README.md
In polak51/WitOMI: Within Outlying Mean Indexes: Refining the OMI Analysis
Subniche Documentation for the Within Outlying Mean Indexes calculations (WitOMI)
subniche is an open-source R (R Core Team, 2017) package that implements functions useful for the calculation of the Within Outlying Mean Indexes (WitOMI) and their respective statistical tests (Karasiewicz, 2017; Karasiewicz et al., 2017). It consists of complementary calculations to the Outlying Mean Index analysis (OMI) (Dolédec et al., 2000). OMI analysis can be implemented with the niche function from the ade4 package (Dray and Dufour, 2007). WitOMI analysis enables to explore niche shift in a community and environmental constraints within an Euclidean space, with graphical displays. The method allows to divide the realized niche, estimated from the niche function into subniches defined by a factor (time, space), which creates the subsets of habitat conditions.
To get started
Install and load the R package from CRAN using the following commands on the R console:
install.packages("subniche")
library(subniche)
## Loading required package: ade4
## Loading required package: ade4
` To install the most recent version from GitHub:
install.packages("devtools")
devtools::install_github("KarasiewiczStephane/WitOMI")
library(subniche)
install.packages("knitr")
library(knitr)
Generate the data required to run this example
Get the data set “drome” from the subniche package (Karasiewicz et al., 2017). This data set includes, fish species occurence along with environmental variables.
drome
Performing the OMI analysis
The first step consists of performing a PCA on the environmental table.
dudi1 <- dudi.pca(drome$env[,-1], scale = TRUE, scan = FALSE, nf = 3)
scatter(dudi1)
The graph displays the correlation between the different environmental variables. Then, the OMI analysis uses the standardized environmental table from the PCA in combination with the faunistic data to perform the OMI analysis. The standardized environmental table can be extracted from the dudi1 with $tab as follow:
kable(dudi1$tab)
| Altitude| DSou| slope| width| Lmin| Dicharge|
|-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|
| 0.1752807| -0.4171664| -0.0573732| -0.1082431| 0.1345316| -0.9354324|
| -0.1279075| -0.0076779| 0.2603861| -0.1082431| 0.9474368| -0.4717552|
| -0.6837525| 1.0569922| -0.4633989| 1.2112919| 2.1667946| 0.1542089|
| -0.8353466| 1.4664807| -0.8870779| 1.2112919| 2.9119577| 0.5483344|
| -1.0374720| 1.7121738| -0.8870779| -0.1082431| 3.3184103| 0.7801730|
| -0.0268448| -0.9085526| -0.0044133| -1.0978944| -0.4751473| -1.2252306|
| -0.2037046| -0.6628595| 0.2603861| -0.4381269| 0.1345316| -0.8890647|
| -0.2289702| -0.5809618| 0.2603861| -0.4381269| 0.1345316| -0.8890647|
| -0.3047673| -0.4990641| -0.2868660| 0.2216407| 0.1345316| -0.8890647|
| -0.4310957| -0.2533710| 0.1015064| 0.2216407| 0.3716289| -0.7847373|
| -0.5068927| -0.1714733| -0.0573732| -0.4381269| 0.3716289| -0.7847373|
| -0.5826898| -0.0076779| -0.4457456| -0.4381269| 1.3877604| -0.2515086|
| -0.8858779| 0.6475037| -0.5693187| 0.8814082| 2.1667946| 0.1773927|
| -0.9111436| 0.8112991| -0.8341181| 1.5411757| 2.4377630| 0.3049039|
| -0.9869407| 1.0569922| -1.1165707| 2.5308270| 2.4377630| 0.3049039|
| 3.1566310| -1.3180411| -0.3574792| -0.7680106| -0.4751473| -0.8658808|
| 2.9545056| -1.0723480| -0.3574792| -0.1082431| -1.0848262| -0.4949391|
| 1.5396274| -0.3352687| -0.0044133| 2.5308270| 0.4732421| 0.3512716|
| -0.4058300| 0.2380152| 0.2427328| 2.8607107| 1.2184052| 1.2206663|
| 0.3774061| -1.3999388| 1.4431567| -1.7576619| -1.0780520| -1.4454772|
| 0.0236866| -1.2361434| 1.3548902| -1.4277781| -0.8815999| -1.2252306|
| -0.2289702| -0.9904503| 1.0194776| -0.7680106| -0.8815999| -1.2252306|
| -0.8858779| -0.3352687| -0.1279864| -0.4381269| -0.2041789| -0.1935490|
| -1.1132691| 0.2380152| -1.2931037| 0.2216407| -0.2041789| -0.1935490|
| -1.1890661| 0.5656060| -1.3284103| 0.8814082| 0.2700158| 0.5483344|
| -1.2648632| 0.8522479| -1.4872899| -0.7680106| 0.2700158| 0.7801730|
| 2.0449411| -1.2361434| 0.4722256| -0.7680106| -0.8138578| -0.6688180|
| 1.6912215| -0.8266549| -0.1456397| -1.0978944| -0.4074052| 0.2585362|
| 1.4890961| -0.2533710| -1.2931037| 0.2216407| -0.0009526| 1.4061371|
| 1.2869706| -0.1714733| -1.3284103| 0.2216407| -0.0009526| 1.4061371|
| 0.9332511| -0.9904503| 0.3839591| -1.0978944| -0.8815999| -0.9122485|
| 0.7311256| -0.5809618| 0.1368130| -0.7680106| -0.7799868| -0.6340422|
| 0.4279375| -0.2533710| -0.0573732| 0.2216407| -0.4751473| 0.3512716|
| 0.0236866| 0.2380152| 0.2250795| 0.8814082| -0.3057921| 0.8265407|
| -0.1784389| 0.4837083| 0.2250795| 0.8814082| -0.2719210| 0.9308680|
| 1.2869706| -1.6456319| 3.4732853| -1.7576619| -1.0238583| -1.3573786|
| 0.7816570| -1.1951946| 1.9551021| -1.0978944| -0.9493420| -1.1440871|
| 0.6805943| -1.1542457| 1.9551021| -1.0978944| -0.9493420| -1.1440871|
| 0.3016091| -0.6628595| -0.2339061| -0.4381269| -0.7461157| -0.5644907|
| -0.1279075| -0.0076779| -0.0573732| -0.1082431| -0.6106315| -0.1008135|
| -0.1784389| 0.0742198| -0.3927858| 0.2216407| -0.6106315| -0.1008135|
| -0.2289702| 0.2380152| -0.3927858| 0.5515244| -0.6106315| -0.1008135|
| -0.2795016| 0.4018106| -0.7105450| -0.4381269| -0.3735342| 0.5831102|
| -0.5321584| 0.8931968| -0.4987055| -0.7680106| -0.2719210| 0.8729084|
| -0.7848152| 1.3845830| -0.4633989| -0.1082431| -0.0686947| 1.3597694|
| -1.0880034| 2.2035600| -0.9753444| 0.2216407| 0.2700158| 2.5189622|
| -1.2395975| 2.8587416| -1.1871839| 1.2112919| 0.2700158| 2.8667200|
| 0.3774061| -1.4818365| 3.2261392| -1.0978944| -0.9696647| -1.4338853|
| -0.2289702| -0.9904503| 1.1077441| -0.7680106| -0.3396631| -0.8658808|
| -0.8353466| -0.3352687| -0.4987055| -0.7680106| 0.1345316| -0.4369795|
| -1.2395975| 0.8112991| -1.0459576| 0.2216407| -0.2719210| 0.2237604|
| 0.5290002| -1.0723480| 1.4431567| -1.0978944| -0.2719210| -0.7847373|
| 0.0236866| -0.3352687| 0.1544663| 0.2216407| 0.0667895| -0.4949391|
| -0.3047673| 0.0742198| 0.1544663| 0.2216407| 0.2700158| -0.2978763|
| -0.8858779| 0.9750945| -0.3574792| 1.2112919| -0.8138578| 0.1078412|
| -0.9364093| 1.1388899| -0.3574792| -0.7680106| -0.8138578| 0.1078412|
| -1.1890661| 1.7940715| -1.0106510| 1.2112919| -1.0780520| 1.7075272|
| -1.3406602| 2.2854577| -1.0636109| 0.8814082| -1.0780520| 2.7508008|
| 1.5901588| -1.0723480| 1.2842770| -1.0978944| -0.6783736| -0.8195131|
| 1.1859079| -0.8266549| 0.5781453| 1.8710595| -0.6783736| -0.8195131|
| 0.8321884| -0.4171664| 0.3839591| -0.4381269| -0.2719210| -0.0544458|
| 0.2763434| 0.0742198| 1.0018243| -0.4381269| -0.2041789| 0.0498815|
| 0.0994836| 0.1561175| 0.2074262| 0.2216407| 0.0667895| 0.5831102|
| -0.3805643| 0.9750945| -0.6752384| 0.2216407| 0.5409842| 1.4177290|
The next step performs the OMI analysis (Dolédec et al., 2000) by implementing the function niche from the ade4 package (Dray and Dufour, 2007).
nic1 <- niche(dudi1, drome$fish, scann = FALSE)
nic1
## Niche analysis
## call: niche(dudiX = dudi1, Y = drome$fish, scannf = FALSE)
## class: niche dudi
##
## $rank (rank) : 6
## $nf (axis saved) : 2
## $RV (RV coeff) :
##
## eigen values: 1.001 0.05759 0.01671 0.004818 0.001209 ...
##
## vector length mode content
## 1 $eig 6 numeric eigen values
## 2 $lw 13 numeric row weigths (crossed array)
## 3 $cw 6 numeric col weigths (crossed array)
##
## data.frame nrow ncol content
## 1 $tab 13 6 crossed array (averaging species/sites)
## 2 $li 13 2 species coordinates
## 3 $l1 13 2 species normed scores
## 4 $co 6 2 variables coordinates
## 5 $c1 6 2 variables normed scores
## 6 $ls 64 2 sites coordinates
## 7 $as 3 2 axis upon niche axis
plot(nic1)
The graph displays the results of the OMI analysis within a two-dimensional Euclidean space. It represents the species niche position (labels in Samples and Species, top-right) and breadth (ellipses in Niches, bottom-right graph), within the environmental gradients (summarized by Variables). The species niche parameters (Inertia, OMI, Tol and Rtol) are calculated with the niche.parameter function from the ade4 package (Dray and Dufour, 2007). More details about the niche parameters calculation are available in Dolédec et al. (2000).
kable(niche.param(nic1))
| | inertia| OMI| Tol| Rtol| omi| tol| rtol|
|-----|----------:|-----------:|----------:|----------:|-----:|-----:|-----:|
| SCU | 5.503685| 2.1488218| 1.5045397| 1.8503234| 39.0| 27.3| 33.6|
| OTR | 5.714861| 0.0777737| 2.4253953| 3.2116918| 1.4| 42.4| 56.2|
| YTR | 5.765225| 0.2630852| 1.6882102| 3.8139292| 4.6| 29.3| 66.2|
| MIN | 5.318492| 0.4460395| 2.5225244| 2.3499277| 8.4| 47.4| 44.2|
| STO | 5.908881| 0.7656741| 2.4125454| 2.7306619| 13.0| 40.8| 46.2|
| BLA | 4.989501| 0.4550558| 2.2738372| 2.2606074| 9.1| 45.6| 45.3|
| SOB | 3.714084| 0.0710025| 0.5129189| 3.1301626| 1.9| 13.8| 84.3|
| SON | 6.384992| 4.8872633| 0.7488491| 0.7488797| 76.5| 11.7| 11.7|
| NAS | 12.598843| 10.1993126| 1.6200685| 0.7794621| 81.0| 12.9| 6.2|
| GUD | 8.427877| 4.3080073| 0.9543342| 3.1655359| 51.1| 11.3| 37.6|
| CHU | 5.584996| 1.5776519| 1.6933570| 2.3139866| 28.2| 30.3| 41.4|
| STR | 9.917999| 5.2753874| 1.1855758| 3.4570355| 53.2| 12.0| 34.9|
| BAR | 8.344112| 3.8305122| 1.3016577| 3.2119418| 45.9| 15.6| 38.5|
The test for statistical significance can now be implemented. It corresponds to the comparison between the observed species' marginality and the simulated values, hereafter 100 random permutations, under the null hypothesis that each species is indifferent to its environment (Dolédec et al., 2000).
rtest(nic1,100)
## class: krandtest lightkrandtest
## Monte-Carlo tests
## Call: as.krandtest(sim = t(sim), obs = obs)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 SCU 2.14882183 1.6898494 greater 0.07920792
## 2 OTR 0.07777369 6.6301270 greater 0.00990099
## 3 YTR 0.26308521 10.3258807 greater 0.00990099
## 4 MIN 0.44603947 9.3195260 greater 0.00990099
## 5 STO 0.76567408 14.1890327 greater 0.00990099
## 6 BLA 0.45505584 16.9938878 greater 0.00990099
## 7 SOB 0.07100247 -0.6060945 greater 0.71287129
## 8 SON 4.88726327 3.6023144 greater 0.01980198
## 9 NAS 10.19931259 6.1321233 greater 0.00990099
## 10 GUD 4.30800731 17.7215982 greater 0.00990099
## 11 CHU 1.57765192 26.9201641 greater 0.00990099
## 12 STR 5.27538741 7.8434366 greater 0.00990099
## 13 BAR 3.83051220 12.5058985 greater 0.00990099
## 14 OMI.mean 2.63889133 15.6134372 greater 0.00990099
Subsets creation
Herein, for the sake of the example, we will create two subsets. A factor needs to be defined, in order to divide the data set in two subsets. We use the functions cutree and hclust to create a cluster, k=2, following the ward.D method. The function creates the subsets based on a dissimilarity matrix created with dist, with the SUs of nic1. It divided the sampling domain into two subsets along the first OMI axis. Blue points correspond to downstream habitat conditions while red points are the upstream conditions.
# Creation of the factor
fact <- as.factor(as.numeric(cutree(hclust(dist(nic1$ls, "euclidean"), "ward.D"),k=2)))
# plotting the two subsets
s.class(nic1$ls, fact, col=c("red", "blue"),cellipse=0, cpoint=2, pch=3)
s.chull(nic1$ls, fact, col=c("red", "blue"),optchull = 1, add.plot = T)
#According to the previous graph,(Variable in plot(nic1)), group 1 and 2 corresponds to upstream and downstream conditions respectively.
levels(fact)<- c("up", "down")
Now that we have the factor for dividing the niche, the subniche function can be implemented as follow:
subnic1 <- subniche(nic1, fact)
subnic1
## WitOMI calculation
## call: niche(dudiX = dudi1, Y = drome$fish, scannf = FALSE)
## class: subniche dudi
##
## $rank (rank) : 6
## $nf (axis saved) : 2
## $RV (RV coeff) :
##
## eigen values: 1.001 0.05759 0.01671 0.004818 0.001209 ...
##
## vector length mode content
## 1 $eig 6 numeric eigen values
## 2 $lw 13 numeric row weigths (crossed array)
## 3 $cw 6 numeric col weigths (crossed array)
## 4 $factor 64 numeric factor used for creating subsets
##
## data.frame nrow ncol content
## 1 $tab 13 6 crossed array (averaging species/sites)
## 2 $li 13 2 species coordinates
## 3 $l1 13 2 species normed scores
## 4 $co 6 2 variables coordinates
## 5 $c1 6 2 variables normed scores
## 6 $ls 64 2 sites coordinates
## 7 $as 3 2 axis upon niche axis
## 8 $G_k 2 2 G_k coordinates
## 9 $sub 26 2 species coordinates within each subset
The subniche function only adds itemsto the niche list, which are $factor, $G_k and $sub. The function does not change the previous results obtained with the OMI analysis from the niche function. The following functions, plot, refparam and rtest, are used to display the same results as in the ade4 package:
plot(subnic1)
The niche parameters from the OMI analysis can still be extracted with its respective significant test with refparam. The test for significance can also be applied, with rtest function.
kable(refparam(subnic1))
| | inertia| OMI| Tol| Rtol| omi| tol| rtol|
|-----|----------:|-----------:|----------:|----------:|-----:|-----:|-----:|
| SCU | 5.503685| 2.1488218| 1.5045397| 1.8503234| 39.0| 27.3| 33.6|
| OTR | 5.714861| 0.0777737| 2.4253953| 3.2116918| 1.4| 42.4| 56.2|
| YTR | 5.765225| 0.2630852| 1.6882102| 3.8139292| 4.6| 29.3| 66.2|
| MIN | 5.318492| 0.4460395| 2.5225244| 2.3499277| 8.4| 47.4| 44.2|
| STO | 5.908881| 0.7656741| 2.4125454| 2.7306619| 13.0| 40.8| 46.2|
| BLA | 4.989501| 0.4550558| 2.2738372| 2.2606074| 9.1| 45.6| 45.3|
| SOB | 3.714084| 0.0710025| 0.5129189| 3.1301626| 1.9| 13.8| 84.3|
| SON | 6.384992| 4.8872633| 0.7488491| 0.7488797| 76.5| 11.7| 11.7|
| NAS | 12.598843| 10.1993126| 1.6200685| 0.7794621| 81.0| 12.9| 6.2|
| GUD | 8.427877| 4.3080073| 0.9543342| 3.1655359| 51.1| 11.3| 37.6|
| CHU | 5.584996| 1.5776519| 1.6933570| 2.3139866| 28.2| 30.3| 41.4|
| STR | 9.917999| 5.2753874| 1.1855758| 3.4570355| 53.2| 12.0| 34.9|
| BAR | 8.344112| 3.8305122| 1.3016577| 3.2119418| 45.9| 15.6| 38.5|
rtest(subnic1)
## class: krandtest lightkrandtest
## Monte-Carlo tests
## Call: as.krandtest(sim = t(sim), obs = obs)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Permutation number: 99
## Test Obs Std.Obs Alter Pvalue
## 1 SCU 2.14882183 1.9780204 greater 0.07
## 2 OTR 0.07777369 5.5075313 greater 0.01
## 3 YTR 0.26308521 12.1858520 greater 0.01
## 4 MIN 0.44603947 7.6868549 greater 0.01
## 5 STO 0.76567408 14.4029032 greater 0.01
## 6 BLA 0.45505584 20.5927841 greater 0.01
## 7 SOB 0.07100247 -0.7547935 greater 0.77
## 8 SON 4.88726327 2.7026550 greater 0.04
## 9 NAS 10.19931259 5.4283545 greater 0.01
## 10 GUD 4.30800731 20.1586180 greater 0.01
## 11 CHU 1.57765192 19.5749034 greater 0.01
## 12 STR 5.27538741 9.0049294 greater 0.01
## 13 BAR 3.83051220 12.8552496 greater 0.01
## 14 OMI.mean 2.63889133 13.9245420 greater 0.01
Calculation of the WitOMI (Spatial example)
The result of the test on marginality from the OMI analysis are important for the relevance of the WitOMI calculation. The following functions will still calculate the WitOMI even if the OMI are not significant. To be valid, the WitOMI analysis must be done only on species with significant OMI. Then, the probability for the WitOMI to be significant within the subset GK corresponds to the $witomigtest$SubniPvalue wihtin the rtestrefor function for WitOMIG. And the equivalent for the WitOMIGK is $witomig_ktest$SubniPvalue in the rtestsubor.
margvectdisplays the marginality vector of the suborigins,GK within the overall available habitat conditions (black polygon), the available resources under each subset habitat conditions (red polygons) and the species subniche (species label).
margvect(subnic1)
subplot displays the subset's constraint of habitat conditions (red polygons), found within the overall available habitat conditions (black polygons), and the corresponding species subniche positions. The arrows represent the marginality vectors from the sub-orgins GK (red dots) and corresponds to the graphical representation of the WitOMIGK.
subplot(subnic1)
subparam.refor calculates the species marginality (WitOMIG), tolerance (Tol) and residual tolerance (Rtol) to the average habitat conditions (G). For details about the parameters calculation see Karasiewicz et al. (2017).
kable(subparam.refor(subnic1))
| | inertia| WitOMIG| Tol| Rtol| witomig| tol| rtol|
|---------|----------:|-----------:|----------:|----------:|--------:|-----:|-----:|
| SCUup | 3.399535| 0.8822018| 1.3160556| 1.2012780| 26.0| 38.7| 35.3|
| OTRup | 4.743091| 1.1205164| 1.6167368| 2.0058378| 23.6| 34.1| 42.3|
| YTRup | 4.915133| 1.1479897| 1.6704008| 2.0967423| 23.4| 34.0| 42.7|
| MINup | 2.824565| 0.3293892| 0.9591758| 1.5360003| 11.7| 34.0| 54.4|
| STOup | 2.855969| 0.1982016| 0.6736997| 1.9840678| 6.9| 23.6| 69.5|
| BLAup | 2.308870| 0.2007444| 0.7128120| 1.3953135| 8.7| 30.9| 60.4|
| SOBup | 2.051601| 0.5881637| 0.6693959| 0.7940418| 28.7| 32.6| 38.7|
| SONup | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| NASup | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| GUDup | 1.164352| 0.2547094| 0.4719874| 0.4376555| 21.9| 40.5| 37.6|
| CHUup | 1.265915| 0.0259759| 0.1325920| 1.1073474| 2.1| 10.5| 87.5|
| STRup | 1.857806| 1.8578063| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| BARup | 1.658331| 0.5157807| 0.0683361| 1.0742143| 31.1| 4.1| 64.8|
| SCUdown | 9.711984| 8.2109981| 0.0907551| 1.4102305| 84.5| 0.9| 14.5|
| OTRdown | 8.435816| 4.1418643| 1.0867398| 3.2072121| 49.1| 12.9| 38.0|
| YTRdown | 9.165592| 5.5840648| 1.5929660| 1.9885608| 60.9| 17.4| 21.7|
| MINdown | 8.505175| 4.6131522| 0.8145921| 3.0774309| 54.2| 9.6| 36.2|
| STOdown | 9.283153| 4.9264247| 0.8757168| 3.4810114| 53.1| 9.4| 37.5|
| BLAdown | 8.608352| 4.5345833| 0.8829193| 3.1908492| 52.7| 10.3| 37.1|
| SOBdown | 6.831239| 3.0898598| 1.0683130| 2.6730658| 45.2| 15.6| 39.1|
| SONdown | 6.384992| 4.8872633| 0.7488491| 0.7488797| 76.5| 11.7| 11.7|
| NASdown | 12.598843| 10.1993126| 1.6200685| 0.7794621| 81.0| 12.9| 6.2|
| GUDdown | 9.465524| 5.5154381| 0.5116158| 3.4384699| 58.3| 5.4| 36.3|
| CHUdown | 8.608352| 4.5345833| 0.8829193| 3.1908492| 52.7| 10.3| 37.1|
| STRdown | 11.069455| 6.8646154| 0.5809199| 3.6239195| 62.0| 5.2| 32.7|
| BARdown | 9.458409| 5.2563916| 0.5836204| 3.6183965| 55.6| 6.2| 38.3|
rtestrefor calculates the subniches' parameters from G with the respective significant test. The output of the rtestrefor function is an object of class list, which includes the results of the WitOMIG significant test, under each subset defined by the $factor.
refor <- rtestrefor(subnic1,100)
First,for each of the two subsets, the subset statictical significance is tested with the null hypothesis that GK is not different from the overall average habitat conditions represented by G. The subsets P value is equal to the product of equiprobabilities to find the same mean value for each variables (Karasiewicz et al., 2017).
