sco.distri: Representation by mean- standard deviation of a set of weight...

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

Representation by mean- standard deviation of a set of weight distributions on a numeric score

Description

represents the mean- standard deviation of a set of weight distributions on a numeric score.

Usage

sco.distri(score, df, y.rank = TRUE, csize = 1, labels = names(df), 
    clabel = 1, xlim = NULL, grid = TRUE, cgrid = 0.75,
    include.origin = TRUE, origin = 0, sub = NULL, csub = 1)

Arguments

score	a numeric vector
df	a data frame with only positive or null values
y.rank	a logical value indicating whether the means should be classified in ascending order
csize	an integer indicating the size segment
labels	a vector of strings of characters for the labels of the variables
clabel	if not NULL, a character size for the labels, used with par("cex")*clabel
xlim	the ranges to be encompassed by the x axis, if NULL they are computed
grid	a logical value indicating whether the scale vertical lines should be drawn
cgrid	a character size, parameter used with par("cex")*cgrid to indicate the mesh of the scale
include.origin	a logical value indicating whether the point "origin" should be belonged to the graph space
origin	the fixed point in the graph space, for example c(0,0) the origin axes
sub	a string of characters to be inserted as legend
csub	a character size for the legend, used with par("cex")*csub

Value

returns an invisible data.frame with means and variances

Author(s)

Daniel Chessel

Examples

if(!adegraphicsLoaded()) {
  w <- seq(-1, 1, le = 200)
  distri <- data.frame(lapply(1:50, 
    function(x) sample((200:1)) * ((w >= (- x / 50)) & (w <= x / 50))))
  names(distri) <- paste("w", 1:50, sep = "")
  par(mfrow = c(1, 2))
  sco.distri(w, distri, csi = 1.5)
  sco.distri(w, distri, y.rank = FALSE, csi = 1.5)
  par(mfrow = c(1, 1))
  
  data(rpjdl)
  coa2 <- dudi.coa(rpjdl$fau, FALSE)
  sco.distri(coa2$li[, 1], rpjdl$fau, lab = rpjdl$frlab, clab = 0.8)
  
  data(doubs)
  par(mfrow = c(2, 2))
  poi.coa <- dudi.coa(doubs$fish, scann = FALSE)
  sco.distri(poi.coa$l1[, 1], doubs$fish)
  poi.nsc <- dudi.nsc(doubs$fish, scann = FALSE)
  sco.distri(poi.nsc$l1[, 1], doubs$fish)
  s.label(poi.coa$l1)
  s.label(poi.nsc$l1)
  
  data(rpjdl)
  fau.coa <- dudi.coa(rpjdl$fau, scann = FALSE)
  sco.distri(fau.coa$l1[,1], rpjdl$fau)
  fau.nsc <- dudi.nsc(rpjdl$fau, scann = FALSE)
  sco.distri(fau.nsc$l1[,1], rpjdl$fau)
  s.label(fau.coa$l1)
  s.label(fau.nsc$l1)
  
  par(mfrow = c(1, 1))
}

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