Dear Mr. Bolker,
Thank you for the fast response.
I also know that a poisson (or negative binomial ) regression of glm is
generally modelled using an offset variable.
In this case, when a weights term instead of the offset is used, this
gave me significant coefficients of covariance.
I understand that the weights function for exponential family
distributions in glm affects the variance of response variable.
I was just wondering whether my first model is a completely wrong model
and the use of offset variable is valid in the case that
response variable is not proportional to offset variable such as my
dataset.
Sincerely,
Joon-Taek
2023년 10월 29일 (일) 오전 3:25, Ben Bolker <bbol...@gmail.com
<mailto:bbol...@gmail.com>>님이 작성:
Using an offset of log(Effort) as in your second model is the more
standard way to approach this problem; it corresponds to assuming that
catch is strictly proportional to effort. Adding log(Effort) as a
covariate (as illustrated below) tests whether a power-law model (catch
propto (Effort)^(b+1), b!=0) is a better description of the data. (In
this case it is not, although the confidence intervals on b are very
wide, indicating that we have very little information -- this is not
surprising since the proportional range of effort is very small
(246-258) in this data set.
In general you should *not* check overdispersion of the raw data
(i.e., the *marginal distribution* of the data, you should check
overdispersion of a fitted (e.g. Poisson) model, as below.
cheers
Ben Bolker
edata <- data.frame(Catch, Effort, xx1, xx2, xx3)
## graphical exploration
library(ggplot2); theme_set(theme_bw())
library(tidyr)
edata_long <- edata |> pivot_longer(names_to="var", cols =-c("Catch",
"Effort"))
ggplot(edata_long, aes(value, Catch)) +
geom_point(alpha = 0.2, aes(size = Effort)) +
facet_wrap(~var, scale="free_x") +
geom_smooth(method = "glm", method.args = list(family =
"quasipoisson"))
#
library(MASS)
g1 <- glm.nb(Catch~xx1+xx2+xx3+offset(log(Effort)), data=edata)
g2 <- update(g1, . ~ . + log(Effort))
g0 <- glm(Catch~xx1+xx2+xx3+offset(log(Effort)), data=edata,
family = poisson)
performance::check_overdispersion(g0)
summary(g1)
summary(g2)
options(digits = 3)
confint(g2)
summary(g1)
On 2023-10-28 3:30 a.m., 유준택 wrote:
> Colleagues,
>
>
>
> I have a dataset that includes five variables.
>
> - Catch: the catch number counted in some species (ind.)
>
> - Effort: fishing effort (the number of fishing vessels)
>
> - xx1, xx2, xx3: some environmental factors
>
> As an overdispersion test on the “Catch” variable, I modeled with
negative
> binomial distribution using a GLM. The “Effort” variable showed a
gradually
> decreasing trend during the study period. I was able to get the
results I
> wanted when considered “Effort” function as a weights function in the
> negative binomial regression as follows:
>
>
>
> library(qcc)
>
>
Catch=c(25,2,7,6,75,5,1,4,66,15,9,25,40,8,7,4,36,11,1,14,141,9,74,38,126,3)
>
>
Effort=c(258,258,258,258,258,258,258,254,252,252,252,252,252,252,252,252,252,252,252,248,246,246,246,246,246,246)
>
>
xx1=c(0.8,0.5,1.2,0.5,1.1,1.1,1.0,0.6,0.9,0.5,1.2,0.6,1.2,0.7,1.0,0.6,1.6,0.7,0.8,0.6,1.7,0.9,1.1,0.5,1.4,0.5)
>
>
xx2=c(1.7,1.6,2.7,2.6,1.5,1.5,2.8,2.5,1.7,1.9,2.2,2.4,1.6,1.4,3.0,2.4,1.4,1.5,2.2,2.3,1.7,1.7,1.9,1.9,1.4,1.4)
>
>
xx3=c(188,40,2,10,210,102,117,14,141,28,48,15,220,115,10,14,320,20,3,10,400,150,145,160,460,66)
>
> #
>
> edata <- data.frame(Catch, Effort, xx1, xx2, xx3)
>
> #
>
> qcc.overdispersion.test(edata$Catch, type="poisson")
>
> #
>
> summary(glm.nb(Catch~xx1+xx2+xx3, weights=Effort, data=edata))
>
> summary(glm.nb(Catch~xx1+xx2+xx3+offset(log(Effort)), data=edata))
>
>
>
> I am not sure the application of the weights function to the negative
> binomial regression is correct. Also I wonder if there is a
better way
> doing this. Can anyone help?
>
> [[alternative HTML version deleted]]
>
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