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Method to Analyse a pair of tables : Environmental and Faunistic Data

niche.Rd

performs a special multivariate analysis for ecological data.

Usage

niche(dudiX, Y, scannf = TRUE, nf = 2)
# S3 method for niche
print(x, ...) 
# S3 method for niche
plot(x, xax = 1, yax = 2, ...)
niche.param(x)
# S3 method for niche
rtest(xtest,nrepet=99, ...)

Arguments

dudiX

a duality diagram providing from a function dudi.coa, dudi.pca, ... using an array sites-variables

Y

a data frame sites-species according to dudiX$tab with no columns of zero

scannf

a logical value indicating whether the eigenvalues bar plot should be displayed

nf

if scannf FALSE, an integer indicating the number of kept axes

x

an object of class niche

...

further arguments passed to or from other methods

xax, yax

the numbers of the x-axis and the y-axis

xtest

an object of class niche

nrepet

the number of permutations for the testing procedure

Value

Returns a list of the class niche (sub-class of dudi) containing :

rank

an integer indicating the rank of the studied matrix

nf

an integer indicating the number of kept axes

RV

a numeric value indicating the RV coefficient

eig

a numeric vector with the all eigenvalues

lw

a data frame with the row weigths (crossed array)

tab

a data frame with the crossed array (averaging species/sites)

li

a data frame with the species coordinates

l1

a data frame with the species normed scores

co

a data frame with the variable coordinates

c1

a data frame with the variable normed scores

ls

a data frame with the site coordinates

as

a data frame with the axis upon niche axis

References

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

Author

Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr
Stéphane Dray stephane.dray@univ-lyon1.fr

Examples

data(doubs)
dudi1 <- dudi.pca(doubs$env, scale = TRUE, scan = FALSE, nf = 3)
nic1 <- niche(dudi1, doubs$fish, scann = FALSE)

if(adegraphicsLoaded()) {
  g1 <- s.traject(dudi1$li, plab.cex = 0, plot = FALSE)
  g2 <- s.traject(nic1$ls, plab.cex = 0, plot = FALSE)
  g3 <- s.corcircle(nic1$as, plot = FALSE)
  g4 <- s.arrow(nic1$c1, plot = FALSE)
  G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2))
  
  glist <- list()
  for(i in 1:ncol(doubs$fish))
    glist[[i]] <- s.distri(nic1$ls, dfdistri = doubs$fish[, i], psub.text = names(doubs$fish)[i], 
      plot = FALSE, storeData = TRUE)
  G2 <- ADEgS(glist, layout = c(5, 6))
  
  G3 <- s.arrow(nic1$li, plab.cex = 0.7)  
    
} else {
  par(mfrow = c(2, 2))
  s.traject(dudi1$li, clab = 0)
  s.traject(nic1$ls, clab = 0)
  s.corcircle(nic1$as)
  s.arrow(nic1$c1)

  par(mfrow = c(5, 6))
  for (i in 1:27) s.distri(nic1$ls, as.data.frame(doubs$fish[,i]),
    csub = 2, sub = names(doubs$fish)[i])
    
  par(mfrow = c(1, 1))
  s.arrow(nic1$li, clab = 0.7)

}




data(trichometeo)
pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE)
nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE)
plot(nic1)
#> Error in s.corcircle(dfxy = nic1$as, xax = 1, yax = 2, plot = FALSE, storeData = TRUE,     pos = -3, psub = list(text = "Unconstrained axes"), pbackground = list(        box = FALSE), plabels = list(cex = 1.25)): non convenient selection for dfxy (can not be converted to dataframe)
niche.param(nic1)
#>       inertia         OMI       Tol     Rtol  omi  tol rtol
#> Che  6.433882  2.77316816 1.0214504 2.639263 43.1 15.9 41.0
#> Hyc 11.914482  4.44884944 2.3877161 5.077916 37.3 20.0 42.6
#> Hym 10.573796  0.09548554 2.5386420 7.939669  0.9 24.0 75.1
#> Hys  7.625791  0.63040842 0.7348512 6.260531  8.3  9.6 82.1
#> Psy 10.470153  0.43447855 3.9237418 6.111932  4.1 37.5 58.4
#> Aga  7.430579  1.29116377 1.5507447 4.588670 17.4 20.9 61.8
#> Glo 14.360078  6.17685139 4.7591657 3.424061 43.0 33.1 23.8
#> Ath 11.244671  1.79679264 2.7654073 6.682471 16.0 24.6 59.4
#> Cea 18.711518 12.23859181 4.1775853 2.295341 65.4 22.3 12.3
#> Ced 11.789951  0.87321186 3.2451344 7.671604  7.4 27.5 65.1
#> Set 12.607986  4.28597109 3.7224679 4.599547 34.0 29.5 36.5
#> All  6.805252  0.72091250 1.2144331 4.869906 10.6 17.8 71.6
#> Han 10.368865  1.20620645 3.3672977 5.795361 11.6 32.5 55.9
#> Hfo 17.543552  6.75786236 7.3444406 3.441250 38.5 41.9 19.6
#> Hsp 13.976515  2.89982751 5.6222008 5.454487 20.7 40.2 39.0
#> Hve 12.253601  4.59849113 3.5177233 4.137387 37.5 28.7 33.8
#> Sta  9.391826  0.58873968 2.5226450 6.280442  6.3 26.9 66.9
rtest(nic1,19)
#> class: krandtest lightkrandtest 
#> Monte-Carlo tests
#> Call: as.krandtest(sim = t(sim), obs = obs)
#> 
#> Number of tests:   18 
#> 
#> Adjustment method for multiple comparisons:   none 
#> Permutation number:   19 
#>        Test         Obs    Std.Obs   Alter Pvalue
#> 1       Che  2.77316816 -0.1927634 greater   0.50
#> 2       Hyc  4.44884944  0.4707573 greater   0.40
#> 3       Hym  0.09548554  0.3328397 greater   0.25
#> 4       Hys  0.63040842 -0.9158369 greater   0.95
#> 5       Psy  0.43447855  9.5057897 greater   0.05
#> 6       Aga  1.29116377  6.3553886 greater   0.05
#> 7       Glo  6.17685139  8.8071797 greater   0.05
#> 8       Ath  1.79679264  1.7288853 greater   0.10
#> 9       Cea 12.23859181  3.6177651 greater   0.05
#> 10      Ced  0.87321186  5.6192601 greater   0.05
#> 11      Set  4.28597109 11.3786632 greater   0.05
#> 12      All  0.72091250  1.8204238 greater   0.10
#> 13      Han  1.20620645  1.0912766 greater   0.15
#> 14      Hfo  6.75786236  5.6871922 greater   0.05
#> 15      Hsp  2.89982751  5.3417149 greater   0.05
#> 16      Hve  4.59849113  2.8173690 greater   0.05
#> 17      Sta  0.58873968  6.8783393 greater   0.05
#> 18 OMI.mean  3.04805955  8.4859875 greater   0.05
#> 

data(rpjdl)
plot(niche(dudi.pca(rpjdl$mil, scan = FALSE), rpjdl$fau, scan = FALSE))