Thanks Jarrod,

My models are running much faster now, I have not yet found a phylogeny
small enough where using a covariance matrix for tree tips gives better
results.
cheers,
Luis Verde

On Wed, Sep 12, 2012 at 2:24 AM, Jarrod Hadfield <j.hadfield@ed.ac.uk>wrote:

> Hi,
>
> You will be best pruning the phylogeny for each data set.  You should get
> the same answer if you don't, but it may take longer (both time per
> iteration, and more iterations because of slower mixing). Just creating a
> covariance matrix for the tips is generally a bad idea unless the phylogeny
> is small. Retaining internal nodes allows a sparser inverse, and retains
> structure that algorithms for efficiently solving sparse linear systems can
> recognise and exploit.
>
> The default is to scale the distance from root to tip to one unit. If the
> tree is non-ultrametric this is not possible, and you will get an error
> saying the tree cannot be scaled. You can leave it unscaled, or stretch the
> tree to be ultrametric and then scale.
>
> Cheers,
>
> Jarrod
>
>
>
>
> Quoting Luis Verde <luisd@ciencias.unam.mx> on Fri, 7 Sep 2012 17:02:54
> +1000:
>
>  Hi,
>>
>> I've been using MCMCglmm to do ordinal regressions with phylogenetic
>> structure. At the moment I've been using the ginverse argument, and a
>> sparse inverse matrix created with
>>
>>  inverseA(phylo.object)$Ainv
>>>
>>
>> after running MCMCglmm, I get the warning that "some combinations in
>> animal
>> do not exist and missing records have been generated"
>>
>> I assume this is because internal nodes are part of the inverse matrix and
>> I only have data for the tips. If I then only use a subset of my data, I
>> get the same warning; but my parameter estimates seem reasonable. Does the
>> program skip these rows internally? I tried to create a matrix for tips
>> only and I would always end up with a matrix too large in size that would
>> crash the computers. This wouldn't happen when using all nodes.
>>
>> My question is, should I be pruning my tree to match every subset of data
>> and then generating an inverse matrix for tips only? Is the default por
>> inverseA to scale branch lengths?
>>
>> best regards,
>>
>> Luis
>>
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>>
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