[R] Help on adding a negative binomial density plot

[David]

Dear David,


thanks ever so much for your answer.


Do you mean predicting the original values based on the fitted model and then comparing observed vs. predicted by, for example, a scatterplot?


Thanks,


David

________________________________
De: David Winsemius <dwinsemius at comcast.net>
Enviado: lunes, 2 de octubre de 2017 18:18:36
Para: David
Cc: R-help
Asunto: Re: [R] Help on adding a negative binomial density plot


> On Oct 2, 2017, at 2:05 AM, David <dasolexa at hotmail.com> wrote:
>
> Dear list,
>
>
> I am just starting on analysis of count data in R 3.4.0. My dataset was obtained from counting particles on a surface before andd after a cleaning process. The sampling positions on the surface are pre-defined and are the same before and after cleaning.  I have ~20% of 0's. I want to know if the cleaning process was useful at reducing the number of particles.
>
>
> I first fit a negative binomial model using
>
>
>> nbFit<-glmer.nb(Count ~ Cleaning + (1|Sampling_point) , data = myCountDB)
>
>
>
> I now would like to add a curve to the histogram representing the negative binomial density function fitted to my data using
>
>
>> curve(dnbinom(x=, size=, prob=, mu=), add=TRUE)

Why not use the predict function in that package?

See ?merMod

--
David.
>
>
> But I am struggling defining the arguments to dnbinom.
>
>
> Using the str() function on the nbFit object I see there are many fields returned. And I get lost reading the ?glmer.nb help, greatly because of my lack of knowledge. Which ones should I use?
>
>
> Thanks ever so much for your valuable help
>
>
> Dave
>
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>
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David Winsemius
Alameda, CA, USA

'Any technology distinguishable from magic is insufficiently advanced.'   -Gehm's Corollary to Clarke's Third Law






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