Data are from an experiment, comparing the potency of the two herbicides glyphosate and bentazone in
white mustard *Sinapis alba*.

1 |

A data frame with 68 observations on the following 3 variables.

`Dose`

a numeric vector containing the dose in g/ha.

`Herbicide`

a factor with levels

`Bentazone`

`Glyphosate`

(the two herbicides applied).`DryMatter`

a numeric vector containing the response (dry matter in g/pot).

The lower and upper limits for the two herbicides can be assumed identical, whereas slopes and ED50 values are different (in the log-logistic model).

Christensen, M. G. and Teicher, H. B., and Streibig, J. C. (2003) Linking fluorescence
induction curve and biomass in herbicide screening, *Pest Management Science*,
**59**, 1303–1310.

See the examples sections for `drm`

and `EDcomp`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Fitting a log-logistic model with
## common lower and upper limits
S.alba.LL.4.1 <- drm(DryMatter~Dose, Herbicide, data=S.alba, fct = LL.4(),
pmodels=data.frame(Herbicide,1,1,Herbicide))
summary(S.alba.LL.4.1)
## Applying the optimal transform-both-sides Box-Cox transformation
## (using the initial model fit)
S.alba.LL.4.2 <- boxcox(S.alba.LL.4.1, method = "anova")
summary(S.alba.LL.4.2)
## Plotting fitted regression curves together with the data
plot(S.alba.LL.4.2)
``` |

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