I used the stepAIC function in the package MASS to do binomial logistic regression on a dataset and the final model derived had an AIC of 57.8. When I tried to replicate the model in jamovi, the AIC was 131. Why the different results? jamovi results attached. Thanks
MASS pkg results:
Call:
glm(formula = phototherapy ~ compatibility + DAT_results + peak_bili,
family = binomial, data = szAIC)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.80278 -0.28119 -0.16732 -0.07112 2.52543
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -8.1175 1.8056 -4.496 6.93e-06 ***
compatibility -1.7169 1.0480 -1.638 0.1014
DAT_results 1.9487 0.9220 2.114 0.0345 *
peak_bili 0.6969 0.1646 4.235 2.29e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 94.419 on 135 degrees of freedom
Residual deviance: 49.809 on 132 degrees of freedom
AIC: 57.809
Number of Fisher Scoring iterations: 7
stepAIC function in MASS pkg vs jamovi: different results
stepAIC function in MASS pkg vs jamovi: different results
- Attachments
-
- 2020-06-21 at 13.34.44 stepAIC.png (84.09 KiB) Viewed 2128 times
Re: stepAIC function in MASS pkg vs jamovi: different result
not sure,
but the models in jamovi are constructed like this:
https://github.com/jamovi/jmv/blob/master/R/logregbin.b.R#L72
and then the AIC is calculated like this:
https://github.com/jamovi/jmv/blob/master/R/logregbin.b.R#L79
the folks responsible for writing the underlying packages may be better equipped to handle questions of why results the differ.
cheers
but the models in jamovi are constructed like this:
https://github.com/jamovi/jmv/blob/master/R/logregbin.b.R#L72
and then the AIC is calculated like this:
https://github.com/jamovi/jmv/blob/master/R/logregbin.b.R#L79
the folks responsible for writing the underlying packages may be better equipped to handle questions of why results the differ.
cheers