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
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)