SSfpl
Self-Starting Nls Four-Parameter Logistic Model
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.
- Keywords
- models
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
inputvalue at the inflection point of the curve. The value ofSSfplwill be midway betweenAandBatxmid. - scal
- a numeric scale parameter on the
inputaxis.
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.
See Also
Examples
library(stats)
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)
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