Dose-response model selection
Model selection by comparison of different models using the following criteria: the log likelihood value, Akaike's information criterion (AIC), the estimated residual standard error or the p-value from a lack-of-fit test.
mselect(object, fctList = NULL, nested = FALSE,
sorted = c("IC", "Res var", "Lack of fit", "no"), linreg = FALSE, icfct = AIC)object |
an object of class 'drc'. |
fctList |
a list of dose-response functions to be compared. |
nested |
logical. TRUE results in F tests between adjacent models (in 'fctList'). Only sensible for nested models. |
sorted |
character string determining according to which criterion the model fits are ranked. |
linreg |
logical indicating whether or not additionally polynomial regression models (linear, quadratic, and cubic models) should be fitted (they could be useful for a kind of informal lack-of-test consideration for the models specified, capturing unexpected departures). |
icfct |
function for supplying the information criterion to be used. |
For Akaike's information criterion and the residual standard error: the smaller the better and for lack-of-fit test (against a one-way ANOVA model): the larger (the p-value) the better. Note that the residual standard error is only available for continuous dose-response data.
Log likelihood values cannot be used for comparison unless the models are nested.
A matrix with one row for each model and one column for each criterion.
Christian Ritz
### Example with continuous/quantitative 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())) ## Model selection including linear, quadratic, and cubic regression models mselect(ryegrass.m1, list(LL.3(), LL.5(), W1.3(), W1.4(), W2.4(), baro5()), linreg = TRUE) ## Comparing nested models mselect(ryegrass.m1, list(LL.5()), nested = TRUE) ### 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()))
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