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08-04-2016 08:42 AM

A user on Twitter asks:

.@SASsoftware Why does the output graph in PROC LOGISTIC PLOTS=ALL show predicted values outside of the range of the independent variable?

— Sam Van Horne (@LearningPlaces) August 4, 2016

I think the explanation might be longer than can fit in a tweet, so I thought I'd post here to see what the experts say. I'm hoping I understand his question correctly. I tried to create an example using the SAS documentation.

```
data Neuralgia;
input Treatment $ Sex $ Age Duration Pain $ @@;
datalines;
P F 68 1 No B M 74 16 No P F 67 30 No
P M 66 26 Yes B F 67 28 No B F 77 16 No
A F 71 12 No B F 72 50 No B F 76 9 Yes
A M 71 17 Yes A F 63 27 No A F 69 18 Yes
B F 66 12 No A M 62 42 No P F 64 1 Yes
A F 64 17 No P M 74 4 No A F 72 25 No
P M 70 1 Yes B M 66 19 No B M 59 29 No
A F 64 30 No A M 70 28 No A M 69 1 No
B F 78 1 No P M 83 1 Yes B F 69 42 No
B M 75 30 Yes P M 77 29 Yes P F 79 20 Yes
A M 70 12 No A F 69 12 No B F 65 14 No
B M 70 1 No B M 67 23 No A M 76 25 Yes
P M 78 12 Yes B M 77 1 Yes B F 69 24 No
P M 66 4 Yes P F 65 29 No P M 60 26 Yes
A M 78 15 Yes B M 75 21 Yes A F 67 11 No
P F 72 27 No P F 70 13 Yes A M 75 6 Yes
B F 65 7 No P F 68 27 Yes P M 68 11 Yes
P M 67 17 Yes B M 70 22 No A M 65 15 No
P F 67 1 Yes A M 67 10 No P F 72 11 Yes
A F 74 1 No B M 80 21 Yes A F 69 3 No
;
ods graphics on;
/* capture value ranges */
proc means data=Neuralgia min max; run;
proc logistic data=Neuralgia plots=all;
class Treatment Sex;
model Pain= Treatment Sex Treatment*Sex Age Duration / expb;
run;
```

Even though MAX of Age is 83, the prediction plot goes beyond that (looks like through Age 90).

Accepted Solutions

Solution

08-04-2016
09:59 AM

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Posted in reply to ChrisHemedinger

08-04-2016 09:42 AM - edited 08-04-2016 12:53 PM

PROC LOGISTIC fits a model. The model is valid for any values of the continuous variables, but most statisticians agree that you should not extrapolate a model to data outside the range of the data.

However, SAS does not enforce that restriction when scoring a model. If you score a regression model by using the SCORE statement, PROC SCORE, or PROC PLM, you can input any value you want for a continuous variable. In the case of PROC SCORE and PROC PLM, the input data set/item store do not even have a copy of the data; they only contain the parameter estimates for the model.

The graph that you are seeing in this example is a view of the MODEL, not a view of the data. In many cases, this is what the analyst wants to see. Notice that this model is evaluated for Duration=16.73, which is not even a value of Duration in the data set.

If you want to see ONLY the predicted values at the data points, you can use the OUTPUT= option to create an output data set that contains the predicted probabilities. You can then use PROC SGPLOT to visualize the predicted values. You should really use a scatter plot for this, but many analysts try to "connect the dots" by using a series plot. Note, however, that if you have repeated values, you might get jagged lines if you attempt to connect the data points.

For your example, the SAS code follows:

```
proc logistic data=Neuralgia plots(only)=effectplot;
class Treatment Sex;
model Pain= Treatment Sex Treatment*Sex Age Duration / expb;
output out=LogiPred predicted=PredProb;
run;
data M;
set LogiPred;
group = Treatment || " " || Sex;
run;
proc sort data=M;
by group age;
run;
```

title "Predicted Probabilities for Data";
proc sgplot data=M;
series x=age y=PredProb / group=group markers;
run;

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Posted in reply to ChrisHemedinger

08-04-2016 09:25 AM

Because what if an 84 year old walks into the neurologist's office tomorrow?

The sigmoid shape of the curve does ensure that predictions fall between 0 and 1, though.

Solution

08-04-2016
09:59 AM

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Posted in reply to ChrisHemedinger

08-04-2016 09:42 AM - edited 08-04-2016 12:53 PM

PROC LOGISTIC fits a model. The model is valid for any values of the continuous variables, but most statisticians agree that you should not extrapolate a model to data outside the range of the data.

However, SAS does not enforce that restriction when scoring a model. If you score a regression model by using the SCORE statement, PROC SCORE, or PROC PLM, you can input any value you want for a continuous variable. In the case of PROC SCORE and PROC PLM, the input data set/item store do not even have a copy of the data; they only contain the parameter estimates for the model.

The graph that you are seeing in this example is a view of the MODEL, not a view of the data. In many cases, this is what the analyst wants to see. Notice that this model is evaluated for Duration=16.73, which is not even a value of Duration in the data set.

If you want to see ONLY the predicted values at the data points, you can use the OUTPUT= option to create an output data set that contains the predicted probabilities. You can then use PROC SGPLOT to visualize the predicted values. You should really use a scatter plot for this, but many analysts try to "connect the dots" by using a series plot. Note, however, that if you have repeated values, you might get jagged lines if you attempt to connect the data points.

For your example, the SAS code follows:

```
proc logistic data=Neuralgia plots(only)=effectplot;
class Treatment Sex;
model Pain= Treatment Sex Treatment*Sex Age Duration / expb;
output out=LogiPred predicted=PredProb;
run;
data M;
set LogiPred;
group = Treatment || " " || Sex;
run;
proc sort data=M;
by group age;
run;
```

title "Predicted Probabilities for Data";
proc sgplot data=M;
series x=age y=PredProb / group=group markers;
run;

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Posted in reply to Rick_SAS

08-04-2016 09:58 AM

Thanks @Rick_SAS. I knew that wouldn't fit in a tweet.

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Posted in reply to ChrisHemedinger

08-04-2016 10:18 AM

Plots show model. Can get pred vals on data by using OUTPUT stmt. [LINK]