I´ve just found this article and I´m writing this email hoping you could shed some light on an analysis I´m performing.

I am trying to analyze some data about animal behaviour and would need some help or advice regarding which non-parametric test should I use.

The variables I have are:

-Response variable: a continuous one (both positive and negative)

-Explicatory variable: a factor with 6 levels

-Random effect variable: as the same animal performing some behavioural task was measured more than once.

As I have a random effect variable, I chose a GLM model. Then, when checking the normality and homoscedasticity assumptions, Shapiro-Wilks test showed there was no normality and QQplots revealed there weren´t patterns nor outliers in my data. So the question would be: which non-parametric test would be optimal in this case, knowing that I would like to perform certain a posteriori comparisons (and not all-against-all comparisons)?

My database has lots of zeros responses in some conditions, I´ve read that for t-students tests lacking of normality due to lots of zeros it´s OK to turn a blind eye on lack of normality (Srivastava, 1958; Sullivan & D'agostino, 1992) ... is there something similar with GLM?

Thank you so much in advance for any advice you could provide.

Kind regards,

Yair Barnatan

Ph.D. Student - Physiology and Molecular Biology Department

Faculty of Science

University of Buenos Aires

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Is it possible to extract the original variables from the "new" variables sorted by decreasing eigenvalues (V1 to V4) ?

Any guidance available ?

Thank you! ]]>

The help to the jagam() function states that you can use "s", "te", "ti" or "t2" splines. So that is probably why it does not work with "ns" splines.

One solution would be not to use JAGS, but STAN for that. There is a packages "brms" which allows you to fit GAMs with any kinds of splines in the Bayesian setting. It's great!

Cheers, Petr ]]>

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jagam(y ~ x1 + ns(x2, df=7), family=possion) ]]>

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I agree with you and usually try to have my scientific work in .csv and .rmd on CDs and DVDs. The most important things are printed. And of course good old field journals are safekeeped. Sometimes they are very helpful in current work. ]]>

I am struggling with this Autocorrelation tests: is there any suggestion to generate correlograms with specific lag distances (in meters) taken from some coordinates in the field? I have some points in a grid and I would like to plot the Moran index to test for autocorrelation (in a specific z variable, measured at each point). at 50, 150, and 200 meters radius in all points. Any suggestion? I don't know how to do. Thanks. ]]>

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