refor[[1]]$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 6
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 Altitude 0.4467398 5.641526 two-sided 0.00990099
## 2 DSou -0.5666773 -6.777756 two-sided 0.00990099
## 3 slope 0.3946332 4.746561 two-sided 0.00990099
## 4 width -0.3153794 -3.288177 two-sided 0.00990099
## 5 Lmin -0.3094155 -3.650813 two-sided 0.00990099
## 6 Dicharge -0.4451747 -4.781605 two-sided 0.00990099
## Subsets Pvalue: 9.420452e-13
## other elements: subpvalue call
refor[[2]]$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 6
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 Altitude -0.9147530 -5.063639 two-sided 0.00990099
## 2 DSou 1.1603393 6.972641 two-sided 0.00990099
## 3 slope -0.8080584 -3.908783 two-sided 0.00990099
## 4 width 0.6457769 3.427746 two-sided 0.00990099
## 5 Lmin 0.6335651 3.642002 two-sided 0.00990099
## 6 Dicharge 0.9115482 5.156716 two-sided 0.00990099
## Subsets Pvalue: 9.420452e-13
## other elements: subpvalue call
Second, the species marginality is tested for statistical significance with the null hypothesis that each species within each subset is uninfluenced by its overall average habitat conditions (ubiquitous), for WitOMIG (Karasiewicz et al., 2017).
refor[[1]]$witomigtest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 SCU 0.88220184 -0.5675372 greater 100 0.7029703 6.622298e-13
## 2 OTR 1.12051638 0.3687786 greater 100 0.3663366 3.451057e-13
## 3 YTR 1.14798971 0.1304604 greater 100 0.4158416 3.917416e-13
## 4 MIN 0.32938916 -2.7444680 greater 100 1.0000000 9.420452e-13
## 5 STO 0.19820159 -3.6764114 greater 100 1.0000000 9.420452e-13
## 6 BLA 0.20074436 -3.9422360 greater 100 1.0000000 9.420452e-13
## 7 SOB 0.58816373 -1.0481577 greater 100 0.9009901 8.487734e-13
## 8 SON NaN NaN greater 97 NA NA
## 9 NAS NaN NaN greater 94 NA NA
## 10 GUD 0.25470940 -1.7428278 greater 100 1.0000000 9.420452e-13
## 11 CHU 0.02597593 -2.9923601 greater 100 1.0000000 9.420452e-13
## 12 STR 1.85780633 0.1069688 greater 100 0.3861386 3.637600e-13
## 13 BAR 0.51578065 -0.9776495 greater 100 0.9207921 8.674278e-13
## 14 OMI.mean 0.64740719 -2.4357870 greater 100 1.0000000 9.420452e-13
##
## other elements: adj.method sub.pvalue subni.pvalue call
refor[[2]]$witomigtest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 SCU 8.210998 0.26817101 greater 86 0.25287356 2.382183e-13
## 2 OTR 4.141864 -1.10185311 greater 100 0.86138614 8.114647e-13
## 3 YTR 5.584065 1.78780845 greater 100 0.03960396 3.730872e-14
## 4 MIN 4.613152 -0.04845032 greater 100 0.56435644 5.316493e-13
## 5 STO 4.926425 0.58490353 greater 100 0.27722772 2.611611e-13
## 6 BLA 4.534583 -0.05263990 greater 100 0.49504950 4.663590e-13
## 7 SOB 3.089860 -1.36484465 greater 100 0.94059406 8.860822e-13
## 8 SON 4.887263 -0.52181416 greater 80 0.58024691 5.466188e-13
## 9 NAS 10.199313 0.62001928 greater 68 0.30434783 2.867094e-13
## 10 GUD 5.515438 0.21504292 greater 100 0.38613861 3.637600e-13
## 11 CHU 4.534583 -0.41713165 greater 100 0.65346535 6.155939e-13
## 12 STR 6.864615 0.28705343 greater 100 0.29702970 2.798154e-13
## 13 BAR 5.256392 -0.08874486 greater 100 0.46534653 4.383775e-13
## 14 OMI.mean 5.566042 0.23045629 greater 100 0.29702970 2.798154e-13
##
## other elements: adj.method sub.pvalue subni.pvalue call
subparam.subor calculates the species marginality (WitOMIGK), tolerance (Tol) and residual tolerance (Rtol) to the subset average habitat conditions (GK). For details about the parameters calculation see Karasiewicz et al. (2017).
kable(subparam.subor(subnic1))
| | inertia| WitOMIG_k| Tol| Rtol| witomig_k| tol| rtol|
|------|---------:|-----------:|----------:|----------:|-----------:|-----:|-----:|
| SCU1 | 4.667445| 2.1501116| 0.8416367| 1.6756969| 46.1| 18.0| 35.9|
| OTR1 | 3.623817| 0.0012426| 0.9635811| 2.6589935| 0.0| 26.6| 73.4|
| YTR1 | 3.769511| 0.0023683| 0.7471943| 3.0199488| 0.1| 19.8| 80.1|
| MIN1 | 2.794045| 0.2988690| 1.0122406| 1.4829355| 10.7| 36.2| 53.1|
| STO1 | 3.170025| 0.5122576| 0.7835477| 1.8742197| 16.2| 24.7| 59.1|
| BLA1 | 2.509801| 0.4016752| 0.7324293| 1.3756961| 16.0| 29.2| 54.8|
| SOB1 | 1.604047| 0.1406096| 0.4103152| 1.0531224| 8.8| 25.6| 65.7|
| SON1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| NAS1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| GUD1 | 2.531369| 1.6217256| 0.0206516| 0.8889913| 64.1| 0.8| 35.1|
| CHU1 | 2.339495| 1.0995556| 0.2824601| 0.9574793| 47.0| 12.1| 40.9|
| STR1 | 2.897381| 2.8973807| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| BAR1 | 2.595143| 1.4525929| 0.8751351| 0.2674153| 56.0| 33.7| 10.3|
| SCU2 | 5.492183| 3.9911970| 0.2671504| 1.2338352| 72.7| 4.9| 22.5|
| OTR2 | 4.471315| 0.1773629| 1.6893396| 2.6046123| 4.0| 37.8| 58.3|
| YTR2 | 4.976729| 1.3952018| 1.2150300| 2.3664967| 28.0| 24.4| 47.6|
| MIN2 | 3.927070| 0.0350466| 0.8819288| 3.0100942| 0.9| 22.5| 76.6|
| STO2 | 4.384113| 0.0273849| 1.2423933| 3.1143349| 0.6| 28.3| 71.0|
| BLA2 | 4.092662| 0.0188934| 0.6783537| 3.3954149| 0.5| 16.6| 83.0|
| SOB2 | 4.175797| 0.4344183| 0.2156571| 3.5257216| 10.4| 5.2| 84.4|
| SON2 | 2.804111| 1.3063827| 0.1263895| 1.3713393| 46.6| 4.5| 48.9|
| NAS2 | 4.402483| 2.0029526| 1.3323835| 1.0671471| 45.5| 30.3| 24.2|
| GUD2 | 4.036922| 0.0868360| 0.9774591| 2.9726266| 2.2| 24.2| 73.6|
| CHU2 | 4.092662| 0.0188934| 0.6783537| 3.3954149| 0.5| 16.6| 83.0|
| STR2 | 4.632032| 0.4271921| 0.8373510| 3.3674884| 9.2| 18.1| 72.7|
| BAR2 | 4.275254| 0.0732369| 0.6000366| 3.6019803| 1.7| 14.0| 84.3|
In a similar way, rtestsubor calculates the subniches' parameters from GK with the respective test for significance. The output of the rtestsubor function is an object of class list, which includes the results of the WitOMIGK significance test under each of the two subsets defined by the $factor.
subor <- rtestsubor(subnic1,100)
As previously, the subsets are tested.
subor[[1]]$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 6
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 Altitude 0.4467398 5.003697 two-sided 0.00990099
## 2 DSou -0.5666773 -6.805470 two-sided 0.00990099
## 3 slope 0.3946332 4.495922 two-sided 0.00990099
## 4 width -0.3153794 -3.813484 two-sided 0.00990099
## 5 Lmin -0.3094155 -3.118210 two-sided 0.01980198
## 6 Dicharge -0.4451747 -4.993129 two-sided 0.00990099
## Subsets Pvalue: 1.88409e-12
## other elements: subpvalue call
subor[[2]]$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 6
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 Altitude -0.9147530 -5.454170 two-sided 0.00990099
## 2 DSou 1.1603393 6.682697 two-sided 0.00990099
## 3 slope -0.8080584 -4.890054 two-sided 0.00990099
## 4 width 0.6457769 3.487026 two-sided 0.00990099
## 5 Lmin 0.6335651 3.509317 two-sided 0.00990099
## 6 Dicharge 0.9115482 4.686616 two-sided 0.00990099
## Subsets Pvalue: 9.420452e-13
## other elements: subpvalue call
The species marginality is then tested for significance with the null hypothesis that each species within a subset is uninfluenced by its subset average habitat conditions (ubiquitous), for WitOMIGK (Karasiewicz et al., 2017).
subor[[1]]$witomig_ktest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 SCU 2.150111587 0.90808913 greater 99 0.09000000 1.695681e-13
## 2 OTR 0.001242635 -1.05509931 greater 100 0.99009901 1.865436e-12
## 3 YTR 0.002368323 -0.95423845 greater 100 0.97029703 1.828127e-12
## 4 MIN 0.298869015 5.93616160 greater 100 0.00990099 1.865436e-14
## 5 STO 0.512257590 11.98368866 greater 100 0.00990099 1.865436e-14
## 6 BLA 0.401675209 14.21278259 greater 100 0.00990099 1.865436e-14
## 7 SOB 0.140609585 -0.09993168 greater 100 0.39603960 7.461744e-13
## 8 SON NaN NaN greater 98 NA NA
## 9 NAS NaN NaN greater 99 NA NA
## 10 GUD 1.621725637 4.92355129 greater 100 0.00990099 1.865436e-14
## 11 CHU 1.099555585 14.17032154 greater 100 0.00990099 1.865436e-14
## 12 STR 2.897380749 2.33730918 greater 100 0.03960396 7.461744e-14
## 13 BAR 1.452592931 4.20572630 greater 100 0.01980198 3.730872e-14
## 14 OMI.mean 0.961671713 1.94127807 greater 100 0.07920792 1.492349e-13
##
## other elements: adj.method sub.pvalue subni.pvalue call
subor[[2]]$witomig_ktest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 SCU 3.99119702 0.5723359 greater 92 0.21505376 2.025904e-13
## 2 OTR 0.17736291 7.3252445 greater 100 0.00990099 9.327181e-15
## 3 YTR 1.39520177 21.0838921 greater 100 0.00990099 9.327181e-15
## 4 MIN 0.03504661 -0.5520602 greater 100 0.83168317 7.834832e-13
## 5 STO 0.02738489 -0.9550790 greater 100 0.93069307 8.767550e-13
## 6 BLA 0.01889343 -0.8622151 greater 100 0.88118812 8.301191e-13
## 7 SOB 0.43441828 0.1539739 greater 100 0.30693069 2.891426e-13
## 8 SON 1.30638269 -0.8024550 greater 82 0.80722892 7.604462e-13
## 9 NAS 2.00295261 -0.8385115 greater 77 0.71794872 6.763402e-13
## 10 GUD 0.08683603 -0.8933709 greater 100 0.96039604 9.047365e-13
## 11 CHU 0.01889343 -1.1158357 greater 100 0.98019802 9.233909e-13
## 12 STR 0.42719213 -0.8614058 greater 97 0.89795918 8.459182e-13
## 13 BAR 0.07323689 -1.1277643 greater 100 0.99009901 9.327181e-13
## 14 OMI.mean 0.76884605 -0.6540869 greater 100 0.72277228 6.808842e-13
##
## other elements: adj.method sub.pvalue subni.pvalue call
Exercise (temporal example)
Now, try to do the same work but with the data ardecheinv. Herein, the invertebrates' abundances need to be log(x+1) transform prior the analysis, and the first column of the $env data are the seasons use to divide the dataset.
Therefore, the creation of the required factor is done by the following line:
fact <- factor(archecheinv$env[,1])
Replace spring and autumn by numbers to allow the subniche function to work properly. After the OMI analysis and WitOMI calculations, you should obtain the following results:
| | inertia| OMI| Tol| Rtol| omi| tol| rtol|
|--------|---------:|----------:|----------:|----------:|------:|-----:|-----:|
| AFLU | 5.538347| 1.6300864| 1.1574213| 2.7508396| 29.4| 20.9| 49.7|
| ANTO | 6.261168| 2.8559783| 0.4809695| 2.9242199| 45.6| 7.7| 46.7|
| ATSP | 5.261425| 0.7738686| 1.9442760| 2.5432800| 14.7| 37.0| 48.3|
| BASP | 4.951897| 0.1540045| 0.9205257| 3.8773669| 3.1| 18.6| 78.3|
| BIMI | 6.440541| 2.6656032| 1.2165765| 2.5583616| 41.4| 18.9| 39.7|
| BFAS | 5.522998| 1.6334715| 0.8316546| 3.0578719| 29.6| 15.1| 55.4|
| CASP | 4.752588| 2.0915508| 0.6997262| 1.9613111| 44.0| 14.7| 41.3|
| CESP | 5.991585| 1.3620235| 1.7438590| 2.8857024| 22.7| 29.1| 48.2|
| CERA | 5.243130| 0.4385214| 1.5649566| 3.2396515| 8.4| 29.8| 61.8|
| CLEP | 5.031965| 0.4363223| 1.3835927| 3.2120500| 8.7| 27.5| 63.8|
| CMAR | 5.296909| 2.0940725| 1.1020999| 2.1007368| 39.5| 20.8| 39.7|
| CHIR | 4.439955| 0.4947663| 1.5231051| 2.4220840| 11.1| 34.3| 54.6|
| CPIC | 5.506342| 2.8291243| 0.5206856| 2.1565322| 51.4| 9.5| 39.2|
| DRSP | 4.496590| 1.0682956| 1.9036269| 1.5246673| 23.8| 42.3| 33.9|
| DUSP | 5.329099| 0.1398475| 1.7382410| 3.4510110| 2.6| 32.6| 64.8|
| ECSP | 5.350165| 0.3283927| 1.4704735| 3.5512986| 6.1| 27.5| 66.4|
| ECTE | 5.455395| 3.0887228| 0.1196580| 2.2470143| 56.6| 2.2| 41.2|
| ELMA | 3.176472| 0.7910221| 0.0376397| 2.3478099| 24.9| 1.2| 73.9|
| EPEO | 5.682921| 1.2701977| 0.7480074| 3.6647159| 22.4| 13.2| 64.5|
| EVIR | 5.302216| 0.3443063| 1.2761101| 3.6817993| 6.5| 24.1| 69.4|
| EPAR | 3.932729| 0.1404872| 0.7954177| 2.9968238| 3.6| 20.2| 76.2|
| ESSP | 4.773562| 0.1428212| 1.0243322| 3.6064087| 3.0| 21.5| 75.5|
| EPYG | 4.864853| 0.1428271| 0.8576808| 3.8643452| 2.9| 17.6| 79.4|
| EGEN | 5.046325| 1.9052672| 0.8579907| 2.2830675| 37.8| 17.0| 45.2|
| GASP | 5.691912| 0.3561383| 1.1723435| 4.1634306| 6.3| 20.6| 73.1|
| HYSP | 8.352748| 8.3527477| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| HEXO | 5.243021| 0.3580747| 1.1967967| 3.6881491| 6.8| 22.8| 70.3|
| HPEL | 3.568855| 1.4801591| 0.4150086| 1.6736876| 41.5| 11.6| 46.9|
| HYDR | 5.424177| 0.3446764| 1.0092149| 4.0702859| 6.4| 18.6| 75.0|
| HYDS | 6.176471| 2.7519145| 0.8340139| 2.5905427| 44.6| 13.5| 41.9|
| LEPT | 5.337927| 2.7371006| 0.4678790| 2.1329476| 51.3| 8.8| 40.0|
| LESP | 4.424214| 0.3139611| 0.8986846| 3.2115689| 7.1| 20.3| 72.6|
| LOPAad | 4.965245| 1.2010608| 1.9737829| 1.7904015| 24.2| 39.8| 36.1|
| LOPAla | 4.572070| 0.4750266| 1.2483663| 2.8486766| 10.4| 27.3| 62.3|
| MPOW | 5.765380| 1.2113295| 2.0688707| 2.4851794| 21.0| 35.9| 43.1|
| MYSA | 4.199512| 3.3382344| 0.2800060| 0.5812719| 79.5| 6.7| 13.8|
| ORHE | 5.578876| 1.7719543| 0.8907733| 2.9161486| 31.8| 16.0| 52.3|
| ONSP | 5.183675| 1.1029607| 1.7398668| 2.3408479| 21.3| 33.6| 45.2|
| ORTH | 5.122163| 0.2310466| 1.2565769| 3.6345398| 4.5| 24.5| 71.0|
| ORSP | 6.830422| 1.3221938| 2.3259105| 3.1823176| 19.4| 34.1| 46.6|
| OTROad | 5.323551| 0.3196624| 0.7949867| 4.2089018| 6.0| 14.9| 79.1|
| OTROla | 5.520143| 0.2338567| 1.2078521| 4.0784347| 4.2| 21.9| 73.9|
| PISI | 6.092811| 1.1586152| 2.0241946| 2.9100014| 19.0| 33.2| 47.8|
| POLY | 6.946072| 3.4561798| 0.5871372| 2.9027547| 49.8| 8.5| 41.8|
| PBIF | 5.230149| 0.1404078| 0.9126282| 4.1771134| 2.7| 17.4| 79.9|
| PPUS | 4.760375| 0.1258586| 0.8095743| 3.8249417| 2.6| 17.0| 80.3|
| RASP | 6.912935| 6.1246587| 0.5175577| 0.2707183| 88.6| 7.5| 3.9|
| RHYP | 5.925556| 1.9250904| 1.1395839| 2.8608814| 32.5| 19.2| 48.3|
| RHIP | 5.509859| 0.5317665| 1.7133248| 3.2647681| 9.7| 31.1| 59.3|
| SIGN | 5.690358| 0.2369562| 1.2683164| 4.1850853| 4.2| 22.3| 73.5|
| SARG | 5.338509| 0.5093751| 1.2407905| 3.5883437| 9.5| 23.2| 67.2|
| SIMU | 4.429029| 0.3726825| 1.1175113| 2.9388347| 8.4| 25.2| 66.4|
| STEN | 6.268569| 0.5701184| 1.5868239| 4.1116266| 9.1| 25.3| 65.6|
| SCAN | 6.356376| 1.3990981| 1.3987842| 3.5584937| 22.0| 22.0| 56.0|
| TANYP | 4.883393| 0.7337598| 1.6914515| 2.4581815| 15.0| 34.6| 50.3|
| TANYT | 5.335505| 0.1691889| 1.3199566| 3.8463591| 3.2| 24.7| 72.1|
| TFLU | 6.940545| 2.0277114| 1.5558618| 3.3569723| 29.2| 22.4| 48.4|
## class: krandtest lightkrandtest
## Monte-Carlo tests
## Call: as.krandtest(sim = t(sim), obs = obs)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 AFLU 1.6300864 1.7253541 greater 0.08910891
## 2 ANTO 2.8559783 3.3678147 greater 0.02970297
## 3 ATSP 0.7738686 1.9292439 greater 0.04950495
## 4 BASP 0.1540045 4.5751519 greater 0.00990099
## 5 BIMI 2.6656032 1.9070044 greater 0.05940594
## 6 BFAS 1.6334715 11.7370567 greater 0.00990099
## 7 CASP 2.0915508 14.4059836 greater 0.00990099
## 8 CESP 1.3620235 1.2553447 greater 0.11881188
## 9 CERA 0.4385214 1.7755189 greater 0.05940594
## 10 CLEP 0.4363223 8.2682846 greater 0.00990099
## 11 CMAR 2.0940725 14.8806714 greater 0.00990099
## 12 CHIR 0.4947663 1.2367689 greater 0.10891089
## 13 CPIC 2.8291243 7.0460467 greater 0.00990099
## 14 DRSP 1.0682956 2.7308803 greater 0.02970297
## 15 DUSP 0.1398475 2.0311775 greater 0.04950495
## 16 ECSP 0.3283927 3.8656990 greater 0.01980198
## 17 ECTE 3.0887228 1.5055852 greater 0.11881188
## 18 ELMA 0.7910221 -0.9326867 greater 0.83168317
## 19 EPEO 1.2701977 0.5411500 greater 0.20792079
## 20 EVIR 0.3443063 2.0042738 greater 0.06930693
## 21 EPAR 0.1404872 0.2023392 greater 0.31683168
## 22 ESSP 0.1428212 1.9794915 greater 0.05940594
## 23 EPYG 0.1428271 5.0843127 greater 0.00990099
## 24 EGEN 1.9052672 4.0699261 greater 0.01980198
## 25 GASP 0.3561383 2.2299864 greater 0.02970297
## 26 HYSP 8.3527477 0.9782508 greater 0.17821782
## 27 HEXO 0.3580747 4.4458230 greater 0.01980198
## 28 HPEL 1.4801591 2.2740531 greater 0.03960396
## 29 HYDR 0.3446764 5.4555549 greater 0.00990099
## 30 HYDS 2.7519145 2.8138801 greater 0.02970297
## 31 LEPT 2.7371006 0.9106221 greater 0.13861386
## 32 LESP 0.3139611 1.9692738 greater 0.05940594
## 33 LOPAad 1.2010608 3.5963104 greater 0.01980198
## 34 LOPAla 0.4750266 5.7482753 greater 0.00990099
## 35 MPOW 1.2113295 0.4274899 greater 0.23762376
## 36 MYSA 3.3382344 0.5204062 greater 0.24752475
## 37 ORHE 1.7719543 12.0081382 greater 0.00990099
## 38 ONSP 1.1029607 1.5271297 greater 0.09900990
## 39 ORTH 0.2310466 11.2649694 greater 0.00990099
## 40 ORSP 1.3221938 1.5224705 greater 0.08910891
## 41 OTROad 0.3196624 4.3031837 greater 0.00990099
## 42 OTROla 0.2338567 1.9429847 greater 0.04950495
## 43 PISI 1.1586152 1.3013958 greater 0.10891089
## 44 POLY 3.4561798 3.4885975 greater 0.00990099
## 45 PBIF 0.1404078 1.0713244 greater 0.10891089
## 46 PPUS 0.1258586 -1.2344247 greater 0.96039604
## 47 RASP 6.1246587 3.2833229 greater 0.01980198
## 48 RHYP 1.9250904 6.4752391 greater 0.00990099
## 49 RHIP 0.5317665 5.4734818 greater 0.00990099
## 50 SIGN 0.2369562 2.9948880 greater 0.03960396
## 51 SARG 0.5093751 10.0000653 greater 0.00990099
## 52 SIMU 0.3726825 -0.8452247 greater 0.80198020
## 53 STEN 0.5701184 0.1009888 greater 0.33663366
## 54 SCAN 1.3990981 2.2577607 greater 0.03960396
## 55 TANYP 0.7337598 2.8492499 greater 0.01980198
## 56 TANYT 0.1691889 2.1522444 greater 0.06930693
## 57 TFLU 2.0277114 1.0036981 greater 0.19801980
## 58 OMI.mean 1.3369324 8.6489846 greater 0.00990099
| | inertia| WitOMIG| Tol| Rtol| witomig| tol| rtol|
