braincousens {drc} | R Documentation |
'braincousens' provides a very general way of specifying Brain-Cousens' modified log- logistic model for describing hormesis, under various constraints on the parameters.
braincousens(lowerc = c(-Inf, -Inf, -Inf, -Inf, -Inf), upperc = c(Inf, Inf, Inf, Inf, Inf), fixed = c(NA, NA, NA, NA, NA), names = c("b", "c", "d", "e", "f"), scaleDose = TRUE, fctName, fctText)
lowerc |
numeric vector. The lower bound on parameters. Default is minus infinity. |
upperc |
numeric vector. The upper bound on parameters. Default is plus infinity. |
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 default is reasonable (see under 'Usage'). The order of the parameters is: b, c, d, e, f (see under 'Details'). |
scaleDose |
logical. If TRUE dose values are scaled around 1 during estimation; this is required for datasets where all dose values are small. |
fctName |
optional character string used internally by convenience functions. |
fctText |
optional character string used internally by convenience functions. |
The Brain-Cousens model is given by the expression
f(x) = c + frac{d-c+fx}{1+exp(b(log(x)-log(e)))}
which is a five-parameter model.
It is a modification of the four-parameter logistic curve to take hormesis into account.
The value returned is a list containing the non-linear function, the self starter function, the parameter names and additional model specific objects.
This function is for use with the function drm
or multdrc
.
Christian Ritz
Brain, P. and Cousens, R. (1989) An equation to describe dose responses where there is stimulation of growth at low doses, Weed Research, 29, 93–96.
The convenience functions of braincousens
are BC.4
and BC.5
.
## Brain-Cousens model with the constraint b>1 model1 <- multdrc(ryegrass, fct=braincousens(fixed=c(NA, NA, NA, NA, NA), lowerc=c(1, -Inf, -Inf, -Inf, -Inf)), control=mdControl(constr=TRUE)) summary(model1) ## Brain-Cousens model with the constraint f>0 model2 <- multdrc(ryegrass, fct=braincousens(fixed=c(NA, NA, NA, NA, NA), lowerc=c(-Inf, -Inf, -Inf, -Inf, 0)), control=mdControl(constr=TRUE)) summary(model2) rm(model1, model2)