Hiya,
I'm using simple glm binomial models to test the effect of treatment
(factor, 3 levels) on infection prevalence (infected/uninfected):
ad3<-glm(Infection~ecs, family=binomial, data=eilb)
but summary() function returns for each of the factor-level coefficients
against the control treatment:
> summary(ad3)
Call:
glm(formula = Infection ~ ecs, family = binomial, data = eilb)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.4006 -1.0383 -0.9005 1.3232 1.4823
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.3365 0.5855 -0.575 0.566
ecsminus2 -0.3567 0.8473 -0.421 0.674
ecsplus2 0.8473 0.9361 0.905 0.365
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 43.860 on 31 degrees of freedom
Residual deviance: 42.162 on 29 degrees of freedom
AIC: 48.162
Number of Fisher Scoring iterations: 4
> str(eilb)
'data.frame': 32 obs. of 59 variables:
(...)
$ ecs : Factor w/ 3 levels "control","minus2",..: 2 3 3 3 2
2 1 1 1 1 ...
$ Infection : Factor w/ 2 levels "Infected","Uninfected": 1 1 1 1
1 1 1 1 1 1 ...
What I want to know is whether the treatment in general had an effect on
infection prevalence, not the difference between respective factor levels.
If it was a general linear model I could switch between using lm() and
aov() functions, but how can I proceed here? I sense I'm missing something
obvious, so I'll appreciate your help!
cheers,
kasia
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