R: Lack-of-fit test for the mean structure based on cumulated...

lin.test {drc} | R Documentation

## Lack-of-fit test for the mean structure based on cumulated residuals

### Description

The function provides a lack-of-fit test for the mean structure based on cumulated residuals from the model fit.

### Usage

      lin.test(object, noksSim = 20, seed = 20070325, plotit = TRUE,
      log = "", bp = 0.01, xlab, ylab, ylim, ...)

### Arguments

`object` | object of class 'drc'.
`noksSim` | numeric specifying the number of simulations used to obtain the p-value.
`seed` | numeric specifying the seed value for the random number generator.
`plotit` | logical indicating whether or not the observed cumulated residual process should be plotted. Default is to plot the process.
`log` | character string which should contains '"x"' if the x axis is to be logarithmic, '"y"' if the y axis is to be logarithmic and '"xy"' or '"yx"' if both axes are to be logarithmic. The default is "x". The empty string "" yields the original axes.
`bp` | numeric value specifying the break point below which the dose is zero (the amount of stretching on the dose axis above zero in order to create the visual illusion of a logarithmic scale including 0).
`xlab` | string character specifying an optional label for the x axis.
`ylab` | character string specifying an optional label for the y axis.
`ylim` | numeric vector of length two, containing the lower and upper limit for the y axis.
`...` | additional arguments to be passed further to the basic `plot` method.

### Details

The function provides a graphical model checking of the mean structure in a dose-response model. The graphical display is supplemented by a p-value based on a supremum-type test.

The test is applicable even in cases where data are non-normal or exhibit variance heterogeneity.

### Value

A p-value for test of the null hypothesis that the mean structure is appropriate. Ritz and Martinussen (2009) provide the details.

### Author(s)

Christian Ritz

### References

Ritz, C and Martinussen, T. (2009) Lack-of-fit tests for assessing mean structures for continuous dose-response data, Submitted manuscript

### See Also

Other available lack-of-fit tests are the Neill test (`neill.test`) and ANOVA-based test (`modelFit`).

### Examples

    ## Fitting a log-logistic model to the dataset 'etmotc'
    etmotc.m1<-drm(rgr1~dose1, data=etmotc[1:15,], fct=LL.4())

    ## Test based on umulated residuals
    lin.test(etmotc.m1, 1000)
    #lin.test(etmotc.m1, 10000, plotit = FALSE)  # more precise

    ## Fitting an exponential model to the dataset 'O.mykiss'
    O.mykiss.m1<-drm(weight~conc, data=O.mykiss, fct=EXD.2(), na.action=na.omit)

    ## ANOVA-based test
    modelFit(O.mykiss.m1)

    ## Test based on umulated residuals
    lin.test(O.mykiss.m1, log = "", cl = 0.2, xlab = "Dose (mg/l)", main = "B", ylim = c(-0.6, 0.6))
    #lin.test(O.mykiss.m1, noksSim = 10000, plotit = FALSE)  # more precise

[Package drc version 3.0-1 Index]