## Herbicide applied to Galium aparine

### Description

Small plants of *Galium aparine*, growing in pots in a green house, were sprayed with the technical
grade phenmidipham herbicide either alone or in mixture with an ester of oleic acid.
The plants were allowed to grow in the green house for 14 days after herbicide treatment.
Then the dry matter was measured per pot.

### Usage

data(G.aparine)

### Format

A data frame with 240 observations on the following 3 variables.

`dose`

a numeric vector of dose value (g/ha)

`drymatter`

a numeric vector of dry matter weights (mg/pot)

`treatment`

a numeric vector giving the grouping: 0: control, 1,2: herbicide formulations

### Source

Cabanne, F., Gaudry, J. C. and Streibig, J. C. (1999) Influence of alkyl oleates on efficacy
of phenmedipham applied as an acetone:water solution on Galium aparine,
*Weed Research*, **39**, 57–67.

### Examples

## Fitting a model with a common control (so a single upper limit: "1")
G.aparine.m1 <- drm(drymatter ~ dose, treatment, data = G.aparine,
pmodels = data.frame(treatment, treatment, 1, treatment), fct = LL.4())
## Visual inspection of fit
plot(G.aparine.m1, broken = TRUE)
## Lack of fit test
modelFit(G.aparine.m1)
## Summary output
summary(G.aparine.m1)
## Predicted values with se and confidence intervals
#predict(G.aparine.m1, interval = "confidence")
# long output
## Calculating the relative potency
EDcomp(G.aparine.m1, c(50,50))
## Showing the relative potency as a
## function of the response level
relpot(G.aparine.m1)
relpot(G.aparine.m1, interval = "delta")
# appears constant!
## Response level in percent
relpot(G.aparine.m1, scale = "percent")
## Fitting a reduced model (with a common slope parameter)
G.aparine.m2 <- drm(drymatter ~ dose, treatment, data = G.aparine,
pmodels = data.frame(1, treatment, 1, treatment), fct = LL.4())
anova(G.aparine.m2, G.aparine.m1)
## Showing the relative potency
relpot(G.aparine.m2)
## Fitting the same model in a different parameterisation
G.aparine.m3 <- drm(drymatter ~ dose, treatment, data = G.aparine,
pmodels = data.frame(treatment, treatment, 1, treatment), fct = LL2.4())
EDcomp(G.aparine.m3, c(50, 50), logBase = exp(1))