| 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 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. |
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.
Jose Pinheiro and Douglas Bates
data( ChickWeight ) 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)