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pink_poodle
Barite | Level 11

I am doing reference cell coding with a binary categorical predictor.

According to max likelihood table,

Intercept = 0.2335, and that is the predicted logit probability of the reference level.

However, the effect plot shows a dot at >0.50 for the predicted probability of that level.

Why is that? Please let me know possible explanations.

proc logistic data=have plots(only)=(effect oddsratio);
    class x(ref='<=60 min')/ param=ref;
    model y(event='1')= x / clodds=pl;
run;

 

1 ACCEPTED SOLUTION

Accepted Solutions
Rick_SAS
SAS Super FREQ

You can get the exact predicted values by using

ods output effectplot=EFPLOT;

proc logistic....

run;

proc print data=EFPLOT; run;

 

The estimate for the Intercept (reference level) is 0.2335.

Then the predicted value for the reference category is 

p = logistic( 0.2335 );

which is 0.5581. This looks like the height of the dot for the reference category, so it looks like all is well. If the height is different from 0.5581, let me know.

 

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6 REPLIES 6
Rick_SAS
SAS Super FREQ

Can you upload the image so we can see it?

pink_poodle
Barite | Level 11

maximum likelihood tablemaximum likelihood tableeffect ploteffect plot

Rick_SAS
SAS Super FREQ

You can get the exact predicted values by using

ods output effectplot=EFPLOT;

proc logistic....

run;

proc print data=EFPLOT; run;

 

The estimate for the Intercept (reference level) is 0.2335.

Then the predicted value for the reference category is 

p = logistic( 0.2335 );

which is 0.5581. This looks like the height of the dot for the reference category, so it looks like all is well. If the height is different from 0.5581, let me know.

 

pink_poodle
Barite | Level 11

The ods output value is 0.558105622519328. Thank you!

pink_poodle
Barite | Level 11

Double-checked the logistic function:

 

logit(p) = ln(odds) 

let logit(p) = a

odds = p / ( 1 - p )

p / (1 - p) = e^a

p  = e^a - p*e^a

p*(1 + e^a) = e^a

p = e^a / (1 + e^a) = logistic(a)

 

 

 

Rick_SAS
SAS Super FREQ

Yes. Equivalently, p = 1 / (1 + exp(-a)), which is shorter to write.

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