Description Usage Arguments Details Value Note Author(s) Examples

`optimum`

can be used to obtain the optimal scalar Mean Square Error (MSE) values and its corresponding parameter values (`k`

and/or `d`

) of all estimators and the optimum Prediction Sum of Square (PRESS) values and its corresponding parameter values `k`

and `d`

of some of the estimators considered in this package.

1 2 |

`formula` |
in this section interested model should be given. This should be given as a |

`r` |
is a |

`R` |
is a |

`dpn` |
dispersion matrix of vector of disturbances of linear restricted model, |

`delt` |
values of |

`aa1` |
adjusted parameters of Type (1) Adjusted Liu Estimators and that should be a set of scalars belongs to real number system. Values for “aa1” should be given as a |

`aa2` |
adjusted parameters of Type (2) Adjusted Liu Estimators and that should be a set of scalars belongs to real number system. Values for “aa2” should be given as a |

`aa3` |
adjusted parameters of Type (3) Adjusted Liu Estimators and that should be a set of scalars belongs to real number system. Values for “aa3” should be given as a |

`k` |
a vector of set of numeric values. See ‘Examples’. |

`d` |
a vector of set of numeric values. See ‘Examples’. |

`press` |
an optional object specifying the PRESS values. That is, if “press=TRUE” then summary of PRESS of some of the estimators are returned with corresponding |

`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.

Optimum scalar MSE values of all estimators can be found for a given range of parameters. Hence the best estimator can be found based on the MSE criteria. Further prior information should be given in order to obtained the results.

The way of finding `aa1`

, `aa2`

and `aa3`

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**

By default, `optimum`

returns the optimum scalar MSE values and corresponding parameter values of all estimators. If “press=TRUE” then `optimum`

return the optimum PRESS values and corresponding parameter values of some of the estimators.

Conversion of estimators and corresponding `k`

and/or `d`

values are given below.

`SRRE = MIXE k=0`

`OGSRRE = MIXE k=0`

`RE = OLS k=0`

`OGRE = OLS k=0`

`RLE = RLS d=1`

`OGRLE = RLS d=1`

`LE = OLS d=1`

`OGLE = OLS d=1`

`RRRE = RLS k=0`

`OGRRRE = RLS k=0`

`SRLE = MIXE d=1`

`OGSRLE = MIXE d=1`

`AURE = OLS k=0`

`OGAURE = OLS k=0`

`AULE = OLS d=1`

`OGAULE = OLS d=1`

`LTE1 = RE d=0`

`OGLTE1 = RE d=0`

`LTE1 = OLS k=0 and d=0`

`OGLTE1 = OLS k=0 and d=0`

`LTE2 = RE d=0`

`OGLTE2 = RE d=0`

`LTE2 = OLS k=0 and d=0`

`OGLTE2 = OLS k=0 and d=0`

P.Wijekoon, A.Dissanayake

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
## portland cement data set 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.
``` |

lrmest documentation built on May 29, 2017, 9:02 a.m.

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