envalysis (version 0.5.5)

mselect: envalysis reimplementation of mselect

Description

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.

Usage

mselect(object, fctList = NULL, nested = FALSE, 
sorted = c("IC", "Res var", "Lack of fit", "no"), linreg = FALSE, icfct = AIC)

Value

A matrix with one row for each model and one column for each criterion.

Arguments

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. AIC and BIC are two options.

Author

Christian Ritz, Zacharias Steinmetz

Details

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.

Examples

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()))