[R-sig-eco] Question about a resampling procedure with LME

Thu Nov 17 02:57:23 CET 2016

Dear Thierry,

Thank you for your remarks.

I agree with you that I should use pool as a fixed effect.

Let me explain some points you've raised:

Bird diversity was obtained at the pool level. Unfortunately I don't have
the coordinates of zooplankton (they're 50 cm apart). Only pool coordinates
(n=2).

Thank you again

Best regards,

2016-11-16 7:01 GMT-02:00 Thierry Onkelinx <thierry.onkelinx at inbo.be>:

> Dear Thiago,
>
> A few remarks.
>
> Two pools are not enough to get reliable estimates for the random effect.
> Although it is conceptually a random effect, it is better to use it as a
> fixed effect.
>
> At which level did you sample the bird diversity? I assume that it would
> be much larger than 10 x 15 cm.  Is it a pool level or a smaller section of
> the pool. Do you have coordinates of the sampling locations of both
> zooplankton and the birds?
>
> Best regards,
>
> Thierry
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
> Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
>
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> 2016-11-14 3:28 GMT+01:00 Thiago Gonçalves-Souza <
> tgoncalves.souza at gmail.com>:
>
>> Hello all,
>>
>>
>>
>> First, I'm sorry for this long question, guys.
>>
>>
>> I’d like to know if the diversity of waterbirds affect zooplankton
>> diversity in natural temporary pools over two seasonal periods (wet and
>> dry). I’ve sampled zooplankton in 38 10x15 cm plots on the pool’s shore
>> sediment (in the same place where birds usually) in both seasonal periods.
>> I used a model of bird feet and simulated them walking on the sediment of
>> each plot. Then, I washed the bird feet (of each plot separately) into
>> containers with filtered water. The zooplankton that emerged from this
>> water were identified.
>>
>>
>> Unfortunately, I was not able to collect birds at the same plot (and at
>> the
>> same time) that I simulated their footprint. Rather, I’ve made 38 samples
>> (during 9 months immediately after each seasonal period) of bird
>> communities (direct observation on each pool).
>>
>>
>> Thus, I do not believe that can use any classic model (such as anova
>> designs) because birds and zooplankton were not collected at the same
>> sampling unit.
>>
>> To do this, I thought to randomly select *10* plots of each combination of
>> zooplankton sampling, that is: 10 samples from pool 1/wet season, 10 from
>> pool 1/dry season, 10 from pool 2/wet season and 10 from pool 2/dry
>> season.
>> I did the same with bird samples, selecting 10 samples by pool and season
>> combination. Then, we run a lme model using species richness of birds
>> (independent variable) and zooplankton (dependent variable), and season as
>> independent variable. The variables were nested in pool. I repeated (for
>> loop) this procedure 1000 times and calculated the number of times that
>> the
>> model was significant (p < 0.05) and the accompanying F values (for bird
>> richness and season effects).
>>
>>
>> I’d like to hear from you if this procedure make any sense. Please, see
>> below a reproducible example. Thank you very much!
>>
>>
>> Note: I’m pretty sure that the for loop could be shorter ;)
>>
>>
>>
>> library(nlme)
>>
>>
>>
>> #### Fake da ta
>>
>>
>>
>> ## zooplankton table
>>
>> ric.zoo <- rnorm(120, 20, 2)
>>
