g02faf
g02faf
© Numerical Algorithms Group, 2002.
Purpose
G02FAF Calculates standardized residuals and influence statistics
Synopsis
[sres,ifail] = g02faf(n,ip,res,h,rms<,ifail>)
Description
For the general linear regression model
y=X(beta)+(epsilon)
where y is a vector of length n of the dependent variable,
X is an n by p matrix of the independent variables,
(beta) is a vector of length p of unknown parameters,
and (epsilon) is a vector of length n of unknown random errors
2
such that var(epsilon)=(sigma) I.
The residuals are given by:
^ ^^^^^^
r=y-y=y-X(beta)
^ ^^^^^^
The fitted values, y=X(beta), can be written as Hy for an n by n
matrix H. The ith diagonal elements of H, h , give a measure of
i
the influence of the ith values of the independent variables on
the fitted regression model. The values of r and the h are
i
returned by G02DAF.
G02FAF calculates statistics which help to indicate if an
observation is extreme and having an undue influence on the fit
of the regression model. Two types of standardised residual are
calculated:
(a) The ith residual is standardised by its variance when the
2 2
estimate of (sigma) , s , is calculated from all the data;
known as internal studentization.
r
i
RI = --------
i ____
s /1-h
\/ i
(b) The ith residual is standardised by its variance when the
2 2
estimate of (sigma) , s is calculated from the data
-i
excluding the ith observation; known as external
studentization.
r ________
i / n-p-1
RE = ----------=r / -------
i ____ i / 2
s /1-h / n-p-RI
-i\/ i \/ i
The two measures of influence are:
(a) Cook's D
h
1 2 i
D = -RE ----
i p i 1-h
i
(b) Atkinson's T
_____________
/ ( h )
/ ( n-p)( i )
T =|RE | / ( ---)( ----).
i i / ( p )( 1-h )
\/ ( i)
Parameters
g02faf
Required Input Arguments:
n integer
ip integer
res (:) real
h (:) real
rms real
Optional Input Arguments: <Default>
ifail integer -1
Output Arguments:
sres (:,4) real
ifail integer