# subniche: The Within Outlying Mean Indexes calculation In subniche: Within Outlying Mean Indexes: Refining the OMI Analysis

## Description

The indexes allows to divide the niche, estimated from the niche function in the ade4 package into subniches defined by a factor, which creates the subsets. See details for more information.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39``` ```subniche(nic, factor) ## S3 method for class 'subkrandtest' print(x, ...) ## S3 method for class 'subnikrandtest' print(x, ...) ## S3 method for class 'subniche' print(x, ...) ## S3 method for class 'subniche' plot(x, xax = 1, yax = 2, ...) margvect(x, xax = 1, yax = 2, colo = NULL, ...) subplot(x, xax = 1, yax = 2, colo = NULL, ...) ## S3 method for class 'subniche' summary(object, ...) refparam(x) ## S3 method for class 'subniche' rtest(xtest, nrepet = 99, ...) subparam.refor(x) rtestrefor(x, nrepet) subparam.subor(x) rtestsubor(x, nrepet) subkrandtest(sim, obs, alter = "greater", call = match.call(), names = colnames(sim), p.adjust.method = "none") subnikrandtest(sim, obs, alter = "greater", subpvalue, call = match.call(), names = colnames(sim), p.adjust.method = "none") ```

## Arguments

 `nic` an object of class `niche`. `factor` a factor which will defined the subsets within which the subniches will be calculated (the same length of the number of sites) `x` an object of class `subniche`. `...` further arguments passed to or from other methods `xax` specify the x column in your matrix `yax` specify the y column in your matrix `colo` string of character specifying the subsets color. Default color is rezd. `object` an object of class `subniche`. `xtest` an object of class `subniche`. `nrepet` the number of permutations for the testing procedure `sim` a numeric vector of simulated values `obs` a numeric vector of an observed value `alter` a character string specifying the alternative hypothesis, must be one of "greater" (default), "less" or "two-sided".he length must be equal to the length of the vector obs, values are recycled if shorter. `call` a call order `names` a vector of names for tests `p.adjust.method` a string indicating a method for multiple adjustment, see p.adjust.methods for possible choices. `subpvalue` the subset pvalue resulting from `subkrandtest` function

## Details

The Within Outlying Mean Index analysis is a statistical exploratory niche analysis which provides observation of niche shift and/or conservatism, of an entire community,at different subcales (temporal ,spatial and/or finer biological organisation level), and comparable under the same environmental gradients. This hindcasting multivariate analysis is based on the OMI analysis (Doledec et al. 2000) which is used as reference. The niches refinement is inspired by the K-select (Calenge et al. 2005) which emphasizes the limiting factors in habitat use in design II and III (Thomas and Taylor, 1990).The different estimations should help understand:

1. the environmental factors defining a species' reference niche, under on the full scale, within a community.

2. the environmental factors defining a species' subniches, under each subsets, within a community.

The subniches parameters can be calculated from both the reference origin,G, which corresponds to the reference plan origin, and from G_k, which corresponds to the suborigins. G is the graphical representation of the mean environmental conditions encountered over the full scale of the data. G_k is the mean environmental conditions encountered at a subset defined by the factor. They are complementary has you can compare:

1. a single species' subniches to G.

2. the community' subniches to G_k at a specific subset.

The subniches of a single species can only be compared to G as it is the common origin to all subsets. Whereas G_k is only common to the species found within the subset. So comparing different subniches of one species, found within different subsets, is only relevant to G. The community's subniches can be compared to both G and G_k, but G, being the mean environmental conditions found within the full scale, will not express the specificity of the environmental conditions that the species encountered at the subset. G_k, being the mean environmental conditions of the subset, will reflect the atypical value of the environmental condition, making the comparison of the community's subniches parameters more relevant. More information on the ecological concept can be found in Karasiewicz et al. 2017.

For more details description on the package use:https://github.com/KarasiewiczStephane/WitOMI.

## Value

Adds items in the niche list and changing the class into `subniche` containing:

`factor` the factor use to divide the environmental and species matrix into submatrices.

`G_k` a dataframe with the sub-origins, G_k.

`sub` a dataframe with the species subniche coordinates

## Author(s)

Stephane Karasiewicz, [email protected]

## References

Karasiewicz S.,Doledec S.and Lefebvre S. (2017). Within outlying mean indexes: refining the OMI analysis for the realized niche decomposition. PeerJ 5:e3364. https://doi.org/10.7717/peerj.3364.

Calenge C., Dufour A.B. and Maillard D. (2005). K-select analysis: a new method to analyse habitat selection in radio-tracking studies. Ecological modelling, 186, 143-153.

Doledec S., Chessel D. and Gimaret C. (2000). Niche separation in community analysis: a new method. Ecology,81, 2914-1927.

