a continuous distribution it
is difficult. I'd certainly better take a descriptive way of presenting my data
for
sparse pollen types.
Best wishes
Valérie
Message du 04/02/13 à 13h15
De : Liz Pryde
A : v_coudr...@voila.fr
Copie à :
Objet : Re: [R-sig-eco] proportion data with many zeros
Hi
,
Valérie
Message du 02/02/13 à 20h47
De : Liz Pryde
A : v_coudr...@voila.fr
Copie à : Cade Brian , r-sig-ecology@r-project.org
Objet : Re: [R-sig-eco] proportion data with many zeros
Have you plotted the raw data to have a look at the distribution?
You could try another exponential
quasipoisson on raw
counts or quasibinomial on proportion gives me awful distributions of
residuals
and
meaningless results.
Valérie
Message du 01/02/13 à 17h22
De : Cade, Brian
A : v_coudr...@voila.fr
Copie à : r-sig-ecology@r-project.org
Objet : Re: [R-sig-eco] proportion data
on proportion gives me awful distributions of
residuals and
meaningless results.
Valérie
Message du 01/02/13 à 17h22
De : Cade, Brian
A : v_coudr...@voila.fr
Copie à : r-sig-ecology@r-project.org
Objet : Re: [R-sig-eco] proportion data with many zeros
For a fully parametric approach
Dear all, I am trying to test how the proportion of pollen of different plants
found in the brood cells of a wild bee changes over time. I conducted 4
sampling sessions
(thus time is a factor with 4 levels) and collected several pollen samples for
each time point (300 pollen grains counted for
For a fully parametric approach, you might want to use of zero-inflated
beta distribution (e.g., as available in gamlss package), which is designed
for zero-inflated proportions. Or for a semi-parametric approach, you
could estimated a sequence of quantile regression estimates (e.g., in
package
