# Modeling count data over years through poisson regression

I have the following problem. I am trying to use the poisson regression on my data, which look like this:

Year   Resistance.proportion
1990            0.367
1991            0.678
1992            0.786


I am using the following code in R to do the modeling:

> model <- glm(Resistance.proportion ~ Year, data=sulfodf_for_poisson_r, family = poisson(link=log))


After executing the command, I get the following message in R:

Warning messages:
1: In dpois(y, mu, log = TRUE) : non-integer x = 0.362069
2: In dpois(y, mu, log = TRUE) : non-integer x = 0.375000
3: In dpois(y, mu, log = TRUE) : non-integer x = 0.723684
4: In dpois(y, mu, log = TRUE) : non-integer x = 0.458333
5: In dpois(y, mu, log = TRUE) : non-integer x = 0.595238
6: In dpois(y, mu, log = TRUE) : non-integer x = 0.666667
7: In dpois(y, mu, log = TRUE) : non-integer x = 0.875000
8: In dpois(y, mu, log = TRUE) : non-integer x = 0.583333
9: In dpois(y, mu, log = TRUE) : non-integer x = 0.321429
10: In dpois(y, mu, log = TRUE) : non-integer x = 0.933333


Is the problem here really that the numbers are floats and have to be integers or is there something more subtle? Furthermore, if I summarize the model, I get an AIC that is infinite, which is also a bad thing as I understood.

I have looked through several questions as Fitting a Poisson GLM in R - issues with rates vs. counts, but they do not provide an helpful answer to me.

So my questions are:

1.)Why am I getting these errors and how can I resolve them?

2.) Once the model is correct, what is a good way to graphically represent it?

• Your title says 'count data' but the example data seems to contain proportions rather than counts Commented Apr 4, 2016 at 11:36
• You could try beta regression instead of Poisson. Commented Apr 4, 2016 at 11:42
• Oh man...that was a pretty stupid error. yes, transformed the count data into proportions. Commented Apr 4, 2016 at 11:44
• @PeterFlom thanks, i will read up on beta-regression! Commented Apr 4, 2016 at 11:49