modelFit.RdChecking the fit of dose-response model by means of formal significance tests or graphical procedures.
modelFit(object, test = NULL, method = c("gof", "cum"))
| object | object of class 'drc' |
|---|---|
| test | character string defining the test method to apply |
| method | character string specifying the method to be used for assessing the model fit |
Currently two methods are available. For continuous data the clasical lack-of-fit test is applied (Bates and Watts, 1988). The test compares the dose-response model to a more general ANOVA model using an approximate F-test. For quantal data the crude goodness-of-fit test based on Pearson's statistic is used.
None of these tests are very powerful. A significant test result is more alarming than a non-significant one.
An object of class 'anova' which will be displayed in much the same way as an ordinary ANOVA table.
Bates, D. M. and Watts, D. G. (1988) Nonlinear Regression Analysis and Its Applications, New York: Wiley \& Sons (pp. 103--104).
## Comparing the four-parameter log-logistic model ## to a one-way ANOVA model using an approximate F test ## in other words applying a lack-of-fit test ryegrass.m1 <- drm(rootl ~ conc, data = ryegrass, fct = W1.4()) modelFit(ryegrass.m1)#> Lack-of-fit test #> #> ModelDf RSS Df F value p value #> ANOVA 17 5.1799 #> DRC model 20 6.0242 3 0.9236 0.4506