Michaelis-Menten model
The functions can be used to fit (shifted) Michaelis-Menten models that are used for modeling enzyme kinetics, weed densities etc.
MM.2(fixed = c(NA, NA), names = c("d", "e"), ...)
MM.3(fixed = c(NA, NA, NA), names = c("c", "d", "e"), ...)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 ":"). |
... |
additional arguments from convenience functions to |
The model is defined by the three-parameter model function
f(x, (c, d, e)) = c + \frac{d-c}{1+(e/x)}
It is an increasing as a function of the dose x, attaining the lower limit c at dose 0 (x=0) and the upper limit d for infinitely large doses. The parameter e corresponds to the dose yielding a response halfway between c and d.
The common two-parameter Michaelis-Menten model (MM.2) is obtained by
setting c equal to 0.
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.
At the moment the implementation cannot deal with infinite concentrations.
Christian Ritz
## Fitting Michaelis-Menten model 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) ED(met.mm.m1, c(10, 50)) ## Calculating bioefficacy: approach 1 coef(met.mm.m1)[4] / coef(met.mm.m1)[5] * 100 ## Calculating bioefficacy: approach 2 EDcomp(met.mm.m1, c(50,50)) ## Simplified models met.mm.m2a <- drm(gain~dose, product, data=methionine, fct=MM.3(), pmodels = list(~1, ~factor(product), ~1)) anova(met.mm.m2a, met.mm.m1) # model reduction not possible met.mm.m2b <- drm(gain~dose, product, data=methionine, fct=MM.3(), pmodels = list(~1, ~1, ~factor(product))) anova(met.mm.m2b, met.mm.m1) # model reduction not possible
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