statforbiology (version 0.9.9)

logCurve: 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"))

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.

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.

Author

Andrea Onofri

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.

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() )