|--------------|---------:|----------:|----------:|----------:|--------:|-----:|-----:|
| AFLUautumn | 5.047993| 4.9912783| 0.0398153| 0.0168989| 98.9| 0.8| 0.3|
| ANTOautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| ATSPautumn | 5.322417| 1.3233915| 1.6872135| 2.3118117| 24.9| 31.7| 43.4|
| BASPautumn | 4.016310| 0.9472722| 0.8623746| 2.2066629| 23.6| 21.5| 54.9|
| BIMIautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| BFASautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| CASPautumn | 5.169176| 2.4639684| 0.7484637| 1.9567438| 47.7| 14.5| 37.9|
| CESPautumn | 6.887544| 6.8875440| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| CERAautumn | 4.970323| 4.9703232| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| CLEPautumn | 4.994338| 1.7047381| 1.3970321| 1.8925682| 34.1| 28.0| 37.9|
| CMARautumn | 4.965081| 3.4695335| 0.5973335| 0.8982140| 69.9| 12.0| 18.1|
| CHIRautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| CPICautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| DRSPautumn | 3.752796| 2.1145534| 0.7093201| 0.9289223| 56.3| 18.9| 24.8|
| DUSPautumn | 4.780569| 1.0076983| 1.7193447| 2.0535264| 21.1| 36.0| 43.0|
| ECSPautumn | 5.492605| 0.5709870| 1.2247275| 3.6968904| 10.4| 22.3| 67.3|
| ECTEautumn | 4.842414| 3.2464421| 0.3220876| 1.2738847| 67.0| 6.7| 26.3|
| ELMAautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| EPEOautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| EVIRautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| EPARautumn | 4.005941| 0.5643963| 0.6642875| 2.7772567| 14.1| 16.6| 69.3|
| ESSPautumn | 4.768717| 0.4737399| 0.5447734| 3.7502039| 9.9| 11.4| 78.6|
| EPYGautumn | 4.737186| 0.4865543| 0.4658465| 3.7847857| 10.3| 9.8| 79.9|
| EGENautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| GASPautumn | 2.580370| 0.6172684| 0.3452494| 1.6178520| 23.9| 13.4| 62.7|
| HYSPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| HEXOautumn | 4.456510| 2.3703453| 0.3042154| 1.7819496| 53.2| 6.8| 40.0|
| HPELautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| HYDRautumn | 5.115527| 0.7610947| 1.3776055| 2.9768267| 14.9| 26.9| 58.2|
| HYDSautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| LEPTautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| LESPautumn | 2.629375| 1.2380197| 0.2587213| 1.1326343| 47.1| 9.8| 43.1|
| LOPAadautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| LOPAlaautumn | 3.718404| 1.9067401| 0.3147357| 1.4969284| 51.3| 8.5| 40.3|
| MPOWautumn | 7.192349| 2.0009281| 2.7526841| 2.4387369| 27.8| 38.3| 33.9|
| MYSAautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| ORHEautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| ONSPautumn | 3.998315| 1.1210944| 0.9542170| 1.9230038| 28.0| 23.9| 48.1|
| ORTHautumn | 4.351143| 0.8960762| 1.5707221| 1.8843446| 20.6| 36.1| 43.3|
| ORSPautumn | 6.830422| 1.3221938| 2.3259105| 3.1823176| 19.4| 34.1| 46.6|
| OTROadautumn | 6.230148| 2.5941353| 0.9993173| 2.6366950| 41.6| 16.0| 42.3|
| OTROlaautumn | 5.142497| 0.6819671| 0.5516613| 3.9088689| 13.3| 10.7| 76.0|
| PISIautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| POLYautumn | 5.874718| 3.7305124| 0.7851956| 1.3590103| 63.5| 13.4| 23.1|
| PBIFautumn | 4.147923| 2.1961661| 0.5297757| 1.4219813| 52.9| 12.8| 34.3|
| PPUSautumn | 3.999069| 2.8780882| 0.7630461| 0.3579344| 72.0| 19.1| 9.0|
| RASPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| RHYPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| RHIPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| SIGNautumn | 5.241407| 0.8293657| 2.0007788| 2.4112627| 15.8| 38.2| 46.0|
| SARGautumn | 4.379817| 2.4492822| 0.8672941| 1.0632409| 55.9| 19.8| 24.3|
| SIMUautumn | 4.103898| 1.0638770| 1.3530498| 1.6869710| 25.9| 33.0| 41.1|
| STENautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| SCANautumn | 6.471128| 0.9813382| 0.6184927| 4.8712967| 15.2| 9.6| 75.3|
| TANYPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| TANYTautumn | 4.459319| 1.4061285| 0.7890246| 2.2641654| 31.5| 17.7| 50.8|
| TFLUautumn | 5.678182| 2.2777933| 0.4658585| 2.9345304| 40.1| 8.2| 51.7|
| AFLUspring | 5.593913| 2.4605247| 0.5429197| 2.5904682| 44.0| 9.7| 46.3|
| ANTOspring | 6.261168| 2.8559783| 0.4809695| 2.9242199| 45.6| 7.7| 46.7|
| ATSPspring | 4.959373| 0.9321612| 2.4084852| 1.6187263| 18.8| 48.6| 32.6|
| BASPspring | 5.216445| 0.3663442| 1.2783080| 3.5717924| 7.0| 24.5| 68.5|
| BIMIspring | 6.440541| 2.6656032| 1.2165765| 2.5583616| 41.4| 18.9| 39.7|
| BFASspring | 5.522998| 1.6334715| 0.8316546| 3.0578719| 29.6| 15.1| 55.4|
| CASPspring | 3.820359| 2.2812516| 0.4450021| 1.0941047| 59.7| 11.6| 28.6|
| CESPspring | 5.832151| 1.5201320| 1.7183686| 2.5936503| 26.1| 29.5| 44.5|
| CERAspring | 5.248000| 0.4657628| 1.4545148| 3.3277225| 8.9| 27.7| 63.4|
| CLEPspring | 5.039830| 0.4812193| 0.9568038| 3.6018068| 9.5| 19.0| 71.5|
| CMARspring | 5.364636| 2.1665091| 0.9555239| 2.2426034| 40.4| 17.8| 41.8|
| CHIRspring | 4.439955| 0.4947663| 1.5231051| 2.4220840| 11.1| 34.3| 54.6|
| CPICspring | 5.506342| 2.8291243| 0.5206856| 2.1565322| 51.4| 9.5| 39.2|
| DRSPspring | 8.570756| 3.3680622| 2.2610122| 2.9416821| 39.3| 26.4| 34.3|
| DUSPspring | 5.850675| 0.5912963| 1.6446603| 3.6147179| 10.1| 28.1| 61.8|
| ECSPspring | 5.331930| 0.5271468| 1.4828117| 3.3219716| 9.9| 27.8| 62.3|
| ECTEspring | 6.681357| 6.6813566| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| ELMAspring | 3.176472| 0.7910221| 0.0376397| 2.3478099| 24.9| 1.2| 73.9|
| EPEOspring | 5.682921| 1.2701977| 0.7480074| 3.6647159| 22.4| 13.2| 64.5|
| EVIRspring | 5.302216| 0.3443063| 1.2761101| 3.6817993| 6.5| 24.1| 69.4|
| EPARspring | 3.917797| 0.2686018| 0.5560763| 3.0931191| 6.9| 14.2| 79.0|
| ESSPspring | 4.774797| 0.3230870| 0.5041956| 3.9475140| 6.8| 10.6| 82.7|
| EPYGspring | 5.037744| 0.6458358| 1.4545934| 2.9373151| 12.8| 28.9| 58.3|
| EGENspring | 5.046325| 1.9052672| 0.8579907| 2.2830675| 37.8| 17.0| 45.2|
| GASPspring | 5.867186| 0.4291003| 1.0866893| 4.3513962| 7.3| 18.5| 74.2|
| HYSPspring | 8.352748| 8.3527477| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| HEXOspring | 5.323090| 0.4469288| 0.9911319| 3.8850296| 8.4| 18.6| 73.0|
| HPELspring | 3.568855| 1.4801591| 0.4150086| 1.6736876| 41.5| 11.6| 46.9|
| HYDRspring | 5.442886| 0.4174195| 0.9798750| 4.0455915| 7.7| 18.0| 74.3|
| HYDSspring | 6.176471| 2.7519145| 0.8340139| 2.5905427| 44.6| 13.5| 41.9|
| LEPTspring | 5.337927| 2.7371006| 0.4678790| 2.1329476| 51.3| 8.8| 40.0|
| LESPspring | 4.612694| 0.4520373| 1.0916182| 3.0690387| 9.8| 23.7| 66.5|
| LOPAadspring | 4.965245| 1.2010608| 1.9737829| 1.7904015| 24.2| 39.8| 36.1|
| LOPAlaspring | 5.113748| 0.3732577| 0.4726950| 4.2677952| 7.3| 9.2| 83.5|
| MPOWspring | 4.927375| 2.1214892| 1.6490874| 1.1567981| 43.1| 33.5| 23.5|
| MYSAspring | 4.199512| 3.3382344| 0.2800060| 0.5812719| 79.5| 6.7| 13.8|
| ORHEspring | 5.578876| 1.7719543| 0.8907733| 2.9161486| 31.8| 16.0| 52.3|
| ONSPspring | 5.582926| 2.8517365| 0.3795820| 2.3516074| 51.1| 6.8| 42.1|
| ORTHspring | 5.389232| 0.3900032| 0.9052110| 4.0940178| 7.2| 16.8| 76.0|
| ORSPspring | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| OTROadspring | 5.254247| 0.4060305| 0.9786089| 3.8696076| 7.7| 18.6| 73.6|
| OTROlaspring | 6.321554| 0.7368166| 1.8764679| 3.7082700| 11.7| 29.7| 58.7|
| PISIspring | 6.092811| 1.1586152| 2.0241946| 2.9100014| 19.0| 33.2| 47.8|
| POLYspring | 8.244171| 7.1591537| 0.3273256| 0.7576920| 86.8| 4.0| 9.2|
| PBIFspring | 5.286846| 0.1779238| 1.0255683| 4.0833537| 3.4| 19.4| 77.2|
| PPUSspring | 4.955463| 0.4637935| 3.2353458| 1.2563236| 9.4| 65.3| 25.4|
| RASPspring | 6.912935| 6.1246587| 0.5175577| 0.2707183| 88.6| 7.5| 3.9|
| RHYPspring | 5.925556| 1.9250904| 1.1395839| 2.8608814| 32.5| 19.2| 48.3|
| RHIPspring | 5.509859| 0.5317665| 1.7133248| 3.2647681| 9.7| 31.1| 59.3|
| SIGNspring | 6.115133| 2.0334064| 1.6570358| 2.4246905| 33.3| 27.1| 39.7|
| SARGspring | 5.630294| 0.5753024| 0.9397895| 4.1152018| 10.2| 16.7| 73.1|
| SIMUspring | 6.133137| 6.1331366| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| STENspring | 6.268569| 0.5701184| 1.5868239| 4.1116266| 9.1| 25.3| 65.6|
| SCANspring | 6.295929| 3.0027724| 0.4501125| 2.8430438| 47.7| 7.1| 45.2|
| TANYPspring | 4.883393| 0.7337598| 1.6914515| 2.4581815| 15.0| 34.6| 50.3|
| TANYTspring | 5.481883| 0.3222465| 0.9109765| 4.2486596| 5.9| 16.6| 77.5|
| TFLUspring | 8.759160| 5.3988068| 0.8297366| 2.5306165| 61.6| 9.5| 28.9|
## $autumn
## $autumn$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 5
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 FST -0.095707902 -0.93792273 two-sided 0.39603960
## 2 Depth 0.330509141 2.39455007 two-sided 0.00990099
## 3 SPS 0.495802058 4.28536734 two-sided 0.00990099
## 4 Froude -0.155231548 -1.19848456 two-sided 0.21782178
## 5 Rough 0.002382221 -0.09980932 two-sided 0.89108911
## Subsets Pvalue: 7.535608e-06
## other elements: subpvalue call
##
## $autumn$witomigtest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 AFLU 4.9912783 3.10875366 greater 100 0.02970297 2.238299e-07
## 2 ANTO NaN NaN greater 98 NA NA
## 3 ATSP 1.3233915 0.26054005 greater 100 0.29702970 2.238299e-06
## 4 BASP 0.9472722 4.12709607 greater 100 0.00990099 7.460998e-08
## 5 BIMI NaN NaN greater 93 NA NA
## 6 BFAS NaN NaN greater 100 NA NA
## 7 CASP 2.4639684 3.86835956 greater 100 0.00990099 7.460998e-08
## 8 CESP 6.8875440 2.51998997 greater 100 0.04950495 3.730499e-07
## 9 CERA 4.9703232 11.07073398 greater 100 0.00990099 7.460998e-08
## 10 CLEP 1.7047381 7.46805126 greater 100 0.00990099 7.460998e-08
## 11 CMAR 3.4695335 7.27334162 greater 100 0.00990099 7.460998e-08
## 12 CHIR NaN NaN greater 100 NA NA
## 13 CPIC NaN NaN greater 100 NA NA
## 14 DRSP 2.1145534 1.83765489 greater 100 0.06930693 5.222699e-07
## 15 DUSP 1.0076983 4.49581672 greater 100 0.00990099 7.460998e-08
## 16 ECSP 0.5709870 0.27475974 greater 100 0.34653465 2.611349e-06
## 17 ECTE 3.2464421 0.17446519 greater 88 0.29213483 2.201414e-06
## 18 ELMA NaN NaN greater 75 NA NA
## 19 EPEO NaN NaN greater 99 NA NA
## 20 EVIR NaN NaN greater 100 NA NA
## 21 EPAR 0.5643963 0.09594693 greater 100 0.35643564 2.685959e-06
## 22 ESSP 0.4737399 -0.07754896 greater 100 0.56435644 4.252769e-06
## 23 EPYG 0.4865543 0.48034280 greater 100 0.20792079 1.566810e-06
## 24 EGEN NaN NaN greater 100 NA NA
## 25 GASP 0.6172684 -0.03832010 greater 100 0.45544554 3.432059e-06
## 26 HYSP NaN NaN greater 38 NA NA
## 27 HEXO 2.3703453 9.57245416 greater 100 0.00990099 7.460998e-08
## 28 HPEL NaN NaN greater 100 NA NA
## 29 HYDR 0.7610947 1.32560003 greater 100 0.12871287 9.699298e-07
## 30 HYDS NaN NaN greater 98 NA NA
## 31 LEPT NaN NaN greater 89 NA NA
## 32 LESP 1.2380197 2.51568053 greater 100 0.03960396 2.984399e-07
## 33 LOPAad NaN NaN greater 100 NA NA
## 34 LOPAla 1.9067401 6.41542785 greater 100 0.00990099 7.460998e-08
## 35 MPOW 2.0009281 0.10850304 greater 100 0.38613861 2.909789e-06
## 36 MYSA NaN NaN greater 76 NA NA
## 37 ORHE NaN NaN greater 100 NA NA
## 38 ONSP 1.1210944 -0.19260730 greater 100 0.50495050 3.805109e-06
## 39 ORTH 0.8960762 4.37355835 greater 100 0.00990099 7.460998e-08
## 40 ORSP 1.3221938 -0.21300412 greater 99 0.49000000 3.692448e-06
## 41 OTROad 2.5941353 8.32955073 greater 100 0.00990099 7.460998e-08
## 42 OTROla 0.6819671 0.55088726 greater 100 0.22772277 1.716030e-06
## 43 PISI NaN NaN greater 100 NA NA
## 44 POLY 3.7305124 1.03807592 greater 100 0.13861386 1.044540e-06
## 45 PBIF 2.1961661 8.13800603 greater 100 0.00990099 7.460998e-08
## 46 PPUS 2.8780882 0.63911223 greater 100 0.17821782 1.342980e-06
## 47 RASP NaN NaN greater 83 NA NA
## 48 RHYP NaN NaN greater 100 NA NA
## 49 RHIP NaN NaN greater 100 NA NA
## 50 SIGN 0.8293657 1.50009635 greater 100 0.09900990 7.460998e-07
## 51 SARG 2.4492822 12.21785529 greater 100 0.00990099 7.460998e-08
## 52 SIMU 1.0638770 -0.58139916 greater 100 0.69306931 5.222699e-06
## 53 STEN NaN NaN greater 99 NA NA
## 54 SCAN 0.9813382 -0.40391275 greater 100 0.55445545 4.178159e-06
## 55 TANYP NaN NaN greater 100 NA NA
## 56 TANYT 1.4061285 5.30804758 greater 100 0.00990099 7.460998e-08
## 57 TFLU 2.2777933 -0.14701472 greater 97 0.43877551 3.306440e-06
## 58 OMI.mean 1.9584239 4.90557362 greater 100 0.00990099 7.460998e-08
##
## other elements: adj.method sub.pvalue subni.pvalue call
##
##
## $spring
## $spring$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 5
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 FST 0.092892964 1.0049170 two-sided 0.31683168
## 2 Depth -0.320788284 -3.0814553 two-sided 0.00990099
## 3 SPS -0.481219645 -3.6328831 two-sided 0.00990099
## 4 Froude 0.150665914 1.4666792 two-sided 0.14851485
## 5 Rough -0.002312156 -0.1493797 two-sided 0.89108911
## Subsets Pvalue: 4.110332e-06
## other elements: subpvalue call
##
## $spring$witomigtest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 AFLU 2.4605247 0.43855286 greater 100 0.23762376 9.767125e-07
## 2 ANTO 2.8559783 0.16875948 greater 100 0.31683168 1.302283e-06
## 3 ATSP 0.9321612 -0.17932166 greater 100 0.46534653 1.912729e-06
## 4 BASP 0.3663442 -0.32426024 greater 100 0.53465347 2.197603e-06
## 5 BIMI 2.6656032 -0.14068152 greater 92 0.41935484 1.723688e-06
## 6 BFAS 1.6334715 1.63368764 greater 100 0.04950495 2.034818e-07
## 7 CASP 2.2812516 2.69515862 greater 100 0.02970297 1.220891e-07
## 8 CESP 1.5201320 -0.24455525 greater 98 0.49494949 2.034407e-06
## 9 CERA 0.4657628 -0.76409501 greater 100 0.79207921 3.255708e-06
## 10 CLEP 0.4812193 0.04242279 greater 100 0.46534653 1.912729e-06
## 11 CMAR 2.1665091 4.21557418 greater 100 0.00990099 4.069635e-08
## 12 CHIR 0.4947663 -0.65386801 greater 100 0.74257426 3.052227e-06
## 13 CPIC 2.8291243 1.18137131 greater 100 0.12871287 5.290526e-07
## 14 DRSP 3.3680622 2.96896593 greater 100 0.01980198 8.139271e-08
## 15 DUSP 0.5912963 0.62148959 greater 100 0.22772277 9.360161e-07
## 16 ECSP 0.5271468 0.01120943 greater 100 0.41584158 1.709247e-06
## 17 ECTE 6.6813566 1.17774092 greater 85 0.10465116 4.301510e-07
## 18 ELMA 0.7910221 -1.14000003 greater 72 0.90410959 3.716190e-06
## 19 EPEO 1.2701977 -0.65639153 greater 97 0.76530612 3.145662e-06
## 20 EVIR 0.3443063 -0.68719153 greater 100 0.67326733 2.767352e-06
## 21 EPAR 0.2686018 -1.02865659 greater 100 0.85148515 3.499886e-06
## 22 ESSP 0.3230870 -0.79606867 greater 100 0.80198020 3.296405e-06
## 23 EPYG 0.6458358 2.17534645 greater 100 0.03960396 1.627854e-07
## 24 EGEN 1.9052672 1.03728742 greater 100 0.10891089 4.476599e-07
## 25 GASP 0.4291003 -0.70741489 greater 100 0.71287129 2.930137e-06
## 26 HYSP 8.3527477 0.89265789 greater 47 0.25000000 1.027583e-06
## 27 HEXO 0.4469288 -0.33768309 greater 100 0.58415842 2.401085e-06
## 28 HPEL 1.4801591 -0.06972933 greater 100 0.41584158 1.709247e-06
## 29 HYDR 0.4174195 -0.42750824 greater 100 0.65346535 2.685959e-06
## 30 HYDS 2.7519145 0.15504324 greater 99 0.32000000 1.315306e-06
## 31 LEPT 2.7371006 -0.37433813 greater 85 0.50000000 2.055166e-06
## 32 LESP 0.4520373 -0.36408430 greater 100 0.61386139 2.523174e-06
## 33 LOPAad 1.2010608 0.11065675 greater 100 0.37623762 1.546461e-06
## 34 LOPAla 0.3732577 -0.71705119 greater 100 0.72277228 2.970834e-06
## 35 MPOW 2.1214892 -0.11364171 greater 99 0.39000000 1.603029e-06
## 36 MYSA 3.3382344 -0.44156444 greater 76 0.59740260 2.455523e-06
## 37 ORHE 1.7719543 2.12491130 greater 100 0.04950495 2.034818e-07
## 38 ONSP 2.8517365 1.35172557 greater 100 0.09900990 4.069635e-07
## 39 ORTH 0.3900032 -0.20137012 greater 100 0.55445545 2.278996e-06
## 40 ORSP NaN NaN greater 100 NA NA
## 41 OTROad 0.4060305 -0.54304267 greater 100 0.72277228 2.970834e-06
## 42 OTROla 0.7368166 0.46514677 greater 100 0.26732673 1.098802e-06
## 43 PISI 1.1586152 -0.23324070 greater 100 0.54455446 2.238299e-06
## 44 POLY 7.1591537 1.80153881 greater 99 0.07000000 2.877232e-07
## 45 PBIF 0.1779238 -1.25703929 greater 100 0.94059406 3.866154e-06
## 46 PPUS 0.4637935 -0.99129897 greater 99 0.93000000 3.822609e-06
## 47 RASP 6.1246587 0.81132685 greater 90 0.23076923 9.485381e-07
## 48 RHYP 1.9250904 1.78499822 greater 100 0.05940594 2.441781e-07
## 49 RHIP 0.5317665 -0.28848663 greater 100 0.56435644 2.319692e-06
## 50 SIGN 2.0334064 7.29772288 greater 100 0.00990099 4.069635e-08
## 51 SARG 0.5753024 0.52721047 greater 100 0.29702970 1.220891e-06
## 52 SIMU 6.1331366 3.62535573 greater 100 0.01980198 8.139271e-08
## 53 STEN 0.5701184 -0.70139134 greater 99 0.81000000 3.329369e-06
## 54 SCAN 3.0027724 1.41007250 greater 100 0.12871287 5.290526e-07
## 55 TANYP 0.7337598 -0.05306232 greater 100 0.42574257 1.749943e-06
## 56 TANYT 0.3222465 -0.76219532 greater 100 0.78217822 3.215012e-06
## 57 TFLU 5.3988068 0.84120522 greater 99 0.17000000 6.987564e-07
## 58 OMI.mean 1.8649566 2.07409190 greater 100 0.02970297 1.220891e-07
##
## other elements: adj.method sub.pvalue subni.pvalue call
| | inertia| WitOMIG_k| Tol| Rtol| witomig_k| tol| rtol|
