ade4 (version 1.2-2)

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)

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

Examples

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$poi, scann = FALSE)
sco.distri(poi.coa$l1[,1], doubs$poi)
poi.nsc <- dudi.nsc(doubs$poi, scann = FALSE)
sco.distri(poi.nsc$l1[,1], doubs$poi)
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))