gompGrowth {drc}R Documentation

Gompertz growth models

Description

Gompertz growth model, with biologically meaningful parameters. Different parameterisations have been included for specific cases and needs.

Usage

gompGrowth.1(fixed = c(NA, NA, NA), names = c("c", "m", "plateau"))
gompGrowth.2(fixed = c(NA, NA, NA), names = c("c", "d", "plateau"))
gompGrowth.3(fixed = c(NA, NA, NA), names = c("b", "c", "plateau"))

Arguments

fixed numeric vector. Specifies which parameters are fixed and at what value they are fixed. NAs for parameter that are not fixed.
names vector of character strings giving the names of the parameters (should not contain ":"). The default parameter names are: init, m, plateau.

Details

The Gompertz growth model is a Gompertz curve, that has been reparameterised to include some biologically meaningful parameters. The mean function for gompGrowth.1() is:

f(x) = f(x) = plateau * exp ( - (m/c) * exp ( - c * x ) )

The parameter plateau is the final plant weight, reached for x going to infinity the parameter c is relative growth rate at inflection point and the parameter m is the initial relative growth rate (when x=0). Thus the curve is monotonously increasing in x. The mean function for gompGrowth.2() is:

f(x) = plateau * exp ( - exp ( c * ( d - x ) ))

where the parameter c is the relative growth rate at inflection point and the parameter d is the abscissa of the inflection point. The mean function for gompGrowth.3() is the classical Gompertz function:

f(x) = plateau * exp ( - b * exp ( - c * x ) )

where b is proportional to the initial relative growth rate (m = b * c).

Value

A list of class drcMean, containing the mean function, the self starter function, the parameter names.

Note

Growth functions are generally fitted on log-transformed weight data, which equals to setting bc parameter to 0

Author(s)

Andrea Onofri

References

Roderick Hunt, 1982. Plant Growth Curves. Edward Arnold Publisher, Great Britain, 248 pp

Examples


## Fitting a Gompertz growth curve

beet.model <- drm(weightInf ~ DAE, data  = beetGrowth, fct=gompGrowth.1())
plot(beet.model, log = "")
summary(beet.model)


[Package drc version 1.6-1 Index]