bca: Between-Class Analysis

In ade4: Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

Between-Class Analysis

Description

Performs a particular case of a Principal Component Analysis with respect to Instrumental Variables (pcaiv), in which there is only a single factor as explanatory variable.

Usage

## S3 method for class 'dudi'
bca(x, fac, scannf = TRUE, nf = 2, ...) 

Arguments

x	a duality diagram, object of class dudi from one of the functions dudi.coa, dudi.pca,...
fac	a factor partitioning the rows of dudi$tab in classes
scannf	a logical value indicating whether the eigenvalues barplot should be displayed
nf	if scannf FALSE, a numeric value indicating the number of kept axes
...	further arguments passed to or from other methods

Value

Returns a list of class dudi, subclass 'between' containing

tab	a data frame class-variables containing the means per class for each variable
cw	a numeric vector of the column weigths
lw	a numeric vector of the class weigths
eig	a numeric vector with all the eigenvalues
rank	the rank of the analysis
nf	an integer value indicating the number of kept axes
c1	a data frame with the column normed scores
l1	a data frame with the class normed scores
co	a data frame with the column coordinates
li	a data frame with the class coordinates
call	the matching call
ratio	the bewteen-class inertia percentage
ls	a data frame with the row coordinates
as	a data frame containing the projection of inertia axes onto between axes

Note

To avoid conflict names with the base:::within function, the function within is now deprecated and removed. To be consistent, the between function is also deprecated and is replaced by the method bca.dudi of the new generic bca function.

Author(s)

Daniel Chessel
Anne-Béatrice Dufour [e-mail]

References

Dolédec, S. and Chessel, D. (1987) Rythmes saisonniers et composantes stationnelles en milieu aquatique I- Description d'un plan d'observations complet par projection de variables. Acta Oecologica, Oecologia Generalis, 8, 3, 403–426.

Examples

data(meaudret)
pca1 <- dudi.pca(meaudret$env, scan = FALSE, nf = 4)
pca2 <- dudi.pca(meaudret$spe, scal = FALSE, scan = FALSE, nf = 4)
bet1 <- bca(pca1, meaudret$design$site, scan = FALSE, nf = 2)
bet2 <- bca(pca2, meaudret$design$site, scan = FALSE, nf = 2)
  
if(adegraphicsLoaded()) {
  g1 <- s.class(pca1$li, meaudret$design$site, psub.text = "Principal Component Analysis (env)",
    plot = FALSE)
  g2 <- s.class(pca2$li, meaudret$design$site, psub.text = "Principal Component Analysis (spe)",
    plot = FALSE)
  g3 <- s.class(bet1$ls, meaudret$design$site, psub.text = "Between sites PCA (env)", plot = FALSE)
  g4 <- s.class(bet2$ls, meaudret$design$site, psub.text = "Between sites PCA (spe)", plot = FALSE)
  G <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2))
  
} else {
  par(mfrow = c(2, 2))
  s.class(pca1$li, meaudret$design$site, sub = "Principal Component Analysis (env)", csub = 1.75)
  s.class(pca2$li, meaudret$design$site, sub = "Principal Component Analysis (spe)", csub = 1.75)
  s.class(bet1$ls, meaudret$design$site, sub = "Between sites PCA (env)", csub = 1.75)
  s.class(bet2$ls, meaudret$design$site, sub = "Between sites PCA (spe)", csub = 1.75)
  par(mfrow = c(1, 1))
}

coib <- coinertia(bet1, bet2, scann = FALSE)
plot(coib)

ade4 documentation built on Feb. 16, 2023, 7:58 p.m.