SScousens85 | R Documentation |
These functions provide the rectangula hyperbola that was devided by Cousens (1985)
for modelling the relationship between crop yield and weed density. The function was
derived from the yield-loss function, and contains parameters that are revelant for
competition studies. These functions provide the
equation (cousens85.fun), the self-starters for the nls
function (NLS.cousens85) and the self-starters for
the drm
function in the drc package (DRC.cousens85)
cousens85.fun(predictor, Ywf, i, A)
NLS.cousens85(predictor, Ywf, i, A)
DRC.cousens85(fixed = c(NA, NA, NA), names = c("Ywf", "i", "A"))
predictor |
a numeric vector of values at which to evaluate the model |
Ywf |
model parameter (Weed-free yield) |
i |
model parameter (initial slope) |
A |
model parameter (maximum percentage yield loss) |
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. The default is usually reasonable. |
This equation is parameterised as:
f(x) = Ywf \, \frac{(1 - (i predictor)} {(100 \, (1 + i \, predictor/A)))}
It depicts a decreasing curve with no inflection point. The curve is equal to 'Ywf' when x = 0 and the lower asymptote is at 'A' multiplied by 'Ywf/100'
cousens85.fun, NLS.cousens85 return a numeric value, while DRC.cousens85 return a list containing the nonlinear function and the self starter function
Andrea Onofri
Ratkowsky, DA (1990) Handbook of nonlinear regression models. New York (USA): Marcel Dekker Inc.
Onofri, A. (2020). A collection of self-starters for nonlinear regression in R. See: https://www.statforbiology.com/2020/stat_nls_usefulfunctions/
Cousens, R., 1985. A simple model relating yield loss to weed density. Annals of Applied Biology 107, 239–252. https://doi.org/10.1111/j.1744-7348.1985.tb01567.x
library(statforbiology)
dataset <- getAgroData("Sinapis")
# nls fit
mod.nls <- nls(yield ~ NLS.cousens85(density, Ywf, i, A),
data = dataset )
summary(mod.nls)
mod.nls2 <- drm(yield ~ density, fct = DRC.cousens85(), data = dataset )
summary(mod.nls2)
plot(mod.nls2)
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