Will,
 Is there any reason you can't fit a zero-inflated Poisson model here? I
think there are various functions that can do this in packages such as pscl
and glmmADMB.
cheers
rob

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====== "How can anyone be enlightened, when truth is so poorly lit" =======




On 5 November 2012 01:41, Seth W. Bigelow <seth@swbigelow.net> wrote:

> Will, if your category UM is all zeros, there can be no variance to it, so
> I would say that you cannot make any statistical inference about it.
> Presumably there is no option of collecting more data? Certainly changing
> one of the data points from zero to one gives a cleaner-looking estimate of
> standard error, but it's an incorrect one.
>
> I would consider leaving the UM category out of the formal data analysis.
> You can still report & discuss the findings regarding the UM category, but
> by keeping the made-up variance out of the statistical analysis you can
> avoid giving that impression that the statistical results are contaminated
> in some way.
>
> --Seth
>
> -----Original Message-----
> From: r-sig-geo-bounces@r-project.org [mailto:
> r-sig-geo-bounces@r-project.org] On Behalf Of WillM
> Sent: Saturday, November 03, 2012 7:16 PM
> To: r-sig-geo@r-project.org
> Subject: [R-sig-Geo] Problem with categorical variable coefficients and se
> in glm
>
> Hi all
>
> I'm hoping that this is something that people deal with regularly and you
> can help me out quickly even though it is a bit more of a stats question
> than R.
>
> I have a dataset where data$Resp is a count response variable (lots of 0s)
> so I used a negative binomial glm with a categorical response variable. The
> categories are the types of vegetation that I stratified my sampling by -
> so
> they are not an arbitrary post hoc decision.
>
> The UM category only has 0's for a response and produces a large
> coefficient
> and large standard error (see output below). So I added a small number (1)
> to one row of the UM category to explore what was happening and get a
> better
> result. With a continuous response variable you can add a very small number
> (say 0.001) so that it is still representative of 0, but with this count
> data, 1 is the minimum.
>
> I get a better estimate, but is there some better way of dealing with this
> type of situation? I could possibly combine UM and UB categories, but I did
> want to keep them separate.
>
> Thanks alot :)
>
> >data
>
>    Resp Cat
> 1     0   D
> 2     0   D
> 3     0   D
> 4     0   D
> 5     3   D
> 6     0   D
> 7     0   D
> 8     0   D
> 9    11   F
> 10   11   F
> 11    3   F
> 12   14   F
> 13   19   F
> 14   41   F
> 15   12   S
> 16   55   S
> 17    3   S
> 18    0   S
> 19    0   S
> 20   30   F
> 21    4   F
> 22   10   F
> 23   99  DS
> 24    3  DS
> 25    1  DS
> 26    7  DS
> 27    4  DS
> 28    0  DS
> 29    2  DS
> 30    1  DS
> 31    0  UB
> 32    0  UB
> 33    0  UB
> 34    0  UB
> 35    1  UB
> 36    0  UM
> 37    0  UM
> 38    0  UM
> 39    0  UM
> 40    0  UM
>
> > mod.nb <- glm.nb(data$Resp ~ data$Cat, data=data)
>
> > summary(mod.nb)
>
> Call:
> glm.nb(formula = data$Resp ~ data$Cat, data = data, init.theta =
> 0.5087557508,
>     link = log)
>
> Deviance Residuals:
>      Min        1Q    Median        3Q       Max
> -1.85799  -0.75714  -0.58082  -0.00009   1.95946
>
> Coefficients:
>              Estimate Std. Error z value Pr(>|z|)
> (Intercept)   -0.9808     0.7609  -1.289 0.197409
> data$CatF      3.7464     0.8969   4.177 2.95e-05 ***
> data$CatS      3.6199     0.9932   3.645 0.000268 ***
> data$CatDS     3.6636     0.9128   4.013 5.99e-05 ***
> data$CatUB    -0.6286     1.4043  -0.448 0.654427
> data$CatUM   -18.3218  4215.7113  -0.004 0.996532
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> (Dispersion parameter for Negative Binomial(0.5088) family taken to be 1)
>
>     Null deviance: 77.163  on 39  degrees of freedom
> Residual deviance: 33.700  on 34  degrees of freedom
> AIC: 191.12
>
> Number of Fisher Scoring iterations: 1
>
>
>               Theta:  0.509
>           Std. Err.:  0.152
>
>  2 x log-likelihood:  -177.120
>
> > data1<-data
> > data1[40,1]<- 1 #add a small value to one of the UM categories
> > mod.nb1 <- glm.nb(data1$Resp ~ data1$Cat, data=data1)     #run model
> again
>
> > summary(mod.nb1)
>
> Call:
> glm.nb(formula = data1$Resp ~ data1$Cat, data = data1, init.theta =
> 0.515774723,
>     link = log)
>
> Deviance Residuals:
>     Min       1Q   Median       3Q      Max
> -1.8671  -0.7593  -0.5814  -0.2098   1.9726
>
> Coefficients:
>             Estimate Std. Error z value Pr(>|z|)
> (Intercept)  -0.9808     0.7587  -1.293 0.196112
> data1$CatF    3.7464     0.8934   4.194 2.75e-05 ***
> data1$CatS    3.6199     0.9888   3.661 0.000251 ***
> data1$CatDS   3.6636     0.9092   4.030 5.59e-05 ***
> data1$CatUB  -0.6286     1.4012  -0.449 0.653712
> data1$CatUM  -0.6286     1.4012  -0.449 0.653712
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> (Dispersion parameter for Negative Binomial(0.5158) family taken to be 1)
>
>     Null deviance: 76.133  on 39  degrees of freedom
> Residual deviance: 36.328  on 34  degrees of freedom
> AIC: 196.69
>
> Number of Fisher Scoring iterations: 1
>
>
>               Theta:  0.516
>           Std. Err.:  0.152
>
>  2 x log-likelihood:  -182.686
>
>
> Thanks!
>
>
>
> --
> View this message in context:
> http://r-sig-geo.2731867.n2.nabble.com/Problem-with-categorical-variable-coefficients-and-se-in-glm-tp7581579.html
> Sent from the R-sig-geo mailing list archive at Nabble.com.
>
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