This is not actually a question about R but rather about understanding
generalized linear models. If you are going to use such models you
need to learn what they are and what they mean. The book "An
Introduction to Generalized Linear Models" by Annette Dobson and
Adrian Barnett (Chapman and Hall/CRC) might be a good place to
start.
cheers,
Rolf Turner
On 16/09/12 05:48, lpchre wrote:
I am trying to do a quasipoisson regression to know if the frequency of
drinking of my subject is related to temperature. The problem is that I'm
not sure how to interpret my result.
1) Since my result is signifiant, can I tell that the frequency of drinking
of my subject increase linearly or exponentially?
2) When I want to quantify the increase, do I need to do an exponential
transformation of my coefficient before I can interpret the result?
See below the result of my regression.
Call:
glm(formula = Freqtot_drink ~ temper, family = quasipoisson,
data = data_t, na.action = na.omit)
Deviance Residuals:
Min 1Q Median 3Q Max
-4.477 -1.663 -1.150 -0.196 8.613
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.88645 1.29215 -6.103 1.47e-08 ***
temper 0.33970 0.04525 7.508 1.44e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasipoisson family taken to be 7.589744)
Null deviance: 1023.37 on 115 degrees of freedom
Residual deviance: 561.11 on 114 degrees of freedom
(11 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 6
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