Yes. relevel looks good! I will give that a try. Thanks!
Katharine B. Miller, PhD Research Fisheries Biologist NMFS, Alaska Fisheries Science Center 17109 Lena Loop Rd Juneau, AK 99801 (907) 789-6410 (907) 789-6094 (fax) On Thu, Feb 25, 2016 at 2:12 PM, Bert Gunter <bgunter.4...@gmail.com> wrote: > Ah yes. Forgot about relevel(). That would be simpler. > > Cheers, > Bert > Bert Gunter > > "The trouble with having an open mind is that people keep coming along > and sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > > On Thu, Feb 25, 2016 at 1:19 PM, Marc Schwartz <marc_schwa...@me.com> > wrote: > > Hi, > > > > Well...as I understand the question: > > > > If Katharine wants to use treatment contrasts, which are the default, > the easiest thing to do may be use to ?relevel to specify the reference > level for the IV factor. > > > > However, as Bert notes, there are other contrasts that can be used, > which would affect the interpretation of the comparisons of the factor > levels and the help pages that he references would cover a high level > review of the options, with more detail provided in the reference therein. > > > > Also, if there is an excess of zeroes, you may need to consider the use > of a zero inflated model and the 'pscl' package on CRAN is worthy of > consideration, along with its vignette on count regression models: > > > > https://cran.r-project.org/web/packages/pscl/ > > https://cran.r-project.org/web/packages/pscl/vignettes/countreg.pdf > > > > The ?vuong test in that package can also be helpful for comparing > zero-inflated models with non zero-inflated models. > > > > Regards, > > > > Marc Schwartz > > > > > >> On Feb 25, 2016, at 2:48 PM, Bert Gunter <bgunter.4...@gmail.com> > wrote: > >> > >> You can re-set the contrasts for the factor, though whether this is > >> "easier" is a matter of personal preference. > >> > >> See ?C or ?contrasts, which I understand to be alternative ways of > >> doing the same thing (and would appreciate correction is this is > >> wrong). Or this can be done through the "contrasts" argument of > >> glm.nb. > >> > >> Cheers, > >> Bert > >> > >> > >> > >> > >> Bert Gunter > >> > >> "The trouble with having an open mind is that people keep coming along > >> and sticking things into it." > >> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > >> > >> > >> On Thu, Feb 25, 2016 at 10:53 AM, Katharine Miller - NOAA Federal > >> <katharine.mil...@noaa.gov> wrote: > >>> Hi, > >>> I am using the glm.nb function to evaluate differences in the catch of > >>> individual species of fish across three river tributaries. The > dependent > >>> variable is catch per unit effort (CPUE), and the independent variable > is > >>> the the tributary (Trib_cat). CPUE is derived from the fish counts > divided > >>> by the effort, so the response is not a count per se, but I think the > >>> negative binomial is appropriate because of the large numbers of zeros > in > >>> the dependent variable. Trib_cat is a column with 1, 2, or 3 depending > on > >>> which tributary it is representing. > >>> > >>> I have the following code: > >>> > >>> dataS<-read.table("OtherSpecies.txt", sep="", header=T) > >>> > >>> dataS <- within(dataS, { > >>> Trib_cat <- factor(Trib_cat, levels = 1:3, labels = c("MM", "NM", > "SM")) > >>> Year<-factor(Year) > >>> }) > >>> > >>> ## separate out the species of interest > >>> coregonid<-subset(dataS, Spec_age=="Coregonid") > >>> > >>> fit.CT<-glm.nb(CPUE ~ Trib_cat, data=coregonid, link = log) > >>> summary(fit.CT) > >>> > >>> The result comes out like this: > >>> Call: > >>> glm.nb(formula = CPUE ~ Trib_cat, data = coregonid4, init.theta = > >>> 0.1723775759, > >>> link = log) > >>> > >>> Deviance Residuals: > >>> Min 1Q Median 3Q Max > >>> -0.9239 -0.8055 -0.6687 -0.6286 2.9020 > >>> > >>> Coefficients: > >>> Estimate Std. Error z value Pr(>|z|) > >>> (Intercept) -0.7805 0.2497 -3.125 0.00178 ** > >>> Trib_catNM -0.2140 0.3485 -0.614 0.53921 > >>> Trib_catSM 0.7393 0.3296 2.243 0.02488 * > >>> --- > >>> > >>> This gives me the difference in CPUE between the NM and SM tributaries > >>> compared to the MM tributary (acting here as the reference group). > What I > >>> need is to compare all of the tributaries with all the others - so I > need > >>> to run the model twice with a different reference group on the second > run. > >>> I can do this by changing the numbering of the Trib_cat field in the > >>> underlying database (e.g. changing MM from 1 to 3, SM from 2 to 1, > etc) and > >>> re-running the model, but I, as I have a number of species to do this > for, > >>> I was wondering if there was an easier way to specify which group to > use as > >>> the reference group when calling the model. > >>> > >>> Thanks for any help. > > > [[alternative HTML version deleted]] ______________________________________________ R-help@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.
