# [R] How can I extract information from list which class is nls

David Winsemius dwinsemius at comcast.net
Wed Apr 27 16:12:54 CEST 2011

```On Apr 27, 2011, at 9:28 AM, Schatzi wrote:

> Here is more information on the equation. It is a growth function:
>
> Growth = a + b*(1-exp(-k*time))
>
> where a, b and k are parameters. I wanted to test the difference in
> total growth between treatments and the parameters a + b represent
> total growth. Thus, I figured that I could add the parameters and
> the SE to test the treatment differences. I also need the rate
> constant k and the initial values a and their treatment differences.
> I found the equation I posted on this website:
> http://sci.tech-archive.net/Archive/sci.math/2004-07/4780.html
> This book was referenced: Kendall & Stuart's Advanced Theory of
> Statistics

You should look at the vignettes and examples in the `fda` package.
fda == Functional Data Analysis and the authors have a book that adds
further background.

http://ego.psych.mcgill.ca/misc/fda/ex-growth-a2.html

You might also try:
RSiteSearch("growth curve")

--
David.
>
> Thanks for all you your help.
>
>
> From: ml-node+3477116-1659458804-211414 at n4.nabble.com [mailto:ml-node+3477116-1659458804-211414 at n4.nabble.com
> ]
> Sent: Tuesday, April 26, 2011 09:29 PM
> Subject: Re: How can I extract information from list which class is
> nls
>
> On Tue, Apr 26, 2011 at 2:21 PM, Schatzi <[hidden email]</user/
> SendEmail.jtp?type=node&node=3477116&i=0&by-user=t>> wrote:
>
>> How do I extract the standard error of the parameter estimates?
>>
>> Also, if I would like to add two parameters together (x+y), can I
>> use this
>> equation to calculate the new standard error?:
>> x = parameter 1
>> y = parameter 2
>> xSE = SE parameter 1
>> ySE = SE parameter 2
>>
>> NewSE=(x+y)*sqrt((xSE/x)^2+(ySE/y)^2)
>
> Try taking the square roots of their variance:
>
>> example(nls)
>> sqrt(diag(vcov(fm1DNase1)))
>      Asym       xmid       scal
> 0.07815395 0.08135321 0.03227080
>>
>> # it gives the same result as another solution
>> # offered on this thread
>> coef(summary(fm1DNase1))[, "Std. Error"]
>      Asym       xmid       scal
> 0.07815395 0.08135321 0.03227080
>
>
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