I'm working in R, using glm.nb (of the MASS package) to model count data with a negative binomial regression model. I'd like to get the standardized (beta) coefficients from the model, but am given the unstandardized (b "Estimate") coefficients.
The R documentation does not seem to show of a way to retrieve the standardized beta weights easily for a negative bionomial regression model.
The R script is something like:
library("MASS")
nb = glm.nb(responseCountVar ~ predictor1 + predictor2 +
predictor3 + predictor4 + predictor5 + predictor6 +
predictor7 + predictor8 + predictor9 + predictor10 +
predictor11 + predictor12 + predictor13 + predictor14 +
predictor15 + predictor16 + predictor17 + predictor18 +
predictor19 + predictor20 + predictor21,
data=myData, control=glm.control(maxit=125))
summary(nb)
and the output of the above is:
Deviance Residuals:
Min 1Q Median 3Q Max
-5.1462 -1.0080 -0.4247 0.2277 3.4336
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.059e+00 3.782e-01 -8.088 6.05e-16 ***
predictor1 -2.447e+00 4.930e-01 -4.965 6.88e-07 ***
predictor2 -1.004e+00 1.313e-01 -7.650 2.00e-14 ***
predictor3 1.158e+00 1.440e-01 8.047 8.46e-16 ***
predictor4 1.334e+00 7.034e-02 18.970 < 2e-16 ***
predictor5 9.862e-01 2.006e-01 4.915 8.87e-07 ***
predictor6 1.166e+00 2.378e+00 0.490 0.62392
predictor7 -1.057e-01 1.494e-01 -0.707 0.47936
predictor8 4.051e-01 7.318e-02 5.536 3.10e-08 ***
predictor9 -3.320e-01 1.132e-01 -2.933 0.00336 **
predictor10 3.761e-01 1.561e-01 2.409 0.01600 *
predictor11 8.660e-02 4.332e-02 1.999 0.04557 *
predictor12 -1.583e-01 2.044e-01 -0.774 0.43872
predictor13 6.404e-02 3.972e-03 16.122 < 2e-16 ***
predictor14 4.264e-03 2.297e-04 18.563 < 2e-16 ***
predictor15 3.279e-03 5.697e-04 5.755 8.68e-09 ***
predictor16 3.487e-03 3.447e-03 1.012 0.31177
predictor17 1.534e-04 1.647e-04 0.931 0.35182
predictor18 -7.606e-05 9.021e-05 -0.843 0.39917
predictor19 2.536e-04 1.733e-05 14.633 < 2e-16 ***
predictor20 2.997e-02 4.977e-03 6.021 1.73e-09 ***
predictor21 2.756e+01 3.508e+00 7.856 3.98e-15 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Negative Binomial(0.9232) family taken to be 1)
Null deviance: 5631.1 on 1835 degrees of freedom
Residual deviance: 2120.7 on 1814 degrees of freedom
AIC: 19268
Number of Fisher Scoring iterations: 1
Theta: 0.9232
Std. Err.: 0.0282
2 x log-likelihood: -19221.9910
My question is: Is there a way to get the beta weights, or do I need to try to convert my unstandardized b coefficients to standardized beta coefficients (if so, how would I do that)?