I have a question. How on earth is it possible to have a negative estimate for a form of a nominal variable (two forms: "HM" and "LM") when it should be positive? I'm modelling a positive continuous variable (rarefied species richness) by using a single nominal predictor. See the output summary below and the box plot. How is this even possible?
P.S. This is my first gamma glm ever
Call:
glm(formula = Rarified.Richness ~ Macrophyte, family = "Gamma",
data = beysehir.diversity.indices.macr)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.39143 -0.09652 -0.00911 0.07579 0.43107
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.082461 0.006693 12.320 2.28e-07 ***
MacrophyteLM -0.024028 0.010597 -2.267 0.0468 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Gamma family taken to be 0.05929918)
Null deviance: 0.87647 on 11 degrees of freedom
Residual deviance: 0.59465 on 10 degrees of freedom
AIC: 65.46
Number of Fisher Scoring iterations: 4
"LM" does have a higher mean value for the response. Shouldn't it has a positive estimate then?
P.S. KS-test and chisq tests both report a good fit for this model.
BONUS QUESTION: Do you think there is any other problem with this model?