|---------|---------:|-----------:|----------:|----------:|-----------:|-----:|-----:|
| AFLU1 | 5.620936| 5.5642217| 0.0360651| 0.0206490| 99.0| 0.6| 0.4|
| ANTO1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| ATSP1 | 5.175405| 1.1763795| 2.3858548| 1.6131705| 22.7| 46.1| 31.2|
| BASP1 | 3.884823| 0.8157855| 1.6171870| 1.4518505| 21.0| 41.6| 37.4|
| BIMI1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| BFAS1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| CASP1 | 4.659261| 1.9540531| 0.8433682| 1.8618393| 41.9| 18.1| 40.0|
| CESP1 | 4.984683| 4.9846831| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| CERA1 | 3.987971| 3.9879708| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| CLEP1 | 4.909637| 1.6200372| 2.0090550| 1.2805453| 33.0| 40.9| 26.1|
| CMAR1 | 4.803559| 3.3080114| 0.8245203| 0.6710272| 68.9| 17.2| 14.0|
| CHIR1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| CPIC1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| DRSP1 | 3.714868| 2.0766254| 0.9507084| 0.6875340| 55.9| 25.6| 18.5|
| DUSP1 | 4.669630| 0.8967591| 2.6173587| 1.1555125| 19.2| 56.1| 24.7|
| ECSP1 | 5.223511| 0.3018932| 2.4656636| 2.4559543| 5.8| 47.2| 47.0|
| ECTE1 | 4.282558| 2.6865859| 0.0458742| 1.5500981| 62.7| 1.1| 36.2|
| ELMA1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| EPEO1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| EVIR1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| EPAR1 | 3.552155| 0.1106110| 1.4819049| 1.9596393| 3.1| 41.7| 55.2|
| ESSP1 | 4.419685| 0.1247073| 2.0704114| 2.2245660| 2.8| 46.8| 50.3|
| EPYG1 | 4.322019| 0.0713870| 2.4834894| 1.7671428| 1.7| 57.5| 40.9|
| EGEN1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| GASP1 | 2.505812| 0.5427110| 0.4469468| 1.5161545| 21.7| 17.8| 60.5|
| HYSP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| HEXO1 | 3.947786| 1.8616211| 0.6252779| 1.4608870| 47.2| 15.8| 37.0|
| HPEL1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| HYDR1 | 4.883217| 0.5287849| 2.1845547| 2.1698775| 10.8| 44.7| 44.4|
| HYDS1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| LEPT1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| LESP1 | 2.415512| 1.0241563| 0.5868012| 0.8045543| 42.4| 24.3| 33.3|
| LOPAad1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| LOPAla1 | 3.483575| 1.6719107| 0.6582340| 1.1534301| 48.0| 18.9| 33.1|
| MPOW1 | 7.279741| 2.0883204| 2.2426765| 2.9487445| 28.7| 30.8| 40.5|
| MYSA1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| ORHE1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| ONSP1 | 4.234140| 1.3569194| 1.5778631| 1.2993576| 32.0| 37.3| 30.7|
| ORTH1 | 4.365602| 0.9105355| 2.1991369| 1.2559299| 20.9| 50.4| 28.8|
| ORSP1 | 6.422186| 0.9139582| 2.3678119| 3.1404162| 14.2| 36.9| 48.9|
| OTROad1 | 5.847957| 2.2119449| 1.8459240| 1.7900883| 37.8| 31.6| 30.6|
| OTROla1 | 4.677074| 0.2165436| 2.2520955| 2.2084347| 4.6| 48.2| 47.2|
| PISI1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| POLY1 | 4.504666| 2.3604599| 0.7878648| 1.3563411| 52.4| 17.5| 30.1|
| PBIF1 | 4.272253| 2.3204958| 0.8146753| 1.1370817| 54.3| 19.1| 26.6|
| PPUS1 | 4.280653| 3.1596723| 0.8806195| 0.2403611| 73.8| 20.6| 5.6|
| RASP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| RHYP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| RHIP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| SIGN1 | 5.201174| 0.7891329| 3.0374197| 1.3746218| 15.2| 58.4| 26.4|
| SARG1 | 4.591782| 2.6612472| 1.0885512| 0.8419839| 58.0| 23.7| 18.3|
| SIMU1 | 3.996263| 0.9562425| 2.4510003| 0.5890204| 23.9| 61.3| 14.7|
| STEN1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| SCAN1 | 5.786297| 0.2965078| 0.4136070| 5.0761824| 5.1| 7.1| 87.7|
| TANYP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| TANYT1 | 3.906339| 0.8531489| 0.9303935| 2.1227966| 21.8| 23.8| 54.3|
| TFLU1 | 4.652959| 1.2525696| 0.0278353| 3.3725536| 26.9| 0.6| 72.5|
| AFLU2 | 5.889237| 2.7558493| 0.7029799| 2.4304079| 46.8| 11.9| 41.3|
| ANTO2 | 7.083839| 3.6786494| 0.4050870| 3.0001024| 51.9| 5.7| 42.4|
| ATSP2 | 4.891195| 0.8639830| 1.4160532| 2.6111583| 17.7| 29.0| 53.4|
| BASP2 | 4.890760| 0.0406599| 2.1106210| 2.7394794| 0.8| 43.2| 56.0|
| BIMI2 | 5.673801| 1.8988628| 0.4534084| 3.3215297| 33.5| 8.0| 58.5|
| BFAS2 | 4.857406| 0.9678796| 1.6202229| 2.2693036| 19.9| 33.4| 46.7|
| CASP2 | 3.980211| 2.4411040| 0.4639540| 1.0751528| 61.3| 11.7| 27.0|
| CESP2 | 6.177622| 1.8656031| 1.8455125| 2.4665064| 30.2| 29.9| 39.9|
| CERA2 | 5.361050| 0.5788122| 1.8880373| 2.8942000| 10.8| 35.2| 54.0|
| CLEP2 | 4.682735| 0.1241242| 2.2629018| 2.2957089| 2.7| 48.3| 49.0|
| CMAR2 | 4.800920| 1.6027928| 1.5454976| 1.6526296| 33.4| 32.2| 34.4|
| CHIR2 | 4.322612| 0.3774233| 1.0898553| 2.8553338| 8.7| 25.2| 66.1|
| CPIC2 | 5.717512| 3.0402945| 0.5568210| 2.1203968| 53.2| 9.7| 37.1|
| DRSP2 | 7.419486| 2.2167917| 2.1269874| 3.0757068| 29.9| 28.7| 41.5|
| DUSP2 | 5.375101| 0.1157230| 3.0826266| 2.1767516| 2.2| 57.4| 40.5|
| ECSP2 | 4.862670| 0.0578862| 2.5673879| 2.2373955| 1.2| 52.8| 46.0|
| ECTE2 | 8.151691| 8.1516905| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| ELMA2 | 4.029052| 1.6436023| 0.0110160| 2.3744336| 40.8| 0.3| 58.9|
| EPEO2 | 5.036529| 0.6238052| 1.5791415| 2.8335819| 12.4| 31.4| 56.3|
| EVIR2 | 5.198365| 0.2404560| 2.0712503| 2.8866591| 4.6| 39.8| 55.5|
| EPAR2 | 3.753191| 0.1039955| 0.9788830| 2.6703124| 2.8| 26.1| 71.1|
| ESSP2 | 4.539844| 0.0881344| 1.0956259| 3.3560837| 1.9| 24.1| 73.9|
| EPYG2 | 4.998468| 0.6065596| 1.5793650| 2.8125435| 12.1| 31.6| 56.3|
| EGEN2 | 5.010104| 1.8690463| 0.9064748| 2.2345834| 37.3| 18.1| 44.6|
| GASP2 | 5.509508| 0.0714224| 2.1874052| 3.2506803| 1.3| 39.7| 59.0|
| HYSP2 | 6.946494| 6.9464943| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| HEXO2 | 4.934166| 0.0580045| 2.5800676| 2.2960939| 1.2| 52.3| 46.5|
| HPEL2 | 3.736686| 1.6479900| 0.4430363| 1.6456599| 44.1| 11.9| 44.0|
| HYDR2 | 5.032585| 0.0071187| 1.8487522| 3.1767142| 0.1| 36.7| 63.1|
| HYDS2 | 7.159464| 3.7349069| 0.7539792| 2.6705774| 52.2| 10.5| 37.3|
| LEPT2 | 6.712646| 4.1118194| 0.2087109| 2.3921156| 61.3| 3.1| 35.6|
| LESP2 | 4.398537| 0.2378806| 1.3146253| 2.8460317| 5.4| 29.9| 64.7|
| LOPAad2 | 4.865602| 1.1014180| 2.0359307| 1.7282537| 22.6| 41.8| 35.5|
| LOPAla2 | 4.881240| 0.1407503| 1.2113913| 3.5290988| 2.9| 24.8| 72.3|
| MPOW2 | 4.573233| 1.7673471| 1.3401219| 1.4657636| 38.6| 29.3| 32.1|
| MYSA2 | 4.421343| 3.5600647| 0.3999980| 0.4612800| 80.5| 9.0| 10.4|
| ORHE2 | 4.925288| 1.1183665| 1.7257631| 2.0811588| 22.7| 35.0| 42.3|
| ONSP2 | 5.752368| 3.0211784| 0.3593429| 2.3718465| 52.5| 6.2| 41.2|
| ORTH2 | 5.008129| 0.0088998| 2.5004187| 2.4988101| 0.2| 49.9| 49.9|
| ORSP2 | NaN| NaN| NaN| NaN| NaN| NaN| NaN|
| OTROad2 | 5.012399| 0.1641826| 1.6013576| 3.2468588| 3.3| 31.9| 64.8|
| OTROla2 | 6.399000| 0.8142621| 2.5716818| 3.0130560| 12.7| 40.2| 47.1|
| PISI2 | 6.174527| 1.2403312| 2.0185171| 2.9156789| 20.1| 32.7| 47.2|
| POLY2 | 9.477574| 8.3925566| 0.3483423| 0.7366753| 88.6| 3.7| 7.8|
| PBIF2 | 5.172676| 0.0637543| 1.3545450| 3.7543770| 1.2| 26.2| 72.6|
| PPUS2 | 5.253758| 0.7620889| 2.5991370| 1.8925324| 14.5| 49.5| 36.0|
| RASP2 | 6.358315| 5.5700388| 0.5376887| 0.2505873| 87.6| 8.5| 3.9|
| RHYP2 | 5.278459| 1.2779932| 1.6076088| 2.3928564| 24.2| 30.5| 45.3|
| RHIP2 | 5.297488| 0.3193947| 2.0800676| 2.8980253| 6.0| 39.3| 54.7|
| SIGN2 | 5.443125| 1.3613993| 1.3428192| 2.7389070| 25.0| 24.7| 50.3|
| SARG2 | 5.112210| 0.0572188| 1.8163081| 3.2386832| 1.1| 35.5| 63.4|
| SIMU2 | 6.807988| 6.8079877| 0.0000000| 0.0000000| 100.0| 0.0| 0.0|
| STEN2 | 5.869217| 0.1707668| 3.1871376| 2.5113129| 2.9| 54.3| 42.8|
| SCAN2 | 6.542266| 3.2491099| 0.5550257| 2.7381305| 49.7| 8.5| 41.9|
| TANYP2 | 4.953800| 0.8041675| 1.9167004| 2.2329326| 16.2| 38.7| 45.1|
| TANYT2 | 5.221442| 0.0618055| 1.5156228| 3.6440133| 1.2| 29.0| 69.8|
| TFLU2 | 9.985720| 6.6253669| 0.8064942| 2.5538589| 66.3| 8.1| 25.6|
## $autumn
## $autumn$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 5
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 FST -0.095707902 -0.8500764 two-sided 0.38613861
## 2 Depth 0.330509141 2.6502707 two-sided 0.01980198
## 3 SPS 0.495802058 3.8951903 two-sided 0.00990099
## 4 Froude -0.155231548 -1.1984950 two-sided 0.25742574
## 5 Rough 0.002382221 0.1115240 two-sided 0.94059406
## Subsets Pvalue: 1.833094e-05
## other elements: subpvalue call
##
## $autumn$witomig_ktest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 AFLU 5.56422172 4.7157933 greater 100 0.00990099 1.814944e-07
## 2 ANTO NaN NaN greater 99 NA NA
## 3 ATSP 1.17637948 0.9608107 greater 100 0.14851485 2.722417e-06
## 4 BASP 0.81578553 11.2081232 greater 100 0.00990099 1.814944e-07
## 5 BIMI NaN NaN greater 96 NA NA
## 6 BFAS NaN NaN greater 100 NA NA
## 7 CASP 1.95405306 5.4880781 greater 100 0.00990099 1.814944e-07
## 8 CESP 4.98468314 2.4878749 greater 100 0.04950495 9.074722e-07
## 9 CERA 3.98797079 9.2265229 greater 100 0.00990099 1.814944e-07
## 10 CLEP 1.62003719 15.0262330 greater 100 0.00990099 1.814944e-07
## 11 CMAR 3.30801135 11.2586970 greater 100 0.00990099 1.814944e-07
## 12 CHIR NaN NaN greater 100 NA NA
## 13 CPIC NaN NaN greater 100 NA NA
## 14 DRSP 2.07662540 1.9105845 greater 100 0.06930693 1.270461e-06
## 15 DUSP 0.89675913 10.3692312 greater 100 0.00990099 1.814944e-07
## 16 ECSP 0.30189321 1.0987540 greater 100 0.09900990 1.814944e-06
## 17 ECTE 2.68658590 -0.1771901 greater 94 0.48421053 8.876033e-06
## 18 ELMA NaN NaN greater 81 NA NA
## 19 EPEO NaN NaN greater 100 NA NA
## 20 EVIR NaN NaN greater 100 NA NA
## 21 EPAR 0.11061095 -0.3249294 greater 100 0.47524752 8.711733e-06
## 22 ESSP 0.12470729 0.8467007 greater 100 0.23762376 4.355866e-06
## 23 EPYG 0.07138701 0.7113649 greater 100 0.18811881 3.448394e-06
## 24 EGEN NaN NaN greater 100 NA NA
## 25 GASP 0.54271103 1.0390179 greater 100 0.14851485 2.722417e-06
## 26 HYSP NaN NaN greater 57 NA NA
## 27 HEXO 1.86162108 12.5078562 greater 100 0.00990099 1.814944e-07
## 28 HPEL NaN NaN greater 100 NA NA
## 29 HYDR 0.52878494 4.5671524 greater 100 0.00990099 1.814944e-07
## 30 HYDS NaN NaN greater 98 NA NA
## 31 LEPT NaN NaN greater 93 NA NA
## 32 LESP 1.02415632 3.9051372 greater 100 0.01980198 3.629889e-07
## 33 LOPAad NaN NaN greater 100 NA NA
## 34 LOPAla 1.67191072 11.1164571 greater 100 0.00990099 1.814944e-07
## 35 MPOW 2.08832037 0.1901046 greater 99 0.27000000 4.949353e-06
## 36 MYSA NaN NaN greater 64 NA NA
## 37 ORHE NaN NaN greater 100 NA NA
## 38 ONSP 1.35691944 0.7957905 greater 100 0.17821782 3.266900e-06
## 39 ORTH 0.91053552 18.9386921 greater 100 0.00990099 1.814944e-07
## 40 ORSP 0.91395817 -0.2212642 greater 100 0.39603960 7.259777e-06
## 41 OTROad 2.21194486 12.4787089 greater 100 0.00990099 1.814944e-07
## 42 OTROla 0.21654356 0.5536612 greater 100 0.25742574 4.718855e-06
## 43 PISI NaN NaN greater 100 NA NA
## 44 POLY 2.36045987 0.8460642 greater 100 0.18811881 3.448394e-06
## 45 PBIF 2.32049577 15.5261977 greater 100 0.00990099 1.814944e-07
## 46 PPUS 3.15967229 1.1302462 greater 100 0.09900990 1.814944e-06
## 47 RASP NaN NaN greater 86 NA NA
## 48 RHYP NaN NaN greater 100 NA NA
## 49 RHIP NaN NaN greater 100 NA NA
## 50 SIGN 0.78913290 5.9368163 greater 100 0.00990099 1.814944e-07
## 51 SARG 2.66124718 28.4580286 greater 100 0.00990099 1.814944e-07
## 52 SIMU 0.95624252 -0.3365549 greater 100 0.49504950 9.074722e-06
## 53 STEN NaN NaN greater 99 NA NA
## 54 SCAN 0.29650781 -0.6823962 greater 100 0.80198020 1.470105e-05
## 55 TANYP NaN NaN greater 100 NA NA
## 56 TANYT 0.85314894 8.5087049 greater 100 0.00990099 1.814944e-07
## 57 TFLU 1.25256959 -0.3526573 greater 98 0.50505051 9.258049e-06
## 58 OMI.mean 1.64733126 4.6319226 greater 100 0.00990099 1.814944e-07
##
## other elements: adj.method sub.pvalue subni.pvalue call
##
##
## $spring
## $spring$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 5
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 FST 0.092892964 0.67535719 two-sided 0.53465347
## 2 Depth -0.320788284 -2.68914880 two-sided 0.01980198
## 3 SPS -0.481219645 -3.96215890 two-sided 0.00990099
## 4 Froude 0.150665914 1.23714387 two-sided 0.21782178
## 5 Rough -0.002312156 -0.06131542 two-sided 0.94059406
## Subsets Pvalue: 2.147648e-05
## other elements: subpvalue call
##
## $spring$witomig_ktest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 AFLU 2.755849341 1.20570963 greater 100 0.10891089 2.339023e-06
## 2 ANTO 3.678649417 1.46558307 greater 100 0.07920792 1.701108e-06
## 3 ATSP 0.863982951 0.09537015 greater 100 0.35643564 7.654984e-06
## 4 BASP 0.040659929 -0.37989530 greater 100 0.56435644 1.212039e-05
## 5 BIMI 1.898862829 -0.43393861 greater 93 0.56382979 1.210908e-05
## 6 BFAS 0.967879551 1.90272607 greater 100 0.07920792 1.701108e-06
## 7 CASP 2.441104022 7.04244690 greater 100 0.00990099 2.126385e-07
## 8 CESP 1.865603098 0.16288540 greater 100 0.29702970 6.379154e-06
## 9 CERA 0.578812207 0.52437558 greater 100 0.26732673 5.741238e-06
## 10 CLEP 0.124124205 0.16089053 greater 100 0.32673267 7.017069e-06
## 11 CMAR 1.602792826 3.58960345 greater 100 0.01980198 4.252769e-07
## 12 CHIR 0.377423333 -0.18105066 greater 100 0.44554455 9.568730e-06
## 13 CPIC 3.040294484 2.94285045 greater 100 0.02970297 6.379154e-07
## 14 DRSP 2.216791717 2.10771008 greater 100 0.04950495 1.063192e-06
## 15 DUSP 0.115722951 -0.06800970 greater 100 0.30693069 6.591792e-06
## 16 ECSP 0.057886165 -0.79469324 greater 100 0.72277228 1.552261e-05
## 17 ECTE 8.151690494 1.77994072 greater 87 0.07954545 1.708357e-06
## 18 ELMA 1.643602322 -0.74415282 greater 75 0.69736842 1.497702e-05
## 19 EPEO 0.623805205 -0.71711064 greater 98 0.78787879 1.692087e-05
## 20 EVIR 0.240456014 -0.01533892 greater 100 0.41584158 8.930815e-06
## 21 EPAR 0.103995528 -0.52099100 greater 100 0.63366337 1.360886e-05
## 22 ESSP 0.088134358 -0.43667510 greater 100 0.57425743 1.233303e-05
## 23 EPYG 0.606559568 12.92843248 greater 100 0.00990099 2.126385e-07
## 24 EGEN 1.869046253 1.28397197 greater 100 0.13861386 2.976938e-06
## 25 GASP 0.071422352 -0.81941175 greater 100 0.81188119 1.743635e-05
## 26 HYSP 6.946494324 0.90807772 greater 61 0.19354839 4.156739e-06
## 27 HEXO 0.058004536 -0.83718353 greater 100 0.79207921 1.701108e-05
## 28 HPEL 1.647990021 0.33726257 greater 100 0.23762376 5.103323e-06
## 29 HYDR 0.007118745 -1.35842322 greater 100 1.00000000 2.147648e-05
## 30 HYDS 3.734906945 0.96068519 greater 99 0.11000000 2.362413e-06
## 31 LEPT 4.111819413 0.26272930 greater 90 0.29670330 6.372143e-06
## 32 LESP 0.237880602 -0.11221807 greater 100 0.46534653 9.994007e-06
## 33 LOPAad 1.101418022 0.67061086 greater 100 0.19801980 4.252769e-06
## 34 LOPAla 0.140750264 -0.19717236 greater 100 0.44554455 9.568730e-06
## 35 MPOW 1.767347058 0.02092202 greater 100 0.38613861 8.292900e-06
## 36 MYSA 3.560064717 -0.16860859 greater 76 0.49350649 1.059878e-05
## 37 ORHE 1.118366495 2.31644155 greater 100 0.05940594 1.275831e-06
## 38 ONSP 3.021178449 1.64655564 greater 100 0.09900990 2.126385e-06
## 39 ORTH 0.008899823 -1.03551132 greater 100 0.94059406 2.020065e-05
## 40 ORSP NaN NaN greater 100 NA NA
## 41 OTROad 0.164182633 -0.27654657 greater 100 0.55445545 1.190775e-05
## 42 OTROla 0.814262062 3.80193284 greater 100 0.01980198 4.252769e-07
## 43 PISI 1.240331162 0.47585817 greater 100 0.25742574 5.528600e-06
## 44 POLY 8.392556643 4.14318650 greater 99 0.01000000 2.147648e-07
## 45 PBIF 0.063754284 -0.65858405 greater 100 0.70297030 1.509733e-05
## 46 PPUS 0.762088874 -0.76183201 greater 99 0.78000000 1.675166e-05
## 47 RASP 5.570038758 0.67463118 greater 91 0.26086957 5.602561e-06
## 48 RHYP 1.277993230 1.57061290 greater 100 0.07920792 1.701108e-06
## 49 RHIP 0.319394665 0.64628842 greater 100 0.18811881 4.040131e-06
## 50 SIGN 1.361399251 8.94619480 greater 100 0.00990099 2.126385e-07
## 51 SARG 0.057218796 -0.43512833 greater 100 0.63366337 1.360886e-05
## 52 SIMU 6.807987747 3.90914932 greater 100 0.00990099 2.126385e-07
## 53 STEN 0.170766804 -0.86762968 greater 100 0.99009901 2.126385e-05
## 54 SCAN 3.249109937 2.18882501 greater 100 0.05940594 1.275831e-06
## 55 TANYP 0.804167503 1.47653777 greater 100 0.05940594 1.275831e-06
## 56 TANYT 0.061805460 -0.59478883 greater 100 0.70297030 1.509733e-05
## 57 TFLU 6.625366853 1.32603512 greater 95 0.10416667 2.237134e-06
## 58 OMI.mean 1.807675271 4.15780127 greater 100 0.00990099 2.126385e-07
##
## other elements: adj.method sub.pvalue subni.pvalue call
Reference
Dolédec, S., Chessel, D., Gimaret-Carpentier, C., 2000. Niche separation in community analysis: A new method. Ecology 81, 2914. doi:doi:10.1890/0012-9658(2000)081[2914:NSICAA]2.0.CO;2
Dray, S., Dufour, A., 2007. The ade4 package: Implementing the duality diagram for ecologists. Journal of Statistical Software 22, 1–20.
