## Fitting a model with a common control (so a single upper limit: "1")
G.aparine.m1 <- drm(drymatter ~ dose, treatment, data = G.aparine,
pmodels = data.frame(treatment, treatment, 1, treatment), fct = LL.4())
## Visual inspection of fit
plot(G.aparine.m1, broken = TRUE)
## Lack of fit test
modelFit(G.aparine.m1)
## Summary output
summary(G.aparine.m1)
## Predicted values with se and confidence intervals
#predict(G.aparine.m1, interval = "confidence")
# long output
## Calculating the relative potency
EDcomp(G.aparine.m1, c(50,50))
## Showing the relative potency as a
## function of the response level
relpot(G.aparine.m1)
relpot(G.aparine.m1, interval = "delta")
# appears constant!
## Response level in percent
relpot(G.aparine.m1, scale = "percent")
## Fitting a reduced model (with a common slope parameter)
G.aparine.m2 <- drm(drymatter ~ dose, treatment, data = G.aparine,
pmodels = data.frame(1, treatment, 1, treatment), fct = LL.4())
anova(G.aparine.m2, G.aparine.m1)
## Showing the relative potency
relpot(G.aparine.m2)
## Fitting the same model in a different parameterisation
G.aparine.m3 <- drm(drymatter ~ dose, treatment, data = G.aparine,
pmodels = data.frame(treatment, treatment, 1, treatment), fct = LL2.4())
EDcomp(G.aparine.m3, c(50, 50), logBase = exp(1))
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