Thomas, D.L., Taylor, E.J. (1990). Study Designs and Tests for Comparing Resource Use and Availability II. Natl. Widl. 54(2), 322-330.

niche niche.param

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```library(subniche) data(doubs) dudi1 <- dudi.pca(doubs\$env, scale = TRUE, scan = FALSE, nf = 3) nic1 <- niche(dudi1, doubs\$fish, scann = FALSE) # number of sites N <- dim(nic1\$ls)[1] #Create a factor which defines the subsets fact <- factor(c(rep(1,N/2),rep(2,N/2))) # nic1 will be use as reference and fact will be use to define the subniches environment subnic1 <- subniche(nic1, fact) # the following two functions do the same display, plot.refniche is adapted to subniche objects plot(nic1) plot(subnic1) #Display the marginality vector of the suborigins and the species subniche margvect(subnic1) #Display the subset's polygon, found within the overall environment's chull, #and the corresponding species positions subplot(subnic1) # The following two functions do the same display, refparam is adapted to subniche objects niche.param(nic1) refparam(subnic1) # The following two functions do the same display, rtest is adapted to subniche objects rtest(nic1,10) rtest(subnic1,10) #Calculates the subniches' parameters from G with the corresponding rtest subparam.refor(subnic1) rtestrefor(subnic1,10) #Calculates the subniches' parameters from G_k with the corresponding rtest subparam.subor(subnic1) rtestsubor(subnic1,10) ```

