Thank you both, I will try using a zero inflated negative binomial as suggested. I had success with negative binomial on previous runs but only when I had fewer covariates and only ran a portion (10%) of the data. I may also try to reduce the number of covariates in the model (i.e., combine some of my landcover [LCOVER] classifications). I have considered using logistic regression and may end up trying that. I appreciate both of your input and will let you know (i.e., post) the results of these suggestions and what ends up working for the benefit of you and others. Thank you for your time and quick responses!! Nate Nathan Svoboda Graduate Research Assistant Carnivore Ecology Lab Mississippi State University
________________________________ From: Marc Schwartz [mailto:marc_schwa...@me.com] Sent: Thu 5/24/2012 3:09 PM To: Nathan Svoboda Cc: David Winsemius; r-help@r-project.org Subject: Re: [R] R Error: System is computationally singular Nathan, This does help, as in the first cut you provided, there was no variability in LOCS for LCOVER >= 5 and you have very few values of LOCS > 0 (you still do, relative to the scale of the total). Have you tried using a zero inflated negative binomial model (dist = "negbin") rather than poisson? I am not sure that the assumption of a zero inflated poisson distribution is reasonable with your data. Also, at least in this cut of the data, you have no 4's in LOCS and no 8's in LCOVER (same as before). If my math is correct only 0.006% of your LOCS values are > 0. I am also not convinced that you have enough data to differentiate between 1 and >=1 of whatever it is you are counting in LOCS. If that is the case, you might want to consider using logistic regression with a dichotomous response variable of LOCS == 0 versus LOCS >= 1. You seem to be in the general realm of very rare events given the distribution of LOCS in your data. Regards, Marc Schwartz On May 24, 2012, at 2:41 PM, Nathan Svoboda wrote: > Hi David, > > My apologies, I am not sure if this makes a big difference in your assessment > of the problem, but the results I just sent were only from a portion (1/15) > of the data. The dataset is rather large and the computer I am currently > using to set up the models is limited in its capabilities to analyze large > datasets. When I run the code you provided on a larger portion of the data > (1/2) this is the output I receive: > > LCOVER > LOCS 1 2 3 4 5 6 7 9 > 0 1692196 630659 550623 6140352 180896 255512 785929 63756 > 1 141 30 48 279 9 14 36 1 > 2 17 4 5 14 3 3 4 1 > 3 0 0 0 3 0 0 1 0 > 5 2 0 0 0 0 0 0 0 > > Thanks again for your time and assistance, > > Nate > > Nathan Svoboda > Graduate Research Assistant > Mississippi State University > > > On May 24, 2012, at 1:57 PM, Nathan Svoboda wrote: > >> Greetings, >> >> I am trying to fit a zero-inflated Poisson model using zeroinfl() >> from the >> pscl library. I have 5 covariates (4 continuous, 1 categorical); the >> categorical variable has 7 levels. I have had success fitting >> models that >> contain only the continuous covariates; however, when I add the >> categorical >> variable to any of the models (or if I run it by itself) I get the >> following >> error: >> >> Error in solve.default(as.matrix(fit$hessian)) : >> >> system is computationally singular: reciprocal condition number = >> 3.46934e-20 >> >> The code I am using is: >> >> library(pscl) >> f1 <- formula(LOCS ~ as.factor(LCOVER) + D_ROADS + D_WATER + D_EDGE + >> D_GRASS) >> ZIP1 <- zeroinfl(f1, dist="poisson", link = "logit", data = FAWNS) >> >> There is no correlation between my covariates. Also, I tried >> reducing my >> categorical covariate to 3 levels and still receive the same error. >> Can >> anyone suggest why I may be getting this error when I add the >> categorical >> covariate? >> > > What does this show: > > with( FAWNS, table(LOCS, LCOVER) ) > > -- > David Winsemius, MD > West Hartford, CT [[alternative HTML version deleted]] ______________________________________________ R-help@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.
