Description Usage Arguments Details Value Author(s) References See Also Examples
This function can be used to find the Type (1) Adjusted Liu Estimated values, corresponding scalar Mean Square Error (MSE) value and Prediction Sum of Square (PRESS) value in the linear model. Further the variation of MSE and PRESS values can be shown graphically.
1 |
formula |
in this section interested model should be given. This should be given as a |
k |
a single numeric value or a vector of set of numeric values. See ‘Examples’. |
d |
a single numeric value or a vector of set of numeric values. See ‘Examples’. |
aa |
this is a set of scalars belongs to real number system. Values for “aa” should be given as a |
press |
if “press=TRUE” then all the PRESS values and its corresponding parameter values are returned. Otherwise all the scalar MSE values and its corresponding parameter values are returned. |
data |
an optional data frame, list or environment containing the variables in the model. If not found in |
na.action |
if the dataset contain |
... |
currently disregarded. |
Since formula has an implied intercept term, use either y ~ x - 1
or y ~ 0 + x
to remove the intercept.
In order to get the best results, optimal values for k
,d
and aa
should be selected.
The way of finding aa
can be determined from Rong,Jian-Ying (2010) Adjustive Liu Type Estimators in linear regression models in communication in statistics-simulation and computation, volume 39
Use matplot
so as to obtain the variation of scalar MSE values and PRESS values graphically. See ‘Examples’.
If k
and d
are single numeric values then alte1
returns the Type (1) Adjusted Liu Estimated values, standard error values, t statistic values, p value, corresponding scalar MSE value and PRESS value.
If k
and d
are vector of set of numeric values then alte1
returns the matrix of scalar MSE values and if “press=TRUE” then alte1
returns the matrix of PRESS values of Type (1) Adjusted Liu Estimator by representing k
and d
as column names and row names respectively.
P.Wijekoon, A.Dissanayake
Rong,Jian-Ying (2010) Adjustive Liu Type Estimators in linear regression models in communication in statistics-simulation and computation, volume 39 DOI:10.1080/03610918.2010.484120
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Portland cement data set is used.
data(pcd)
k<-0.1650
d<--0.1300
aa<-c(0.958451,1.021155,0.857821,1.040296)
alte1(Y~X1+X2+X3+X4-1,k,d,aa,data=pcd) # Model without the intercept is considered.
## To obtain the variation of MSE of Type (1) Adjusted Liu Estimator.
data(pcd)
k<-c(0:5/10)
d<-c(5:20/10)
aa<-c(0.958451,1.021155,0.857821,1.040296)
msemat<-alte1(Y~X1+X2+X3+X4-1,k,d,aa,data=pcd)
matplot(d,alte1(Y~X1+X2+X3+X4-1,k,d,aa,data=pcd),type="l",ylab=c("MSE"),
main=c("Plot of MSE of Type (1) Adjusted Liu Estimator"),
cex.lab=0.6,adj=1,cex.axis=0.6,cex.main=1,las=1,lty=3)
text(y=msemat[1,],x=d[1],labels=c(paste0("k=",k)),pos=4,cex=0.6)
## Use "press=TRUE" to obtain the variation of PRESS of Type (1) Adjusted Liu Estimator.
|
$`*****Type (1) Adjusted Liu Estimator*****`
Estimate Standard_error t_statistic p_value
X1 2.1015 0.1775 11.8377 0.000
X2 1.1778 0.0489 24.0657 0.000
X3 0.6504 0.1368 4.7543 0.001
X4 0.5060 0.0431 11.7481 0.000
$`****Mean Square Error value*****`
MSE
0.0545
$`*****Prediction Sum of Squares value*****`
PRESS
[1,] 94.623
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