space.only <- gam(count~s(x, y), data=all.data, family = "poisson")

I get the following warning:

Residual degrees of freedom are negative or zero. This occurs when the sum of the parametric and nonparametric degrees of freedom exceeds the number of observations. The model is probably too complex for the amount of data available.

Is this a result of using a dataset with a limited number of samples? I assume that real datasets will tend to have enough samples for this to not be an issue?

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For the record, i added EcoGenetics to the parcours (instead of spdep) for my real data (30 coordinates, distance range 0.5m to 11m, z is species abundance), and forced it to 15 distance bins (for ncf, i had to give a increment leading to 15 bins). The results were roughly comparable, if not for slightly different distance bin means. I find it curious that pgirmess doubled the amount of comparisons (as if a distance matrix wasnt split by diagonale):

um(unlist(df.eco$Z.size))

[1] 435

> sum(unlist(df.ncf$n))

[1] 435

> sum(unlist(df.pgir$n))

[1] 870

Anyway, here is the plot. Do you have an idea (if you even look at this post anymore^^), to force the three packages to use the exact same distances?

]]>How can we interpret the results?

]]>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