>> pool <- factor(rep(c("pool1", "pool2"), 60))
>>
>> season <- factor(rep(rep(c("dry", "wet"), each=30), 2))
>>
>> tab <- data.frame(pool, season, ric.zoo)
>>
>>
>>
>> ## birds table
>>
>> ric.bird <- rnorm(76, 10, 2)
>>
>> pool_b <- factor(rep(c("pool1", "pool2"), 38))
>>
>> season_b <- factor(rep(rep(c("dry", "wet"), each=19), 2))
>>
>> tab_b <- data.frame(pool=pool_b, season=season_b, ric.bird)
>>
>>
>>
>> ## Dataset to be used in for loops
>>
>> bird.p.val <- numeric()
>>
>> season.p.val <- numeric()
>>
>> bird.f <- numeric()
>>
>> season.f <- numeric()
>>
>> pool.sort <- rep(c("pool1", "pool2"), each=20)
>>
>> season.sort <- rep(rep(c("wet", "dry"), each=10), 2)
>>
>> factor.sort <- data.frame(pool=pool.sort, season=season.sort)
>>
>> nreps <- 1000
>>
>>
>>
>>
>>
>> for(i in 1:nreps){
>>
>>   subset1 <- subset(tab, pool=="pool1"&season=="wet")
>>
>>   subsample1 <- subset1[sample(nrow(subset1), 10),][,3]
>>
>>   subset2 <- subset(tab, pool=="pool1"&season=="dry")
>>
>>   subsample2 <- subset2[sample(nrow(subset2), 10),][,3]
>>
>>   subset3 <- subset(tab, pool=="pool2"&season=="wet")
>>
>>   subsample3 <- subset3[sample(nrow(subset3), 10),][,3]
>>
>>   subset4 <- subset(tab, pool=="pool2"&season=="dry")
>>
>>   subsample4 <- subset4[sample(nrow(subset4), 10),][,3]
>>
>>   subbird1 <- subset(tab_b, pool=="pool1"&season=="wet")
>>
>>   subsbird1 <- subbird1[sample(nrow(subbird1), 10),][,3]
>>
>>   subbird2 <- subset(tab_b, pool=="pool1"&season=="dry")
>>
>>   subsbird2 <- subbird2[sample(nrow(subbird2), 10),][,3]
>>
>>   subbird3 <- subset(tab_b, pool=="pool2"&season=="wet")
>>
>>   subsbird3 <- subbird3[sample(nrow(subbird3), 10),][,3]
>>
>>   subbird4 <- subset(tab_b, pool=="pool2"&season=="dry")
>>
>>   subsbird4 <- subbird4[sample(nrow(subbird4), 10),][,3]
>>
>>   ric.sort.zoo <- c(subsample1, subsample2, subsample3, subsample4)
>>
>>   ric.sort.birds <- c(subsbird1, subsbird2, subsbird3, subsbird4)
>>
>>   df40 <- data.frame(ric.zoo=ric.sort.zoo, ric.bird=ric.sort.birds,
>> factor.sort)
>>
>>   lm.nested <- lme(ric.zoo~ric.bird+season, random=~1|pool, data=df40,
>> method="REML")#MODELO
>>
>>   mod <- anova.lme(lm.nested, type="sequential", adjustSigma = FALSE)
>>
>>   bird.p.val[i] <- mod$`p-value`[2]
>>
>>   season.p.val[i] <- mod$`p-value`[3]
>>
>>   bird.f[i] <- mod$`F-value`[2]
>>
>>   season.f[i] <- mod$`F-value`[3]
>>
>> }
>>
>>
>>
>>
>>
>> sum(bird.p.val<0.05 )/nreps
>>
>> sum(season.p.val<0.05)/nreps
>>
>> --
>>
>> *Thiago Gonçalves-SouzaProfessor Adjunto*
>>
>> *Universidade Federal Rural de Pernambuco (UFRPE)*
>> *Departamento de Biologia / Área de Ecologia *
>>
>> *E-mail alternativo: thiagoaracno at gmail.com <thiagoaracno at gmail.com>Home
>> page:<http://thiagocalvesouza.wix.com/ecoffun
>> <http://thiagocalvesouza.wix.com/ecoffun>**>*
>> *Google Scholar:
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>
>
>

-- 

*Thiago Gonçalves-SouzaProfessor Adjunto*

*Universidade Federal Rural de Pernambuco (UFRPE)*
*Departamento de Biologia / Área de Ecologia *

*E-mail alternativo: thiagoaracno at gmail.com <thiagoaracno at gmail.com>Home
page:<http://thiagocalvesouza.wix.com/ecoffun
<http://thiagocalvesouza.wix.com/ecoffun>**>*
*Google Scholar:
<http://scholar.google.com.br/citations?user=TjaP2l8AAAAJ&hl=pt-BR
<http://scholar.google.com.br/citations?user=TjaP2l8AAAAJ&hl=pt-BR>>*

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