MM {drc}R Documentation

Michaelis-Menten model

Description

The functions can be used to fit (shifted) Michaelis-Menten models that are used for modeling enzyme kinetics, weed densities etc.

Usage

 
  MM.2(fixed = c(NA, NA), names = c("Vm", "K"))
  
  MM.3(fixed = c(NA, NA, NA), names = c("y0", "Vm", "K"))  

Arguments

fixed numeric vector. Specifies which parameters are fixed and at what value they are fixed. NAs for parameter that are not fixed.
names a vector of character strings giving the names of the parameters (should not contain ":"). The order of the parameters is: y0, Vm, K (see under 'Details').

Details

The model is given by the expression

f(x) = y0 + frac{Vm x}{K + x}

It is a three-parameter model. A commonly used derived two-parameter model (MM.2) is obtained by setting y0=0.

Value

A list of class drcMean, containing the mean function, the self starter function, the parameter names and other components such as derivatives and a function for calculating ED values.

Note

At the moment the implementation cannot deal with infinite concentrations.

Author(s)

Christian Ritz

See Also

Related models are the asymptotic regression models AR.2 and AR.3.

Examples


met.mm.m1<-drm(gain~dose, product, data=methionine, fct=MM.3(), pmodels = list(~1, ~factor(product), ~factor(product)))
plot(met.mm.m1, log = "", ylim=c(1450, 1800))
summary(met.mm.m1)

## Calculating bioefficacy: approach 1
coef(met.mm.m1)[4] / coef(met.mm.m1)[5] * 100

## Calculating bioefficacy: approach 2
SI(met.mm.m1, c(50,50))

## Simplified models
met.mm.m2<-drm(gain~dose, product, data=methionine, fct=MM.3(), pmodels = list(~1, ~factor(product), ~1))
anova(met.mm.m2, met.mm.m1)  # model reduction not possible

met.mm.m3<-drm(gain~dose, product, data=methionine, fct=MM.3(), pmodels = list(~1, ~1, ~factor(product)))
anova(met.mm.m3, met.mm.m1)  # model reduction not possible


[Package drc version 1.4-2 Index]