ade4 (version 1.7-19)

sco.distri: 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)

Value

returns an invisible data.frame with means and variances

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

Author

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))
}