Dear friends,
Could you please help me with analysis? I am afraid that it involves crossed random effects in the mixed-effect constrained ordination setting, so to say. Goal: Show an effect of the species trait (single one) and treatment (four levels, quantitative scale) on parameters. Trait x treatment interaction is possible. If possible, show changes through time. Data: Individuals of 7 species, subjected to 4 treatment levels (fully factorial) - from 6 to 12 individuals in each combination. Each individual scored 4 times (same times: 1st wk, 2nd wk, 3rd wk, 4th wk). Several (10) parameters scored every time on each individual. What I did: In order to avoid multiple testing (the parameters are likely to be correlated to each other), I decided to use multivariate analysis (RDA). I am by far much more accustomed to vegan than ade4, so excuse me if I use some "veganisms". Predictors: time, trait, treatment (possibly with interactions), conditioned on individual identity to avoid treating records from the same plant as independent. Variance partitioning. Here comes the problem: how to set permutations in order to be able to report p_vals (some people just are not happy without them)? Since individuals of the same species share the same trait value, maybe the right way is to: shift observations within individual (if time is among predictors for the particular model) and permute trait value among species. Is it so? Is this treatment of the pseudoreplication at the species level (i.e. only in the significance testing, not in the ordination per se) ok? I also tried to use different approach: I averaged all params individual-wise (getting rid of temporal pseudoreplication, but also time effects), and subsequently I averaged the result within treatment x species levels. I assume that I can go for simple free permutations this way? Pity is that this way, I cannot see development in time. And another way: I averaged params for species x treatment x time groups, ignoring interdependence of records from the same individual, hoping that the effect of an individual "dissolves" in the average. Is that meaningful? If yes, what is the appropriate permutation structure in this case? But maybe I miss something and there are better ways how to deal with this problem? Any suggestions (ok: not any, just those made in an attempt to help :-) ) are appreciated. With the best regards, Martin Weiser _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
This doesn't sound like an ordination problem: it's more similar to a
standard mixed model regression. There are several ways to approach this, for example Ben Bolker has some (old) notes here: <https://rpubs.com/bbolker/3336> I'd suggest you start by running the analyses on each trait individually, as that way you can make sure you have the model sorted correctly. The step up to the multivariate model just needs some work understanding what the relevant R package wants. HTH Bob On 12/01/17 14:12, Martin Weiser wrote: > Dear friends, > > Could you please help me with analysis? I am afraid that it involves > crossed random effects in the mixed-effect constrained ordination > setting, so to say. > > Goal: > Show an effect of the species trait (single one) and treatment (four > levels, quantitative scale) on parameters. Trait x treatment > interaction is possible. If possible, show changes through time. > > Data: > Individuals of 7 species, subjected to 4 treatment levels (fully > factorial) - from 6 to 12 individuals in each combination. Each > individual scored 4 times (same times: 1st wk, 2nd wk, 3rd wk, 4th wk). > Several (10) parameters scored every time on each individual. > > What I did: > In order to avoid multiple testing (the parameters are likely to be > correlated to each other), I decided to use multivariate analysis > (RDA). I am by far much more accustomed to vegan than ade4, so excuse > me if I use some "veganisms". Predictors: time, trait, treatment > (possibly with interactions), conditioned on individual identity to > avoid treating records from the same plant as independent. Variance > partitioning. > > Here comes the problem: how to set permutations in order to be able to > report p_vals (some people just are not happy without them)? Since > individuals of the same species share the same trait value, maybe the > right way is to: shift observations within individual (if time is among > predictors for the particular model) and permute trait value among > species. Is it so? Is this treatment of the pseudoreplication at the > species level (i.e. only in the significance testing, not in the > ordination per se) ok? > > I also tried to use different approach: I averaged all params > individual-wise (getting rid of temporal pseudoreplication, but also > time effects), and subsequently I averaged the result within treatment > x species levels. I assume that I can go for simple free permutations > this way? Pity is that this way, I cannot see development in time. > > And another way: I averaged params for species x treatment x time > groups, ignoring interdependence of records from the same individual, > hoping that the effect of an individual "dissolves" in the average. Is > that meaningful? If yes, what is the appropriate permutation structure > in this case? > > But maybe I miss something and there are better ways how to deal with > this problem? > > Any suggestions (ok: not any, just those made in an attempt to help :-) > ) are appreciated. > > With the best regards, > Martin Weiser > > _______________________________________________ > R-sig-ecology mailing list > [hidden email] > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > -- Bob O'Hara NEW EMAIL: [hidden email] NOTE NEW ADDRESS!!! Institutt for matematiske fag NTNU 7491 Trondheim Norway Mobile: +49 1515 888 5440 Journal of Negative Results - EEB: www.jnr-eeb.org _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