### Example output

```Loading required package: ade4
inertia       OMI       Tol     Rtol  omi  tol rtol
Cogo  5.592708 2.6560006 0.8167845 2.119923 47.5 14.6 37.9
Satr  9.365634 3.5242789 1.6636579 4.177697 37.6 17.8 44.6
Phph  7.383686 2.0454327 1.4133937 3.924860 27.7 19.1 53.2
Neba  7.302860 1.3244021 1.9888040 3.989653 18.1 27.2 54.6
Thth  6.189998 2.6687690 0.5632649 2.957964 43.1  9.1 47.8
Teso  5.781321 2.0966432 1.0009551 2.683722 36.3 17.3 46.4
Chna  6.459330 3.0036630 0.9205975 2.535070 46.5 14.3 39.2
Chto  4.791591 2.0228932 0.7891362 1.979562 42.2 16.5 41.3
Lele  8.398481 0.5832650 3.0201993 4.795016  6.9 36.0 57.1
Lece  9.047967 0.6959801 3.4093956 4.942591  7.7 37.7 54.6
Baba  6.869906 2.8240029 1.1654461 2.880457 41.1 17.0 41.9
Spbi  7.323117 3.6481419 1.5108744 2.164101 49.8 20.6 29.6
Gogo  9.894373 2.2051499 2.8315484 4.857674 22.3 28.6 49.1
Eslu  9.938425 1.5285287 4.4766835 3.933213 15.4 45.0 39.6
Pefl  7.714630 1.4293101 3.8431119 2.442208 18.5 49.8 31.7
Rham  8.698681 4.9065929 1.3761486 2.415940 56.4 15.8 27.8
Legi  8.905122 4.8252988 1.3311280 2.748696 54.2 14.9 30.9
Scer 12.235545 4.2331196 3.1770688 4.825356 34.6 26.0 39.4
Cyca  9.149580 5.2211179 1.6530441 2.275418 57.1 18.1 24.9
Titi  7.790130 1.8106553 3.3438706 2.635605 23.2 42.9 33.8
Abbr  9.531568 5.8043296 1.3360409 2.391197 60.9 14.0 25.1
Icme 12.076075 8.4683537 1.1244446 2.483276 70.1  9.3 20.6
Acce 11.084037 5.7953055 1.1416389 4.147093 52.3 10.3 37.4
Ruru  8.519888 1.9475787 2.8232349 3.749074 22.9 33.1 44.0
Blbj  9.545674 5.6984685 1.1892862 2.657919 59.7 12.5 27.8
Alal 12.048146 4.9846448 1.7027475 5.360754 41.4 14.1 44.5
Anan  9.645091 5.7954132 1.3592329 2.490445 60.1 14.1 25.8
inertia       OMI       Tol     Rtol  omi  tol rtol
Cogo  5.592708 2.6560006 0.8167845 2.119923 47.5 14.6 37.9
Satr  9.365634 3.5242789 1.6636579 4.177697 37.6 17.8 44.6
Phph  7.383686 2.0454327 1.4133937 3.924860 27.7 19.1 53.2
Neba  7.302860 1.3244021 1.9888040 3.989653 18.1 27.2 54.6
Thth  6.189998 2.6687690 0.5632649 2.957964 43.1  9.1 47.8
Teso  5.781321 2.0966432 1.0009551 2.683722 36.3 17.3 46.4
Chna  6.459330 3.0036630 0.9205975 2.535070 46.5 14.3 39.2
Chto  4.791591 2.0228932 0.7891362 1.979562 42.2 16.5 41.3
Lele  8.398481 0.5832650 3.0201993 4.795016  6.9 36.0 57.1
Lece  9.047967 0.6959801 3.4093956 4.942591  7.7 37.7 54.6
Baba  6.869906 2.8240029 1.1654461 2.880457 41.1 17.0 41.9
Spbi  7.323117 3.6481419 1.5108744 2.164101 49.8 20.6 29.6
Gogo  9.894373 2.2051499 2.8315484 4.857674 22.3 28.6 49.1
Eslu  9.938425 1.5285287 4.4766835 3.933213 15.4 45.0 39.6
Pefl  7.714630 1.4293101 3.8431119 2.442208 18.5 49.8 31.7
Rham  8.698681 4.9065929 1.3761486 2.415940 56.4 15.8 27.8
Legi  8.905122 4.8252988 1.3311280 2.748696 54.2 14.9 30.9
Scer 12.235545 4.2331196 3.1770688 4.825356 34.6 26.0 39.4
Cyca  9.149580 5.2211179 1.6530441 2.275418 57.1 18.1 24.9
Titi  7.790130 1.8106553 3.3438706 2.635605 23.2 42.9 33.8
Abbr  9.531568 5.8043296 1.3360409 2.391197 60.9 14.0 25.1
Icme 12.076075 8.4683537 1.1244446 2.483276 70.1  9.3 20.6
Acce 11.084037 5.7953055 1.1416389 4.147093 52.3 10.3 37.4
Ruru  8.519888 1.9475787 2.8232349 3.749074 22.9 33.1 44.0
Blbj  9.545674 5.6984685 1.1892862 2.657919 59.7 12.5 27.8
Alal 12.048146 4.9846448 1.7027475 5.360754 41.4 14.1 44.5
Anan  9.645091 5.7954132 1.3592329 2.490445 60.1 14.1 25.8
class: krandtest lightkrandtest
Monte-Carlo tests
Call: as.krandtest(sim = t(sim), obs = obs)

Number of tests:   28

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test       Obs    Std.Obs   Alter     Pvalue
1      Cogo 2.6560006  0.4942337 greater 0.27272727
2      Satr 3.5242789 10.4280811 greater 0.09090909
3      Phph 2.0454327 15.4065073 greater 0.09090909
4      Neba 1.3244021 11.8027713 greater 0.09090909
5      Thth 2.6687690  2.5847519 greater 0.09090909
6      Teso 2.0966432  0.2906676 greater 0.36363636
7      Chna 3.0036630  4.8516364 greater 0.09090909
8      Chto 2.0228932  3.8565434 greater 0.09090909
9      Lele 0.5832650  2.0470427 greater 0.18181818
10     Lece 0.6959801  3.5651680 greater 0.09090909
11     Baba 2.8240029  5.2601421 greater 0.09090909
12     Spbi 3.6481419  3.7800197 greater 0.09090909
13     Gogo 2.2051499  5.0914137 greater 0.09090909
14     Eslu 1.5285287  1.4678679 greater 0.18181818
15     Pefl 1.4293101  2.5526742 greater 0.09090909
