Hi,
If your adventurous you could probably write your own model in R or Stan
or similar. The wrapped Normal or the Von Mise distribution are circular
and map from 0 to 2pi. My suggestion would be to include a species level
phylogenetic "random effect" for the mean parameter to account for the
aut
The alternative to MCMCglmm would be to use stan or bugs for writing your
own sampling statement + priors. You'll have more control than with
MCMCglmm, but it will have even more of a learning curve.
Using stan will also most likely be faster than using any single R package.
Cheers,
Peter
On We
Hi Andrea,
To paraphrase Gelman and Hill 2007 on regression modeling: a coefficient
estimate is considered significant when the mean/modal estimate is more
than 2 standard errors away from 0. This means, your beta estimate
(e.g. 0.1292656)
is known with some kind of error. If the estimate - 2*erro
Quick correction: index variables are transformed into n x (k - 1) matrices
of INDICATOR variables.
On Wed, Feb 4, 2015 at 5:25 PM, Peter Smits wrote:
> Hi Will,
>
> Quick answer to that question: yes.
>
> The key is that categorical variables cannot be modeled directly in a
Hi Will,
Quick answer to that question: yes.
The key is that categorical variables cannot be modeled directly in a GLM
framework. These categorical variables, or index variables, are transformed
into n x (k - 1) matrices of index variables. These index variables are
binary where a 1 corresponds t
scaling of branch lengths, I agree with Joe that there is
> nothing particular about 1, other than providing an easier interpretation
> for the numerical value of the phylogenetic variance.
> Cheers,
> Cecile.
>
>
> On 12/14/2014 01:03 PM, Peter Smits wrote:
>
>>
m of the shared branch lengths. Is
this appropriate? Why or why not?
Any input would be much appreciated.
Cheers,
Peter Smits
On Mon, Aug 11, 2014 at 3:34 PM, Edwin Lebrija Trejos
wrote:
>
> Dear all,
>
>
> I am plant ecologist using a 2-level hierarchical Bayesian model
> (inpleme