Dear drc-Team,
for some reason, mselect() only returns 0 values for the LOF (Lack of fit) when comparing multiple models. However, when I am running modelFit() I get p values clearly above the sign. threshold of 0.05.
To reproduce the error I used the ryegrass dataset as shown below:
> # Fitting a four-parameter log-logistic model
> ryegrass.LL.4 <- drm(rootl ~ conc, data = ryegrass, fct = LL.4())
> modelFit(ryegrass.LL.4) # LOF pvalue of 0.9451 <- this is NOT identical with the results of mselect
Lack-of-fit test
ModelDf RSS Df F value p value
ANOVA 17 5.1799
DRC model 20 5.4002 3 0.2411 0.8665
> AIC(ryegrass.LL.4) # IC = 41.82703 <- this is identical with the results of mselect
[1] 42.31029
> m.ls = list(LL.5(),LL.3(),W2.4(),W1.4())
> mselect(ryegrass.LL.4, m.ls)
logLik IC Lack of fit Res var
W2.4 -15.91352 41.82703 0 0.2646283
LL.4 -16.15514 42.31029 0 0.2700107
LL.5 -15.87828 43.75656 0 0.2777393
W1.4 -17.46720 44.93439 0 0.3012075
LL.3 -18.60413 45.20827 0 0.3153724
From my understanding of the mselect() and modelFit() function the computation of the Lack of fit values is based on a more general ANOVA model. Hence it should return similar p-values, shouldn't it?
I am using drc package version 3.2.0 and R version 4.2.1 on a Windows System.
Dear drc-Team,
for some reason,
mselect()only returns 0 values for the LOF (Lack of fit) when comparing multiple models. However, when I am runningmodelFit()I get p values clearly above the sign. threshold of 0.05.To reproduce the error I used the ryegrass dataset as shown below:
From my understanding of the mselect() and modelFit() function the computation of the Lack of fit values is based on a more general ANOVA model. Hence it should return similar p-values, shouldn't it?
I am using drc package version 3.2.0 and R version 4.2.1 on a Windows System.