16     Rham 4.9065929 11.7399705 greater 0.09090909
17     Legi 4.8252988  9.6577136 greater 0.09090909
18     Scer 4.2331196  4.8793255 greater 0.09090909
19     Cyca 5.2211179  3.2745221 greater 0.09090909
20     Titi 1.8106553  2.3639897 greater 0.09090909
21     Abbr 5.8043296  8.4839271 greater 0.09090909
22     Icme 8.4683537  6.0123844 greater 0.09090909
23     Acce 5.7953055  6.8539460 greater 0.09090909
24     Ruru 1.9475787  5.8980271 greater 0.09090909
25     Blbj 5.6984685 10.0525558 greater 0.09090909
26     Alal 4.9846448  6.1228845 greater 0.09090909
27     Anan 5.7954132 10.4330054 greater 0.09090909
28 OMI.mean 3.3980496 14.3581702 greater 0.09090909

class: krandtest lightkrandtest
Monte-Carlo tests
Call: as.krandtest(sim = t(sim), obs = obs)

Number of tests:   28

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test       Obs    Std.Obs   Alter     Pvalue
1      Cogo 2.6560006  0.5236533 greater 0.18181818
2      Satr 3.5242789 12.6941629 greater 0.09090909
3      Phph 2.0454327 10.9625239 greater 0.09090909
4      Neba 1.3244021  6.9945578 greater 0.09090909
5      Thth 2.6687690  0.3228457 greater 0.45454545
6      Teso 2.0966432  0.7037263 greater 0.27272727
7      Chna 3.0036630  2.4844170 greater 0.09090909
8      Chto 2.0228932  2.1638432 greater 0.09090909
9      Lele 0.5832650  0.3372337 greater 0.27272727
10     Lece 0.6959801  6.2144069 greater 0.09090909
11     Baba 2.8240029  4.7352815 greater 0.09090909
12     Spbi 3.6481419  5.4222098 greater 0.09090909
13     Gogo 2.2051499 10.4128199 greater 0.09090909
14     Eslu 1.5285287  5.4331305 greater 0.09090909
15     Pefl 1.4293101  1.8433520 greater 0.18181818
16     Rham 4.9065929  3.8654597 greater 0.09090909
17     Legi 4.8252988 15.9247788 greater 0.09090909
18     Scer 4.2331196  6.0142245 greater 0.09090909
19     Cyca 5.2211179  6.6508094 greater 0.09090909
20     Titi 1.8106553  2.7048275 greater 0.09090909
21     Abbr 5.8043296  5.4527932 greater 0.09090909
22     Icme 8.4683537  6.8115633 greater 0.09090909
23     Acce 5.7953055  5.7922958 greater 0.09090909
24     Ruru 1.9475787  3.3540114 greater 0.09090909
25     Blbj 5.6984685 13.0585351 greater 0.09090909
26     Alal 4.9846448 11.2181079 greater 0.09090909
27     Anan 5.7954132  8.9699684 greater 0.09090909
28 OMI.mean 3.3980496 21.5205785 greater 0.09090909

inertia  WitOMIG         Tol      Rtol witomig  tol rtol
Cogo1  7.045272 4.299120 0.611056034 2.1350954    61.0  8.7 30.3
Satr1 10.166060 4.809033 1.099795163 4.2572324    47.3 10.8 41.9
Phph1  8.800128 3.925657 0.783819306 4.0906518    44.6  8.9 46.5
Neba1  9.324223 4.205376 0.917731295 4.2011155    45.1  9.8 45.1
Thth1  6.138099 3.969489 0.301609758 1.8670002    64.7  4.9 30.4
Teso1  8.804533 6.896211 0.705686549 1.2026356    78.3  8.0 13.7
Chna1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Chto1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Lele1  9.105805 3.247430 0.310610493 5.5477644    35.7  3.4 60.9
Lece1  7.636651 2.511241 0.428051767 4.6973588    32.9  5.6 61.5
Baba1 10.817788 9.820319 0.553858874 0.4436107    90.8  5.1  4.1
Spbi1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Gogo1  9.149357 3.069763 0.255345818 5.8242481    33.6  2.8 63.7
Eslu1  8.038841 5.390231 0.628215152 2.0203947    67.1  7.8 25.1
Pefl1  7.488585 6.179629 0.052022168 1.2569343    82.5  0.7 16.8
Rham1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Legi1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Scer1  6.029779 6.029779 0.000000000 0.0000000   100.0  0.0  0.0
Cyca1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Titi1  8.132365 4.501950 0.510551566 3.1198638    55.4  6.3 38.4
Abbr1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Icme1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Acce1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Ruru1  5.764182 4.960883 0.043319141 0.7599796    86.1  0.8 13.2
Blbj1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Alal1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Anan1       NaN      NaN         NaN       NaN     NaN  NaN  NaN
Cogo2  1.598157 1.126400 0.001527593 0.4702290    70.5  0.1 29.4
Satr2  3.648305 1.495458 0.452287950 1.7005586    41.0 12.4 46.6
Phph2  2.780251 1.482589 0.158509115 1.1391528    53.3  5.7 41.0
Neba2  3.421842 1.459884 0.257501940 1.7044555    42.7  7.5 49.8
