Re: [R-sig-phylo] Non Parametric PGLS

in the ratio are thus different. Best, Julien From: sclaramunt...@gmail.com Date: Thu, 18 Jun 2015 12:52:11 -0500 To: r-sig-phylo@r-project.org Subject: Re: [R-sig-phylo] Non Parametric PGLS Hi Sergio, If what you want to do is to partition your morphometric data into size

Re: [R-sig-phylo] Non Parametric PGLS

whether or not the shape is independent of the measure of size (the geometric mean of all others measurements). I attach a picture to illustrate the bias introduced but it should be easy to simulate this issue with R... Hope it makes sense, Julien Subject: Re: [R-sig-phylo] Non Parametric PGLS From

Re: [R-sig-phylo] Non Parametric PGLS

: sclaramunt...@gmail.com Date: Fri, 19 Jun 2015 08:57:11 -0500 To: r-sig-phylo@r-project.org Subject: Re: [R-sig-phylo] Non Parametric PGLS Hi Julien, Mosimann’s method was proposed for the study of allometry, so it would be strange if it only applies to isometric cases. Can you provide

Re: [R-sig-phylo] Non Parametric PGLS

Hi Sérgio. What Simon ( I, in my blog post) suggested is that to test the 'normality assumption' you need to first transform the residuals with the inverse Cholesky decomposite matrix. This will give you a vector in which the values should be normal independent (assuming that the

Re: [R-sig-phylo] Non Parametric PGLS

Hi Liam, Again, thank you for the answer. Yes, I'm aware that they are phylogenetically correlated and that they need to be subsequently analysed with methods such as PGLS. In fact, when I apply a normality test to ( chol(solve(vcv(tree)))%*%residuals(fit))the transformed residuals are normally

Re: [R-sig-phylo] Non Parametric PGLS

Hi Sergio, If what you want to do is to partition your morphometric data into size and shape components, then you can use Mosimann’s methods which do not require regressions or phylogenetic corrections. Mosimann, J. E. 1970. Size allometry: size and shape variables with charac- terization of

Re: [R-sig-phylo] Non Parametric PGLS

bias in the ratio are thus different. Best, Julien From: sclaramunt...@gmail.com Date: Thu, 18 Jun 2015 12:52:11 -0500 To: r-sig-phylo@r-project.org Subject: Re: [R-sig-phylo] Non Parametric PGLS Hi Sergio, If what you want to do is to partition your morphometric data into size and shape

Re: [R-sig-phylo] Non Parametric PGLS

Hello again, Thanks for your help. This kind of solved my problem. I normally use some kind of test (shapiro or komolgorov) to test for normality. I know histograms or qqnorm plots a more helpful, but they are more vulnerable to each others interpretation. So, just to make clear one thing: these

Re: [R-sig-phylo] Non Parametric PGLS

Hi Sérgio. Liam is right. But we do expect the normalised residuals to be approximately Normal. You can calculate the normalised residuals by pre-multiplying the raw residuals by the inverse of the Cholesky decomposition of the phylogenetic variance-covariance matrix, and then dividing by

Re: [R-sig-phylo] Non Parametric PGLS

Sergio, You can fit a non-Gaussian phylo regression with MCMCglmm. HTH, Dan. On Jun 17, 2015, at 9:40 AM, Sergio Ferreira Cardoso sff.card...@campus.fct.unl.pt wrote: Hello all, I'm having a problem with a Multiple Regression PGLS analysis that I'm performing. The residuals are

Re: [R-sig-phylo] Non Parametric PGLS

Hi Sérgio. It might be worth pointing out that we do not expect that the residuals from a phylogenetic regression to be normal. I described this with respect to the phylogenetic ANOVA on my blog (http://blog.phytools.org/2013/02/a-comment-on-distribution-of-residuals.html), but this applies

[R-sig-phylo] Non Parametric PGLS

Hello all, I'm having a problem with a Multiple Regression PGLS analysis that I'm performing. The residuals are not normal and it's difficult to bring them to normality. In these cases, are there any alternatives to the linear model? I know that for non-phylogenetic analyses other models exist,