Karasiewicz, S., 2017. Subniche: Within outlying mean indexes: Refining the omi analysis. doi:https://zenodo.org/badge/DOI/10.5281/zenodo.438723.svg
Karasiewicz S, Dolédec S, Lefebvre S. (2017) Within outlying mean indexes: refining the OMI analysis for the realized niche decomposition. PeerJ, 5:e3364. doi:https://doi.org/10.7717/peerj.3364
polak51/WitOMI documentation built on April 4, 2020, 6:14 p.m.
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Subniche Documentation for the Within Outlying Mean Indexes calculations (WitOMI)
subniche is an open-source R (R Core Team, 2017) package that implements functions useful for the calculation of the Within Outlying Mean Indexes (WitOMI) and their respective statistical tests (Karasiewicz, 2017; Karasiewicz et al., 2017). It consists of complementary calculations to the Outlying Mean Index analysis (OMI) (Dolédec et al., 2000). OMI analysis can be implemented with the niche function from the ade4 package (Dray and Dufour, 2007). WitOMI analysis enables to explore niche shift in a community and environmental constraints within an Euclidean space, with graphical displays. The method allows to divide the realized niche, estimated from the niche function into subniches defined by a factor (time, space), which creates the subsets of habitat conditions.
To get started
Install and load the R package from CRAN using the following commands on the R console:
install.packages("subniche")
library(subniche)
## Loading required package: ade4
## Loading required package: ade4
` To install the most recent version from GitHub:
install.packages("devtools")
devtools::install_github("KarasiewiczStephane/WitOMI")
library(subniche)
install.packages("knitr")
library(knitr)
Generate the data required to run this example
Get the data set “drome” from the subniche package (Karasiewicz et al., 2017). This data set includes, fish species occurence along with environmental variables.
drome
Performing the OMI analysis
The first step consists of performing a PCA on the environmental table.
dudi1 <- dudi.pca(drome$env[,-1], scale = TRUE, scan = FALSE, nf = 3)
scatter(dudi1)
The graph displays the correlation between the different environmental variables. Then, the OMI analysis uses the standardized environmental table from the PCA in combination with the faunistic data to perform the OMI analysis. The standardized environmental table can be extracted from the dudi1 with $tab as follow:
kable(dudi1$tab)
| Altitude| DSou| slope| width| Lmin| Dicharge| |-----------:|-----------:|-----------:|-----------:|-----------:|-----------:| | 0.1752807| -0.4171664| -0.0573732| -0.1082431| 0.1345316| -0.9354324| | -0.1279075| -0.0076779| 0.2603861| -0.1082431| 0.9474368| -0.4717552| | -0.6837525| 1.0569922| -0.4633989| 1.2112919| 2.1667946| 0.1542089| | -0.8353466| 1.4664807| -0.8870779| 1.2112919| 2.9119577| 0.5483344| | -1.0374720| 1.7121738| -0.8870779| -0.1082431| 3.3184103| 0.7801730| | -0.0268448| -0.9085526| -0.0044133| -1.0978944| -0.4751473| -1.2252306| | -0.2037046| -0.6628595| 0.2603861| -0.4381269| 0.1345316| -0.8890647| | -0.2289702| -0.5809618| 0.2603861| -0.4381269| 0.1345316| -0.8890647| | -0.3047673| -0.4990641| -0.2868660| 0.2216407| 0.1345316| -0.8890647| | -0.4310957| -0.2533710| 0.1015064| 0.2216407| 0.3716289| -0.7847373| | -0.5068927| -0.1714733| -0.0573732| -0.4381269| 0.3716289| -0.7847373| | -0.5826898| -0.0076779| -0.4457456| -0.4381269| 1.3877604| -0.2515086| | -0.8858779| 0.6475037| -0.5693187| 0.8814082| 2.1667946| 0.1773927| | -0.9111436| 0.8112991| -0.8341181| 1.5411757| 2.4377630| 0.3049039| | -0.9869407| 1.0569922| -1.1165707| 2.5308270| 2.4377630| 0.3049039| | 3.1566310| -1.3180411| -0.3574792| -0.7680106| -0.4751473| -0.8658808| | 2.9545056| -1.0723480| -0.3574792| -0.1082431| -1.0848262| -0.4949391| | 1.5396274| -0.3352687| -0.0044133| 2.5308270| 0.4732421| 0.3512716| | -0.4058300| 0.2380152| 0.2427328| 2.8607107| 1.2184052| 1.2206663| | 0.3774061| -1.3999388| 1.4431567| -1.7576619| -1.0780520| -1.4454772| | 0.0236866| -1.2361434| 1.3548902| -1.4277781| -0.8815999| -1.2252306| | -0.2289702| -0.9904503| 1.0194776| -0.7680106| -0.8815999| -1.2252306| | -0.8858779| -0.3352687| -0.1279864| -0.4381269| -0.2041789| -0.1935490| | -1.1132691| 0.2380152| -1.2931037| 0.2216407| -0.2041789| -0.1935490| | -1.1890661| 0.5656060| -1.3284103| 0.8814082| 0.2700158| 0.5483344| | -1.2648632| 0.8522479| -1.4872899| -0.7680106| 0.2700158| 0.7801730| | 2.0449411| -1.2361434| 0.4722256| -0.7680106| -0.8138578| -0.6688180| | 1.6912215| -0.8266549| -0.1456397| -1.0978944| -0.4074052| 0.2585362| | 1.4890961| -0.2533710| -1.2931037| 0.2216407| -0.0009526| 1.4061371| | 1.2869706| -0.1714733| -1.3284103| 0.2216407| -0.0009526| 1.4061371| | 0.9332511| -0.9904503| 0.3839591| -1.0978944| -0.8815999| -0.9122485| | 0.7311256| -0.5809618| 0.1368130| -0.7680106| -0.7799868| -0.6340422| | 0.4279375| -0.2533710| -0.0573732| 0.2216407| -0.4751473| 0.3512716| | 0.0236866| 0.2380152| 0.2250795| 0.8814082| -0.3057921| 0.8265407| | -0.1784389| 0.4837083| 0.2250795| 0.8814082| -0.2719210| 0.9308680| | 1.2869706| -1.6456319| 3.4732853| -1.7576619| -1.0238583| -1.3573786| | 0.7816570| -1.1951946| 1.9551021| -1.0978944| -0.9493420| -1.1440871| | 0.6805943| -1.1542457| 1.9551021| -1.0978944| -0.9493420| -1.1440871| | 0.3016091| -0.6628595| -0.2339061| -0.4381269| -0.7461157| -0.5644907| | -0.1279075| -0.0076779| -0.0573732| -0.1082431| -0.6106315| -0.1008135| | -0.1784389| 0.0742198| -0.3927858| 0.2216407| -0.6106315| -0.1008135| | -0.2289702| 0.2380152| -0.3927858| 0.5515244| -0.6106315| -0.1008135| | -0.2795016| 0.4018106| -0.7105450| -0.4381269| -0.3735342| 0.5831102| | -0.5321584| 0.8931968| -0.4987055| -0.7680106| -0.2719210| 0.8729084| | -0.7848152| 1.3845830| -0.4633989| -0.1082431| -0.0686947| 1.3597694| | -1.0880034| 2.2035600| -0.9753444| 0.2216407| 0.2700158| 2.5189622| | -1.2395975| 2.8587416| -1.1871839| 1.2112919| 0.2700158| 2.8667200| | 0.3774061| -1.4818365| 3.2261392| -1.0978944| -0.9696647| -1.4338853| | -0.2289702| -0.9904503| 1.1077441| -0.7680106| -0.3396631| -0.8658808| | -0.8353466| -0.3352687| -0.4987055| -0.7680106| 0.1345316| -0.4369795| | -1.2395975| 0.8112991| -1.0459576| 0.2216407| -0.2719210| 0.2237604| | 0.5290002| -1.0723480| 1.4431567| -1.0978944| -0.2719210| -0.7847373| | 0.0236866| -0.3352687| 0.1544663| 0.2216407| 0.0667895| -0.4949391| | -0.3047673| 0.0742198| 0.1544663| 0.2216407| 0.2700158| -0.2978763| | -0.8858779| 0.9750945| -0.3574792| 1.2112919| -0.8138578| 0.1078412| | -0.9364093| 1.1388899| -0.3574792| -0.7680106| -0.8138578| 0.1078412| | -1.1890661| 1.7940715| -1.0106510| 1.2112919| -1.0780520| 1.7075272| | -1.3406602| 2.2854577| -1.0636109| 0.8814082| -1.0780520| 2.7508008| | 1.5901588| -1.0723480| 1.2842770| -1.0978944| -0.6783736| -0.8195131| | 1.1859079| -0.8266549| 0.5781453| 1.8710595| -0.6783736| -0.8195131| | 0.8321884| -0.4171664| 0.3839591| -0.4381269| -0.2719210| -0.0544458| | 0.2763434| 0.0742198| 1.0018243| -0.4381269| -0.2041789| 0.0498815| | 0.0994836| 0.1561175| 0.2074262| 0.2216407| 0.0667895| 0.5831102| | -0.3805643| 0.9750945| -0.6752384| 0.2216407| 0.5409842| 1.4177290|
The next step performs the OMI analysis (Dolédec et al., 2000) by implementing the function niche from the ade4 package (Dray and Dufour, 2007).
nic1 <- niche(dudi1, drome$fish, scann = FALSE)
nic1
## Niche analysis
## call: niche(dudiX = dudi1, Y = drome$fish, scannf = FALSE)
## class: niche dudi
##
## $rank (rank) : 6
## $nf (axis saved) : 2
## $RV (RV coeff) :
##
## eigen values: 1.001 0.05759 0.01671 0.004818 0.001209 ...
##
## vector length mode content
## 1 $eig 6 numeric eigen values
## 2 $lw 13 numeric row weigths (crossed array)
## 3 $cw 6 numeric col weigths (crossed array)
##
## data.frame nrow ncol content
## 1 $tab 13 6 crossed array (averaging species/sites)
## 2 $li 13 2 species coordinates
## 3 $l1 13 2 species normed scores
## 4 $co 6 2 variables coordinates
## 5 $c1 6 2 variables normed scores
## 6 $ls 64 2 sites coordinates
## 7 $as 3 2 axis upon niche axis
plot(nic1)
The graph displays the results of the OMI analysis within a two-dimensional Euclidean space. It represents the species niche position (labels in Samples and Species, top-right) and breadth (ellipses in Niches, bottom-right graph), within the environmental gradients (summarized by Variables). The species niche parameters (Inertia, OMI, Tol and Rtol) are calculated with the niche.parameter function from the ade4 package (Dray and Dufour, 2007). More details about the niche parameters calculation are available in Dolédec et al. (2000).
kable(niche.param(nic1))
| | inertia| OMI| Tol| Rtol| omi| tol| rtol| |-----|----------:|-----------:|----------:|----------:|-----:|-----:|-----:| | SCU | 5.503685| 2.1488218| 1.5045397| 1.8503234| 39.0| 27.3| 33.6| | OTR | 5.714861| 0.0777737| 2.4253953| 3.2116918| 1.4| 42.4| 56.2| | YTR | 5.765225| 0.2630852| 1.6882102| 3.8139292| 4.6| 29.3| 66.2| | MIN | 5.318492| 0.4460395| 2.5225244| 2.3499277| 8.4| 47.4| 44.2| | STO | 5.908881| 0.7656741| 2.4125454| 2.7306619| 13.0| 40.8| 46.2| | BLA | 4.989501| 0.4550558| 2.2738372| 2.2606074| 9.1| 45.6| 45.3| | SOB | 3.714084| 0.0710025| 0.5129189| 3.1301626| 1.9| 13.8| 84.3| | SON | 6.384992| 4.8872633| 0.7488491| 0.7488797| 76.5| 11.7| 11.7| | NAS | 12.598843| 10.1993126| 1.6200685| 0.7794621| 81.0| 12.9| 6.2| | GUD | 8.427877| 4.3080073| 0.9543342| 3.1655359| 51.1| 11.3| 37.6| | CHU | 5.584996| 1.5776519| 1.6933570| 2.3139866| 28.2| 30.3| 41.4| | STR | 9.917999| 5.2753874| 1.1855758| 3.4570355| 53.2| 12.0| 34.9| | BAR | 8.344112| 3.8305122| 1.3016577| 3.2119418| 45.9| 15.6| 38.5|
The test for statistical significance can now be implemented. It corresponds to the comparison between the observed species' marginality and the simulated values, hereafter 100 random permutations, under the null hypothesis that each species is indifferent to its environment (Dolédec et al., 2000).
rtest(nic1,100)
## class: krandtest lightkrandtest
## Monte-Carlo tests
## Call: as.krandtest(sim = t(sim), obs = obs)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 SCU 2.14882183 1.6898494 greater 0.07920792
## 2 OTR 0.07777369 6.6301270 greater 0.00990099
## 3 YTR 0.26308521 10.3258807 greater 0.00990099
## 4 MIN 0.44603947 9.3195260 greater 0.00990099
## 5 STO 0.76567408 14.1890327 greater 0.00990099
## 6 BLA 0.45505584 16.9938878 greater 0.00990099
## 7 SOB 0.07100247 -0.6060945 greater 0.71287129
## 8 SON 4.88726327 3.6023144 greater 0.01980198
## 9 NAS 10.19931259 6.1321233 greater 0.00990099
## 10 GUD 4.30800731 17.7215982 greater 0.00990099
## 11 CHU 1.57765192 26.9201641 greater 0.00990099
## 12 STR 5.27538741 7.8434366 greater 0.00990099
## 13 BAR 3.83051220 12.5058985 greater 0.00990099
## 14 OMI.mean 2.63889133 15.6134372 greater 0.00990099
Subsets creation
Herein, for the sake of the example, we will create two subsets. A factor needs to be defined, in order to divide the data set in two subsets. We use the functions cutree and hclust to create a cluster, k=2, following the ward.D method. The function creates the subsets based on a dissimilarity matrix created with dist, with the SUs of nic1. It divided the sampling domain into two subsets along the first OMI axis. Blue points correspond to downstream habitat conditions while red points are the upstream conditions.
# Creation of the factor
fact <- as.factor(as.numeric(cutree(hclust(dist(nic1$ls, "euclidean"), "ward.D"),k=2)))
# plotting the two subsets
s.class(nic1$ls, fact, col=c("red", "blue"),cellipse=0, cpoint=2, pch=3)
s.chull(nic1$ls, fact, col=c("red", "blue"),optchull = 1, add.plot = T)
#According to the previous graph,(Variable in plot(nic1)), group 1 and 2 corresponds to upstream and downstream conditions respectively.
levels(fact)<- c("up", "down")
Now that we have the factor for dividing the niche, the subniche function can be implemented as follow:
subnic1 <- subniche(nic1, fact)
subnic1
## WitOMI calculation
## call: niche(dudiX = dudi1, Y = drome$fish, scannf = FALSE)
## class: subniche dudi
##
## $rank (rank) : 6
## $nf (axis saved) : 2
## $RV (RV coeff) :
##
## eigen values: 1.001 0.05759 0.01671 0.004818 0.001209 ...
##
## vector length mode content
## 1 $eig 6 numeric eigen values
## 2 $lw 13 numeric row weigths (crossed array)
## 3 $cw 6 numeric col weigths (crossed array)
## 4 $factor 64 numeric factor used for creating subsets
##
## data.frame nrow ncol content
## 1 $tab 13 6 crossed array (averaging species/sites)
## 2 $li 13 2 species coordinates
## 3 $l1 13 2 species normed scores
## 4 $co 6 2 variables coordinates
## 5 $c1 6 2 variables normed scores
## 6 $ls 64 2 sites coordinates
## 7 $as 3 2 axis upon niche axis
## 8 $G_k 2 2 G_k coordinates
## 9 $sub 26 2 species coordinates within each subset
The subniche function only adds itemsto the niche list, which are $factor, $G_k and $sub. The function does not change the previous results obtained with the OMI analysis from the niche function. The following functions, plot, refparam and rtest, are used to display the same results as in the ade4 package:
plot(subnic1)
The niche parameters from the OMI analysis can still be extracted with its respective significant test with refparam. The test for significance can also be applied, with rtest function.
kable(refparam(subnic1))
| | inertia| OMI| Tol| Rtol| omi| tol| rtol| |-----|----------:|-----------:|----------:|----------:|-----:|-----:|-----:| | SCU | 5.503685| 2.1488218| 1.5045397| 1.8503234| 39.0| 27.3| 33.6| | OTR | 5.714861| 0.0777737| 2.4253953| 3.2116918| 1.4| 42.4| 56.2| | YTR | 5.765225| 0.2630852| 1.6882102| 3.8139292| 4.6| 29.3| 66.2| | MIN | 5.318492| 0.4460395| 2.5225244| 2.3499277| 8.4| 47.4| 44.2| | STO | 5.908881| 0.7656741| 2.4125454| 2.7306619| 13.0| 40.8| 46.2| | BLA | 4.989501| 0.4550558| 2.2738372| 2.2606074| 9.1| 45.6| 45.3| | SOB | 3.714084| 0.0710025| 0.5129189| 3.1301626| 1.9| 13.8| 84.3| | SON | 6.384992| 4.8872633| 0.7488491| 0.7488797| 76.5| 11.7| 11.7| | NAS | 12.598843| 10.1993126| 1.6200685| 0.7794621| 81.0| 12.9| 6.2| | GUD | 8.427877| 4.3080073| 0.9543342| 3.1655359| 51.1| 11.3| 37.6| | CHU | 5.584996| 1.5776519| 1.6933570| 2.3139866| 28.2| 30.3| 41.4| | STR | 9.917999| 5.2753874| 1.1855758| 3.4570355| 53.2| 12.0| 34.9| | BAR | 8.344112| 3.8305122| 1.3016577| 3.2119418| 45.9| 15.6| 38.5|
rtest(subnic1)
## class: krandtest lightkrandtest
## Monte-Carlo tests
## Call: as.krandtest(sim = t(sim), obs = obs)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Permutation number: 99
## Test Obs Std.Obs Alter Pvalue
## 1 SCU 2.14882183 1.9780204 greater 0.07
## 2 OTR 0.07777369 5.5075313 greater 0.01
## 3 YTR 0.26308521 12.1858520 greater 0.01
## 4 MIN 0.44603947 7.6868549 greater 0.01
## 5 STO 0.76567408 14.4029032 greater 0.01
## 6 BLA 0.45505584 20.5927841 greater 0.01
## 7 SOB 0.07100247 -0.7547935 greater 0.77
## 8 SON 4.88726327 2.7026550 greater 0.04
## 9 NAS 10.19931259 5.4283545 greater 0.01
## 10 GUD 4.30800731 20.1586180 greater 0.01
## 11 CHU 1.57765192 19.5749034 greater 0.01
## 12 STR 5.27538741 9.0049294 greater 0.01
## 13 BAR 3.83051220 12.8552496 greater 0.01
## 14 OMI.mean 2.63889133 13.9245420 greater 0.01
Calculation of the WitOMI (Spatial example)
The result of the test on marginality from the OMI analysis are important for the relevance of the WitOMI calculation. The following functions will still calculate the WitOMI even if the OMI are not significant. To be valid, the WitOMI analysis must be done only on species with significant OMI. Then, the probability for the WitOMI to be significant within the subset GK corresponds to the $witomigtest$SubniPvalue wihtin the rtestrefor function for WitOMIG. And the equivalent for the WitOMIGK is $witomig_ktest$SubniPvalue in the rtestsubor.
margvectdisplays the marginality vector of the suborigins,GK within the overall available habitat conditions (black polygon), the available resources under each subset habitat conditions (red polygons) and the species subniche (species label).
margvect(subnic1)
subplot displays the subset's constraint of habitat conditions (red polygons), found within the overall available habitat conditions (black polygons), and the corresponding species subniche positions. The arrows represent the marginality vectors from the sub-orgins GK (red dots) and corresponds to the graphical representation of the WitOMIGK.
subplot(subnic1)
subparam.refor calculates the species marginality (WitOMIG), tolerance (Tol) and residual tolerance (Rtol) to the average habitat conditions (G). For details about the parameters calculation see Karasiewicz et al. (2017).
kable(subparam.refor(subnic1))
| | inertia| WitOMIG| Tol| Rtol| witomig| tol| rtol| |---------|----------:|-----------:|----------:|----------:|--------:|-----:|-----:| | SCUup | 3.399535| 0.8822018| 1.3160556| 1.2012780| 26.0| 38.7| 35.3| | OTRup | 4.743091| 1.1205164| 1.6167368| 2.0058378| 23.6| 34.1| 42.3| | YTRup | 4.915133| 1.1479897| 1.6704008| 2.0967423| 23.4| 34.0| 42.7| | MINup | 2.824565| 0.3293892| 0.9591758| 1.5360003| 11.7| 34.0| 54.4| | STOup | 2.855969| 0.1982016| 0.6736997| 1.9840678| 6.9| 23.6| 69.5| | BLAup | 2.308870| 0.2007444| 0.7128120| 1.3953135| 8.7| 30.9| 60.4| | SOBup | 2.051601| 0.5881637| 0.6693959| 0.7940418| 28.7| 32.6| 38.7| | SONup | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | NASup | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | GUDup | 1.164352| 0.2547094| 0.4719874| 0.4376555| 21.9| 40.5| 37.6| | CHUup | 1.265915| 0.0259759| 0.1325920| 1.1073474| 2.1| 10.5| 87.5| | STRup | 1.857806| 1.8578063| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | BARup | 1.658331| 0.5157807| 0.0683361| 1.0742143| 31.1| 4.1| 64.8| | SCUdown | 9.711984| 8.2109981| 0.0907551| 1.4102305| 84.5| 0.9| 14.5| | OTRdown | 8.435816| 4.1418643| 1.0867398| 3.2072121| 49.1| 12.9| 38.0| | YTRdown | 9.165592| 5.5840648| 1.5929660| 1.9885608| 60.9| 17.4| 21.7| | MINdown | 8.505175| 4.6131522| 0.8145921| 3.0774309| 54.2| 9.6| 36.2| | STOdown | 9.283153| 4.9264247| 0.8757168| 3.4810114| 53.1| 9.4| 37.5| | BLAdown | 8.608352| 4.5345833| 0.8829193| 3.1908492| 52.7| 10.3| 37.1| | SOBdown | 6.831239| 3.0898598| 1.0683130| 2.6730658| 45.2| 15.6| 39.1| | SONdown | 6.384992| 4.8872633| 0.7488491| 0.7488797| 76.5| 11.7| 11.7| | NASdown | 12.598843| 10.1993126| 1.6200685| 0.7794621| 81.0| 12.9| 6.2| | GUDdown | 9.465524| 5.5154381| 0.5116158| 3.4384699| 58.3| 5.4| 36.3| | CHUdown | 8.608352| 4.5345833| 0.8829193| 3.1908492| 52.7| 10.3| 37.1| | STRdown | 11.069455| 6.8646154| 0.5809199| 3.6239195| 62.0| 5.2| 32.7| | BARdown | 9.458409| 5.2563916| 0.5836204| 3.6183965| 55.6| 6.2| 38.3|
rtestrefor calculates the subniches' parameters from G with the respective significant test. The output of the rtestrefor function is an object of class list, which includes the results of the WitOMIG significant test, under each subset defined by the $factor.
refor <- rtestrefor(subnic1,100)
First,for each of the two subsets, the subset statictical significance is tested with the null hypothesis that GK is not different from the overall average habitat conditions represented by G. The subsets P value is equal to the product of equiprobabilities to find the same mean value for each variables (Karasiewicz et al., 2017).
