Title:	Within Outlying Mean Indexes: Refining the 'OMI' Analysis
Version:	1.6
Date:	2026-01-20
Author:	Stephane Karasiewicz [aut, cre]
Maintainer:	Stephane Karasiewicz <skaraz.science@gmail.com>
BugReports:	https://github.com/KarasiewiczStephane/WitOMI/issues
Description:	Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays. For details see Karasiewicz 'et al.' (2017) <doi:10.7717/peerj.3364>.
Depends:	R (≥ 3.5.0)
Imports:	ade4, polyclip, wordcloud
Suggests:	adegraphics, ape, CircStats, deldir, lattice, MASS, pixmap, spdep, splancs, waveslim
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
RoxygenNote:	7.3.3
Encoding:	UTF-8
NeedsCompilation:	no
LazyData:	true
Packaged:	2026-01-20 13:44:56 UTC; skaraz
Repository:	CRAN
Date/Publication:	2026-01-23 21:20:02 UTC

Temporal data

Description

The ardecheinv data are the temporal example used as an ecological application of the WitOMI calculations in Karasiewicz et al. (2017).

Usage

data(ardecheinv)

Format

The ardecheinv is a list of 3 components.

env is a dataframe of 67 rows with 5 environmental tables, collected in spring and autumn.

Invertebrates is a dataframe of 67 rows and 57 species of invertebrates.

code is a dataframe with 57 rows and 2 columns, the species scientific name and their respective code.

Source

Merigoux, S. and Doledec, S. (2004). Hydraulic requirements of stream communities: A case study on invertebrates. Freshwater Biology, 49(5), 600-613. doi:10.1111/j.1365-2427.2004.01214.x.

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. doi:10.7717/peerj.3364.

Calculate Convex Hull

Description

Internal function to calculate 2D convex hull coordinates and area. This function replaces the dependency on SIBER::siberConvexhull.

Usage

convexhull(x, y)

Arguments

Value

A list containing:

Spatial data

Description

The drome data are the spatial example used as an ecological application of the WitOMI calculations in Karasiewicz et al. (2017).

Usage

data(drome)

Format

The drome is a list of 3 components.

env is a dataframe with 64 rows with 6 environmental tables, collected in 10 different rivers.

fish is a dataframe with 64 rows and 13 columns (12 fish species, including young and older trouts).

code is a dataframe with 13 rows and 2 columns, the species, common and scientific, name and their respective code.

Source

Doledec S., Chessel D. and Gimaret C. (2000). Niche separation in community analysis: a new method. Ecology,81(10), 2914-1927.doi:10.2307/177351.

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.doi:10.7717/peerj.3364.

The eigenvalue of the OMI analysis

Description

The function plot the eigenvalues of the OMI analysis

Usage

eigenbar(
  subnic,
  col.sel = "black",
  col.unsel = "grey",
  ylab = "Eigen values in %",
  names.arg = NULL,
  main = NA,
  ...
)

Arguments

Details

The black bars represents the selected axes for the OMI analysis See doi:10.7717/peerj.3364 for more details on the subniche concept.

Examples

library(subniche)
library(ade4)
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)
eigenbar(subnic1)

Summarizes the results of the species subniche

Description

The function plot the resulting species subniche of the WitOMI calculation.

Usage

## S3 method for class 'subniche'
plot(
  x,
  xax = 1,
  yax = 2,
  ax.angle.arrow = 20,
  ax.col.arrow = "black",
  ax.length.arrow = 0.1,
  ax.lwd.arrow = 1,
  ax.leg.posi = "bottomleft",
  ax.leg.cex = 1.2,
  eig.col.chos = "black",
  eig.col.left = "gray",
  eig.leg.posi = "topright",
  eig.leg.cex = 1.2,
  su.leg.posi = "bottomleft",
  su.leg.cex = 1.2,
  col.axis = "azure3",
  lty.axis = 2,
  lwd.axis = 2,
  var.col.arrow = "black",
  var.length.arrow = 0.1,
  var.lwd.arrow = 1,
  var.angle.arrow = 20,
  var.leg.posi = "bottomleft",
  var.leg.cex = 1.2,
  fac.var.lab = 1.2,
  col.var = "black",
  col.su = "black",
  col.G_k = "red",
  nic.leg.posi = "bottomleft",
  nic.leg.cex = 1.2,
  sub.leg.cex = 1.2,
  sub.leg.posi = "bottomleft",
  pch.su = 16,
  cex.su = 1,
  border.E = "#92c5de",
  col.E = "#92c5de",
  lty.E = 1,
  border.K = "black",
  col.K = "#2c7fb8",
  lty.K = 1,
  show.lines = FALSE,
  ...
)

Arguments

Details

The function illustrate the results of subniche calculation with a great deal of customization parameters.

