[R] No answer in anova.nls

[Nazareno Andrade]

(sorry if this arrives multiple times, I sent it from the wrong email
address to the r-help the first time)

Thanks for both answers. I'll look into that.

I understand I can take do a qualitative evaluation of the fits using
visual tests, but a problem I have is that I'd like to quantify in how
many out of hundreds of downloads each model fits better the data. I
have some a hypothesis that there are two group of downloads, one
modeled by each function. Would there be any automated method for
quantifying this in R?

thank you again,
Nazareno

On Tue, Aug 12, 2008 at 9:19 AM, Bert Gunter <gunter.berton at gene.com> wrote:
> To add to Brian's points (which you should heed!) -- you **may** find it
> also useful to look at (possibly smoothed) residuals to compare lack of fit
> from your alternative models. If any shows up, some subject matter knowledge
> might lead you to choose one or the other of your models -- or neither.
>
> -- Bert Gunter
> Genentech
>
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On
> Behalf Of Prof Brian Ripley
> Sent: Monday, August 11, 2008 11:34 PM
> To: Nazareno Andrade
> Cc: r-help
> Subject: Re: [R] No answer in anova.nls
>
> The reason for no F test showing up is that the additional df is 0 and the
> F value is Inf.  But the underlying problem is that your models are not
> nested and so ANOVA between them is invalid.
>
> I suggest you seek help from a local statistician: your misunderstanding
> and your question about model adequacy are subtle statistical issues and
> not help on R.
>
> On Mon, 11 Aug 2008, Nazareno Andrade wrote:
>
>> Dear R-helpers,
>>
>> I am trying to check whether a model of the form y(t) = a/(1 +b*t) fits
> the
>> curve of downloads per day of a file in a specific online community better
>> than a model of the form y(t) = a*exp(-b*t). For that, I used nls to fit
>> both models and I am now trying to compare the fits with anova. The
> problem
>> I find is that anova does not report an F statistic or a p-value when I
>> compare these two models.
>>
>> The data for a file is typically the following:
>>> d
>>   V1  V2
>> 1   1 293
>> 2   2 101
>> 3   3  63
>> 4   4  53
>> 5   5  42
>> 6   6  19
>> 7   7  28
>> 8   8  23
>> 9   9  18
>> 10 10  17
>> 11 11  14
>> 12 12  18
>> 13 13   5
>> 14 14   9
>> 15 15  10
>> 16 15   0
>>
>> My code:
>>
>> d <-
> read.table(url("http://ece.ubc.ca/~nazareno/85247.arrivalRates<http://ece.ub
> c.ca/%7Enazareno/85247.arrivalRates>
>> "))
>> plot(d)
>> f.exp.nw <- nls(V2 ~ a. * exp(-b. * V1), data = d, list( a. = d$V2[1], b.
> =
>> 0.05))
>> f.exp5.nw <- nls(V2 ~ a. / (1+ b. *V1), data = d, list( a. = d$V2[1], b. =
>> 2))
>> lines(d$V1, predict(f.exp.nw), col = "royalblue")
>> lines(d$V1, predict(f.exp5.nw), col = "orange")
>>
>> anova(f.exp.nw, f.exp5.nw)
>>
>> However, the output from anova.nls is:
>>
>> Analysis of Variance Table
>>
>> Model 1: V2 ~ a. * exp(-b. * V1)
>> Model 2: V2 ~ a./(1 + b. * V1)
>>  Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
>> 1     13     4994.9
>> 2     13      314.7  0    0.0
>>
>> and I cannot interpretate the lack of an F value. Looking at the
>> implementation of the anova.nls() function, this seems to be related to
> the
>> fact that the residuals' degrees of freedom are the same, but I could not
>> find anywhere more information on whether they were required to be
>> different. Thus, I'd greatly appreciate if you could spot any mistakes I
>> might be doing or a (preferably online) reference for more on this issue.
>>
>>
>> As a side question, it would be great also if someone with more experience
>> on this matter could confirm with me that the proper direction for
> checking
>> whether "the y(t) = a/(1 +b*t) form models more precisely the behavior of
>> downloads of files in this communtiy" by quantifying for how many files it
>> outperforms the exponential model.
>>
>> thank you very much in advance,
>> Nazareno
>>
>>       [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> --
> Brian D. Ripley,                  ripley at stats.ox.ac.uk
> Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
> University of Oxford,             Tel:  +44 1865 272861 (self)
> 1 South Parks Road,                     +44 1865 272866 (PA)
> Oxford OX1 3TG, UK                Fax:  +44 1865 272595
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>