Thth2  6.397592 3.615121 1.553925768 1.2285445    56.5 24.3 19.2
Teso2  3.060430 1.256070 0.177715399 1.6266442    41.0  5.8 53.2
Chna2  6.459330 3.003663 0.920597499 2.5350698    46.5 14.3 39.2
Chto2  4.791591 2.022893 0.789136180 1.9795616    42.2 16.5 41.3
Lele2  8.124678 2.687707 1.411140189 4.0258308    33.1 17.4 49.6
Lece2  9.772696 3.866684 1.453076893 4.4529348    39.6 14.9 45.6
Baba2  6.573814 3.216743 1.137779059 2.2192924    48.9 17.3 33.8
Spbi2  7.323117 3.648142 1.510874391 2.1641009    49.8 20.6 29.6
Gogo2 10.021184 3.956758 1.440829189 4.6235965    39.5 14.4 46.1
Eslu2 10.489917 4.937736 1.410933856 4.1412476    47.1 13.5 39.5
Pefl2  7.769192 4.018942 1.622862723 2.1273873    51.7 20.9 27.4
Rham2  8.698681 4.906593 1.376148555 2.4159397    56.4 15.8 27.8
Legi2  8.905122 4.825299 1.331127966 2.7486955    54.2 14.9 30.9
Scer2 12.888783 6.155192 1.599190420 5.1344008    47.8 12.4 39.8
Cyca2  9.149580 5.221118 1.653044096 2.2754181    57.1 18.1 24.9
Titi2  7.716134 4.169011 1.237200958 2.3099213    54.0 16.0 29.9
Abbr2  9.531568 5.804330 1.336040926 2.3911974    60.9 14.0 25.1
Icme2 12.076075 8.468354 1.124444616 2.4832764    70.1  9.3 20.6
Acce2 11.084037 5.795305 1.141638947 4.1470926    52.3 10.3 37.4
Ruru2  9.039832 3.920043 1.228012868 3.8917765    43.4 13.6 43.1
Blbj2  9.545674 5.698468 1.189286249 2.6579192    59.7 12.5 27.8
Alal2 12.048146 4.984645 1.702747532 5.3607540    41.4 14.1 44.5
Anan2  9.645091 5.795413 1.359232867 2.4904452    60.1 14.1 25.8
\$`1`
\$`1`\$Subsettest
class: subkrandtest
Monte-Carlo tests
Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")

Number of tests:   11

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test        Obs     Std.Obs     Alter     Pvalue
1   dfs -0.8603563 -4.57884443 two-sided 0.09090909
2   alt  0.8413738  3.73847448 two-sided 0.09090909
3   slo  0.6719200  3.30202517 two-sided 0.09090909
4   flo -0.7343498 -5.33581005 two-sided 0.09090909
5    pH  0.1366260  0.05532833 two-sided 1.00000000
6   har -0.4844664 -2.25792091 two-sided 0.09090909
7   pho -0.4568414 -2.45679417 two-sided 0.09090909
8   nit -0.7834262 -7.29416818 two-sided 0.09090909
9   amm -0.4543625 -2.62441603 two-sided 0.09090909
10  oxy  0.5127452  2.40635588 two-sided 0.09090909
11  bdo -0.3869307 -2.12448056 two-sided 0.09090909
Subsets Pvalue: 3.855433e-11
other elements: subpvalue call

\$`1`\$witomigtest
class: subnikrandtest
Monte-Carlo tests
Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest\$subpvalue)

Number of tests:   28

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test      Obs     Std.Obs   Alter     Pvalue  SubniPvalue
1      Cogo 4.299120 -0.32435869 greater 0.54545455 2.102963e-11
2      Satr 4.809033 -0.08145415 greater 0.63636364 2.453457e-11
3      Phph 3.925657 -0.32723746 greater 0.63636364 2.453457e-11
4      Neba 4.205376 -0.09948895 greater 0.63636364 2.453457e-11
5      Thth 3.969489 -0.84889706 greater 0.90909091 3.504939e-11
6      Teso 6.896211  0.09572681 greater 0.36363636 1.401976e-11
7      Chna      NaN         NaN greater         NA           NA
8      Chto      NaN         NaN greater         NA           NA
9      Lele 3.247430 -1.98088588 greater 1.00000000 3.855433e-11
10     Lece 2.511241 -1.71414005 greater 1.00000000 3.855433e-11
11     Baba 9.820319  2.77243684 greater 0.09090909 3.504939e-12
12     Spbi      NaN         NaN greater         NA           NA
13     Gogo 3.069763 -0.93085520 greater 0.81818182 3.154445e-11
14     Eslu 5.390231  0.82453308 greater 0.27272727 1.051482e-11
15     Pefl 6.179629  1.15226317 greater 0.27272727 1.051482e-11
16     Rham      NaN         NaN greater         NA           NA
17     Legi      NaN         NaN greater         NA           NA
18     Scer 6.029779  0.49694341 greater 0.18181818 7.009878e-12
19     Cyca      NaN         NaN greater         NA           NA
20     Titi 4.501950 -0.31523785 greater 0.45454545 1.752469e-11
21     Abbr      NaN         NaN greater         NA           NA
22     Icme      NaN         NaN greater         NA           NA
23     Acce      NaN         NaN greater         NA           NA
24     Ruru 4.960883 -0.54796836 greater 0.72727273 2.803951e-11
25     Blbj      NaN         NaN greater         NA           NA
26     Alal      NaN         NaN greater         NA           NA
