Representation by mean- standard deviation of a set of weight distributions on a numeric score
represents the mean- standard deviation of a set of weight distributions on a numeric score.
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)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 |
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 |
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 |
returns an invisible data.frame with means and variances
Daniel Chessel
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))
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