mr.test.RdThe function provides a lack-of-fit test for the mean structure based on the Mizon-Richard test as compared to a specific alternative model.
mr.test(object1, object2, object, x, var.equal = TRUE, component = 1)
| object1 | object of class 'drc' (null model). |
|---|---|
| object2 | object of class 'drc' (alternative model). |
| object | object of class 'drc' (fitted model under alternative). |
| x | numeric vector of dose values. |
| var.equal | logical indicating whether or not equal variances can be assumed across doses. |
| component | numeric vector specifying the component(s) in the parameter vector to use in the test. |
The function provides a p-value indicating whether or not the mean structure is appropriate.
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 chosen mean structure is appropriate as compared to the alternative mean structure provided (see Ritz and Martinussen (2011) for a detailed explanation).
Ritz, C and Martinussen, T. (2011) Lack-of-fit tests for assessing mean structures for continuous dose-response data, Environmental and Ecological Statistics, 18, 349--366
This functionality is still experimental: Currently, the null and alternative models are hardcoded! In the future the function will be working for null and alternative models specified by the user.
See also modelFit for details on the related lack-of-fit test against an ANOVA model.
## Fitting log-logistic and Weibull models ## The Weibull model is the alternative etmotc.m1<-drm(rgr1~dose1, data=etmotc[1:15,], fct=LL.4()) etmotc.m2 <- update(etmotc.m1, fct=W1.4()) ## Fitting the fitted model (using the alternative model) etmotc.m3 <- drm(fitted(etmotc.m1)~dose1, data=etmotc[1:15,], fct=W1.4()) ## Handling missing values xVec <- etmotc[1:15,]$dose1 xVec[1:8] <- 1e-10 # avoiding 0's ## Obtaining the Mizon-Richard test mr.test(etmotc.m1, etmotc.m2, etmotc.m3, xVec, var.equal = FALSE)#> Statistic p-value Difference SE #> -1.65084985 0.09876924 -0.01936982 0.01173324