SSbragg | R Documentation |
These functions provide the Bragg's equations, that is based on
the normal (Gaussian) distribution and it supports a maximum,
a minimum and inflection points. These functions provide the
equations with 4 (bragg.4.fun) and 3 (bragg.3.fun) parameters
with self-starters for the nls
function (NLS.bragg.4, NLS.bragg.3) and the self-starters for
the drm
function in the drc package (DRC.bragg.4, DRC.bragg.3)
bragg.4.fun(X, b, c, d, e)
bragg.3.fun(X, b, d, e)
NLS.bragg.4(X, b, c, d, e)
NLS.bragg.3(X, b, d, e)
DRC.bragg.4()
DRC.bragg.3()
X |
a numeric vector of values at which to evaluate the model |
b |
model parameter (relates to slope at inflection point) |
d |
model parameter (maximum value) |
e |
model parameter (abscissa at maximum value) |
c |
model parameter (lower asymptote) |
The Bragg's equation is parameterised as:
f(x) = c + \left(d - c \right) \, \exp(- b \cdot (X - e)^2)
for the 4-parameter model. For the 3-parameter model, c is equal to 0. It depicts a bell-shaped curve
bragg.4.fun, bragg.3.fun, NLS.bragg.4 and NLS.bragg.3 return a numeric value, while DRC.bragg.4 and DRC.bragg.3 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/
library(statforbiology)
X <- c(5, 10, 15, 20, 25, 30, 35, 40, 45, 50)
Y1 <- c(0.1, 2, 5.7, 9.3, 19.7, 28.4, 20.3, 6.6, 1.3, 0.1)
Y2 <- Y1 + 2
# nls fit
mod.nls <- nls(Y1 ~ NLS.bragg.3(X, b, d, e) )
mod.nls2 <- nls(Y2 ~ NLS.bragg.4(X, b, c, d, e) )
# drm fit
mod.drc <- drm(Y1 ~ X, fct = DRC.bragg.3() )
mod.drc2 <- drm(Y2 ~ X, fct = DRC.bragg.4() )
plot(mod.drc, ylim = c(0, 30), log = "")
plot(mod.drc2, add = TRUE, col = "red")
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