mselect: Dose-response model selection

In drc: Analysis of Dose-Response Curves

Description

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.

Usage

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

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.

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.

Value

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

Author(s)

Christian Ritz

Examples

### 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()))

drc documentation built on May 1, 2019, 8:43 p.m.