refor[[1]]$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 6
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 Altitude 0.4467398 5.641526 two-sided 0.00990099
## 2 DSou -0.5666773 -6.777756 two-sided 0.00990099
## 3 slope 0.3946332 4.746561 two-sided 0.00990099
## 4 width -0.3153794 -3.288177 two-sided 0.00990099
## 5 Lmin -0.3094155 -3.650813 two-sided 0.00990099
## 6 Dicharge -0.4451747 -4.781605 two-sided 0.00990099
## Subsets Pvalue: 9.420452e-13
## other elements: subpvalue call
refor[[2]]$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 6
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 Altitude -0.9147530 -5.063639 two-sided 0.00990099
## 2 DSou 1.1603393 6.972641 two-sided 0.00990099
## 3 slope -0.8080584 -3.908783 two-sided 0.00990099
## 4 width 0.6457769 3.427746 two-sided 0.00990099
## 5 Lmin 0.6335651 3.642002 two-sided 0.00990099
## 6 Dicharge 0.9115482 5.156716 two-sided 0.00990099
## Subsets Pvalue: 9.420452e-13
## other elements: subpvalue call
Second, the species marginality is tested for statistical significance with the null hypothesis that each species within each subset is uninfluenced by its overall average habitat conditions (ubiquitous), for WitOMIG (Karasiewicz et al., 2017).
refor[[1]]$witomigtest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 SCU 0.88220184 -0.5675372 greater 100 0.7029703 6.622298e-13
## 2 OTR 1.12051638 0.3687786 greater 100 0.3663366 3.451057e-13
## 3 YTR 1.14798971 0.1304604 greater 100 0.4158416 3.917416e-13
## 4 MIN 0.32938916 -2.7444680 greater 100 1.0000000 9.420452e-13
## 5 STO 0.19820159 -3.6764114 greater 100 1.0000000 9.420452e-13
## 6 BLA 0.20074436 -3.9422360 greater 100 1.0000000 9.420452e-13
## 7 SOB 0.58816373 -1.0481577 greater 100 0.9009901 8.487734e-13
## 8 SON NaN NaN greater 97 NA NA
## 9 NAS NaN NaN greater 94 NA NA
## 10 GUD 0.25470940 -1.7428278 greater 100 1.0000000 9.420452e-13
## 11 CHU 0.02597593 -2.9923601 greater 100 1.0000000 9.420452e-13
## 12 STR 1.85780633 0.1069688 greater 100 0.3861386 3.637600e-13
## 13 BAR 0.51578065 -0.9776495 greater 100 0.9207921 8.674278e-13
## 14 OMI.mean 0.64740719 -2.4357870 greater 100 1.0000000 9.420452e-13
##
## other elements: adj.method sub.pvalue subni.pvalue call
refor[[2]]$witomigtest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 SCU 8.210998 0.26817101 greater 86 0.25287356 2.382183e-13
## 2 OTR 4.141864 -1.10185311 greater 100 0.86138614 8.114647e-13
## 3 YTR 5.584065 1.78780845 greater 100 0.03960396 3.730872e-14
## 4 MIN 4.613152 -0.04845032 greater 100 0.56435644 5.316493e-13
## 5 STO 4.926425 0.58490353 greater 100 0.27722772 2.611611e-13
## 6 BLA 4.534583 -0.05263990 greater 100 0.49504950 4.663590e-13
## 7 SOB 3.089860 -1.36484465 greater 100 0.94059406 8.860822e-13
## 8 SON 4.887263 -0.52181416 greater 80 0.58024691 5.466188e-13
## 9 NAS 10.199313 0.62001928 greater 68 0.30434783 2.867094e-13
## 10 GUD 5.515438 0.21504292 greater 100 0.38613861 3.637600e-13
## 11 CHU 4.534583 -0.41713165 greater 100 0.65346535 6.155939e-13
## 12 STR 6.864615 0.28705343 greater 100 0.29702970 2.798154e-13
## 13 BAR 5.256392 -0.08874486 greater 100 0.46534653 4.383775e-13
## 14 OMI.mean 5.566042 0.23045629 greater 100 0.29702970 2.798154e-13
##
## other elements: adj.method sub.pvalue subni.pvalue call
subparam.subor calculates the species marginality (WitOMIGK), tolerance (Tol) and residual tolerance (Rtol) to the subset average habitat conditions (GK). For details about the parameters calculation see Karasiewicz et al. (2017).
kable(subparam.subor(subnic1))
| | inertia| WitOMIG_k| Tol| Rtol| witomig_k| tol| rtol| |------|---------:|-----------:|----------:|----------:|-----------:|-----:|-----:| | SCU1 | 4.667445| 2.1501116| 0.8416367| 1.6756969| 46.1| 18.0| 35.9| | OTR1 | 3.623817| 0.0012426| 0.9635811| 2.6589935| 0.0| 26.6| 73.4| | YTR1 | 3.769511| 0.0023683| 0.7471943| 3.0199488| 0.1| 19.8| 80.1| | MIN1 | 2.794045| 0.2988690| 1.0122406| 1.4829355| 10.7| 36.2| 53.1| | STO1 | 3.170025| 0.5122576| 0.7835477| 1.8742197| 16.2| 24.7| 59.1| | BLA1 | 2.509801| 0.4016752| 0.7324293| 1.3756961| 16.0| 29.2| 54.8| | SOB1 | 1.604047| 0.1406096| 0.4103152| 1.0531224| 8.8| 25.6| 65.7| | SON1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | NAS1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | GUD1 | 2.531369| 1.6217256| 0.0206516| 0.8889913| 64.1| 0.8| 35.1| | CHU1 | 2.339495| 1.0995556| 0.2824601| 0.9574793| 47.0| 12.1| 40.9| | STR1 | 2.897381| 2.8973807| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | BAR1 | 2.595143| 1.4525929| 0.8751351| 0.2674153| 56.0| 33.7| 10.3| | SCU2 | 5.492183| 3.9911970| 0.2671504| 1.2338352| 72.7| 4.9| 22.5| | OTR2 | 4.471315| 0.1773629| 1.6893396| 2.6046123| 4.0| 37.8| 58.3| | YTR2 | 4.976729| 1.3952018| 1.2150300| 2.3664967| 28.0| 24.4| 47.6| | MIN2 | 3.927070| 0.0350466| 0.8819288| 3.0100942| 0.9| 22.5| 76.6| | STO2 | 4.384113| 0.0273849| 1.2423933| 3.1143349| 0.6| 28.3| 71.0| | BLA2 | 4.092662| 0.0188934| 0.6783537| 3.3954149| 0.5| 16.6| 83.0| | SOB2 | 4.175797| 0.4344183| 0.2156571| 3.5257216| 10.4| 5.2| 84.4| | SON2 | 2.804111| 1.3063827| 0.1263895| 1.3713393| 46.6| 4.5| 48.9| | NAS2 | 4.402483| 2.0029526| 1.3323835| 1.0671471| 45.5| 30.3| 24.2| | GUD2 | 4.036922| 0.0868360| 0.9774591| 2.9726266| 2.2| 24.2| 73.6| | CHU2 | 4.092662| 0.0188934| 0.6783537| 3.3954149| 0.5| 16.6| 83.0| | STR2 | 4.632032| 0.4271921| 0.8373510| 3.3674884| 9.2| 18.1| 72.7| | BAR2 | 4.275254| 0.0732369| 0.6000366| 3.6019803| 1.7| 14.0| 84.3|
In a similar way, rtestsubor calculates the subniches' parameters from GK with the respective test for significance. The output of the rtestsubor function is an object of class list, which includes the results of the WitOMIGK significance test under each of the two subsets defined by the $factor.
subor <- rtestsubor(subnic1,100)
As previously, the subsets are tested.
subor[[1]]$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 6
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 Altitude 0.4467398 5.003697 two-sided 0.00990099
## 2 DSou -0.5666773 -6.805470 two-sided 0.00990099
## 3 slope 0.3946332 4.495922 two-sided 0.00990099
## 4 width -0.3153794 -3.813484 two-sided 0.00990099
## 5 Lmin -0.3094155 -3.118210 two-sided 0.01980198
## 6 Dicharge -0.4451747 -4.993129 two-sided 0.00990099
## Subsets Pvalue: 1.88409e-12
## other elements: subpvalue call
subor[[2]]$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 6
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 Altitude -0.9147530 -5.454170 two-sided 0.00990099
## 2 DSou 1.1603393 6.682697 two-sided 0.00990099
## 3 slope -0.8080584 -4.890054 two-sided 0.00990099
## 4 width 0.6457769 3.487026 two-sided 0.00990099
## 5 Lmin 0.6335651 3.509317 two-sided 0.00990099
## 6 Dicharge 0.9115482 4.686616 two-sided 0.00990099
## Subsets Pvalue: 9.420452e-13
## other elements: subpvalue call
The species marginality is then tested for significance with the null hypothesis that each species within a subset is uninfluenced by its subset average habitat conditions (ubiquitous), for WitOMIGK (Karasiewicz et al., 2017).
subor[[1]]$witomig_ktest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 SCU 2.150111587 0.90808913 greater 99 0.09000000 1.695681e-13
## 2 OTR 0.001242635 -1.05509931 greater 100 0.99009901 1.865436e-12
## 3 YTR 0.002368323 -0.95423845 greater 100 0.97029703 1.828127e-12
## 4 MIN 0.298869015 5.93616160 greater 100 0.00990099 1.865436e-14
## 5 STO 0.512257590 11.98368866 greater 100 0.00990099 1.865436e-14
## 6 BLA 0.401675209 14.21278259 greater 100 0.00990099 1.865436e-14
## 7 SOB 0.140609585 -0.09993168 greater 100 0.39603960 7.461744e-13
## 8 SON NaN NaN greater 98 NA NA
## 9 NAS NaN NaN greater 99 NA NA
## 10 GUD 1.621725637 4.92355129 greater 100 0.00990099 1.865436e-14
## 11 CHU 1.099555585 14.17032154 greater 100 0.00990099 1.865436e-14
## 12 STR 2.897380749 2.33730918 greater 100 0.03960396 7.461744e-14
## 13 BAR 1.452592931 4.20572630 greater 100 0.01980198 3.730872e-14
## 14 OMI.mean 0.961671713 1.94127807 greater 100 0.07920792 1.492349e-13
##
## other elements: adj.method sub.pvalue subni.pvalue call
subor[[2]]$witomig_ktest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 14
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 SCU 3.99119702 0.5723359 greater 92 0.21505376 2.025904e-13
## 2 OTR 0.17736291 7.3252445 greater 100 0.00990099 9.327181e-15
## 3 YTR 1.39520177 21.0838921 greater 100 0.00990099 9.327181e-15
## 4 MIN 0.03504661 -0.5520602 greater 100 0.83168317 7.834832e-13
## 5 STO 0.02738489 -0.9550790 greater 100 0.93069307 8.767550e-13
## 6 BLA 0.01889343 -0.8622151 greater 100 0.88118812 8.301191e-13
## 7 SOB 0.43441828 0.1539739 greater 100 0.30693069 2.891426e-13
## 8 SON 1.30638269 -0.8024550 greater 82 0.80722892 7.604462e-13
## 9 NAS 2.00295261 -0.8385115 greater 77 0.71794872 6.763402e-13
## 10 GUD 0.08683603 -0.8933709 greater 100 0.96039604 9.047365e-13
## 11 CHU 0.01889343 -1.1158357 greater 100 0.98019802 9.233909e-13
## 12 STR 0.42719213 -0.8614058 greater 97 0.89795918 8.459182e-13
## 13 BAR 0.07323689 -1.1277643 greater 100 0.99009901 9.327181e-13
## 14 OMI.mean 0.76884605 -0.6540869 greater 100 0.72277228 6.808842e-13
##
## other elements: adj.method sub.pvalue subni.pvalue call
Exercise (temporal example)
Now, try to do the same work but with the data ardecheinv. Herein, the invertebrates' abundances need to be log(x+1) transform prior the analysis, and the first column of the $env data are the seasons use to divide the dataset.
Therefore, the creation of the required factor is done by the following line:
fact <- factor(archecheinv$env[,1])
Replace spring and autumn by numbers to allow the subniche function to work properly. After the OMI analysis and WitOMI calculations, you should obtain the following results:
| | inertia| OMI| Tol| Rtol| omi| tol| rtol| |--------|---------:|----------:|----------:|----------:|------:|-----:|-----:| | AFLU | 5.538347| 1.6300864| 1.1574213| 2.7508396| 29.4| 20.9| 49.7| | ANTO | 6.261168| 2.8559783| 0.4809695| 2.9242199| 45.6| 7.7| 46.7| | ATSP | 5.261425| 0.7738686| 1.9442760| 2.5432800| 14.7| 37.0| 48.3| | BASP | 4.951897| 0.1540045| 0.9205257| 3.8773669| 3.1| 18.6| 78.3| | BIMI | 6.440541| 2.6656032| 1.2165765| 2.5583616| 41.4| 18.9| 39.7| | BFAS | 5.522998| 1.6334715| 0.8316546| 3.0578719| 29.6| 15.1| 55.4| | CASP | 4.752588| 2.0915508| 0.6997262| 1.9613111| 44.0| 14.7| 41.3| | CESP | 5.991585| 1.3620235| 1.7438590| 2.8857024| 22.7| 29.1| 48.2| | CERA | 5.243130| 0.4385214| 1.5649566| 3.2396515| 8.4| 29.8| 61.8| | CLEP | 5.031965| 0.4363223| 1.3835927| 3.2120500| 8.7| 27.5| 63.8| | CMAR | 5.296909| 2.0940725| 1.1020999| 2.1007368| 39.5| 20.8| 39.7| | CHIR | 4.439955| 0.4947663| 1.5231051| 2.4220840| 11.1| 34.3| 54.6| | CPIC | 5.506342| 2.8291243| 0.5206856| 2.1565322| 51.4| 9.5| 39.2| | DRSP | 4.496590| 1.0682956| 1.9036269| 1.5246673| 23.8| 42.3| 33.9| | DUSP | 5.329099| 0.1398475| 1.7382410| 3.4510110| 2.6| 32.6| 64.8| | ECSP | 5.350165| 0.3283927| 1.4704735| 3.5512986| 6.1| 27.5| 66.4| | ECTE | 5.455395| 3.0887228| 0.1196580| 2.2470143| 56.6| 2.2| 41.2| | ELMA | 3.176472| 0.7910221| 0.0376397| 2.3478099| 24.9| 1.2| 73.9| | EPEO | 5.682921| 1.2701977| 0.7480074| 3.6647159| 22.4| 13.2| 64.5| | EVIR | 5.302216| 0.3443063| 1.2761101| 3.6817993| 6.5| 24.1| 69.4| | EPAR | 3.932729| 0.1404872| 0.7954177| 2.9968238| 3.6| 20.2| 76.2| | ESSP | 4.773562| 0.1428212| 1.0243322| 3.6064087| 3.0| 21.5| 75.5| | EPYG | 4.864853| 0.1428271| 0.8576808| 3.8643452| 2.9| 17.6| 79.4| | EGEN | 5.046325| 1.9052672| 0.8579907| 2.2830675| 37.8| 17.0| 45.2| | GASP | 5.691912| 0.3561383| 1.1723435| 4.1634306| 6.3| 20.6| 73.1| | HYSP | 8.352748| 8.3527477| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | HEXO | 5.243021| 0.3580747| 1.1967967| 3.6881491| 6.8| 22.8| 70.3| | HPEL | 3.568855| 1.4801591| 0.4150086| 1.6736876| 41.5| 11.6| 46.9| | HYDR | 5.424177| 0.3446764| 1.0092149| 4.0702859| 6.4| 18.6| 75.0| | HYDS | 6.176471| 2.7519145| 0.8340139| 2.5905427| 44.6| 13.5| 41.9| | LEPT | 5.337927| 2.7371006| 0.4678790| 2.1329476| 51.3| 8.8| 40.0| | LESP | 4.424214| 0.3139611| 0.8986846| 3.2115689| 7.1| 20.3| 72.6| | LOPAad | 4.965245| 1.2010608| 1.9737829| 1.7904015| 24.2| 39.8| 36.1| | LOPAla | 4.572070| 0.4750266| 1.2483663| 2.8486766| 10.4| 27.3| 62.3| | MPOW | 5.765380| 1.2113295| 2.0688707| 2.4851794| 21.0| 35.9| 43.1| | MYSA | 4.199512| 3.3382344| 0.2800060| 0.5812719| 79.5| 6.7| 13.8| | ORHE | 5.578876| 1.7719543| 0.8907733| 2.9161486| 31.8| 16.0| 52.3| | ONSP | 5.183675| 1.1029607| 1.7398668| 2.3408479| 21.3| 33.6| 45.2| | ORTH | 5.122163| 0.2310466| 1.2565769| 3.6345398| 4.5| 24.5| 71.0| | ORSP | 6.830422| 1.3221938| 2.3259105| 3.1823176| 19.4| 34.1| 46.6| | OTROad | 5.323551| 0.3196624| 0.7949867| 4.2089018| 6.0| 14.9| 79.1| | OTROla | 5.520143| 0.2338567| 1.2078521| 4.0784347| 4.2| 21.9| 73.9| | PISI | 6.092811| 1.1586152| 2.0241946| 2.9100014| 19.0| 33.2| 47.8| | POLY | 6.946072| 3.4561798| 0.5871372| 2.9027547| 49.8| 8.5| 41.8| | PBIF | 5.230149| 0.1404078| 0.9126282| 4.1771134| 2.7| 17.4| 79.9| | PPUS | 4.760375| 0.1258586| 0.8095743| 3.8249417| 2.6| 17.0| 80.3| | RASP | 6.912935| 6.1246587| 0.5175577| 0.2707183| 88.6| 7.5| 3.9| | RHYP | 5.925556| 1.9250904| 1.1395839| 2.8608814| 32.5| 19.2| 48.3| | RHIP | 5.509859| 0.5317665| 1.7133248| 3.2647681| 9.7| 31.1| 59.3| | SIGN | 5.690358| 0.2369562| 1.2683164| 4.1850853| 4.2| 22.3| 73.5| | SARG | 5.338509| 0.5093751| 1.2407905| 3.5883437| 9.5| 23.2| 67.2| | SIMU | 4.429029| 0.3726825| 1.1175113| 2.9388347| 8.4| 25.2| 66.4| | STEN | 6.268569| 0.5701184| 1.5868239| 4.1116266| 9.1| 25.3| 65.6| | SCAN | 6.356376| 1.3990981| 1.3987842| 3.5584937| 22.0| 22.0| 56.0| | TANYP | 4.883393| 0.7337598| 1.6914515| 2.4581815| 15.0| 34.6| 50.3| | TANYT | 5.335505| 0.1691889| 1.3199566| 3.8463591| 3.2| 24.7| 72.1| | TFLU | 6.940545| 2.0277114| 1.5558618| 3.3569723| 29.2| 22.4| 48.4|
## class: krandtest lightkrandtest
## Monte-Carlo tests
## Call: as.krandtest(sim = t(sim), obs = obs)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 AFLU 1.6300864 1.7253541 greater 0.08910891
## 2 ANTO 2.8559783 3.3678147 greater 0.02970297
## 3 ATSP 0.7738686 1.9292439 greater 0.04950495
## 4 BASP 0.1540045 4.5751519 greater 0.00990099
## 5 BIMI 2.6656032 1.9070044 greater 0.05940594
## 6 BFAS 1.6334715 11.7370567 greater 0.00990099
## 7 CASP 2.0915508 14.4059836 greater 0.00990099
## 8 CESP 1.3620235 1.2553447 greater 0.11881188
## 9 CERA 0.4385214 1.7755189 greater 0.05940594
## 10 CLEP 0.4363223 8.2682846 greater 0.00990099
## 11 CMAR 2.0940725 14.8806714 greater 0.00990099
## 12 CHIR 0.4947663 1.2367689 greater 0.10891089
## 13 CPIC 2.8291243 7.0460467 greater 0.00990099
## 14 DRSP 1.0682956 2.7308803 greater 0.02970297
## 15 DUSP 0.1398475 2.0311775 greater 0.04950495
## 16 ECSP 0.3283927 3.8656990 greater 0.01980198
## 17 ECTE 3.0887228 1.5055852 greater 0.11881188
## 18 ELMA 0.7910221 -0.9326867 greater 0.83168317
## 19 EPEO 1.2701977 0.5411500 greater 0.20792079
## 20 EVIR 0.3443063 2.0042738 greater 0.06930693
## 21 EPAR 0.1404872 0.2023392 greater 0.31683168
## 22 ESSP 0.1428212 1.9794915 greater 0.05940594
## 23 EPYG 0.1428271 5.0843127 greater 0.00990099
## 24 EGEN 1.9052672 4.0699261 greater 0.01980198
## 25 GASP 0.3561383 2.2299864 greater 0.02970297
## 26 HYSP 8.3527477 0.9782508 greater 0.17821782
## 27 HEXO 0.3580747 4.4458230 greater 0.01980198
## 28 HPEL 1.4801591 2.2740531 greater 0.03960396
## 29 HYDR 0.3446764 5.4555549 greater 0.00990099
## 30 HYDS 2.7519145 2.8138801 greater 0.02970297
## 31 LEPT 2.7371006 0.9106221 greater 0.13861386
## 32 LESP 0.3139611 1.9692738 greater 0.05940594
## 33 LOPAad 1.2010608 3.5963104 greater 0.01980198
## 34 LOPAla 0.4750266 5.7482753 greater 0.00990099
## 35 MPOW 1.2113295 0.4274899 greater 0.23762376
## 36 MYSA 3.3382344 0.5204062 greater 0.24752475
## 37 ORHE 1.7719543 12.0081382 greater 0.00990099
## 38 ONSP 1.1029607 1.5271297 greater 0.09900990
## 39 ORTH 0.2310466 11.2649694 greater 0.00990099
## 40 ORSP 1.3221938 1.5224705 greater 0.08910891
## 41 OTROad 0.3196624 4.3031837 greater 0.00990099
## 42 OTROla 0.2338567 1.9429847 greater 0.04950495
## 43 PISI 1.1586152 1.3013958 greater 0.10891089
## 44 POLY 3.4561798 3.4885975 greater 0.00990099
## 45 PBIF 0.1404078 1.0713244 greater 0.10891089
## 46 PPUS 0.1258586 -1.2344247 greater 0.96039604
## 47 RASP 6.1246587 3.2833229 greater 0.01980198
## 48 RHYP 1.9250904 6.4752391 greater 0.00990099
## 49 RHIP 0.5317665 5.4734818 greater 0.00990099
## 50 SIGN 0.2369562 2.9948880 greater 0.03960396
## 51 SARG 0.5093751 10.0000653 greater 0.00990099
## 52 SIMU 0.3726825 -0.8452247 greater 0.80198020
## 53 STEN 0.5701184 0.1009888 greater 0.33663366
## 54 SCAN 1.3990981 2.2577607 greater 0.03960396
## 55 TANYP 0.7337598 2.8492499 greater 0.01980198
## 56 TANYT 0.1691889 2.1522444 greater 0.06930693
## 57 TFLU 2.0277114 1.0036981 greater 0.19801980
## 58 OMI.mean 1.3369324 8.6489846 greater 0.00990099
| | inertia| WitOMIG| Tol| Rtol| witomig| tol| rtol| |--------------|---------:|----------:|----------:|----------:|--------:|-----:|-----:| | AFLUautumn | 5.047993| 4.9912783| 0.0398153| 0.0168989| 