mselect {drc} | R Documentation |
Model selection by comparison of different models using the maximum log likelihood value, Akaike's information criterion (AIC), the estimated residual variance and the p-value from a lack-of-fit test as criteria.
mselect(object, fctList = NULL, nested = FALSE)
object |
an object of class 'drc'. |
fctList |
a list of non-linear functions to be compared. |
nested |
logical. TRUE results in F tests between adjacent models (in 'fctList'). Only sensible for nested models. |
The maximum likelihood cannot be used for comparison unless the models are nested.
For Akaike's information criterion and the residual variance: the smaller the better and for lack-of-fit test: the larger the better. Note that the residual variance is only available for continuous dose-response data.
A matrix with one row for each model and one column for each criterion.
Christian Ritz
### Example with continuous data ## Fitting initial four-parameter log-logistic model ryegrass.m1 <- drm(rootl ~ conc, data = ryegrass, fct = LL.4()) ## Model selection mselect(ryegrass.m1, list(LL.3(), LL.5(), W1.3(), W1.4(), W2.4(), baro5())) mselect(ryegrass.m1, list(LL.5()), nested = TRUE) # nested models ### Example with quantal data ## Fitting initial two-parameter log-logistic model earthworms.m1 <- drm(number/total~dose, weights=total, data = earthworms, fct = LL.2(), type = "binomial") ## Comparing 4 models mselect(earthworms.m1, list(W1.2(), W2.2(), LL.3())) # model selection based AIC or p value not working in this example!!!