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RE: st: RE: Interpretation of quadratic terms


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: RE: Interpretation of quadratic terms
Date   Tue, 9 Mar 2010 12:04:29 -0000

Tony is referring to a program -rbinplot- which will be published in
Stata Journal 10(1) as part of a software update for the package
discussed in 

SJ-4-4  gr0009  . . . . . . . . . . Speaking Stata: Graphing model
diagnostics
        . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  N.
J. Cox
        (help anovaplot, indexplot, modeldiag, ofrtplot, ovfplot,
        qfrplot, racplot, rdplot, regplot, rhetplot, rvfplot2,
        rvlrplot, rvpplot2 if installed)
        Q4/04   SJ 4(4):449--475
        plotting diagnostic information calculated from residuals
        and fitted values from regression models with continuous
        responses

Nick 
n.j.cox@durham.ac.uk 

Lachenbruch, Peter

When you think about it, the residual will be 1-predicted or
0-predicted.  Thus, they will plot as two parallel straight lines.

Nick and I (mostly Nick)  wrote a possible solution to this by using
binned plots.  I've been meaning to write it up for the SSC (and maybe
even SJ).  It's based on work by Andrew Gelman reported in his book with
Jennifer Hill.

Rosie Chen

Nick, thank you for the guidance. The model I am estimating is a
logistic regression. What I did to check the plot was to save the
residual of the model, and then plotted the standardized residual
against the predictor. I didn't really found a curve-linear
relationship. 

Is there anything wrong with the way I plot the residual? If not, then
why the inclusion of a quadratic term actually improves the model
fitting when I made a model comparison using the -2log-likelihood? In
addition, the nonsignificant predictor in the original form turned to be
significant after using the quadratic term? Your further advice would be
appreciated.


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