98.9| 0.8| 0.3| | ANTOautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | ATSPautumn | 5.322417| 1.3233915| 1.6872135| 2.3118117| 24.9| 31.7| 43.4| | BASPautumn | 4.016310| 0.9472722| 0.8623746| 2.2066629| 23.6| 21.5| 54.9| | BIMIautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | BFASautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | CASPautumn | 5.169176| 2.4639684| 0.7484637| 1.9567438| 47.7| 14.5| 37.9| | CESPautumn | 6.887544| 6.8875440| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | CERAautumn | 4.970323| 4.9703232| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | CLEPautumn | 4.994338| 1.7047381| 1.3970321| 1.8925682| 34.1| 28.0| 37.9| | CMARautumn | 4.965081| 3.4695335| 0.5973335| 0.8982140| 69.9| 12.0| 18.1| | CHIRautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | CPICautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | DRSPautumn | 3.752796| 2.1145534| 0.7093201| 0.9289223| 56.3| 18.9| 24.8| | DUSPautumn | 4.780569| 1.0076983| 1.7193447| 2.0535264| 21.1| 36.0| 43.0| | ECSPautumn | 5.492605| 0.5709870| 1.2247275| 3.6968904| 10.4| 22.3| 67.3| | ECTEautumn | 4.842414| 3.2464421| 0.3220876| 1.2738847| 67.0| 6.7| 26.3| | ELMAautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | EPEOautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | EVIRautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | EPARautumn | 4.005941| 0.5643963| 0.6642875| 2.7772567| 14.1| 16.6| 69.3| | ESSPautumn | 4.768717| 0.4737399| 0.5447734| 3.7502039| 9.9| 11.4| 78.6| | EPYGautumn | 4.737186| 0.4865543| 0.4658465| 3.7847857| 10.3| 9.8| 79.9| | EGENautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | GASPautumn | 2.580370| 0.6172684| 0.3452494| 1.6178520| 23.9| 13.4| 62.7| | HYSPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | HEXOautumn | 4.456510| 2.3703453| 0.3042154| 1.7819496| 53.2| 6.8| 40.0| | HPELautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | HYDRautumn | 5.115527| 0.7610947| 1.3776055| 2.9768267| 14.9| 26.9| 58.2| | HYDSautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | LEPTautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | LESPautumn | 2.629375| 1.2380197| 0.2587213| 1.1326343| 47.1| 9.8| 43.1| | LOPAadautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | LOPAlaautumn | 3.718404| 1.9067401| 0.3147357| 1.4969284| 51.3| 8.5| 40.3| | MPOWautumn | 7.192349| 2.0009281| 2.7526841| 2.4387369| 27.8| 38.3| 33.9| | MYSAautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | ORHEautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | ONSPautumn | 3.998315| 1.1210944| 0.9542170| 1.9230038| 28.0| 23.9| 48.1| | ORTHautumn | 4.351143| 0.8960762| 1.5707221| 1.8843446| 20.6| 36.1| 43.3| | ORSPautumn | 6.830422| 1.3221938| 2.3259105| 3.1823176| 19.4| 34.1| 46.6| | OTROadautumn | 6.230148| 2.5941353| 0.9993173| 2.6366950| 41.6| 16.0| 42.3| | OTROlaautumn | 5.142497| 0.6819671| 0.5516613| 3.9088689| 13.3| 10.7| 76.0| | PISIautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | POLYautumn | 5.874718| 3.7305124| 0.7851956| 1.3590103| 63.5| 13.4| 23.1| | PBIFautumn | 4.147923| 2.1961661| 0.5297757| 1.4219813| 52.9| 12.8| 34.3| | PPUSautumn | 3.999069| 2.8780882| 0.7630461| 0.3579344| 72.0| 19.1| 9.0| | RASPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | RHYPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | RHIPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | SIGNautumn | 5.241407| 0.8293657| 2.0007788| 2.4112627| 15.8| 38.2| 46.0| | SARGautumn | 4.379817| 2.4492822| 0.8672941| 1.0632409| 55.9| 19.8| 24.3| | SIMUautumn | 4.103898| 1.0638770| 1.3530498| 1.6869710| 25.9| 33.0| 41.1| | STENautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | SCANautumn | 6.471128| 0.9813382| 0.6184927| 4.8712967| 15.2| 9.6| 75.3| | TANYPautumn | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | TANYTautumn | 4.459319| 1.4061285| 0.7890246| 2.2641654| 31.5| 17.7| 50.8| | TFLUautumn | 5.678182| 2.2777933| 0.4658585| 2.9345304| 40.1| 8.2| 51.7| | AFLUspring | 5.593913| 2.4605247| 0.5429197| 2.5904682| 44.0| 9.7| 46.3| | ANTOspring | 6.261168| 2.8559783| 0.4809695| 2.9242199| 45.6| 7.7| 46.7| | ATSPspring | 4.959373| 0.9321612| 2.4084852| 1.6187263| 18.8| 48.6| 32.6| | BASPspring | 5.216445| 0.3663442| 1.2783080| 3.5717924| 7.0| 24.5| 68.5| | BIMIspring | 6.440541| 2.6656032| 1.2165765| 2.5583616| 41.4| 18.9| 39.7| | BFASspring | 5.522998| 1.6334715| 0.8316546| 3.0578719| 29.6| 15.1| 55.4| | CASPspring | 3.820359| 2.2812516| 0.4450021| 1.0941047| 59.7| 11.6| 28.6| | CESPspring | 5.832151| 1.5201320| 1.7183686| 2.5936503| 26.1| 29.5| 44.5| | CERAspring | 5.248000| 0.4657628| 1.4545148| 3.3277225| 8.9| 27.7| 63.4| | CLEPspring | 5.039830| 0.4812193| 0.9568038| 3.6018068| 9.5| 19.0| 71.5| | CMARspring | 5.364636| 2.1665091| 0.9555239| 2.2426034| 40.4| 17.8| 41.8| | CHIRspring | 4.439955| 0.4947663| 1.5231051| 2.4220840| 11.1| 34.3| 54.6| | CPICspring | 5.506342| 2.8291243| 0.5206856| 2.1565322| 51.4| 9.5| 39.2| | DRSPspring | 8.570756| 3.3680622| 2.2610122| 2.9416821| 39.3| 26.4| 34.3| | DUSPspring | 5.850675| 0.5912963| 1.6446603| 3.6147179| 10.1| 28.1| 61.8| | ECSPspring | 5.331930| 0.5271468| 1.4828117| 3.3219716| 9.9| 27.8| 62.3| | ECTEspring | 6.681357| 6.6813566| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | ELMAspring | 3.176472| 0.7910221| 0.0376397| 2.3478099| 24.9| 1.2| 73.9| | EPEOspring | 5.682921| 1.2701977| 0.7480074| 3.6647159| 22.4| 13.2| 64.5| | EVIRspring | 5.302216| 0.3443063| 1.2761101| 3.6817993| 6.5| 24.1| 69.4| | EPARspring | 3.917797| 0.2686018| 0.5560763| 3.0931191| 6.9| 14.2| 79.0| | ESSPspring | 4.774797| 0.3230870| 0.5041956| 3.9475140| 6.8| 10.6| 82.7| | EPYGspring | 5.037744| 0.6458358| 1.4545934| 2.9373151| 12.8| 28.9| 58.3| | EGENspring | 5.046325| 1.9052672| 0.8579907| 2.2830675| 37.8| 17.0| 45.2| | GASPspring | 5.867186| 0.4291003| 1.0866893| 4.3513962| 7.3| 18.5| 74.2| | HYSPspring | 8.352748| 8.3527477| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | HEXOspring | 5.323090| 0.4469288| 0.9911319| 3.8850296| 8.4| 18.6| 73.0| | HPELspring | 3.568855| 1.4801591| 0.4150086| 1.6736876| 41.5| 11.6| 46.9| | HYDRspring | 5.442886| 0.4174195| 0.9798750| 4.0455915| 7.7| 18.0| 74.3| | HYDSspring | 6.176471| 2.7519145| 0.8340139| 2.5905427| 44.6| 13.5| 41.9| | LEPTspring | 5.337927| 2.7371006| 0.4678790| 2.1329476| 51.3| 8.8| 40.0| | LESPspring | 4.612694| 0.4520373| 1.0916182| 3.0690387| 9.8| 23.7| 66.5| | LOPAadspring | 4.965245| 1.2010608| 1.9737829| 1.7904015| 24.2| 39.8| 36.1| | LOPAlaspring | 5.113748| 0.3732577| 0.4726950| 4.2677952| 7.3| 9.2| 83.5| | MPOWspring | 4.927375| 2.1214892| 1.6490874| 1.1567981| 43.1| 33.5| 23.5| | MYSAspring | 4.199512| 3.3382344| 0.2800060| 0.5812719| 79.5| 6.7| 13.8| | ORHEspring | 5.578876| 1.7719543| 0.8907733| 2.9161486| 31.8| 16.0| 52.3| | ONSPspring | 5.582926| 2.8517365| 0.3795820| 2.3516074| 51.1| 6.8| 42.1| | ORTHspring | 5.389232| 0.3900032| 0.9052110| 4.0940178| 7.2| 16.8| 76.0| | ORSPspring | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | OTROadspring | 5.254247| 0.4060305| 0.9786089| 3.8696076| 7.7| 18.6| 73.6| | OTROlaspring | 6.321554| 0.7368166| 1.8764679| 3.7082700| 11.7| 29.7| 58.7| | PISIspring | 6.092811| 1.1586152| 2.0241946| 2.9100014| 19.0| 33.2| 47.8| | POLYspring | 8.244171| 7.1591537| 0.3273256| 0.7576920| 86.8| 4.0| 9.2| | PBIFspring | 5.286846| 0.1779238| 1.0255683| 4.0833537| 3.4| 19.4| 77.2| | PPUSspring | 4.955463| 0.4637935| 3.2353458| 1.2563236| 9.4| 65.3| 25.4| | RASPspring | 6.912935| 6.1246587| 0.5175577| 0.2707183| 88.6| 7.5| 3.9| | RHYPspring | 5.925556| 1.9250904| 1.1395839| 2.8608814| 32.5| 19.2| 48.3| | RHIPspring | 5.509859| 0.5317665| 1.7133248| 3.2647681| 9.7| 31.1| 59.3| | SIGNspring | 6.115133| 2.0334064| 1.6570358| 2.4246905| 33.3| 27.1| 39.7| | SARGspring | 5.630294| 0.5753024| 0.9397895| 4.1152018| 10.2| 16.7| 73.1| | SIMUspring | 6.133137| 6.1331366| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | STENspring | 6.268569| 0.5701184| 1.5868239| 4.1116266| 9.1| 25.3| 65.6| | SCANspring | 6.295929| 3.0027724| 0.4501125| 2.8430438| 47.7| 7.1| 45.2| | TANYPspring | 4.883393| 0.7337598| 1.6914515| 2.4581815| 15.0| 34.6| 50.3| | TANYTspring | 5.481883| 0.3222465| 0.9109765| 4.2486596| 5.9| 16.6| 77.5| | TFLUspring | 8.759160| 5.3988068| 0.8297366| 2.5306165| 61.6| 9.5| 28.9|
## $autumn
## $autumn$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 5
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 FST -0.095707902 -0.93792273 two-sided 0.39603960
## 2 Depth 0.330509141 2.39455007 two-sided 0.00990099
## 3 SPS 0.495802058 4.28536734 two-sided 0.00990099
## 4 Froude -0.155231548 -1.19848456 two-sided 0.21782178
## 5 Rough 0.002382221 -0.09980932 two-sided 0.89108911
## Subsets Pvalue: 7.535608e-06
## other elements: subpvalue call
##
## $autumn$witomigtest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 AFLU 4.9912783 3.10875366 greater 100 0.02970297 2.238299e-07
## 2 ANTO NaN NaN greater 98 NA NA
## 3 ATSP 1.3233915 0.26054005 greater 100 0.29702970 2.238299e-06
## 4 BASP 0.9472722 4.12709607 greater 100 0.00990099 7.460998e-08
## 5 BIMI NaN NaN greater 93 NA NA
## 6 BFAS NaN NaN greater 100 NA NA
## 7 CASP 2.4639684 3.86835956 greater 100 0.00990099 7.460998e-08
## 8 CESP 6.8875440 2.51998997 greater 100 0.04950495 3.730499e-07
## 9 CERA 4.9703232 11.07073398 greater 100 0.00990099 7.460998e-08
## 10 CLEP 1.7047381 7.46805126 greater 100 0.00990099 7.460998e-08
## 11 CMAR 3.4695335 7.27334162 greater 100 0.00990099 7.460998e-08
## 12 CHIR NaN NaN greater 100 NA NA
## 13 CPIC NaN NaN greater 100 NA NA
## 14 DRSP 2.1145534 1.83765489 greater 100 0.06930693 5.222699e-07
## 15 DUSP 1.0076983 4.49581672 greater 100 0.00990099 7.460998e-08
## 16 ECSP 0.5709870 0.27475974 greater 100 0.34653465 2.611349e-06
## 17 ECTE 3.2464421 0.17446519 greater 88 0.29213483 2.201414e-06
## 18 ELMA NaN NaN greater 75 NA NA
## 19 EPEO NaN NaN greater 99 NA NA
## 20 EVIR NaN NaN greater 100 NA NA
## 21 EPAR 0.5643963 0.09594693 greater 100 0.35643564 2.685959e-06
## 22 ESSP 0.4737399 -0.07754896 greater 100 0.56435644 4.252769e-06
## 23 EPYG 0.4865543 0.48034280 greater 100 0.20792079 1.566810e-06
## 24 EGEN NaN NaN greater 100 NA NA
## 25 GASP 0.6172684 -0.03832010 greater 100 0.45544554 3.432059e-06
## 26 HYSP NaN NaN greater 38 NA NA
## 27 HEXO 2.3703453 9.57245416 greater 100 0.00990099 7.460998e-08
## 28 HPEL NaN NaN greater 100 NA NA
## 29 HYDR 0.7610947 1.32560003 greater 100 0.12871287 9.699298e-07
## 30 HYDS NaN NaN greater 98 NA NA
## 31 LEPT NaN NaN greater 89 NA NA
## 32 LESP 1.2380197 2.51568053 greater 100 0.03960396 2.984399e-07
## 33 LOPAad NaN NaN greater 100 NA NA
## 34 LOPAla 1.9067401 6.41542785 greater 100 0.00990099 7.460998e-08
## 35 MPOW 2.0009281 0.10850304 greater 100 0.38613861 2.909789e-06
## 36 MYSA NaN NaN greater 76 NA NA
## 37 ORHE NaN NaN greater 100 NA NA
## 38 ONSP 1.1210944 -0.19260730 greater 100 0.50495050 3.805109e-06
## 39 ORTH 0.8960762 4.37355835 greater 100 0.00990099 7.460998e-08
## 40 ORSP 1.3221938 -0.21300412 greater 99 0.49000000 3.692448e-06
## 41 OTROad 2.5941353 8.32955073 greater 100 0.00990099 7.460998e-08
## 42 OTROla 0.6819671 0.55088726 greater 100 0.22772277 1.716030e-06
## 43 PISI NaN NaN greater 100 NA NA
## 44 POLY 3.7305124 1.03807592 greater 100 0.13861386 1.044540e-06
## 45 PBIF 2.1961661 8.13800603 greater 100 0.00990099 7.460998e-08
## 46 PPUS 2.8780882 0.63911223 greater 100 0.17821782 1.342980e-06
## 47 RASP NaN NaN greater 83 NA NA
## 48 RHYP NaN NaN greater 100 NA NA
## 49 RHIP NaN NaN greater 100 NA NA
## 50 SIGN 0.8293657 1.50009635 greater 100 0.09900990 7.460998e-07
## 51 SARG 2.4492822 12.21785529 greater 100 0.00990099 7.460998e-08
## 52 SIMU 1.0638770 -0.58139916 greater 100 0.69306931 5.222699e-06
## 53 STEN NaN NaN greater 99 NA NA
## 54 SCAN 0.9813382 -0.40391275 greater 100 0.55445545 4.178159e-06
## 55 TANYP NaN NaN greater 100 NA NA
## 56 TANYT 1.4061285 5.30804758 greater 100 0.00990099 7.460998e-08
## 57 TFLU 2.2777933 -0.14701472 greater 97 0.43877551 3.306440e-06
## 58 OMI.mean 1.9584239 4.90557362 greater 100 0.00990099 7.460998e-08
##
## other elements: adj.method sub.pvalue subni.pvalue call
##
##
## $spring
## $spring$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 5
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 FST 0.092892964 1.0049170 two-sided 0.31683168
## 2 Depth -0.320788284 -3.0814553 two-sided 0.00990099
## 3 SPS -0.481219645 -3.6328831 two-sided 0.00990099
## 4 Froude 0.150665914 1.4666792 two-sided 0.14851485
## 5 Rough -0.002312156 -0.1493797 two-sided 0.89108911
## Subsets Pvalue: 4.110332e-06
## other elements: subpvalue call
##
## $spring$witomigtest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 AFLU 2.4605247 0.43855286 greater 100 0.23762376 9.767125e-07
## 2 ANTO 2.8559783 0.16875948 greater 100 0.31683168 1.302283e-06
## 3 ATSP 0.9321612 -0.17932166 greater 100 0.46534653 1.912729e-06
## 4 BASP 0.3663442 -0.32426024 greater 100 0.53465347 2.197603e-06
## 5 BIMI 2.6656032 -0.14068152 greater 92 0.41935484 1.723688e-06
## 6 BFAS 1.6334715 1.63368764 greater 100 0.04950495 2.034818e-07
## 7 CASP 2.2812516 2.69515862 greater 100 0.02970297 1.220891e-07
## 8 CESP 1.5201320 -0.24455525 greater 98 0.49494949 2.034407e-06
## 9 CERA 0.4657628 -0.76409501 greater 100 0.79207921 3.255708e-06
## 10 CLEP 0.4812193 0.04242279 greater 100 0.46534653 1.912729e-06
## 11 CMAR 2.1665091 4.21557418 greater 100 0.00990099 4.069635e-08
## 12 CHIR 0.4947663 -0.65386801 greater 100 0.74257426 3.052227e-06
## 13 CPIC 2.8291243 1.18137131 greater 100 0.12871287 5.290526e-07
## 14 DRSP 3.3680622 2.96896593 greater 100 0.01980198 8.139271e-08
## 15 DUSP 0.5912963 0.62148959 greater 100 0.22772277 9.360161e-07
## 16 ECSP 0.5271468 0.01120943 greater 100 0.41584158 1.709247e-06
## 17 ECTE 6.6813566 1.17774092 greater 85 0.10465116 4.301510e-07
## 18 ELMA 0.7910221 -1.14000003 greater 72 0.90410959 3.716190e-06
## 19 EPEO 1.2701977 -0.65639153 greater 97 0.76530612 3.145662e-06
## 20 EVIR 0.3443063 -0.68719153 greater 100 0.67326733 2.767352e-06
## 21 EPAR 0.2686018 -1.02865659 greater 100 0.85148515 3.499886e-06
## 22 ESSP 0.3230870 -0.79606867 greater 100 0.80198020 3.296405e-06
## 23 EPYG 0.6458358 2.17534645 greater 100 0.03960396 1.627854e-07
## 24 EGEN 1.9052672 1.03728742 greater 100 0.10891089 4.476599e-07
## 25 GASP 0.4291003 -0.70741489 greater 100 0.71287129 2.930137e-06
## 26 HYSP 8.3527477 0.89265789 greater 47 0.25000000 1.027583e-06
## 27 HEXO 0.4469288 -0.33768309 greater 100 0.58415842 2.401085e-06
## 28 HPEL 1.4801591 -0.06972933 greater 100 0.41584158 1.709247e-06
## 29 HYDR 0.4174195 -0.42750824 greater 100 0.65346535 2.685959e-06
## 30 HYDS 2.7519145 0.15504324 greater 99 0.32000000 1.315306e-06
## 31 LEPT 2.7371006 -0.37433813 greater 85 0.50000000 2.055166e-06
## 32 LESP 0.4520373 -0.36408430 greater 100 0.61386139 2.523174e-06
## 33 LOPAad 1.2010608 0.11065675 greater 100 0.37623762 1.546461e-06
## 34 LOPAla 0.3732577 -0.71705119 greater 100 0.72277228 2.970834e-06
## 35 MPOW 2.1214892 -0.11364171 greater 99 0.39000000 1.603029e-06
## 36 MYSA 3.3382344 -0.44156444 greater 76 0.59740260 2.455523e-06
## 37 ORHE 1.7719543 2.12491130 greater 100 0.04950495 2.034818e-07
## 38 ONSP 2.8517365 1.35172557 greater 100 0.09900990 4.069635e-07
## 39 ORTH 0.3900032 -0.20137012 greater 100 0.55445545 2.278996e-06
## 40 ORSP NaN NaN greater 100 NA NA
## 41 OTROad 0.4060305 -0.54304267 greater 100 0.72277228 2.970834e-06
## 42 OTROla 0.7368166 0.46514677 greater 100 0.26732673 1.098802e-06
## 43 PISI 1.1586152 -0.23324070 greater 100 0.54455446 2.238299e-06
## 44 POLY 7.1591537 1.80153881 greater 99 0.07000000 2.877232e-07
## 45 PBIF 0.1779238 -1.25703929 greater 100 0.94059406 3.866154e-06
## 46 PPUS 0.4637935 -0.99129897 greater 99 0.93000000 3.822609e-06
## 47 RASP 6.1246587 0.81132685 greater 90 0.23076923 9.485381e-07
## 48 RHYP 1.9250904 1.78499822 greater 100 0.05940594 2.441781e-07
## 49 RHIP 0.5317665 -0.28848663 greater 100 0.56435644 2.319692e-06
## 50 SIGN 2.0334064 7.29772288 greater 100 0.00990099 4.069635e-08
## 51 SARG 0.5753024 0.52721047 greater 100 0.29702970 1.220891e-06
## 52 SIMU 6.1331366 3.62535573 greater 100 0.01980198 8.139271e-08
## 53 STEN 0.5701184 -0.70139134 greater 99 0.81000000 3.329369e-06
## 54 SCAN 3.0027724 1.41007250 greater 100 0.12871287 5.290526e-07
## 55 TANYP 0.7337598 -0.05306232 greater 100 0.42574257 1.749943e-06
## 56 TANYT 0.3222465 -0.76219532 greater 100 0.78217822 3.215012e-06
## 57 TFLU 5.3988068 0.84120522 greater 99 0.17000000 6.987564e-07
## 58 OMI.mean 1.8649566 2.07409190 greater 100 0.02970297 1.220891e-07
##
## other elements: adj.method sub.pvalue subni.pvalue call
| | inertia| WitOMIG_k| Tol| Rtol| witomig_k| tol| rtol| |---------|---------:|-----------:|----------:|----------:|-----------:|-----:|-----:| | AFLU1 | 5.620936| 5.5642217| 0.0360651| 0.0206490| 99.0| 0.6| 0.4| | ANTO1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | ATSP1 | 5.175405| 1.1763795| 2.3858548| 1.6131705| 22.7| 46.1| 31.2| | BASP1 | 3.884823| 0.8157855| 1.6171870| 1.4518505| 21.0| 41.6| 