SSlogCurve: Logarithmic curve

In OnofriAndreaPG/aomisc: Statistical methods for the agricultural sciences

Logarithmic curve

Description

These functions provide the logarithmic model (logCurve) with self-starter for the nls function and for the drm function in the drc package.

Usage

  logCurve.fun(predictor, a, b)
  NLS.logCurve(predictor, a, b)
  NLS.logCurveNI(predictor, b)
  DRC.logCurve(fixed = c(NA, NA), names = c("a", "b"))

Arguments

predictor	a numeric vector of values at which to evaluate the model.
a	model parameter
b	model parameter
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 reasonable.

Details

The logarithmic curve is given by the following function:

f(x) = a + b \log (X)

This curve crosses the X axis at X = a. We can force it through the origin by setting a = 0; this is possible by setting 'fixed = c(=, NA), while, in the 'nls()' function, we need to use the NLS.logCurveNI()' function.

Value

logCurve.fun, NLS.logCurve and NLS.logCurveNI return a numeric value, while DRC.logCurve returns a list containing the nonlinear function, the self starter function and the parameter names.

Note

DRC.logCurve() is for use with the function drm.

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/

Examples

X <- c(1,2,4,5,7,12)
Y <- c(1.97, 2.32, 2.67, 2.71, 2.86, 3.09)

# lm fit
model <- lm(Y ~ log(X) )

# nls fit
model <- nls(Y ~ NLS.logCurve(X, a, b) )

# drm fit
model <- drm(Y ~ X, fct = DRC.logCurve() )

OnofriAndreaPG/aomisc documentation built on Feb. 26, 2024, 8:21 p.m.