27     Anan      NaN         NaN greater         NA           NA
28 OMI.mean 4.921074 -0.24097451 greater 0.63636364 2.453457e-11

other elements: adj.method sub.pvalue subni.pvalue call

\$`2`
\$`2`\$Subsettest
class: subkrandtest
Monte-Carlo tests
Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")

Number of tests:   11

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test        Obs    Std.Obs     Alter     Pvalue
1   dfs  0.8603563  4.1143754 two-sided 0.09090909
2   alt -0.8413738 -4.5607712 two-sided 0.09090909
3   slo -0.6719200 -2.8381443 two-sided 0.09090909
4   flo  0.7343498  7.0421092 two-sided 0.09090909
5    pH -0.1366260 -0.4045199 two-sided 0.81818182
6   har  0.4844664  2.7264278 two-sided 0.09090909
7   pho  0.4568414  2.9591089 two-sided 0.09090909
8   nit  0.7834262  4.6739286 two-sided 0.09090909
9   amm  0.4543625  2.0325089 two-sided 0.18181818
10  oxy -0.5127452 -2.7430956 two-sided 0.09090909
11  bdo  0.3869307  1.4250241 two-sided 0.27272727
Subsets Pvalue: 1.892667e-10
other elements: subpvalue call

\$`2`\$witomigtest
class: subnikrandtest
Monte-Carlo tests
Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest\$subpvalue)

Number of tests:   28

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test      Obs     Std.Obs   Alter    Pvalue  SubniPvalue
1      Cogo 1.126400 -1.60496405 greater 1.0000000 1.892667e-10
2      Satr 1.495458 -1.35602240 greater 1.0000000 1.892667e-10
3      Phph 1.482589 -2.08917864 greater 1.0000000 1.892667e-10
4      Neba 1.459884 -1.83389728 greater 1.0000000 1.892667e-10
5      Thth 3.615121 -0.77084968 greater 0.7272727 1.376485e-10
6      Teso 1.256070 -0.81601650 greater 1.0000000 1.892667e-10
7      Chna 3.003663 -0.67650631 greater 0.7272727 1.376485e-10
8      Chto 2.022893 -1.85139356 greater 1.0000000 1.892667e-10
9      Lele 2.687707 -0.60604494 greater 0.9090909 1.720606e-10
10     Lece 3.866684 -0.29758791 greater 0.5454545 1.032364e-10
11     Baba 3.216743 -0.80433780 greater 0.8181818 1.548546e-10
12     Spbi 3.648142 -0.57000947 greater 0.6363636 1.204424e-10
13     Gogo 3.956758  0.12233470 greater 0.4545455 8.603032e-11
14     Eslu 4.937736  0.50266278 greater 0.3636364 6.882426e-11
15     Pefl 4.018942 -0.28211119 greater 0.5454545 1.032364e-10
16     Rham 4.906593 -0.32173294 greater 0.7272727 1.376485e-10
17     Legi 4.825299 -0.09385500 greater 0.4545455 8.603032e-11
18     Scer 6.155192  1.05867954 greater 0.1818182 3.441213e-11
19     Cyca 5.221118  0.30677992 greater 0.4545455 8.603032e-11
20     Titi 4.169011 -0.35955046 greater 0.5454545 1.032364e-10
21     Abbr 5.804330  0.16398698 greater 0.3636364 6.882426e-11
22     Icme 8.468354  0.07338725 greater 0.4545455 8.603032e-11
23     Acce 5.795305 -0.11976254 greater 0.5454545 1.032364e-10
24     Ruru 3.920043 -0.68954449 greater 0.9090909 1.720606e-10
25     Blbj 5.698468  0.90540928 greater 0.1818182 3.441213e-11
26     Alal 4.984645  0.13921540 greater 0.4545455 8.603032e-11
27     Anan 5.795413  0.47035238 greater 0.4545455 8.603032e-11
28 OMI.mean 3.982910 -1.31167028 greater 1.0000000 1.892667e-10

other elements: adj.method sub.pvalue subni.pvalue call

inertia  WitOMIG_k        Tol      Rtol witomig_k  tol rtol
Cogo1  7.683680  4.9375290 0.85860713 1.8875443      64.3 11.2 24.6
Satr1  5.590677  0.2336491 0.60312995 4.7538976       4.2 10.8 85.0
Phph1  5.006388  0.1319164 1.72184100 3.1526301       2.6 34.4 63.0
Neba1  5.195194  0.0763470 1.46124941 3.6575974       1.5 28.1 70.4
Thth1  6.686528  4.5179183 0.50877470 1.6598352      67.6  7.6 24.8
Teso1  9.903954  7.9956322 0.79395000 1.1143722      80.7  8.0 11.3
Chna1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Chto1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Lele1  6.384517  0.5261420 1.62810617 4.2302688       8.2 25.5 66.3
Lece1  5.620914  0.4955039 0.93678527 4.1886253       8.8 16.7 74.5
Baba1 12.350955 11.3534854 0.48236362 0.5151059      91.9  3.9  4.2
Spbi1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Gogo1  6.665556  0.5859616 3.86395953 2.2156344       8.8 58.0 33.2
Eslu1  3.229296  0.5806859 0.67272471 1.9758851      18.0 20.8 61.2
Pefl1  2.432845  1.1238890 0.24366337 1.0652930      46.2 10.0 43.8
Rham1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Legi1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Scer1  2.797308  2.7973083 0.00000000 0.0000000     100.0  0.0  0.0
