I'm using Poisson Regression and Negative Binomial regression to estimate temporal trends. My understanding is that the coefficients are in log scale and they have to be translated to data-unit (count per time [ year, month...]) by multiplying them by 100. Is it correct?
Negative binomial example
> library("MASS") > Y= DF$Counts > X= DF$ Years > Nig<- glm.nb(Y~X) > summary(Nig) Call: glm.nb(formula = Y ~ X, init.theta = 6.190108641, link = log) Deviance Residuals: Min 1Q Median 3Q Max -2.19350 -0.81948 -0.06559 0.47013 1.85608 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -18.316582 19.892078 -0.921 0.357 X 0.010564 0.009947 1.062 0.288 (Dispersion parameter for Negative Binomial(6.1901) family taken to be 1) Null deviance: 32.207 on 29 degrees of freedom Residual deviance: 31.059 on 28 degrees of freedom AIC: 210.93 Number of Fisher Scoring iterations: 1 Theta: 6.19 Std. Err.: 2.23 2 x log-likelihood: -204.928
The slope (trend) = 0.01056 on Log scale and to change it to count per year, it has to be multiplied by 100. So the trend = 1.056 count per year