Hi S-listers,
I am looking into the effects of some environmental conditions
(temperature, wind speed, etc) on the foraging activity of bumblebees. I?m
trying to do this using a Poisson GLM with count of foraging bumblebees as
the response and the different environmental conditions as explanatory
variables. I have been looking at each environmental condition by adding it
to a minimal model and testing the change in deviance with an F-test (the
data is slightly over-dispersed). Recently I stumbled on Venables &
Ripley?s cunning addterm function in their MASS library which would save me
a lot of time. Unfortunately I get slightly different F and P values when I
do the test using anova (model1, model2, test = ?F?) and addterm (model1,
model2, test = ?F?). The deviances are the same using both methods, its
just the F and P values that differ. I?m not sure what I?m doing wrong, or
if I?ve got the wrong idea about these functions, and would be really
grateful if anyone could give me a clue.
Thanks loads,
tom
A madeup example of what I?m doing:
bees<- c(6, 8, 10, 8, 14, 3, 6, 5, 12, 4) temp<-
factor(c("hot","hot","hot","hot","hot","cold","cold","cold","cold","cold")
) model1<- glm(bees ~ 1, family = poisson) model2<- glm(bees ~ temp,
family = poisson) library (MASS) anova (model1, model2, test = "F")
Analysis of Deviance Table
Response: bees
Terms Resid. Df Resid. Dev Test Df Deviance F Value Pr(F)
1 1 9 14.62530
2 temp 8 11.23154 1 3.393755 2.201395 0.1761781
addterm (model1, model2, test = "F")
Single term additions
Model:
bees ~ 1
Df Deviance AIC F value Pr(F)
<none> 14.62530 16.62530
temp 1 11.23154 15.23154 2.417303 0.1586095
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