Cyca1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Titi1  4.384417  0.7540019 0.50232377 3.1280916      17.2 11.5 71.3
Abbr1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Icme1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Acce1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Ruru1  3.337607  2.5343078 0.19535208 0.6079467      75.9  5.9 18.2
Blbj1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Alal1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Anan1       NaN        NaN        NaN       NaN       NaN  NaN  NaN
Cogo2  5.152885  4.6811288 0.18121530 0.2905413      90.8  3.5  5.6
Satr2  5.664872  3.5120259 0.43377646 1.7190701      62.0  7.7 30.3
Phph2  4.455101  3.1574389 0.24787671 1.0497852      70.9  5.6 23.6
Neba2  4.329963  2.3680054 0.79955493 1.1624025      54.7 18.5 26.8
Thth2  5.216749  2.4342785 0.05792606 2.7245442      46.7  1.1 52.2
Teso2  5.733520  3.9291600 0.49207782 1.3122818      68.5  8.6 22.9
Chna2  4.370565  0.9148978 0.79027887 2.6653885      20.9 18.1 61.0
Chto2  4.802004  2.0333059 0.42369988 2.3449979      42.3  8.8 48.8
Lele2  6.386763  0.9497922 2.34759773 3.0893733      14.9 36.8 48.4
Lece2  6.221847  0.3158352 2.51094580 3.3950659       5.1 40.4 54.6
Baba2  4.540331  1.1832592 0.37986084 2.9772106      26.1  8.4 65.6
Spbi2  5.145322  1.4703463 0.28636200 3.3886133      28.6  5.6 65.9
Gogo2  6.534522  0.4700963 2.54917803 3.5152476       7.2 39.0 53.8
Eslu2  6.346077  0.7938952 1.91512068 3.6370608      12.5 30.2 57.3
Pefl2  5.254466  1.5042159 0.35147064 3.3987793      28.6  6.7 64.7
Rham2  5.044424  1.2523355 0.61512888 3.1769594      24.8 12.2 63.0
Legi2  5.060639  0.9808150 0.81669840 3.2631251      19.4 16.1 64.5
Scer2  7.758808  1.0252173 2.54039830 4.1931929      13.2 32.7 54.0
Cyca2  5.390604  1.4621416 0.64082019 3.2876420      27.1 11.9 61.0
Titi2  4.519140  0.9720175 0.42636304 3.1207593      21.5  9.4 69.1
Abbr2  5.207288  1.4800497 0.85618389 2.8710544      28.4 16.4 55.1
Icme2  6.182745  2.5750240 1.15468432 2.4530366      41.6 18.7 39.7
Acce2  5.956953  0.6682218 1.54751285 3.7412187      11.2 26.0 62.8
Ruru2  5.557985  0.4381958 1.91523988 3.2045495       7.9 34.5 57.7
Blbj2  5.014302  1.1670965 0.89991564 2.9472898      23.3 17.9 58.8
Alal2  7.168875  0.1053735 1.27292168 5.7905799       1.5 17.8 80.8
Anan2  5.201754  1.3520758 0.90170122 2.9479769      26.0 17.3 56.7
\$`1`
\$`1`\$Subsettest
class: subkrandtest
Monte-Carlo tests
Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")

Number of tests:   11

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test        Obs   Std.Obs     Alter     Pvalue
1   dfs -0.8603563 -3.207702 two-sided 0.09090909
2   alt  0.8413738  4.066338 two-sided 0.09090909
3   slo  0.6719200  4.557428 two-sided 0.09090909
4   flo -0.7343498 -3.601406 two-sided 0.09090909
5    pH  0.1366260  0.571006 two-sided 0.54545455
6   har -0.4844664 -2.093485 two-sided 0.09090909
7   pho -0.4568414 -1.844859 two-sided 0.09090909
8   nit -0.7834262 -5.833650 two-sided 0.09090909
9   amm -0.4543625 -2.679853 two-sided 0.09090909
10  oxy  0.5127452  1.976203 two-sided 0.09090909
11  bdo -0.3869307 -2.374007 two-sided 0.09090909
Subsets Pvalue: 2.102963e-11
other elements: subpvalue call

\$`1`\$witomig_ktest
class: subnikrandtest
Monte-Carlo tests
Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest\$subpvalue)

Number of tests:   28

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test        Obs     Std.Obs   Alter     Pvalue  SubniPvalue
1      Cogo  4.9375290  3.76351371 greater 0.09090909 1.911785e-12
2      Satr  0.2336491 -0.48213426 greater 0.63636364 1.338249e-11
3      Phph  0.1319164  0.16353261 greater 0.45454545 9.558925e-12
4      Neba  0.0763470 -1.36549212 greater 0.90909091 1.911785e-11
5      Thth  4.5179183  1.92770642 greater 0.18181818 3.823570e-12
6      Teso  7.9956322  6.02848029 greater 0.09090909 1.911785e-12
7      Chna        NaN         NaN greater         NA           NA
8      Chto        NaN         NaN greater         NA           NA
9      Lele  0.5261420  0.05219689 greater 0.36363636 7.647140e-12
10     Lece  0.4955039  3.86856225 greater 0.09090909 1.911785e-12
11     Baba 11.3534854 23.98244068 greater 0.09090909 1.911785e-12
