anova.drc.Rd'anova' produces an analysis of variance table for one or two non-linear model fits.
# S3 method for drc anova(object, ..., details = TRUE, test = NULL)
| object | an object of class 'drc'. |
|---|---|
| ... | additional arguments. |
| 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. Use "od" to assess overdispersion for binomial data. |
Specifying only a single object gives a test for lack-of-fit, comparing the non-linear regression model to a more general one-way or two-way ANOVA model.
If two objects are specified a test for reduction from the larger to the smaller model is given. (This only makes statistical sense if the models are nested, that is: one model is a submodel of the other model.)
An object of class 'anova'.
Bates, D. M. and Watts, D. G. (1988) Nonlinear Regression Analysis and Its Applications, New York: Wiley \& Sons (pp. 103--104)
For comparison of nested or non-nested model the function mselectcan also be used.
The function anova.lm for linear models.
## Comparing a Gompertz three- and four-parameter models using an F test 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) # reduction to 'W1.3' not possible (highly significant)#> #> 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 0.0054anova(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