a numeric vector containing the observations for which the density
should be estimated.
n.interval
an integer specifying the number of cells for the histogram.
If NULL, n.interval is estimated by the method specified by
type.nclass.
df
integer specifying the degrees of freedom of the natural cubic spline
used in the Poisson regression fit.
knots.mode
if TRUE the df - 1 knots are centered around the
mode and not the median of the density, where the mode is estimated by the
midpoint of the cell of the histogram that contains the largest number of
observations. If FALSE, the default knots are used in the function ns.
Thus, if FALSE the basis matrix will be generated by ns(x, df = 5).
type.nclass
character string specifying the procedure used to compute the
number of cells of the histogram. Ignored if n.interval is specified.
By default, the method of Wand (1994) with
level = 1 (see the help page of dpih in the package KernSmooth) is used.
For the other choices, see nclass.scott.
addx
should x be added to the output? Necessary when the estimated density
should be plotted by plot(out) or lines(out), where out is
the output of denspr.
Value
An object of class denspr consisting of
y
a numeric vector of the same length as x containing the estimated density
for each of the observations
center
a numeric vector specifying the midpoints of the cells of the histogram
counts
a numeric vector of the same length as center composed of the number
of observations of the corresponding cells
x.mode
the estimated mode
ns.out
the output of ns
type
the method used to estimate the numbers of cells
x
the input vector x if addx = TRUE; otherwise, NULL.
Efron, B., and Tibshirani, R. (1996). Using specially designed exponential
families for density estimation. Annals of Statistics, 24, 2431–2461.
Wand, M.P. (1997). Data-based choice of histogram bin width.
American Statistician, 51, 59–64.
See Also
cat.ebam
Examples
## Not run:
# Generating some random data.
x <- rnorm(10000)
out <- denspr(x, addx=TRUE)
plot(out)
# Or for an asymmetric density.
x <- rchisq(10000, 2)
out <- denspr(x, df=3, addx=TRUE)
plot(out)
## End(Not run)