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

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))

Worked out examples


> library(ade4)
> ### Name: sco.distri
> ### Title: Representation by mean- standard deviation of a set of weight
> ###   distributions on a numeric score
> ### Aliases: sco.distri
> ### Keywords: multivariate hplot
> 
> ### ** 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))
> 
> 
> 
> 

[Package ade4 Index]