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

sco.distri {ade4} | R Documentation

## 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))
    }

[Package ade4 version 1.7-8 Index]