Histogram density estimate
Histogram density estimate for 1- and 2-dimensional data.
histde(x, binw, xmin, xmax, adj=0) ## S3 method for class 'histde' predict(object, ..., x)
x |
matrix of data values |
binw |
(vector) of binwidths |
xmin,xmax |
vector of minimum/maximum values for grid |
adj |
displacement of default anchor point, in percentage of 1 bin |
object |
object of class |
... |
other parameters |
If binw is missing, the default binwidth is b_i = 2*3^(1/(d+2))*pi^(d/(2d+4))*S_i*n^(-1/(d+2)), the
normal scale selector.
If xmin is missing then it defaults to the data minimum. If
xmax is missing then it defaults to the data maximum.
A histogram density estimate is an object of class histde which is a
list with fields:
x |
data points - same as input |
eval.points |
vector or list of points at which the estimate is evaluated |
estimate |
density estimate at |
binw |
(vector of) bandwidths |
nbin |
(vector of) number of bins |
names |
variable names |
## positive data example set.seed(8192) x <- 2^rnorm(100) fhat <- histde(x=x) plot(fhat, col=3) points(c(0.5, 1), predict(fhat, x=c(0.5, 1))) ## large data example on a non-default grid set.seed(8192) x <- rmvnorm.mixt(10000, mus=c(0,0), Sigmas=invvech(c(1,0.8,1))) fhat <- histde(x=x, xmin=c(-5,-5), xmax=c(5,5)) plot(fhat) ## See other examples in ? plot.histde
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