SSfpl: Self-Starting Nls Four-Parameter Logistic Model

Description Usage Arguments Value Author(s) See Also Examples

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

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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

See Also

nls, selfStart

Examples

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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)