The logistic model
The general asymmetric five-parameter logistic model for describing dose-response relationships.
logistic(fixed = c(NA, NA, NA, NA, NA), names = c("b", "c", "d", "e", "f"),
method = c("1", "2", "3", "4"), ssfct = NULL,
fctName, fctText)
L.3(fixed = c(NA, NA, NA), names = c("b", "d", "e"), ...)
L.4(fixed = c(NA, NA, NA, NA), names = c("b", "c", "d", "e"), ...)
L.5(fixed = c(NA, NA, NA, NA, NA), names = c("b", "c", "d", "e", "f"), ...)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: b, c, d, e, f (see under 'Details'). |
method |
character string indicating the self starter function to use. |
ssfct |
a self starter function to be used. |
fctName |
optional character string used internally by convenience functions. |
fctText |
optional character string used internally by convenience functions. |
... |
Additional arguments (see |
The default arguments yields the five-parameter logistic mean function given by the expression
f(x) = c + \frac{d-c}{(1+\exp(b(x - e)))^f}
The model is different from the log-logistic models llogistic and llogistic2
where the term
log(x)
is used instead of
x
.
The model is sometimes referred to as the Boltzmann model.
The value returned is a list containing the nonlinear function, the self starter function and the parameter names.
Christian Ritz
## Fitting the four-parameter logistic model ryegrass.m1 <- drm(rootl ~ conc, data = ryegrass, fct = L.4()) summary(ryegrass.m1) ## Fitting an asymmetric logistic model ## requires installing the package 'NISTnls' # Ratkowsky3.m1 <- drm(y~x, data = Ratkowsky3, # fct = L.5(fixed = c(NA, 0, NA, NA, NA))) # plot(Ratkowsky3.m1) # summary(Ratkowsky3.m1) ## okay agreement with NIST values ## for the two parameters that are the same
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