SSfpl {stats} R Documentation

## 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)

Jose 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
getInitial(weight ~ SSfpl(Time, A, B, xmid, scal), data = Chick.1)
## 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 2.2.1 Index]