SSasymp
Self-Starting Nls Asymptotic Regression Model
This selfStart model evaluates the asymptotic regression
function and its gradient. It has an initial attribute that
will evaluate initial estimates of the parameters Asym, R0,
and lrc for a given set of data.
- Keywords
- models
Usage
SSasymp(input, Asym, R0, lrc)Arguments
- input
- a numeric vector of values at which to evaluate the model.
- Asym
- a numeric parameter representing the horizontal asymptote on
the right side (very large values of
input). - R0
- a numeric parameter representing the response when
inputis zero. - lrc
- a numeric parameter representing the natural logarithm of the rate constant.
Value
-
a numeric vector of the same length as
input. It is the value of
the expression Asym+(R0-Asym)*exp(-exp(lrc)*input). If all of
the arguments Asym, R0, and lrc are
names of objects, the gradient matrix with respect to these names is
attached as an attribute named gradient.
See Also
Examples
library(stats)
Lob.329 <- Loblolly[ Loblolly$Seed == "329", ]
SSasymp( Lob.329$age, 100, -8.5, -3.2 ) # response only
Asym <- 100
resp0 <- -8.5
lrc <- -3.2
SSasymp( Lob.329$age, Asym, resp0, lrc ) # response and gradient
getInitial(height ~ SSasymp( age, Asym, resp0, lrc), data = Lob.329)
## Initial values are in fact the converged values
fm1 <- nls(height ~ SSasymp( age, Asym, resp0, lrc), data = Lob.329)
summary(fm1)
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