Examples

library(subniche)
library(ade4)
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)
plot(subnic1)

Community niche distribution

Description

The function represents the species' niche NR position within the environmental space E.

Usage

plot_NR(
  subnic,
  sig = NULL,
  sig_thres = 0.05,
  xlab = NULL,
  ylab = NULL,
  main = NA,
  col.axis = "azure3",
  lty.axis = 2,
  lwd.axis = 2,
  border.E = "black",
  col.E = "#92c5de",
  lty.E = 1,
  lwd.E = 1,
  pch.NR.pos = 21,
  cex.NR.pos = 1,
  col.NR.pos = "#a1d99b",
  col.NR.pt = "black",
  col.NR.lab = "black",
  cex.NR.lab = NA,
  fac.NR.lab = 1.2,
  col.arrow = "black",
  angle.arrow = 20,
  lwd.arrow = 2,
  length.arrow = 0.1,
  font.sp = 2,
  leg = TRUE,
  posi.leg = "topleft",
  bty.leg = "n",
  ...
)

Arguments

Details

The convex hulls measured is E is the environmental space. The arrows represent the species' NR marginality from the origin G. See doi:10.7717/peerj.3364 for more details on the subniche concept.

Examples

library(subniche)
library(ade4)
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$ls)[1]
#Create a factor which defines the subsets
fact <- factor(c(rep(1,N/2),rep(2,N/2)))
# nic1 will be use as reference and fact will be use to define the subniches environment
subnic1 <- subniche(nic1, fact)
sigi <- rtest(nic1,10)
M <- length(sigi$pvalue)
plot_NR(subnic1, sig=sigi$pvalue[-M], sig_thres= 0.1)

Species niche

Description

The function represents the species' niche NR within the environmental space E.

Usage

plot_NR_sp(
  subnic,
  sp,
  xlab = NULL,
  ylab = NULL,
  main = NA,
  col.axis = "azure3",
  lty.axis = 2,
  lwd.axis = 2,
  border.E = "black",
  col.E = "#92c5de",
  lty.E = 1,
  lwd.E = 1,
  border.NR = "black",
  col.NR = "#fdb462",
  lty.NR = 1,
  lwd.NR = 1,
  pch.NR.pos = 21,
  cex.NR.pos = 1,
  col.NR.pos = "#a1d99b",
  col.NR.pt = "black",
  col.NR.lab = "black",
  cex.NR.lab = NA,
  fac.NR.lab = 1.2,
  col.arrow = "black",
  angle.arrow = 20,
  lwd.arrow = 2,
  length.arrow = 0.1,
  font.sp = 2,
  leg = TRUE,
  posi.leg = "topleft",
  bty.leg = "n",
  ...
)

Arguments

Details

The convex hulls measured is E is the environmental space. The arrows represent the species' NR marginality from the origin G. See doi:10.7717/peerj.3364 for more details on the subniche concept.

Examples

library(subniche)
library(ade4)
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)
plot_NR_sp(subnic1, "Neba")

Communities subniches dynamic

Description

The function represents the species' subniches SR position within the environmental space E.

Usage

plot_dym(
  subnic,
  sig = NULL,
  sig_thres = 0.05,
  xlab = NULL,
  ylab = NULL,
  main = NA,
  col.axis = "azure3",
  lty.axis = 2,
  lwd.axis = 2,
  border.E = "black",
  col.E = "#92c5de",
  lty.E = 1,
  lwd.E = 1,
  pch.SR.pos = 21,
  cex.SR.pos = 1,
  col.SR.pos = "#ffa600",
  col.SR.pt = "black",
  col.SR.lab = "black",
  cex.SR.lab = NA,
  fac.SR.lab = 1.2,
  col.arrow = "black",
  angle.arrow = 20,
  lwd.arrow = 2,
  length.arrow = 0.1,
  font.sp = 2,
  leg = TRUE,
  posi.leg = "topleft",
  bty.leg = "n",
  ...
)

Arguments

Details

The convex hulls measured is E is the environmental space. The arrows represent the species' subniche marginality from the origin G. See doi:10.7717/peerj.3364 for more details on the subniche concept.

