I would like to test the significance of the difference in mean between two independent count samples. I'm doing this with a GLM poisson in R, as shown in the code below:
a=c(0,0,0,0,0,0,0,0,0,0) b=c(1,2,0,1,1,2,0,1,0,2) c=data.frame(sp=c(a,b),grp=c(rep('A',10),rep('B',10))) summary(glm(sp~grp,data=c,family=poisson)) Call: glm(formula = sp ~ grp, family = poisson, data = c) Deviance Residuals: Min 1Q Median 3Q Max -1.41421 -0.00006 -0.00006 0.00000 0.87897 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -20.3 4914.8 -0.004 0.997 grpB 20.3 4914.8 0.004 0.997 (Dispersion parameter for poisson family taken to be 1) Null deviance: 22.1807 on 19 degrees of freedom Residual deviance: 8.3178 on 18 degrees of freedom AIC: 28.159 Number of Fisher Scoring iterations: 18
As you can see, the coefficient values are not reflecting reality. I noticed that this happened because group "A" has mean=0. In this way, I would like to know if there is any way to fix this problem in glm, or if there is any other better method to test my hypothesis.