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species richness, GLM and negative values

On 10/29/2014 04:27 PM, Ludovico Frate wrote:
Use the Poisson to model the number of species in each sample, so use 
data like this:

Plot  Time   DS
1      A         4
1      B         7
2      A         0
2      B         1
3      A         23
3      B         7
...

Then you fit the model

Mgood <- glm(DS ~ Plot + Time, family=poisson())

where Plot and Time are factors (use Plot <- factor(Plot), for example, 
if you need to).

You're interested in the Time effect, which is the average difference 
between the numbers of species in the plots in the different times. The 
Plot effect controls for different plots having different numbers of 
species overall. If you look at summary(Mgood), the Time effect is the 
log of the ratio of the species richnesses in times A and B. It will be 
written as TimeB, which means it's log(E.B/E.A) (where E.A and E.B are 
the expected species richnesses at times A and B). So, for example, an 
estimate of 0.4 would mean that at Time B there are exp(0.4)=1.49 times 
more species at time B than time A.

Bob