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

1 2 |

`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 |

`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 |

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.

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

Christian Ritz

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

Other available lack-of-fit tests are the Neill test (`neill.test`

) and
ANOVA-based test (`modelFit`

).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## 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
``` |

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