# compare quasi-Poisson models with different response variables

My response variable is a combining of two variables which I would like to rescale with different weights. ratio:0.5/0.5; 0.25/0.75; 0.1/0.9. Now I would like to test which is the best fit. The three models have overdispersion so I used quasipoisson regression.

Following former instructions in this matter, I used the F test, but it didn't work because of the different response variable. I got this massage:

    anova(glm0.5_0.5,glm0.75_0.25,glm0.9_0.1, test = "F")

Response: data$dep0.5_0.5 Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL 575 11386.0 academic95 1 3720.1 574 7666.0 231.505 < 2.2e-16 *** israeli_ac95 1 1239.1 573 6426.8 77.113 < 2.2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Warning message: In anova.glmlist(c(list(object), dotargs), dispersion = dispersion, : models with response ‘c("data$dep0.75_0.25", "data\$dep0.9_0.1")’ removed because response differs from model 1