Hi folks;
I've run gam procedure to a data set and got the following output table. I wonder how I can calculate a pseudo R-Squared with the help of deviance.
Number of Observations | 49988 |
---|---|
Number of Missing Observations | 12 |
Distribution | Gaussian |
Link Function | Identity |
Final Number of Backfitting Iterations | 4 |
---|---|
Final Backfitting Criterion | 1.6618101E-9 |
The Deviance of the Final Estimate | 240768.87317 |
The backfitting algorithm converged. |
Thanks!
Issac
One possibility is discussed in Cameron and Trevedi's "Regression Analysis of Count Data" which describes the deviance as the GLM generalization of the sum of squares. They refer to two papers by Cameron and Windmeijer where a pseudo-R-squared is proposed based on a decomposition of the deviance. In essence, the proposal is to use R2 = 1-(D(intercept-only-model)/D(full-model)). I would recommend reviewing the original papers to verify that the interpretation is appropriate for your particular model before using the result.
One possibility is discussed in Cameron and Trevedi's "Regression Analysis of Count Data" which describes the deviance as the GLM generalization of the sum of squares. They refer to two papers by Cameron and Windmeijer where a pseudo-R-squared is proposed based on a decomposition of the deviance. In essence, the proposal is to use R2 = 1-(D(intercept-only-model)/D(full-model)). I would recommend reviewing the original papers to verify that the interpretation is appropriate for your particular model before using the result.
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