| s1d.density {adegraphics} | R Documentation |
1-D plot of a numeric score by density curves
Description
This function represents a score with a density curve for each level of a factor.
Usage
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, ...)
Arguments
score |
a numeric vector (or a data frame) used to produce the plot |
fac |
a factor (or a matrix of factors) to split |
kernel |
the smoothing kernel used, see |
bandwidth |
the kernel bandwidth smoothing parameter |
gridsize |
the number of equally spaced points at which to estimate the density |
col |
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. |
fill |
a logical to yield the polygons density curves filled |
facets |
a factor splitting |
plot |
a logical indicating if the graphics is displayed |
storeData |
a logical indicating if the data are stored in
the returned object. If |
add |
a logical. If |
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 |
... |
additional graphical parameters (see
|
Details
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.
Value
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.
Author(s)
Alice Julien-Laferriere, Aurelie Siberchicot aurelie.siberchicot@univ-lyon1.fr and Stephane Dray
See Also
Examples
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)