I want to make a chart on variables which are taken in 2 different years on 2
the same locations, at different depths, where for every location the
species and the amount of species are determined.
The first chart I want to make is to see how much of every species is
collected in total in a bar or
fferent modeling
approach?
I am mainly interested in the probability of choosing to react (fly or
dive) or not, and then once a reaction has been made, which one is chosen
and how these decisions relate to perpendicular distance to the ship's pat
ic formula
CSHR.shore.fly <- coxph(Surv(entry, exit, to == 1) ~ shore.cat, data
glba.mod)
My variable shore.cat is violating the proportional hazards assumption so I
am trying to add in an interaction with time. Do I interact exit? entry? or
the range of the two?
Thanks, Tim
--
Tim Marcella
Hi,
I cannot figure out how or if I even can plot the results from a nested
multinomial logit model. I am using the mlogit package.
Does anyone have a lead on any tutorials? Both of the vignettes are lacking
plotting instructions.
Thanks, Tim
--
Tim Marcella
[[alternative HTML
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> R-help@r-proj
Sorry I jumped the gun. That does not provide you with the same plot as gg2
that you are aiming for.
-T
On Wed, Apr 16, 2014 at 7:37 PM, Tim Marcella wrote:
> I think all you have to do is add type="response" to your call for the
> predictions.
>
> Does this work fo
uch a chart per Location or Station as called in the
data set and third such a chart per Sa.Code
Gr,
Marcella
2012/11/15 Berend Hasselman
>
> On 15-11-2012, at 11:14, Gaj Stan (BIGCAT) wrote:
>
> > Hello all,
> >
> > I have the following problem:
> >
> > 1)
Hi,
My data is characterized by many zeros (82%) and overdispersion. I have
chosen to model with hurdle regression (pscl package) with a negative
binomial distribution for the count data. In an effort to validate the
model I would like to calculate the RMSE of the predicted vs. the observed
values
Hi,
I am working with hurdle models in the pscl package to model zero inflated
overdispersed count data and want to incorporate censored observations into
the equation. 33% of the observed positive count data is right censored,
i.e. subject lost to follow up during the duration of the study. Can t
Hi,
I am using a two part hurdle model to account for zero inflation and
overdispersion in my count data. I would like to account for a segmented or
breakpoint relationship in the binomial logistic hurdle model and pass
these results onto the count model (negative binomial).
Using the segemented
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