# SSasymp

0th

Percentile

##### 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 input is 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.

nls, selfStart
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)