**Date:** Mon, 27 Jun 2011 15:48:49 -0700
**Reply-To:** David Marso <david.marso@gmail.com>
**Sender:** "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
**From:** David Marso <david.marso@gmail.com>
**Subject:** Re: cross validation using SPSS
**In-Reply-To:** <BANLkTinFXULWi3JF9-B+tF2KZH8M-jZ-yQ@mail.gmail.com>
**Content-Type:** text/plain; charset=us-ascii
OK! First of all it would be nice if you were to provide a reference to
these quantities or a formula.
Sure, I can google but AFAIC it is a pain and you really should save us the
extra research effort!
I made the effort to Google "prediction sum of squares" and located
something which might be useful.
http://webscripts.softpedia.com/script/Scientific-Engineering-Ruby/Statistics-and-Probability/press-35784.html
Given the definition one might be inclined to run a bajillion different
regressions leaving one case out for each regression and then calculating
the residuals for the omitted case based on the regression weights for the
remaining cases. OTOH, this is shear folly as there is a much nicer way to
achieve this.
My initial idea was to create a MATRIX program to calculate the 'hat' matrix
and then go to town with that. My second idea was to see what SPSS will
give you in terms of useful stuff in the SAVE subcommand. Rather than spoil
all the fun I leave you with the following.
You should run this as is and look at the data file after running all three
regressions... Hmmmmm.

data list free / a b c y .
begin data
1 6 3 1 6 3 6 1 5 3 6 5 3 6 1 5 6 3 1 5 6 3 1 5 6 7 3 5 1 2 6 7 3 5 1 7 6 3
7 6 1 3 5 6 7 1 3 6 7 1 5 3 6 7 1 5 3 6 7 1 7 6
end data.
compute id=$casenum.
reg / var a b c y / select id NE 1 / dep y / method enter a b c /
SAVE DRESID (h1) RESID (e1).
reg / var a b c y / select id NE 2 / dep y / method enter a b c /
SAVE DRESID (h2) RESID (e2).
reg / var a b c y / dep y / method enter a b c / SAVE DRESID (h_all)
.
*Note this is merely a pointer in the (hopefully right) direction.

Regarding MSPR (mean squared prediction error). I think you will need to
provide an explicit publically available citation or formula. I found a few
references but did not feel like attempting to make sense of them in the
context of linear Regression. OTOH, I did see a reference to Mallow's Cp as
a scaled version of MSRP.
HTH, David

Mehrshad Koleini wrote:
>
> Dear all
>
>
> Hi. During cross-validation procedure for making a regression model, I
> need
> to obtain PRESSp (prediction sum of squares), and MSPR (mean squared
> prediction error). Does anybody know how I can calculate it by using SPSS
> 17.0 Professor Package or I should use other software?
>
>
>
> Kind regards
>
>
> Mehrshad
>

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