mselect function

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)

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.

Returns

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()))