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:
SSfpl(input, A, B, xmid, scal)
REQUIRED 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.
SEE ALSO:
,
EXAMPLES:
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