| SSfpl {stats} | R Documentation |
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.
SSfpl(input, A, B, xmid, scal)
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 |
B |
a numeric parameter representing the horizontal asymptote on
the right side (very large values of |
xmid |
a numeric parameter representing the |
scal |
a numeric scale parameter on the |
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.
José Pinheiro and Douglas Bates
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)