37.4| | BIMI1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | BFAS1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | CASP1 | 4.659261| 1.9540531| 0.8433682| 1.8618393| 41.9| 18.1| 40.0| | CESP1 | 4.984683| 4.9846831| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | CERA1 | 3.987971| 3.9879708| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | CLEP1 | 4.909637| 1.6200372| 2.0090550| 1.2805453| 33.0| 40.9| 26.1| | CMAR1 | 4.803559| 3.3080114| 0.8245203| 0.6710272| 68.9| 17.2| 14.0| | CHIR1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | CPIC1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | DRSP1 | 3.714868| 2.0766254| 0.9507084| 0.6875340| 55.9| 25.6| 18.5| | DUSP1 | 4.669630| 0.8967591| 2.6173587| 1.1555125| 19.2| 56.1| 24.7| | ECSP1 | 5.223511| 0.3018932| 2.4656636| 2.4559543| 5.8| 47.2| 47.0| | ECTE1 | 4.282558| 2.6865859| 0.0458742| 1.5500981| 62.7| 1.1| 36.2| | ELMA1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | EPEO1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | EVIR1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | EPAR1 | 3.552155| 0.1106110| 1.4819049| 1.9596393| 3.1| 41.7| 55.2| | ESSP1 | 4.419685| 0.1247073| 2.0704114| 2.2245660| 2.8| 46.8| 50.3| | EPYG1 | 4.322019| 0.0713870| 2.4834894| 1.7671428| 1.7| 57.5| 40.9| | EGEN1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | GASP1 | 2.505812| 0.5427110| 0.4469468| 1.5161545| 21.7| 17.8| 60.5| | HYSP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | HEXO1 | 3.947786| 1.8616211| 0.6252779| 1.4608870| 47.2| 15.8| 37.0| | HPEL1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | HYDR1 | 4.883217| 0.5287849| 2.1845547| 2.1698775| 10.8| 44.7| 44.4| | HYDS1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | LEPT1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | LESP1 | 2.415512| 1.0241563| 0.5868012| 0.8045543| 42.4| 24.3| 33.3| | LOPAad1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | LOPAla1 | 3.483575| 1.6719107| 0.6582340| 1.1534301| 48.0| 18.9| 33.1| | MPOW1 | 7.279741| 2.0883204| 2.2426765| 2.9487445| 28.7| 30.8| 40.5| | MYSA1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | ORHE1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | ONSP1 | 4.234140| 1.3569194| 1.5778631| 1.2993576| 32.0| 37.3| 30.7| | ORTH1 | 4.365602| 0.9105355| 2.1991369| 1.2559299| 20.9| 50.4| 28.8| | ORSP1 | 6.422186| 0.9139582| 2.3678119| 3.1404162| 14.2| 36.9| 48.9| | OTROad1 | 5.847957| 2.2119449| 1.8459240| 1.7900883| 37.8| 31.6| 30.6| | OTROla1 | 4.677074| 0.2165436| 2.2520955| 2.2084347| 4.6| 48.2| 47.2| | PISI1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | POLY1 | 4.504666| 2.3604599| 0.7878648| 1.3563411| 52.4| 17.5| 30.1| | PBIF1 | 4.272253| 2.3204958| 0.8146753| 1.1370817| 54.3| 19.1| 26.6| | PPUS1 | 4.280653| 3.1596723| 0.8806195| 0.2403611| 73.8| 20.6| 5.6| | RASP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | RHYP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | RHIP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | SIGN1 | 5.201174| 0.7891329| 3.0374197| 1.3746218| 15.2| 58.4| 26.4| | SARG1 | 4.591782| 2.6612472| 1.0885512| 0.8419839| 58.0| 23.7| 18.3| | SIMU1 | 3.996263| 0.9562425| 2.4510003| 0.5890204| 23.9| 61.3| 14.7| | STEN1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | SCAN1 | 5.786297| 0.2965078| 0.4136070| 5.0761824| 5.1| 7.1| 87.7| | TANYP1 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | TANYT1 | 3.906339| 0.8531489| 0.9303935| 2.1227966| 21.8| 23.8| 54.3| | TFLU1 | 4.652959| 1.2525696| 0.0278353| 3.3725536| 26.9| 0.6| 72.5| | AFLU2 | 5.889237| 2.7558493| 0.7029799| 2.4304079| 46.8| 11.9| 41.3| | ANTO2 | 7.083839| 3.6786494| 0.4050870| 3.0001024| 51.9| 5.7| 42.4| | ATSP2 | 4.891195| 0.8639830| 1.4160532| 2.6111583| 17.7| 29.0| 53.4| | BASP2 | 4.890760| 0.0406599| 2.1106210| 2.7394794| 0.8| 43.2| 56.0| | BIMI2 | 5.673801| 1.8988628| 0.4534084| 3.3215297| 33.5| 8.0| 58.5| | BFAS2 | 4.857406| 0.9678796| 1.6202229| 2.2693036| 19.9| 33.4| 46.7| | CASP2 | 3.980211| 2.4411040| 0.4639540| 1.0751528| 61.3| 11.7| 27.0| | CESP2 | 6.177622| 1.8656031| 1.8455125| 2.4665064| 30.2| 29.9| 39.9| | CERA2 | 5.361050| 0.5788122| 1.8880373| 2.8942000| 10.8| 35.2| 54.0| | CLEP2 | 4.682735| 0.1241242| 2.2629018| 2.2957089| 2.7| 48.3| 49.0| | CMAR2 | 4.800920| 1.6027928| 1.5454976| 1.6526296| 33.4| 32.2| 34.4| | CHIR2 | 4.322612| 0.3774233| 1.0898553| 2.8553338| 8.7| 25.2| 66.1| | CPIC2 | 5.717512| 3.0402945| 0.5568210| 2.1203968| 53.2| 9.7| 37.1| | DRSP2 | 7.419486| 2.2167917| 2.1269874| 3.0757068| 29.9| 28.7| 41.5| | DUSP2 | 5.375101| 0.1157230| 3.0826266| 2.1767516| 2.2| 57.4| 40.5| | ECSP2 | 4.862670| 0.0578862| 2.5673879| 2.2373955| 1.2| 52.8| 46.0| | ECTE2 | 8.151691| 8.1516905| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | ELMA2 | 4.029052| 1.6436023| 0.0110160| 2.3744336| 40.8| 0.3| 58.9| | EPEO2 | 5.036529| 0.6238052| 1.5791415| 2.8335819| 12.4| 31.4| 56.3| | EVIR2 | 5.198365| 0.2404560| 2.0712503| 2.8866591| 4.6| 39.8| 55.5| | EPAR2 | 3.753191| 0.1039955| 0.9788830| 2.6703124| 2.8| 26.1| 71.1| | ESSP2 | 4.539844| 0.0881344| 1.0956259| 3.3560837| 1.9| 24.1| 73.9| | EPYG2 | 4.998468| 0.6065596| 1.5793650| 2.8125435| 12.1| 31.6| 56.3| | EGEN2 | 5.010104| 1.8690463| 0.9064748| 2.2345834| 37.3| 18.1| 44.6| | GASP2 | 5.509508| 0.0714224| 2.1874052| 3.2506803| 1.3| 39.7| 59.0| | HYSP2 | 6.946494| 6.9464943| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | HEXO2 | 4.934166| 0.0580045| 2.5800676| 2.2960939| 1.2| 52.3| 46.5| | HPEL2 | 3.736686| 1.6479900| 0.4430363| 1.6456599| 44.1| 11.9| 44.0| | HYDR2 | 5.032585| 0.0071187| 1.8487522| 3.1767142| 0.1| 36.7| 63.1| | HYDS2 | 7.159464| 3.7349069| 0.7539792| 2.6705774| 52.2| 10.5| 37.3| | LEPT2 | 6.712646| 4.1118194| 0.2087109| 2.3921156| 61.3| 3.1| 35.6| | LESP2 | 4.398537| 0.2378806| 1.3146253| 2.8460317| 5.4| 29.9| 64.7| | LOPAad2 | 4.865602| 1.1014180| 2.0359307| 1.7282537| 22.6| 41.8| 35.5| | LOPAla2 | 4.881240| 0.1407503| 1.2113913| 3.5290988| 2.9| 24.8| 72.3| | MPOW2 | 4.573233| 1.7673471| 1.3401219| 1.4657636| 38.6| 29.3| 32.1| | MYSA2 | 4.421343| 3.5600647| 0.3999980| 0.4612800| 80.5| 9.0| 10.4| | ORHE2 | 4.925288| 1.1183665| 1.7257631| 2.0811588| 22.7| 35.0| 42.3| | ONSP2 | 5.752368| 3.0211784| 0.3593429| 2.3718465| 52.5| 6.2| 41.2| | ORTH2 | 5.008129| 0.0088998| 2.5004187| 2.4988101| 0.2| 49.9| 49.9| | ORSP2 | NaN| NaN| NaN| NaN| NaN| NaN| NaN| | OTROad2 | 5.012399| 0.1641826| 1.6013576| 3.2468588| 3.3| 31.9| 64.8| | OTROla2 | 6.399000| 0.8142621| 2.5716818| 3.0130560| 12.7| 40.2| 47.1| | PISI2 | 6.174527| 1.2403312| 2.0185171| 2.9156789| 20.1| 32.7| 47.2| | POLY2 | 9.477574| 8.3925566| 0.3483423| 0.7366753| 88.6| 3.7| 7.8| | PBIF2 | 5.172676| 0.0637543| 1.3545450| 3.7543770| 1.2| 26.2| 72.6| | PPUS2 | 5.253758| 0.7620889| 2.5991370| 1.8925324| 14.5| 49.5| 36.0| | RASP2 | 6.358315| 5.5700388| 0.5376887| 0.2505873| 87.6| 8.5| 3.9| | RHYP2 | 5.278459| 1.2779932| 1.6076088| 2.3928564| 24.2| 30.5| 45.3| | RHIP2 | 5.297488| 0.3193947| 2.0800676| 2.8980253| 6.0| 39.3| 54.7| | SIGN2 | 5.443125| 1.3613993| 1.3428192| 2.7389070| 25.0| 24.7| 50.3| | SARG2 | 5.112210| 0.0572188| 1.8163081| 3.2386832| 1.1| 35.5| 63.4| | SIMU2 | 6.807988| 6.8079877| 0.0000000| 0.0000000| 100.0| 0.0| 0.0| | STEN2 | 5.869217| 0.1707668| 3.1871376| 2.5113129| 2.9| 54.3| 42.8| | SCAN2 | 6.542266| 3.2491099| 0.5550257| 2.7381305| 49.7| 8.5| 41.9| | TANYP2 | 4.953800| 0.8041675| 1.9167004| 2.2329326| 16.2| 38.7| 45.1| | TANYT2 | 5.221442| 0.0618055| 1.5156228| 3.6440133| 1.2| 29.0| 69.8| | TFLU2 | 9.985720| 6.6253669| 0.8064942| 2.5538589| 66.3| 8.1| 25.6|
## $autumn
## $autumn$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 5
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 FST -0.095707902 -0.8500764 two-sided 0.38613861
## 2 Depth 0.330509141 2.6502707 two-sided 0.01980198
## 3 SPS 0.495802058 3.8951903 two-sided 0.00990099
## 4 Froude -0.155231548 -1.1984950 two-sided 0.25742574
## 5 Rough 0.002382221 0.1115240 two-sided 0.94059406
## Subsets Pvalue: 1.833094e-05
## other elements: subpvalue call
##
## $autumn$witomig_ktest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 AFLU 5.56422172 4.7157933 greater 100 0.00990099 1.814944e-07
## 2 ANTO NaN NaN greater 99 NA NA
## 3 ATSP 1.17637948 0.9608107 greater 100 0.14851485 2.722417e-06
## 4 BASP 0.81578553 11.2081232 greater 100 0.00990099 1.814944e-07
## 5 BIMI NaN NaN greater 96 NA NA
## 6 BFAS NaN NaN greater 100 NA NA
## 7 CASP 1.95405306 5.4880781 greater 100 0.00990099 1.814944e-07
## 8 CESP 4.98468314 2.4878749 greater 100 0.04950495 9.074722e-07
## 9 CERA 3.98797079 9.2265229 greater 100 0.00990099 1.814944e-07
## 10 CLEP 1.62003719 15.0262330 greater 100 0.00990099 1.814944e-07
## 11 CMAR 3.30801135 11.2586970 greater 100 0.00990099 1.814944e-07
## 12 CHIR NaN NaN greater 100 NA NA
## 13 CPIC NaN NaN greater 100 NA NA
## 14 DRSP 2.07662540 1.9105845 greater 100 0.06930693 1.270461e-06
## 15 DUSP 0.89675913 10.3692312 greater 100 0.00990099 1.814944e-07
## 16 ECSP 0.30189321 1.0987540 greater 100 0.09900990 1.814944e-06
## 17 ECTE 2.68658590 -0.1771901 greater 94 0.48421053 8.876033e-06
## 18 ELMA NaN NaN greater 81 NA NA
## 19 EPEO NaN NaN greater 100 NA NA
## 20 EVIR NaN NaN greater 100 NA NA
## 21 EPAR 0.11061095 -0.3249294 greater 100 0.47524752 8.711733e-06
## 22 ESSP 0.12470729 0.8467007 greater 100 0.23762376 4.355866e-06
## 23 EPYG 0.07138701 0.7113649 greater 100 0.18811881 3.448394e-06
## 24 EGEN NaN NaN greater 100 NA NA
## 25 GASP 0.54271103 1.0390179 greater 100 0.14851485 2.722417e-06
## 26 HYSP NaN NaN greater 57 NA NA
## 27 HEXO 1.86162108 12.5078562 greater 100 0.00990099 1.814944e-07
## 28 HPEL NaN NaN greater 100 NA NA
## 29 HYDR 0.52878494 4.5671524 greater 100 0.00990099 1.814944e-07
## 30 HYDS NaN NaN greater 98 NA NA
## 31 LEPT NaN NaN greater 93 NA NA
## 32 LESP 1.02415632 3.9051372 greater 100 0.01980198 3.629889e-07
## 33 LOPAad NaN NaN greater 100 NA NA
## 34 LOPAla 1.67191072 11.1164571 greater 100 0.00990099 1.814944e-07
## 35 MPOW 2.08832037 0.1901046 greater 99 0.27000000 4.949353e-06
## 36 MYSA NaN NaN greater 64 NA NA
## 37 ORHE NaN NaN greater 100 NA NA
## 38 ONSP 1.35691944 0.7957905 greater 100 0.17821782 3.266900e-06
## 39 ORTH 0.91053552 18.9386921 greater 100 0.00990099 1.814944e-07
## 40 ORSP 0.91395817 -0.2212642 greater 100 0.39603960 7.259777e-06
## 41 OTROad 2.21194486 12.4787089 greater 100 0.00990099 1.814944e-07
## 42 OTROla 0.21654356 0.5536612 greater 100 0.25742574 4.718855e-06
## 43 PISI NaN NaN greater 100 NA NA
## 44 POLY 2.36045987 0.8460642 greater 100 0.18811881 3.448394e-06
## 45 PBIF 2.32049577 15.5261977 greater 100 0.00990099 1.814944e-07
## 46 PPUS 3.15967229 1.1302462 greater 100 0.09900990 1.814944e-06
## 47 RASP NaN NaN greater 86 NA NA
## 48 RHYP NaN NaN greater 100 NA NA
## 49 RHIP NaN NaN greater 100 NA NA
## 50 SIGN 0.78913290 5.9368163 greater 100 0.00990099 1.814944e-07
## 51 SARG 2.66124718 28.4580286 greater 100 0.00990099 1.814944e-07
## 52 SIMU 0.95624252 -0.3365549 greater 100 0.49504950 9.074722e-06
## 53 STEN NaN NaN greater 99 NA NA
## 54 SCAN 0.29650781 -0.6823962 greater 100 0.80198020 1.470105e-05
## 55 TANYP NaN NaN greater 100 NA NA
## 56 TANYT 0.85314894 8.5087049 greater 100 0.00990099 1.814944e-07
## 57 TFLU 1.25256959 -0.3526573 greater 98 0.50505051 9.258049e-06
## 58 OMI.mean 1.64733126 4.6319226 greater 100 0.00990099 1.814944e-07
##
## other elements: adj.method sub.pvalue subni.pvalue call
##
##
## $spring
## $spring$Subsettest
## class: subkrandtest
## Monte-Carlo tests
## Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")
##
## Number of tests: 5
##
## Adjustment method for multiple comparisons: none
## Permutation number: 100
## Test Obs Std.Obs Alter Pvalue
## 1 FST 0.092892964 0.67535719 two-sided 0.53465347
## 2 Depth -0.320788284 -2.68914880 two-sided 0.01980198
## 3 SPS -0.481219645 -3.96215890 two-sided 0.00990099
## 4 Froude 0.150665914 1.23714387 two-sided 0.21782178
## 5 Rough -0.002312156 -0.06131542 two-sided 0.94059406
## Subsets Pvalue: 2.147648e-05
## other elements: subpvalue call
##
## $spring$witomig_ktest
## class: subnikrandtest
## Monte-Carlo tests
## Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest$subpvalue)
##
## Number of tests: 58
##
## Adjustment method for multiple comparisons: none
## Test Obs Std.Obs Alter N.perm Pvalue SubniPvalue
## 1 AFLU 2.755849341 1.20570963 greater 100 0.10891089 2.339023e-06
## 2 ANTO 3.678649417 1.46558307 greater 100 0.07920792 1.701108e-06
## 3 ATSP 0.863982951 0.09537015 greater 100 0.35643564 7.654984e-06
## 4 BASP 0.040659929 -0.37989530 greater 100 0.56435644 1.212039e-05
## 5 BIMI 1.898862829 -0.43393861 greater 93 0.56382979 1.210908e-05
## 6 BFAS 0.967879551 1.90272607 greater 100 0.07920792 1.701108e-06
## 7 CASP 2.441104022 7.04244690 greater 100 0.00990099 2.126385e-07
## 8 CESP 1.865603098 0.16288540 greater 100 0.29702970 6.379154e-06
## 9 CERA 0.578812207 0.52437558 greater 100 0.26732673 5.741238e-06
## 10 CLEP 0.124124205 0.16089053 greater 100 0.32673267 7.017069e-06
## 11 CMAR 1.602792826 3.58960345 greater 100 0.01980198 4.252769e-07
## 12 CHIR 0.377423333 -0.18105066 greater 100 0.44554455 9.568730e-06
## 13 CPIC 3.040294484 2.94285045 greater 100 0.02970297 6.379154e-07
## 14 DRSP 2.216791717 2.10771008 greater 100 0.04950495 1.063192e-06
## 15 DUSP 0.115722951 -0.06800970 greater 100 0.30693069 6.591792e-06
## 16 ECSP 0.057886165 -0.79469324 greater 100 0.72277228 1.552261e-05
## 17 ECTE 8.151690494 1.77994072 greater 87 0.07954545 1.708357e-06
## 18 ELMA 1.643602322 -0.74415282 greater 75 0.69736842 1.497702e-05
## 19 EPEO 0.623805205 -0.71711064 greater 98 0.78787879 1.692087e-05
## 20 EVIR 0.240456014 -0.01533892 greater 100 0.41584158 8.930815e-06
## 21 EPAR 0.103995528 -0.52099100 greater 100 0.63366337 1.360886e-05
## 22 ESSP 0.088134358 -0.43667510 greater 100 0.57425743 1.233303e-05
## 23 EPYG 0.606559568 12.92843248 greater 100 0.00990099 2.126385e-07
## 24 EGEN 1.869046253 1.28397197 greater 100 0.13861386 2.976938e-06
## 25 GASP 0.071422352 -0.81941175 greater 100 0.81188119 1.743635e-05
## 26 HYSP 6.946494324 0.90807772 greater 61 0.19354839 4.156739e-06
## 27 HEXO 0.058004536 -0.83718353 greater 100 0.79207921 1.701108e-05
## 28 HPEL 1.647990021 0.33726257 greater 100 0.23762376 5.103323e-06
## 29 HYDR 0.007118745 -1.35842322 greater 100 1.00000000 2.147648e-05
## 30 HYDS 3.734906945 0.96068519 greater 99 0.11000000 2.362413e-06
## 31 LEPT 4.111819413 0.26272930 greater 90 0.29670330 6.372143e-06
## 32 LESP 0.237880602 -0.11221807 greater 100 0.46534653 9.994007e-06
## 33 LOPAad 1.101418022 0.67061086 greater 100 0.19801980 4.252769e-06
## 34 LOPAla 0.140750264 -0.19717236 greater 100 0.44554455 9.568730e-06
## 35 MPOW 1.767347058 0.02092202 greater 100 0.38613861 8.292900e-06
## 36 MYSA 3.560064717 -0.16860859 greater 76 0.49350649 1.059878e-05
## 37 ORHE 1.118366495 2.31644155 greater 100 0.05940594 1.275831e-06
## 38 ONSP 3.021178449 1.64655564 greater 100 0.09900990 2.126385e-06
## 39 ORTH 0.008899823 -1.03551132 greater 100 0.94059406 2.020065e-05
## 40 ORSP NaN NaN greater 100 NA NA
## 41 OTROad 0.164182633 -0.27654657 greater 100 0.55445545 1.190775e-05
## 42 OTROla 0.814262062 3.80193284 greater 100 0.01980198 4.252769e-07
## 43 PISI 1.240331162 0.47585817 greater 100 0.25742574 5.528600e-06
## 44 POLY 8.392556643 4.14318650 greater 99 0.01000000 2.147648e-07
## 45 PBIF 0.063754284 -0.65858405 greater 100 0.70297030 1.509733e-05
## 46 PPUS 0.762088874 -0.76183201 greater 99 0.78000000 1.675166e-05
## 47 RASP 5.570038758 0.67463118 greater 91 0.26086957 5.602561e-06
## 48 RHYP 1.277993230 1.57061290 greater 100 0.07920792 1.701108e-06
## 49 RHIP 0.319394665 0.64628842 greater 100 0.18811881 4.040131e-06
## 50 SIGN 1.361399251 8.94619480 greater 100 0.00990099 2.126385e-07
## 51 SARG 0.057218796 -0.43512833 greater 100 0.63366337 1.360886e-05
## 52 SIMU 6.807987747 3.90914932 greater 100 0.00990099 2.126385e-07
## 53 STEN 0.170766804 -0.86762968 greater 100 0.99009901 2.126385e-05
## 54 SCAN 3.249109937 2.18882501 greater 100 0.05940594 1.275831e-06
## 55 TANYP 0.804167503 1.47653777 greater 100 0.05940594 1.275831e-06
## 56 TANYT 0.061805460 -0.59478883 greater 100 0.70297030 1.509733e-05
## 57 TFLU 6.625366853 1.32603512 greater 95 0.10416667 2.237134e-06
## 58 OMI.mean 1.807675271 4.15780127 greater 100 0.00990099 2.126385e-07
##
## other elements: adj.method sub.pvalue subni.pvalue call
Reference
Dolédec, S., Chessel, D., Gimaret-Carpentier, C., 2000. Niche separation in community analysis: A new method. Ecology 81, 2914. doi:doi:10.1890/0012-9658(2000)081[2914:NSICAA]2.0.CO;2
Dray, S., Dufour, A., 2007. The ade4 package: Implementing the duality diagram for ecologists. Journal of Statistical Software 22, 1–20.
Karasiewicz, S., 2017. Subniche: Within outlying mean indexes: Refining the omi analysis. doi:https://zenodo.org/badge/DOI/10.5281/zenodo.438723.svg
Karasiewicz S, Dolédec S, Lefebvre S. (2017) Within outlying mean indexes: refining the OMI analysis for the realized niche decomposition. PeerJ, 5:e3364. doi:https://doi.org/10.7717/peerj.3364
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