SSW2 {statforbiology} | R Documentation |
Weibull equation (Type II)
Description
These functions provide the Weibull equation (type II), that has an
asymmetric sygmoidal shape and it has been used for bioassay work.
These functions provide the 4-, 3- and 2-parameter equations
(W2.4.fun(), W2.3.fun() and W2.2.fun()) as well as the self-starters
for the nls
function (NLS.W2.4(), NLS.W2.3() and NLS.W2.2()
Usage
W2.4.fun(predictor, b, c, d, e)
W2.3.fun(predictor, b, d, e)
W2.2.fun(predictor, b, e)
NLS.W2.4(predictor, b, c, d, e)
NLS.W2.3(predictor, b, d, e)
NLS.W2.2(predictor, b, e)
Arguments
predictor |
a numeric vector of values at which to evaluate the model |
b |
model parameter (slope at inflection point) |
c |
model parameter (lower asymptote) |
d |
model parameter (higher asymptote) |
e |
model parameter (abscissa at inlection point) |
Details
These functions provide the Weibull (Type I) equation for bioassay work This equation (4-parameters) is parameterised as:
f(x) = c + (d - c) (1 - \exp( - \exp (b \, (\log(x) - \log(e)))))
For the 3- and 2-parameters model, c is equal to 0, while for the 2-parameter model d is equal to 1.
Value
All these functions return a numeric value
Author(s)
Andrea Onofri
References
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/
Ritz, C., Jensen, S.M., Gerhard, D., Streibig, J.C., 2019. Dose-response analysis using R, CRC Press. ed. USA.
Examples
library(statforbiology)
dataset <- getAgroData("brassica")
model <- nls(FW ~ NLS.W2.4(Dose, b, c, d, e), data = dataset)
model <- nls(FW ~ NLS.W2.3(Dose, b, d, e), data = dataset)
model <- nls(FW/max(FW) ~ NLS.W2.2(Dose, b, e), data = dataset)
summary(model)