Lack-of-fit test for the mean structure based on cumulated residuals
The function provides a lack-of-fit test for the mean structure based on cumulated residuals from the model fit.
lin.test(object, noksSim = 20, seed = 20070325, plotit = TRUE, log = "", bp = 0.01, xlab, ylab, ylim, ...)
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 |
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).
## 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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