mselect  R Documentation 
This function should behave just like mselect()
, with the
main difference that model objects are passed through the function instead of
requiring the data to be present in .GlobalEnv
. If you have trouble
with this function, you can use mselect()
instead.
mselect(object, fctList = NULL, nested = FALSE, sorted = c("IC", "Res var", "Lack of fit", "no"), linreg = FALSE, icfct = AIC)
object 
an object of class 
fctList 
a list of doseresponse functions to be compared. 
nested 
logical; 
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 lackoftest 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 lackoffit test (against a oneway ANOVA model): the larger (the pvalue) the better. Note that the residual standard error is only available for continuous doseresponse 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, Zacharias Steinmetz
library(drc) ryegrass.m1 < drm(rootl ~ conc, data = ryegrass, fct = LL.4()) mselect(ryegrass.m1, list(LL.3(), LL.5(), W1.3(), W1.4(), W2.4(), baro5()))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.