1-D plot of a numeric score by means/standard deviations computed using an external table of weights

This function represents a set of distributions on a numeric score using a mean-standard deviation display

Usage

s1d.distri(score, dfdistri, labels = colnames(dfdistri), at = 1:NCOL(dfdistri), 
  yrank = TRUE, sdSize = 1, facets = NULL, plot = TRUE, 
  storeData = TRUE, add = FALSE, pos = -1, ...)

Arguments

score: a numeric vector (or a data frame) used to produce the plot
dfdistri: a data frame containing the mass distribution in which each column is a class
yrank: a logical to draw the distributions sorted by means ascending order
labels: the labels' names drawn for each distribution
at: a numeric vector used as an index
sdSize: a numeric for the size of the standard deviation segments
facets: a factor splitting score so that subsets of the data are represented on different sub-graphics
plot: a logical indicating if the graphics is displayed
storeData: a logical indicating if the data are stored in the returned object. If FALSE, only the names of the data arguments are stored
add: a logical. If TRUE, the graphic is superposed to the graphics already plotted in the current device
pos: an integer indicating the position of the environment where the data are stored, relative to the environment where the function is called. Useful only if storeData is FALSE
...: additional graphical parameters (see adegpar and trellis.par.get)

Details

Graphical parameters for rugs are available in plines of adegpar. Some appropriated graphical parameters in p1d are also available. The weighted means and standard deviations of class are available in the object slot stats using object@stats$means and object@stats$sds.

Value

An object of class ADEg (subclass S1.distri) or ADEgS (if add is TRUE and/or if facets or data frame for score are used).
The result is displayed if plot is TRUE.

Author

Alice Julien-Laferriere, Aurelie Siberchicot aurelie.siberchicot@univ-lyon1.fr and Stephane Dray

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 = "")
g11 <- s1d.distri(w, distri, yrank = TRUE, sdS = 1.5, plot = FALSE)
g12 <- s1d.distri(w, distri, yrank = FALSE, sdS = 1.5, plot = FALSE)
G1 <- ADEgS(c(g11, g12), layout = c(1, 2))


data(rpjdl, package = "ade4")
coa1 <- ade4::dudi.coa(rpjdl$fau, scannf = FALSE)
G2 <- s1d.distri(coa1$li[,1], rpjdl$fau, labels = rpjdl$frlab, 
  plabels = list(cex = 0.8, boxes = list(draw = FALSE)))


if (FALSE) { # \dontrun{
g31 <- s1d.distri(coa1$l1[,1], rpjdl$fau, plabels = list(cex = 0.8, boxes = list(draw = FALSE)), 
  plot = FALSE)
nsc1 <- ade4::dudi.nsc(rpjdl$fau, scannf = FALSE)
g32 <- s1d.distri(nsc1$l1[,1], rpjdl$fau, plabels = list(cex = 0.8, boxes = list(draw = FALSE)), 
  plot = FALSE)
g33 <- s.label(coa1$l1, plot = FALSE)
g34 <- s.label(nsc1$l1, plot = FALSE)
G3 <- ADEgS(c(g31, g32, g33, g34), layout = c(2, 2))
} # }