Re: Deriving Formula from Ordinal Regression Results to Classify New Cases?

Posted by David Marso on
URL: http://spssx-discussion.165.s1.nabble.com/Deriving-Formula-from-Ordinal-Regression-Results-to-Classify-New-Cases-tp5715848p5716049.html

"What is the correct way to apply this line of the algorithm:

   compute #eta0_subj1 = 2.203323 - (1.047664*0 + (-0.058683)*0 +
0.615746*3.260000).

...for predictor variables that have more than one parameter estimate? "

"but if the observed value is the highest possible value, then there is no matching parameter estimate. What am I missing?":  Think about it???  If say both values are the highest category then the calculation is simply the 'threshold'.  Similarly if one or the other is zero...
---
FWIW with fair amount of confidence but not absolute certainty ;-)
These coefficients should be replaced with the full precision values .
--
COMPUTE # =
    SUM((Q5_3 EQ 1)* -.980,(Q5_3 EQ 2)*-.724,(Q5_3 EQ 3)*-.151,(Q5_3 EQ 4)*.461,
            (Q5_4 EQ 1)*-4.65,(Q5_4 EQ 2)*-3.22,(Q5_4 EQ 3)*-2.18288,(Q5_4 EQ 4) *-1.09362 ).
DO REPEAT Eta=Eta1 TO Eta4 / C=-4.03128 -1.61392 1.650025 2.764527 .
+  COMPUTE Eta=C-# .
END REPEAT.

OR

COMPUTE #=0.
DO REPEAT V=1 2 3 4  / C= -0.98 -0.724 -0.15104 0.460543.
+  IF (Q5_3  EQ V) # = #+C.
END REPEAT.
DO REPEAT V=1 2 3 4  / C=-4.65169 -3.22348 -2.18288 -1.09362 .
+  IF (Q5_4 EQ V) # = #+C.
END REPEAT.
       
DO REPEAT Eta=Eta1 TO Eta4 / C=-4.03128 -1.61392 1.650025 2.764527 .
+  COMPUTE Eta=C-# .
END REPEAT.

Don't believe me!!!! VERIFY IT and convince yourself!!!
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Vik Rubenfeld wrote

This is fantastic. I have almost got it.

In the ucla data set, the variables pared and public have just two levels, and so they get only one parameter estimate each.  Some of the variables in my data set have 5 levels, and so get 4 parameter estimates each, one for each level minus the highest level.  Here are the parameter estimates for a test run using two predictor variables:

PLUM Q7 BY Q5_3 Q5_4
  /LINK=LOGIT
  /PRINT=PARAMETER SUMMARY
  /SAVE=ESTPROB.



What is the correct way to apply this line of the algorithm:

   compute #eta0_subj1 = 2.203323 - (1.047664*0 + (-0.058683)*0 +
0.615746*3.260000).

...for predictor variables that have more than one parameter estimate?  In other words, which of the four possible parameter estimates is to be used?  I would have thought it would be the one that matches the observed value of the predictor variable for each case - but if the observed value is the highest possible value, then there is no matching parameter estimate. What am I missing?

I am attaching the test data set used in this example.

test-data.sav