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multdrc(formula, curve, collapse, weights, data=NULL, boxcox = FALSE,
bcAdd = 0, varPower = FALSE, startVal, fct = l4(), na.action = na.fail,
hetvar = NULL, robust="mean", type = "continuous", cm = NULL,
logDose = NULL, fctList = NULL, control = mdControl())
curve
corresponding to control measurements.mdControl
.
If the argument boxcox
is TRUE it may be necessary to constrain the optimisation (using mdControl
)
in order to comply with the requirement of non-negative values for the Box-Cox transformation. Alternatively a constant can be
added in the Box-Cox transformation (using bcAdd
). The residuals available will be based on the transformed response values.
If varPower
is TRUE then 'robust="mean"'. Also if "boxcox=TRUE" then 'varPower=FALSE'.
For robust estimation MAD (median abslolute deviance) is used to estimate the residual variance.nls
can be used for fitting general nonlinear regression models.## Fitting response 'rootl' to dose 'conc' in the dataset 'ryegrass'
## (type ?ryegrass to learn about the data set)
model1 <- multdrc(rootl~conc, data = ryegrass)
summary(model1)
## Fitting a 5-parameter logistic model
model2 <- multdrc(ryegrass, fct = l5())
summary(model2)
## Fitting a 4-parameter logistic model
## with Box-Cox transformation
model3 <- multdrc(ryegrass, boxcox = TRUE)
summary(model3)
## Example using 'varPower' argument
## modelling variance as power of mean
model4 <- multdrc(ryegrass, varPower = TRUE)
summary(model4)
## Example using 'logDose' argument
logdose <- c(-8.045757, -7.568636, -7.086186, -6.607303, -6.130768, -5.653647, -5.180456, -4.698970)
response <- c(7.941, 16.619, 11.021, 19.767, 30.487, 64.434, 74.422, 52.472)
model5 <- multdrc(response ~ logdose, logDose=10)
plot(model5, conLevel=-9) # the default level is too high
## Example using 'fctLink'
model6 <- multdrc(DryMatter ~ Dose,Herbicide,
data=S.alba, fctList=list(l3(), l4()))
summary(model6)
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