drc (version 1.1-0)

mixdrc: Fitting multiple dose-response curves with random effects

Description

'mixdrc' fits non-linear mixed models where random effects are assigned to some or all of the parameters.

Usage

mixdrc(object, random, data, startVal)

Arguments

object

an object of class 'drc'.

random

a character string specifying the random effects. Specification is as in nlme.

data

a data frame containing the variables in the model (not optional).

startVal

a numeric vector of initial parameter values for the fixed effects (optional).

Value

• An object of classes 'drc' and 'nlme'.

Details

The function searches through a range of initial values in order to obtain convergence of the estimation procedure.

See Also

Non-linear mixed models can be fitted using nlme.

Examples

## First fitting a model without random effects

#model1 <- multdrc(SLOPE~DOSE, CURVE, data=PestSci,
#collapse=data.frame(HERBICIDE, 1, 1, HERBICIDE))

## Then fitting the same model (the same fixed effects)
##  but with random effects (assigned to d)

#model2 <- mixdrc(model1, random="d~1|CURVE", data=PestSci)
#compParm(model2, "e", "-")

## Fitting a model without random effects
##  but with a Box-Cox transformation

#model3 <- multdrc(SLOPE~DOSE, CURVE, data=PestSci,
#collapse=data.frame(HERBICIDE, 1, 1, HERBICIDE), boxcox=TRUE)

## Then fitting the same model with random effects assigned to d

#model4 <- mixdrc(model3, random="d~1|CURVE", data=PestSci)
#compParm(model4, "e", "-")

## Fitting a model without random effects, but fixing the c parameter at 0
#model5 <- multdrc(SLOPE~DOSE, CURVE, data=PestSci,
#collapse=data.frame(HERBICIDE, 1, 1, HERBICIDE), boxcox=TRUE,
#fct=l4(fixed=c(NA,0,NA,NA)))

## Fitting the corresponding mixed model with random effects on d
#model6 <- mixdrc(model5, random="d~1|CURVE", data=PestSci)

#rm(model1, model2, model3, model4, model5, model6)