# How to interpret coefficients in a Poisson regression?

How can I interpret the main effects (coefficients for dummy-coded factor) in a Poisson regression?

Assume the following example:

treatment <- factor(rep(c(1, 2), c(43, 41)),
levels = c(1, 2),
labels = c("placebo", "treated"))
improved <- factor(rep(c(1, 2, 3, 1, 2, 3), c(29, 7, 7, 13, 7, 21)),
levels = c(1, 2, 3),
labels = c("none", "some", "marked"))
numberofdrugs <- rpois(84, 10)+1
healthvalue <- rpois(84, 5)
y <- data.frame(healthvalue, numberofdrugs, treatment, improved)
test <- glm(healthvalue ~ numberofdrugs+treatment+improved, y, family=poisson)
summary(test)


The output is:

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)       1.88955    0.19243   9.819   <2e-16 ***
numberofdrugs    -0.02303    0.01624  -1.418    0.156
treatmenttreated -0.01271    0.10861  -0.117    0.907   MAIN EFFECT
improvedsome     -0.13541    0.14674  -0.923    0.356   MAIN EFFECT
improvedmarke    -0.10839    0.12212  -0.888    0.375   MAIN EFFECT
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


I know that the incident rate for numberofdrugs is exp(-0.023)=0.977. But how do I interpret the main effects for the dummy variables?

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This seems like a question better suited for crossvalidated.com –  Sacha Epskamp May 21 '11 at 15:15
1 sec earlier seriously? :) –  Sacha Epskamp May 21 '11 at 15:15
hehe. great minds think alike –  Vincent May 21 '11 at 15:22
@chi: Your edits were excellent. More comprehensive. that the ones I was planning –  DWin May 21 '11 at 19:03

## migrated from stackoverflow.comMay 21 '11 at 21:38

This question came from our site for professional and enthusiast programmers.

The exponentiated 'numberofdrugs' coefficient is the multiplicative term to use to calculate the estimated "healthvalue" when 'numberofdrugs' increases by 1 unit. In the case of categorical (factor) variables, the exponentiated coefficient is the multiplicative term relative to the base (first factor) level for that variable (since R uses treatment contrasts by default). The exp(Intercept) is the baseline rate, and all other estimates would be relative to it.

In your example the estimated 'healthvalue' for someone with 2 drugs, "placebo" and improvement=="none" would be (using addition inside exp as the equivalent of multiplication):

 exp( 1.88955  + 2 *-0.02303 + 0 +0 )
[1] 6.318552


While someone on 4 drugs, 'treated', and 'some' improvement would have an estimated 'healthvalue' of

exp( 1.88955  + 4 *-0.02303  + -0.01271 + -0.13541)
[1] 38.44813


ADDENDUM: The way to return coefficients from regression objects is generally to use the coef() extractor function (done with a different random realization below):

 coef(test)
#   (Intercept)    numberofdrugs treatmenttreated     improvedsome   improvedmarked
#   1.18561313       0.03272109       0.05544510      -0.09295549       0.06248684


So the calculation of the estimate for a subject with 4 drugs, treated, with moderate improvement would be:

 exp( sum( coef(test)[ c(1,2,3,4) ]* c(1,4,1,1) ) )
[1] 3.592999


And the linear predictor for that case should be the sum of:

 coef(test)[c(1,2,3,4)]*c(1,4,1,1)
#    (Intercept)    numberofdrugs treatmenttreated     improvedsome
#     1.18561313       0.13088438       0.05544510      -0.09295549

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+1 on both SO and CV for answering without complaint :) –  Brandon Bertelsen May 21 '11 at 22:52
So it let you vote twice? How nice. I thought offering an R code implementation of the interpretation might rescue it from the forces of classification rectitude. The OP composed a nice self contained example. Maybe I should have demonstrated an extractor function, so I think I will. –  DWin May 22 '11 at 3:00
Yes, I upvoted your response on SO, then it moved here and I upvoted again :) –  Brandon Bertelsen May 22 '11 at 4:35
Thanks so far! I know the realtion between the dummies and cavariables, but I'm just interested of how to interpret the Main effects (I marked them). Is it possible to take teh incidient rate from a Main effects, for example for the dummy treated 'exp(-0.012)=0.99' and interpret it as the rate from which the healtvalue decreases, when switching from reference category to treated? It must be, no? –  MarkDollar May 22 '11 at 8:40
The exponentiated coefficients are always interpreted as ratios. Ratios of 'what' to 'what' depends on the units of analysis. 'Rates' are different, having an implicit number and time value. So if you are willing to change your terminology, then perhaps,'yes". Best answers come from describing the analysis situation fully. –  DWin May 22 '11 at 14:01