3|
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## Portland cement dataset is used. data(pcd) attach(pcd) k<-c(0:3/10) d<-c(-3:3/10) r<-c(2.1930,1.1533,0.75850) R<-c(1,0,0,0,0,1,0,0,0,0,1,0) dpn<-c(0.0439,0.0029,0.0325) delt<-c(0,0,0) aa1<-c(0.958451,1.021155,0.857821,1.040296) aa2<-c(0.345454,1.387888,0.866466,1.354454) aa3<-c(0.344841,1.344723,0.318451,1.523316) optimum(Y~X1+X2+X3+X4-1,r,R,dpn,delt,aa1,aa2,aa3,k,d,data=pcd) # Model without the intercept is considered. ## Use "press=TRUE" to get the optimum PRESS values only for some of # the estimators.