Dear Koilos,
You've neglected to correct the MSE for df. Modifying your example, so that
it actually runs (your original regression doesn't work -- the lengths of x
and y differ):
> y <- (1:100)^2
> x <- 1:100
> lm(y~x) -> linmod # just for the sake of a simple example
>
linmod$residuals[1]^2/(2*sum(linmod$residuals^2)/98)*(hatvalues(linmod)[1]/(
1-hatvalues(linmod)[1])^2)
1
0.09853436
> cooks.distance(linmod)[1]
1
0.09853436
I hope this helps,
John
-----------------------------------------------
John Fox
Senator McMaster Professor of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of kolos.agos...@gmx.net
> Sent: Tuesday, March 12, 2013 3:55 PM
> To: r-help@r-project.org
> Subject: [R] Cook's distance
>
> Dear useRs,
>
> I have some trouble with the calculation of Cook's distance in R.
> The formula for Cook's distance can be found for example here:
> http://en.wikipedia.org/wiki/Cook%27s_distance
>
> I tried to apply it in R:
>
> > y <- (1:400)^2
> > x <- 1:100
> > lm(y~x) -> linmod # just for the sake of a simple example
> >
> linmod$residuals[1]^2/(2*mean(linmod$residuals^2))*(hatvalues(linmod)[1
> ]/(1-hatvalues(linmod)[1])^2)
> 1
> 0.02503195
> > cooks.distance(linmod)[1]
> 1
> 0.02490679
>
> Why differ the two results?
>
> Thanks a lot if somebody have some instructions for me.
>
> Best wishes:
>
> Kolos
>
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