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
 
 
________________________________

From: David Winsemius [mailto:dwinsem...@comcast.net]
Sent: Thu 5/24/2012 1:54 PM
To: Nathan Svoboda
Cc: r-help@r-project.org
Subject: Re: [R] R Error: System is computationally singular




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




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