R help - Mar 2017 - nls fitting & plotting to data subsets defined by combinations of categorical variables

Dear list, 

I want to apply the same nls function to different subsets of a larger
dataset. These subsets are defined as unique combinations of two
(categorical) variables, each one with two levels, so I should obtain 4
sets of parameters after fitting. 

I have managed to do it in a loop, creating different datasets for each
one of the sub-groups, and then applying the function to each one
independently and finally just merging all parameters in a single
dataset, but this seems pretty inefficient. 

I tried to use by and with, but they don't produce the expected result.
Rather, I get 4 sets of exactly the same parameters (?), so I know that
with/by are not actually doing anything, and the function is applied tothe
dataset as a whole.

Here is the call I tried to use:

test <- with(Data, by(Data, list(Type, Phase), function(x) nls(Response
~ k*exp(-((Duration-mu)^2)/(2*sigma^2)), start=c(mu=0,sigma=150,k=0.9),
upper=c(Inf, Inf, 1), algorithm="port", trace=T,
control=CSJ_FitControl)))

Also, I would like to plot the fitted distributions for each sub-group
in the same plot to be able to directly compare them. I figured that,
since I have the base nls function and the resulting parameters for
each subset (stored in a data frame), I should be able to enter these
on a ggplot call to get the 4 regressions lines plotted along with the
data, but I can't get that to work either. Or is it necessary to plot
this at the fitting stage (i.e. with the original data)?

Thanks for any suggestion

Given that nls() often gives failures of the "singular gradient" type,
you may want to give package nlsr a try with the
nlxb() function. It should be able to use analytic gradients on this expression,
has bounds, and uses a Marquardt
stabilized solver. It can still fail, but is more robust than nls().

I suspect that explicit looping is a safer way to proceed than trying to use
by() within with(). Looping is not the slow
part of this set of computations.

JN

On 2017-03-11 10:37 AM, DANIEL PRECIADO wrote:
> Dear list,
>
> I want to apply the same nls function to different subsets of a larger
> dataset. These subsets are defined as unique combinations of two
> (categorical) variables, each one with two levels, so I should obtain 4
> sets of parameters after fitting.
>
> I have managed to do it in a loop, creating different datasets for each
> one of the sub-groups, and then applying the function to each one
> independently and finally just merging all parameters in a single
> dataset, but this seems pretty inefficient.
>
> I tried to use by and with, but they don't produce the expected result.
> Rather, I get 4 sets of exactly the same parameters (?), so I know that
> with/by are not actually doing anything, and the function is applied tothe
dataset as a whole.
>
> Here is the call I tried to use:
>
> test <- with(Data, by(Data, list(Type, Phase), function(x) nls(Response
> ~ k*exp(-((Duration-mu)^2)/(2*sigma^2)), start=c(mu=0,sigma=150,k=0.9),
> upper=c(Inf, Inf, 1), algorithm="port", trace=T,
> control=CSJ_FitControl)))
>
> Also, I would like to plot the fitted distributions for each sub-group
> in the same plot to be able to directly compare them. I figured that,
> since I have the base nls function and the resulting parameters for
> each subset (stored in a data frame), I should be able to enter these
> on a ggplot call to get the 4 regressions lines plotted along with the
> data, but I can't get that to work either. Or is it necessary to plot
> this at the fitting stage (i.e. with the original data)?
>
> Thanks for any suggestion
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> 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.
>