ANOVA Model Comparison for Dose-Response Models

Compares two nested dose-response model fits using a likelihood-ratio test (for binomial data) or an F-test (for continuous data). Two drc objects must be provided. For a lack-of-fit test of a single model, use modelFit instead.

Usage

# S3 method for class 'drc'
anova(object, ..., details = TRUE, test = NULL)

Arguments

object

an object of class ‘drc’.

...

a second object of class ‘drc’ to compare against object. Exactly two models must be supplied; passing a single model will result in an error directing the user to modelFit.

details

logical indicating whether or not details on the models compared should be displayed. Default is TRUE (details are displayed).

test

a character string specifying the test statistic to be applied. For continuous data the default is "F" (F-test); for binomial data the default is "Chisq" (likelihood-ratio test). Use "Chisq" to force a likelihood-ratio test for continuous data.

Value

An object of class ‘anova’ (inheriting from data.frame) with columns for model degrees of freedom, residual sum of squares (or log-likelihood), the difference in degrees of freedom, the test statistic, and the p-value.

Details

Two drc objects must be specified. The function performs a test for reduction from the larger to the smaller model. This only makes statistical sense if the models are nested, that is: one model is a submodel of the other model.

For continuous data an F-test is used by default. For binomial data a likelihood-ratio (chi-square) test is used by default.

If a single model is passed, the function raises an error. To assess the fit of a single dose-response model (lack-of-fit test comparing the model to a more general ANOVA model), use modelFit instead.

See also

modelFit for lack-of-fit testing of a single model, drm for fitting dose-response models, logLik.drc for log-likelihood extraction, summary.drc for model summaries.

Author

Christian Ritz, Hannes Reinwald

Examples

## Comparing two nested models (two-model comparison)
ryegrass.m1 <- drm(rootl ~ conc, data = ryegrass, fct = W1.4())
ryegrass.m2 <- drm(rootl ~ conc, data = ryegrass, fct = W1.3())
anova(ryegrass.m2, ryegrass.m1)
#> 
#> 1st model
#>  fct:      W1.3()
#> 2nd model
#>  fct:      W1.4()
#> 
#> ANOVA table
#> 
#>           ModelDf    RSS Df F value p value
#> 1st model      21 8.9520                   
#> 2nd model      20 6.0242  1  9.7205  0.0054

anova(ryegrass.m2, ryegrass.m1, details = FALSE)  # without details
#> ANOVA table
#> 
#>           ModelDf    RSS Df F value p value
#> 1st model      21 8.9520                   
#> 2nd model      20 6.0242  1  9.7205  0.0054

## For a lack-of-fit test on a single model, use modelFit():
modelFit(ryegrass.m1)
#> Lack-of-fit test
#> 
#>           ModelDf    RSS Df F value p value
#> ANOVA          17 5.1799                   
#> DRC model      20 6.0242  3  0.9236  0.4506