LICurvature: Assement on Local Influence in Case-Weight for the Linear...

In LICurvature: Sensitivity Analysis for Case Weight in Normal Linear Regression

Description

This Package presents a general method for assessing the local influence of minor perturbations of case-weight for the linear regression models. The method relies on a well-behaved likelihood and certain elementary ideas from differential geometry, and seems to provide a relatively simple, unified approach for handling a variety of problems. A distinguishing feature of this method is its use of log-likelihood contours to gauge influence. Although this Package is concerned primarily with local influence, some discussion of assessing global influence, which is a significantly more difficult problem. We use geometric normal curvatures to characterize the behaviour of an influence graph around omega (Generally, omega can reflect any well-defined perturbation scheme and thus is not restricted to be a collection of case weights.),although the essential results can be obtained by using less descriptive We used it in case-Weight for the linear regression models, also recommended a general reference for deciding whether there is notable local sensitivity or not

Usage

LICurvature(ini = NA,X,Xstar,y,n,p, ...)

Arguments

ini	Initial values
X	Covariate matrix
Xstar	Design matrix
y	Continuous response
n	Design matrix
p	The number of covariates
...	Other arguments

Details

Models for LICurvature are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) continuous response vector and terms are a series of terms which specifies a linear predictor for responses.

Value

lmax	Eign vector
Clmax	Normal curvatures for case weight in linear regression models

Note

Supportted by Shahid Beheshti University

Author(s)

Bahrami Samani and ParsaMaram

References

Cook, R. D. (1986). Assessment of local influence (with discussion). J. Roy. Statist. Soc. Ser. B 48: 133-169.

See Also

nlminb,eigen

Examples

data("Bahrami1")
gender<-Bahrami1$ GENDER
duration <-Bahrami1$ DURATION
y<-Bahrami1$  BMI
X=cbind(gender,duration )
Xstar=cbind(1,X)
p=2;
n=15;
ini=c(0,rep(1,p));

LICurvature(ini,X=X,Xstar=Xstar,y=y,n=n,p=p)


## The function is currently defined as
structure(function (x, ...) 
UseMethod("LICurvature"), class = "LICurvature")

LICurvature documentation built on May 30, 2017, 4:18 a.m.