Dear Bob and others,
Thank you very much for your answer. I realized I forgot to mention the nature of the params: these are mostly root system architectural traits, and, as you can imagine, they are just a subset of many possible ones. The goal is merely to show how groups of traits are related to the predictors. In the terms of Ben Bolker's note you kindly pointed to me, imagine that apart of cars and holiday, there will be also swimming pools, paintings, houses and opera visits, restaurant dinners, charity donations. The question is then to show whether increase in individual wealth is more pronounced in obtaining possessions or immaterial "feelgoods". Maybe cars tightly correlate with swimming pools and paintings and do not correlate so tightly with an individual income, feelgoods are correlated to each other and strongly predicted by (correlated to) the income, while houses do not correlate with anything (even though one would guess that they should behave similarly to e.g. cars). If someone forgets to take notes about e.g. paintings, nothing serious happens. P_val is there just to say that the structure of the spendings is probably not random regarding to the income. I hope this made the problem clearer to anyone wishing to join the thread. With the best regards, Martin W. 2017-01-12 14:29 GMT+01:00 Bob O'Hara <[hidden email]>: > This doesn't sound like an ordination problem: it's more similar to a > standard mixed model regression. There are several ways to approach this, > for example Ben Bolker has some (old) notes here: > <https://rpubs.com/bbolker/3336> > > I'd suggest you start by running the analyses on each trait individually, as > that way you can make sure you have the model sorted correctly. The step up > to the multivariate model just needs some work understanding what the > relevant R package wants. > > HTH > > Bob > > On 12/01/17 14:12, Martin Weiser wrote: >> >> Dear friends, >> >> Could you please help me with analysis? I am afraid that it involves >> crossed random effects in the mixed-effect constrained ordination >> setting, so to say. >> >> Goal: >> Show an effect of the species trait (single one) and treatment (four >> levels, quantitative scale) on parameters. Trait x treatment >> interaction is possible. If possible, show changes through time. >> >> Data: >> Individuals of 7 species, subjected to 4 treatment levels (fully >> factorial) - from 6 to 12 individuals in each combination. Each >> individual scored 4 times (same times: 1st wk, 2nd wk, 3rd wk, 4th wk). >> Several (10) parameters scored every time on each individual. >> >> What I did: >> In order to avoid multiple testing (the parameters are likely to be >> correlated to each other), I decided to use multivariate analysis >> (RDA). I am by far much more accustomed to vegan than ade4, so excuse >> me if I use some "veganisms". Predictors: time, trait, treatment >> (possibly with interactions), conditioned on individual identity to >> avoid treating records from the same plant as independent. Variance >> partitioning. >> >> Here comes the problem: how to set permutations in order to be able to >> report p_vals (some people just are not happy without them)? Since >> individuals of the same species share the same trait value, maybe the >> right way is to: shift observations within individual (if time is among >> predictors for the particular model) and permute trait value among >> species. Is it so? Is this treatment of the pseudoreplication at the >> species level (i.e. only in the significance testing, not in the >> ordination per se) ok? >> >> I also tried to use different approach: I averaged all params >> individual-wise (getting rid of temporal pseudoreplication, but also >> time effects), and subsequently I averaged the result within treatment >> x species levels. I assume that I can go for simple free permutations >> this way? Pity is that this way, I cannot see development in time. >> >> And another way: I averaged params for species x treatment x time >> groups, ignoring interdependence of records from the same individual, >> hoping that the effect of an individual "dissolves" in the average. Is >> that meaningful? If yes, what is the appropriate permutation structure >> in this case? >> >> But maybe I miss something and there are better ways how to deal with >> this problem? >> >> Any suggestions (ok: not any, just those made in an attempt to help :-) >> ) are appreciated. >> >> With the best regards, >> Martin Weiser >> >> _______________________________________________ >> R-sig-ecology mailing list >> [hidden email] >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> > > > -- > Bob O'Hara > > NEW EMAIL: [hidden email] > NOTE NEW ADDRESS!!! > Institutt for matematiske fag > NTNU > 7491 Trondheim > Norway > > Mobile: +49 1515 888 5440 > Journal of Negative Results - EEB: www.jnr-eeb.org -- Mgr. Martin Weiser, Ph.D. Tel.: +420 221 95 1654 E-mail: [hidden email] Katedra botaniky; Přírodovědecká fakulta Univerzita Karlova v Praze Přírodovědecká fakulta Benátská 2, 128 43 Praha 2 www.natur.cuni.cz Department of Botany; Faculty of Science Charles University in Prague Faculty of Science Benátská 2, 128 43 Praha 2 www.natur.cuni.cz/en _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
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