SSpoly2: Simple polynomial regression functions

In OnofriAndreaPG/aomisc: Statistical methods for the agricultural sciences

Simple polynomial regression functions

Description

These functions provide the simple polynomial (second order) regression model (poly2), the polynomial regression model with self-starter for the nls function (NLS.poly2) and the polynomial regression function with self-starter for the drm function in the drc package (DRC.poly2). Fitting linear functions with nonlinear least square regression is sub-optimal, but it might be useful for comparing alternative models.

Usage

  poly2.fun(predictor, a, b, c)
  NLS.poly2(predictor, a, b, c)
  DRC.poly2(fixed = c(NA, NA, NA), names = c("a", "b", "c"))

Arguments

predictor	a numeric vector of values at which to evaluate the model
a	numeric. The response when the predictor is equal to 0.
b	numeric. The slope at X = 0
c	numeric. Regression 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 simple polynomial (second order) regression model is given by the following equation:

f(x) = a + b x + c x^2

Value

poly2.fun and NLS.poly2 return a numeric value, while DRC.poly2 returns a list containing the nonlinear function, the self starter function and the parameter names.

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

# Polynomial regression
X <- seq(5, 50, 5)
Y <- c(12.6, 74.1, 157.6, 225.5, 303.4, 462.8, 
       669.9, 805.3, 964.2, 1169)

model <- nls(Y ~ NLS.poly2(X, a, b, c))
summary(model)
model <- drm(Y ~ X, fct = DRC.poly2())
summary(model)

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