Dear list,
I am using glm's to predict count data for a fish species inside and outside
a marine reserve for three different methods of monitoring.
I run glms and figured out the best model using step function for each
methods used.
I predicted two values for my fish counts inside and outside the reserve
using means of each of the covariates (using predict() )
therefore I have only one value for each protection effect (inside/outside),
considered as my mean count.
I used either poisson distribution or negative binomial for the models as
for each techniques, the distribution of the counts for a same species can
be quiet different.
I now need to get a confidence interval for my predicted count and I want to
compute the coefficient of variation.
It looks like the function pois.ci() (package NCstats) gives a confidence
interval for a single given value
or bspln() and bsnb() ("degreenet") gives also CI but using bootstrap. I
therefore need a vector of counts using those latest function, which I don't
have since I have only one predicted values from my Glms.
My questions are the following:
- can I use easily pois.ci to get my confidence interval using my predicted
mean? but It looks like the similar function doesn't exist for the negative
binomial distribution?
- in order to use the bspln and the bsnb function, can I use the covariates
values used to get the parameters of my model to create a "predicted vector"
and then be able to apply those functions on this vector?
I am also not sure about the meaning of the outputs of these two functions.
Which outputs give the CI???
About the coefficient of variation, is it equal to the standard deviation/
mean for all the distributions???
Can I say that for a poisson distribution, it is therefore equal to
1/sqrt(mean) and for a negative binomial distribution, variance = mean +
mean^2/theta (theta the canonical parameter given in glm.nb summary). I can
then calculate my St. Dev and then CV?
I really appreciate your help.
Regards,
--
Stéphanie D'agata
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