| niche {ade4} | R Documentation |
performs a special multivariate analysis for ecological data.
niche(dudiX, Y, scannf = TRUE, nf = 2) ## S3 method for class 'niche': print(x, ...) ## S3 method for class 'niche': plot(x, xax = 1, yax = 2, ...) niche.param(x) ## S3 method for class 'niche': rtest(xtest,nrepet=99, ...)
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$tab with no columns of zero |
scannf |
a logical value indicating whether the eigenvalues bar plot should be displayed |
nf |
if scannf FALSE, an integer indicating the number of kept axes |
x |
an object of class niche |
... |
further arguments passed to or from other methods |
xax, yax |
the numbers of the x-axis and the y-axis |
xtest |
an object of class niche |
nrepet |
the number of permutations for the testing procedure |
Returns a list of the class niche (sub-class of dudi) containing :
rank |
an integer indicating the rank of the studied matrix |
nf |
an integer indicating the number of kept axes |
RV |
a numeric value indicating the RV coefficient |
eig |
a numeric vector with the all eigenvalues |
lw |
a data frame with the row weigths (crossed array) |
tab |
a data frame with the crossed array (averaging species/sites) |
li |
a data frame with the species coordinates |
l1 |
a data frame with the species normed scores |
co |
a data frame with the variable coordinates |
c1 |
a data frame with the variable normed scores |
ls |
a data frame with the site coordinates |
as |
a data frame with the axis upon niche axis |
Daniel Chessel
Anne B Dufour dufour@biomserv.univ-lyon1.fr
Stephane Dray dray@biomserv.univ-lyon1.fr
Dolédec, S., Chessel, D. and Gimaret, C. (2000) Niche separation in community analysis: a new method. Ecology, 81, 2914–1927.
data(doubs)
dudi1 <- dudi.pca(doubs$mil, scale = TRUE, scan = FALSE, nf = 3)
nic1 <- niche(dudi1, doubs$poi, scann = FALSE)
par(mfrow = c(2,2))
s.traject(dudi1$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$poi[,i]),
csub = 2, sub = names(doubs$poi)[i])
par(mfrow = c(1,1))
s.arrow(nic1$li, clab = 0.7)
par(mfrow = c(1,1))
data(trichometeo)
pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE)
nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE)
plot(nic1)
niche.param(nic1)
rtest(nic1,19)
data(rpjdl)
plot(niche(dudi.pca(rpjdl$mil, scan = FALSE), rpjdl$fau, scan = FALSE))
> library(ade4) > ### Name: niche > ### Title: Method to Analyse a pair of tables : Environmental and Faunistic > ### Data > ### Aliases: niche plot.niche print.niche niche.param rtest.niche > ### Keywords: multivariate > > ### ** Examples > > data(doubs) > dudi1 <- dudi.pca(doubs$mil, scale = TRUE, scan = FALSE, nf = 3) > nic1 <- niche(dudi1, doubs$poi, scann = FALSE) > > par(mfrow = c(2,2)) > s.traject(dudi1$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$poi[,i]), + csub = 2, sub = names(doubs$poi)[i])
> > par(mfrow = c(1,1)) > s.arrow(nic1$li, clab = 0.7)
> > par(mfrow = c(1,1)) > data(trichometeo) > pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE) > nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE) > plot(nic1)
> 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
Monte-Carlo tests
Call: as.krandtest(sim = t(sim), obs = obs)
Test number: 18
Permutation number: 19
Test Obs Std.Obs Alter Pvalue
1 Che 2.77316816 -0.4300319 greater 0.60
2 Hyc 4.44884944 0.9686935 greater 0.20
3 Hym 0.09548554 1.5288828 greater 0.15
4 Hys 0.63040842 -1.2178823 greater 0.90
5 Psy 0.43447855 11.1164776 greater 0.05
6 Aga 1.29116377 5.6731334 greater 0.05
7 Glo 6.17685139 10.4320863 greater 0.05
8 Ath 1.79679264 3.2453635 greater 0.05
9 Cea 12.23859181 4.4209467 greater 0.05
10 Ced 0.87321186 6.4112531 greater 0.05
11 Set 4.28597109 12.7850679 greater 0.05
12 All 0.72091250 0.6687294 greater 0.20
13 Han 1.20620645 1.1363174 greater 0.15
14 Hfo 6.75786236 2.9347726 greater 0.10
15 Hsp 2.89982751 9.3821772 greater 0.05
16 Hve 4.59849113 1.5507335 greater 0.10
17 Sta 0.58873968 4.6965029 greater 0.05
18 OMI.mean 3.04805955 7.0232114 greater 0.05
other elements: NULL
>
> data(rpjdl)
> plot(niche(dudi.pca(rpjdl$mil, scan = FALSE), rpjdl$fau, scan = FALSE))
> > > >