Examples

library(subniche)
library(ade4)
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$ls)[1]
#Create a factor which defines the subsets
fact <- factor(c(rep(1,N/2),rep(2,N/2)))
# nic1 will be use as reference and fact will be use to define the subniches environment
subnic1 <- subniche(nic1, fact)
sigg <- rtestrefor(subnic1,10)
sig = c(sigg$`1`$witomigtest$subni.pvalue[-28],sigg$`2`$witomigtest$subni.pvalue[-28])
plot_dym(subnic1, sig=sig, sig_thres= 0.1)

Species subniches dynamic

Description

The function represents the species' subniches SR within its realized niche NR.

Usage

plot_dym_sp(
  subnic,
  sp,
  xlab = NULL,
  ylab = NULL,
  main = NA,
  col.axis = "azure3",
  lty.axis = 2,
  lwd.axis = 2,
  border.E = "black",
  col.E = "#92c5de",
  lty.E = 1,
  lwd.E = 1,
  col.NR = "#fdb462",
  border.NR = "black",
  lty.NR = 1,
  lwd.NR = 1,
  col.NR.lab = "black",
  cex.NR.lab = 0.7,
  pch.NR.pos = 21,
  col.NR.pos = "black",
  col.NR.pt = "black",
  cex.NR.pos = 1,
  border.SR = "black",
  col.SR = "#a1d99b",
  lty.SR = 1,
  lwd.SR = 1,
  col.SR.lab = "black",
  cex.SR.lab = 0.7,
  fac.SR.lab = 1.2,
  pch.SR.pos = 21,
  col.SR.pos = "#ffa600",
  col.SR.pt = "black",
  cex.SR.pos = 1,
  col.arrow = "black",
  angle.arrow = 20,
  lwd.arrow = 2,
  length.arrow = 0.1,
  font.sp = 2,
  leg = TRUE,
  posi.leg = "topleft",
  bty.leg = "n",
  ...
)

Arguments

Details

The convex hulls measured are :

1. E is the environmental space.

2. NR the realized subniche.

3. SR the species realized subniche.

The arrows represent the species' subniche marginality from the origin G. See doi:10.7717/peerj.3364 for more details on the subniche concept.

Examples

library(subniche)
library(ade4)
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)
plot_dym_sp(subnic1, "Neba")

A function to seperate a matrix, by row, into submatrices.

Description

separate matrix by rows into submatrices

Usage

sep.factor.row (x,factor)

Arguments

Value

list of submatrices

Convex hull decomposition

Description

The function is used to calculate the coordinates and area of each convex hull from E environmental space to SR subniche.

Usage

subarea(subnic)

Arguments

Details

The convex hulls measured are :

1. E is the environmental space.

2. K the sub-environmental space.

3. NR the realized subniche.

4. SP the existing fundamental subniche.

5. SB the area of the biological constraint reducing SP.

6. SR the species realized subniche.

See doi:10.7717/peerj.3364 for more details on the subniche concept.

Value

A list containing the coordinates and area of each convex hulls

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. doi:10.7717/peerj.3364.

Examples

library(subniche)
library(ade4)
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)
area_sub <- subarea(subnic1)

The Within Outlying Mean Indexes calculation

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

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'
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

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, skaraz.science@gmail.fr

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. doi:10.7717/peerj.3364.

Calenge C., Dufour A.B. and Maillard D. (2005). K-select analysis: a new method to analyze habitat selection in radio-tracking studies. Ecological modelling, 186, 143-153. doi:10.1016/j.ecolmodel.2004.12.005.

Doledec S., Chessel D. and Gimaret C. (2000). Niche separation in community analysis: a new method. Ecology,81, 2914-1927. doi:10.2307/177351

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

See Also

niche niche.param

Examples

library(ade4)
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
#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)

Sub-community plot under each sub-environmental space K

Description

The function to represent the community subniche position under each subenvironment K with their respective marginality from Gk.