12     Spbi        NaN         NaN greater         NA           NA
13     Gogo  0.5859616  0.45581577 greater 0.36363636 7.647140e-12
14     Eslu  0.5806859  0.23748200 greater 0.36363636 7.647140e-12
15     Pefl  1.1238890  1.16882068 greater 0.18181818 3.823570e-12
16     Rham        NaN         NaN greater         NA           NA
17     Legi        NaN         NaN greater         NA           NA
18     Scer  2.7973083  4.15573331 greater 0.09090909 1.911785e-12
19     Cyca        NaN         NaN greater         NA           NA
20     Titi  0.7540019  1.95245737 greater 0.09090909 1.911785e-12
21     Abbr        NaN         NaN greater         NA           NA
22     Icme        NaN         NaN greater         NA           NA
23     Acce        NaN         NaN greater         NA           NA
24     Ruru  2.5343078  5.97163610 greater 0.09090909 1.911785e-12
25     Blbj        NaN         NaN greater         NA           NA
26     Alal        NaN         NaN greater         NA           NA
27     Anan        NaN         NaN greater         NA           NA
28 OMI.mean  2.5762852 17.31771315 greater 0.09090909 1.911785e-12

other elements: adj.method sub.pvalue subni.pvalue call

\$`2`
\$`2`\$Subsettest
class: subkrandtest
Monte-Carlo tests
Call: subkrandtest(sim = t(Xsim), obs = Xwobs, alter = "two-sided")

Number of tests:   11

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test        Obs   Std.Obs     Alter     Pvalue
1   dfs  0.8603563  5.036922 two-sided 0.09090909
2   alt -0.8413738 -3.465448 two-sided 0.09090909
3   slo -0.6719200 -5.018536 two-sided 0.09090909
4   flo  0.7343498  4.859649 two-sided 0.09090909
5    pH -0.1366260 -1.204469 two-sided 0.27272727
6   har  0.4844664  3.646228 two-sided 0.09090909
7   pho  0.4568414  2.127719 two-sided 0.09090909
8   nit  0.7834262  3.581106 two-sided 0.09090909
9   amm  0.4543625  1.811531 two-sided 0.09090909
10  oxy -0.5127452 -2.276122 two-sided 0.09090909
11  bdo  0.3869307  2.245447 two-sided 0.09090909
Subsets Pvalue: 1.051482e-11
other elements: subpvalue call

\$`2`\$witomig_ktest
class: subnikrandtest
Monte-Carlo tests
Call: subnikrandtest(sim = t(sim), obs = obs, subpvalue = Xtest\$subpvalue)

Number of tests:   28

Adjustment method for multiple comparisons:   none
Permutation number:   10
Test       Obs     Std.Obs   Alter     Pvalue  SubniPvalue
1      Cogo 4.6811288  3.18003960 greater 0.09090909 9.558925e-13
2      Satr 3.5120259  6.56148564 greater 0.09090909 9.558925e-13
3      Phph 3.1574389  3.95455307 greater 0.09090909 9.558925e-13
4      Neba 2.3680054 13.57705395 greater 0.09090909 9.558925e-13
5      Thth 2.4342785 -0.19774919 greater 0.27272727 2.867677e-12
6      Teso 3.9291600  1.62392443 greater 0.18181818 1.911785e-12
7      Chna 0.9148978 -0.12825437 greater 0.45454545 4.779462e-12
8      Chto 2.0333059  0.90175403 greater 0.36363636 3.823570e-12
9      Lele 0.9497922  0.14914713 greater 0.45454545 4.779462e-12
10     Lece 0.3158352  0.98498734 greater 0.27272727 2.867677e-12
11     Baba 1.1832592 -0.14721938 greater 0.36363636 3.823570e-12
12     Spbi 1.4703463  0.62731138 greater 0.18181818 1.911785e-12
13     Gogo 0.4700963 -0.20351273 greater 0.54545455 5.735355e-12
14     Eslu 0.7938952 -0.03849616 greater 0.54545455 5.735355e-12
15     Pefl 1.5042159  0.15049186 greater 0.36363636 3.823570e-12
16     Rham 1.2523355 -0.03681327 greater 0.45454545 4.779462e-12
17     Legi 0.9808150  0.57402218 greater 0.36363636 3.823570e-12
18     Scer 1.0252173 -0.44102041 greater 0.54545455 5.735355e-12
19     Cyca 1.4621416  0.08840199 greater 0.45454545 4.779462e-12
20     Titi 0.9720175  1.42321852 greater 0.09090909 9.558925e-13
21     Abbr 1.4800497  1.49248409 greater 0.09090909 9.558925e-13
22     Icme 2.5750240 -0.25790803 greater 0.54545455 5.735355e-12
23     Acce 0.6682218 -0.67963832 greater 0.72727273 7.647140e-12
24     Ruru 0.4381958 -0.11823417 greater 0.36363636 3.823570e-12
25     Blbj 1.1670965 -0.32401233 greater 0.54545455 5.735355e-12
26     Alal 0.1053735 -1.49018977 greater 1.00000000 1.051482e-11
27     Anan 1.3520758 -0.32544075 greater 0.54545455 5.735355e-12
28 OMI.mean 1.5998609  0.34126518 greater 0.27272727 2.867677e-12

other elements: adj.method sub.pvalue subni.pvalue call
```

subniche documentation built on May 2, 2019, 9:42 a.m.