R: Assessing the model fit

modelFit {drc} | R Documentation

## Assessing the model fit

### Description

Checking the fit of dose-response model by means of formal significance tests or graphical procedures.

### Usage

      modelFit(object, test = NULL, method = c("gof", "cum"))

### Arguments

`object` | object of class 'drc'
`test` | character string defining the test method to apply
`method` | character string specifying the method to be used for assessing the model fit

### Details

Currently two methods are available. For continuous data the clasical lack-of-fit test is applied (Bates and Watts, 1988). The test compares the dose-response model to a more general ANOVA model using an approximate F-test. For quantal data the crude goodness-of-fit test based on Pearson's statistic is used.

None of these tests are very powerful. A significant test result is more alarming than a non-significant one.

### Value

An object of class 'anova' which will be displayed in much the same way as an ordinary ANOVA table.

### Author(s)

Christian Ritz

### References

Bates, D. M. and Watts, D. G. (1988) Nonlinear Regression Analysis and Its Applications, New York: Wiley \& Sons (pp. 103–104).

### Examples

    ## Comparing the four-parameter log-logistic model
    ##  to a one-way ANOVA model using an approximate F test
    ## in other words applying a lack-of-fit test
    ryegrass.m1 <- drm(rootl ~ conc, data = ryegrass, fct = W1.4())
    modelFit(ryegrass.m1)

[Package drc version 3.0-1 Index]