# SSweibull

0th

Percentile

##### Self-Starting Nls Weibull Growth Curve Model

This selfStart model evaluates the Weibull model for growth curve data and its gradient. It has an initial attribute that will evaluate initial estimates of the parameters Asym, Drop, lrc, and pwr for a given set of data.

Keywords
models
##### Usage
SSweibull(x, Asym, Drop, lrc, pwr)
##### Arguments
x
a numeric vector of values at which to evaluate the model.
Asym
a numeric parameter representing the horizontal asymptote on the right side (very small values of x).
Drop
a numeric parameter representing the change from Asym to the y intercept.
lrc
a numeric parameter representing the natural logarithm of the rate constant.
pwr
a numeric parameter representing the power to which x is raised.
##### Details

This model is a generalization of the SSasymp model in that it reduces to SSasymp when pwr is unity.

##### Value

a numeric vector of the same length as x. It is the value of the expression Asym-Drop*exp(-exp(lrc)*x^pwr). If all of the arguments Asym, Drop, lrc, and pwr are names of objects, the gradient matrix with respect to these names is attached as an attribute named gradient.

##### References

Ratkowsky, David A. (1983), Nonlinear Regression Modeling, Dekker. (section 4.4.5)

nls, selfStart, SSasymp
library(stats) Chick.6 <- subset(ChickWeight, (Chick == 6) & (Time > 0)) SSweibull(Chick.6$Time, 160, 115, -5.5, 2.5) # response only Asym <- 160; Drop <- 115; lrc <- -5.5; pwr <- 2.5 SSweibull(Chick.6$Time, Asym, Drop, lrc, pwr) # response and gradient getInitial(weight ~ SSweibull(Time, Asym, Drop, lrc, pwr), data = Chick.6) ## Initial values are in fact the converged values fm1 <- nls(weight ~ SSweibull(Time, Asym, Drop, lrc, pwr), data = Chick.6) summary(fm1)