Usage

subplot(
  subnic,
  main = NULL,
  sig = NULL,
  sig_thres = 0.05,
  xlab = NULL,
  ylab = NULL,
  col.axis = "azure3",
  lty.axis = 2,
  lwd.axis = 2,
  pch.SR.pos = 21,
  cex.SR.pos = 1,
  col.SR.pt = "black",
  col.SR.pos = "#ffa600",
  col.SR.lab = "black",
  cex.SR.lab = NA,
  fac.SR.lab = 1.2,
  border.E = "black",
  col.E = "#92c5de",
  lty.E = 1,
  lwd.E = 1,
  border.K = "black",
  col.K = "#2c7fb8",
  lty.K = 1,
  lwd.K = 1,
  col.arrow = "black",
  angle.arrow = 20,
  lwd.arrow = 2,
  length.arrow = 0.1,
  col.Gk.pos = "red",
  col.Gk.pt = "black",
  cex.Gk.pos = 1,
  pch.Gk.pos = 21,
  col.su = "#b35806",
  pt.su = "black",
  cex.su = 0.7,
  pch.su = 1,
  font.sp = 2,
  leg = TRUE,
  posi.leg = "topleft",
  bty.leg = "n",
  ...
)

Arguments

Examples

library(subniche)
library(ade4)
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$ls)[1]
#Create a factor which defines the subsets
fact <- factor(c(rep(1,N/2),rep(2,N/2)))
# nic1 will be use as reference and fact will be use to define the subniches environment
subnic1 <- subniche(nic1, fact)
#Two graphs are drawn one after the other
siggk <- rtestsubor(subnic1,10)
sig = c(siggk$`1`$witomigktest$subni.pvalue[-28],siggk$`2`$witomigktest$subni.pvalue[-28])
subplot(subnic1, sig = sig, sig_thres= 0.1)

Plot sub-environmental space K in E

Description

The function to represent the sub-environment K in E.

Usage

subplot_K(
  subnic,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  col.axis = "azure3",
  lty.axis = 2,
  lwd.axis = 2,
  border.E = "black",
  col.E = "#92c5de",
  lty.E = 1,
  lwd.E = 1,
  border.K = "black",
  col.K = "#2c7fb8",
  lty.K = 1,
  lwd.K = 1,
  col.Gk.pos = "red",
  col.Gk.pt = "black",
  cex.Gk.pos = 1,
  pch.Gk.pos = 21,
  col.Gk.lab = "black",
  cex.Gk.lab = 0.8,
  fac.Gk.lab = 1.5,
  col.su = "#b35806",
  pt.su = "black",
  cex.su = 0.7,
  pch.su = 1,
  leg = TRUE,
  posi.leg = "topleft",
  bty.leg = "n",
  ...
)

Arguments

Examples

library(subniche)
library(ade4)
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)
#Plot K in E
subplot_K(subnic1)

Plot a species subniche under each sub-environmental space K

Description

The function to represent the species subniche under each subenvironment K with their respective marginality from G_K.

Usage

subplot_sp(
  subnic,
  sp,
  main = NULL,
  col.axis = "azure3",
  lty.axis = 2,
  lwd.axis = 2,
  xlab = NULL,
  ylab = NULL,
  border.E = "black",
  col.E = "#92c5de",
  lty.E = 1,
  lwd.E = 1,
  border.K = "black",
  lwd.K = 1,
  col.K = "#2c7fb8",
  lty.K = 1,
  col.Gk.pos = "red",
  col.Gk.pt = "black",
  cex.Gk.pos = 1,
  pch.Gk.pos = 21,
  border.SP = "#bc5090",
  col.SB = "#ffff99",
  lty.SP = 1,
  lwd.SP = 2,
  border.NR = "#fdb462",
  col.NR = NA,
  lty.NR = 1,
  lwd.NR = 2,
  border.SR = "#a1d99b",
  col.SR = "#a1d99b",
  lty.SR = 1,
  lwd.SR = 1,
  pch.SR.pos = 19,
  cex.SR.pos = 1,
  col.SR.pt = "black",
  col.SR.pos = "black",
  cex.SR.lab = 0.7,
  col.SR.lab = "black",
  fac.SR.lab = 1.2,
  font.sp = 2,
  col.arrow = "black",
  angle.arrow = 20,
  lwd.arrow = 2,
  length.arrow = 0.1,
  leg = TRUE,
  posi.leg = "topleft",
  bty.leg = "n",
  ...
)

Arguments

Examples

library(subniche)
library(ade4)
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$ls)[1]
#Create a factor which defines the subsets
fact <- factor(c(rep(1,N/2),rep(2,N/2)))
# nic1 will be use as reference and fact will be use to define the subniches environment
subnic1 <- subniche(nic1, fact)
eig <- round(subnic1$eig/sum(subnic1$eig)*100,2)[1:2]
#Two graphs are drawn one after the other
subplot_sp(subnic1,"Neba")