Hello,

I have a question about modelling via  glm. I have a dataset (see dput)
that looks like as if it where poisson distributed (actually I would
appreciate that) but it isnt because  mean unequals var.


> mean (x)
[1] 901.7827
> var (x)
[1] 132439.3


Anyway, I tried to model it via poisson and quasipoisson. Actually, just to
get an impression how glm works. But I dont know how to interprete the
data. Of course this is the case because my knowledge concerning logistic
regressions is rather limited. Hoping there is somebody with mercy I would
like to understand which parameters are important, e.g. which paramter
might give me a hint that a poisson model is a bad idea. For hints
concerning some tutorials  about reading glm-output I would appreciate as
well.

Thanks
Wim


> skn300.glmp <- glm (freq~n, data=skn300.tab, family=poisson)
> summary (skn300.glmp)

Call:
glm(formula = freq ~ n, family = poisson, data = skn300.tab)

Deviance Residuals:
    Min       1Q   Median       3Q      Max
-51.332   -9.383   -6.599   -3.959   55.111

Coefficients:
              Estimate Std. Error z value Pr(>|z|)
(Intercept)  7.2374375  0.0093285   775.8   <2e-16 ***
n           -0.0539424  0.0003699  -145.8   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 71731  on 96  degrees of freedom
Residual deviance: 37383  on 95  degrees of freedom
AIC: 37800

Number of Fisher Scoring iterations: 6

>
> skn300.glmq <- glm (freq~n, data=skn300.tab, family=quasipoisson)
> summary (skn300.glmq)

Call:
glm(formula = freq ~ n, family = quasipoisson, data = skn300.tab)

Deviance Residuals:
    Min       1Q   Median       3Q      Max
-51.332   -9.383   -6.599   -3.959   55.111

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)  7.237438   0.186381  38.831  < 2e-16 ***
n           -0.053942   0.007391  -7.298  8.8e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for quasipoisson family taken to be 399.1874)

    Null deviance: 71731  on 96  degrees of freedom
Residual deviance: 37383  on 95  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 6


>  dput (skn300.tab)
structure(list(n = 1:97, freq = c(0L, 0L, 0L, 0L, 1L, 7L, 40L,
100L, 276L, 543L, 952L, 1414L, 1853L, 2199L, 2435L, 2270L, 2042L,
1679L, 1386L, 1108L, 922L, 792L, 642L, 597L, 453L, 424L, 370L,
297L, 278L, 218L, 208L, 172L, 174L, 149L, 124L, 98L, 98L, 67L,
78L, 67L, 46L, 34L, 31L, 42L, 34L, 21L, 28L, 18L, 18L, 18L, 10L,
19L, 6L, 9L, 10L, 6L, 6L, 5L, 3L, 9L, 4L, 3L, 4L, 5L, 2L, 6L,
4L, 2L, 2L, 3L, 3L, 0L, 0L, 0L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 1L, 2L, 1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 1L),
    kum = c(0L, 0L, 0L, 0L, 1L, 8L, 48L, 148L, 424L, 967L, 1919L,
    3333L, 5186L, 7385L, 9820L, 12090L, 14132L, 15811L, 17197L,
    18305L, 19227L, 20019L, 20661L, 21258L, 21711L, 22135L, 22505L,
    22802L, 23080L, 23298L, 23506L, 23678L, 23852L, 24001L, 24125L,
    24223L, 24321L, 24388L, 24466L, 24533L, 24579L, 24613L, 24644L,
    24686L, 24720L, 24741L, 24769L, 24787L, 24805L, 24823L, 24833L,
    24852L, 24858L, 24867L, 24877L, 24883L, 24889L, 24894L, 24897L,
    24906L, 24910L, 24913L, 24917L, 24922L, 24924L, 24930L, 24934L,
    24936L, 24938L, 24941L, 24944L, 24944L, 24944L, 24944L, 24944L,
    24946L, 24947L, 24947L, 24947L, 24947L, 24947L, 24947L, 24948L,
    24948L, 24948L, 24949L, 24951L, 24952L, 24952L, 24952L, 24952L,
    24952L, 24954L, 24954L, 24954L, 24954L, 24955L)), .Names = c("n",
"freq", "kum"), row.names = c(NA, -97L), class = "data.frame")

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