This function represents a score with a density curve for each level of a factor.
s1d.density(score, fac = gl(1, NROW(score)), kernel = c("normal", "box",
"epanech", "biweight", "triweight"), bandwidth = NULL, gridsize = 450,
col = NULL, fill = TRUE, facets = NULL, plot = TRUE, storeData = TRUE,
add = FALSE, pos = -1, ...)
An object of class ADEg
(subclass C1.density
) or ADEgS
(if add
is TRUE
and/or
if facets or data frame for score
or data frame for fac
are used).
The result is displayed if plot
is TRUE
.
a numeric vector (or a data frame) used to produce the plot
a factor (or a matrix of factors) to split score
the smoothing kernel used, see bkde
the kernel bandwidth smoothing parameter
the number of equally spaced points at which to estimate the density
a logical, a color or a colors vector for labels, rugs, lines and polygons according to their factor level. Colors are recycled whether there are not one color by factor level.
a logical to yield the polygons density curves filled
a factor splitting score
so that subsets
of the data are represented on different sub-graphics
a logical indicating if the graphics is displayed
a logical indicating if the data are stored in
the returned object. If FALSE
, only the names of the data
arguments are stored
a logical. If TRUE
, the graphic is superposed to the graphics
already plotted in the current device
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
)
Alice Julien-Laferriere, Aurelie Siberchicot aurelie.siberchicot@univ-lyon1.fr and Stephane Dray
kernel
, bandwidth
and gridsize
are passed as parameters to bkde
function of the KernSmooth
package.
Graphical parameters for rugs are available in plines
of adegpar
and the ones for density curves filled in ppolygons
.
Some appropriated graphical parameters in p1d
are also available.
C1.density
ADEg.C1
score <- c(rnorm(1000, mean = -0.5, sd = 0.5), rnorm(1000, mean = 1))
fac <- rep(c("A", "B"), each = 1000)
s1d.density(score, fac, col = c(2, 4), p1d.reverse = TRUE)
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