R: Self-Starting Nls Four-Parameter Logistic Model

SSfpl {stats}

R Documentation

Self-Starting Nls Four-Parameter Logistic Model

Description

This selfStart model evaluates the four-parameter logistic function and its gradient. It has an initial attribute that will evaluate initial estimates of the parameters A, B, xmid, and scal for a given set of data.

Usage

SSfpl(input, A, B, xmid, scal)

Arguments

`input`	a numeric vector of values at which to evaluate the model.
`A`	a numeric parameter representing the horizontal asymptote on the left side (very small values of `input`).
`B`	a numeric parameter representing the horizontal asymptote on the right side (very large values of `input`).
`xmid`	a numeric parameter representing the `input` value at the inflection point of the curve. The value of `SSfpl` will be midway between `A` and `B` at `xmid`.
`scal`	a numeric scale parameter on the `input` axis.

Value

a numeric vector of the same length as input. It is the value of the expression A+(B-A)/(1+exp((xmid-input)/scal)). If all of the arguments A, B, xmid, and scal are names of objects, the gradient matrix with respect to these names is attached as an attribute named gradient.

Author(s)

José Pinheiro and Douglas Bates

Examples

Chick.1 <- ChickWeight[ChickWeight$Chick == 1, ]
SSfpl(Chick.1$Time, 13, 368, 14, 6)  # response only
A <- 13; B <- 368; xmid <- 14; scal <- 6
SSfpl(Chick.1$Time, A, B, xmid, scal) # response and gradient
print(getInitial(weight ~ SSfpl(Time, A, B, xmid, scal), data = Chick.1),
      digits = 5)
## Initial values are in fact the converged values
fm1 <- nls(weight ~ SSfpl(Time, A, B, xmid, scal), data = Chick.1)
summary(fm1)

[Package stats version 3.4.1 Index]