[R] nls and factor

[Christian Ritz]

Hi Manuel,

an alternative to the approach pointed out by Prof. Ripley is to use
the package 'drc' which allows one or more parameters in a non-linear
regression model to depend on a factor.  

You will need the latest version available at www.bioassay.dk (an older
version is available on CRAN).


Bconc <- runif(30,0.1,10)
Aconc <- runif(30,0.1,10)
At <- runif(30,1,30)
Bt <- runif(30,1,30)

BKm <- 1
AKm <- 0
EBoth <- -0.41

yB <- 100*exp(EBoth*Bt)*Bconc/(BKm+Bconc)+rnorm(30,0,1)
yA <- 75*exp(EBoth*At)*Aconc/(AKm+Aconc)+rnorm(30,0,1)


## Constructing the dataset
tvalues <- c(At,  Bt)
conc <- c(Aconc, Bconc)
yfactor <- factor(rep(c("A", "B"), c(30, 30)))
y <- c(yA, yB)


## Defining the non-linear function
twodimMM <- function()
{
    fct <- function(x, parm)
    {
        parm[, 1]*exp(parm[, 2]*x[, 1])*x[, 2]/(parm[, 3] + x[, 2])
    }

    ssfct <- function(dataset)  # a very simple self starter function
    {
        return(c(90, -0.5, 0.8))
    }

    names <- c("lev", "Ev", "km")  # parameter names

    return(list(fct=fct, ssfct=ssfct, names=names))
}


library(drc)

model1 <- multdrc(y~cbind(tvalues,conc), yfactor, fct=twodimMM())
summary(model1)


## Collapsing the Ev parameters into a single parameter
model2 <- multdrc(y~cbind(tvalues,conc), yfactor,
collapse=data.frame(yfactor,1,yfactor), fct=twodimMM())

anova(model2, model1)  # model reduction is insignificant
summary(model2)



Christian