KDE {fractal} | R Documentation |
Given a training matrix, this function estimates a multidimensional probability density function using the Epanechnikov kernel as a smoother. The density function is estimated at a specified and arbitrary set of points, i.e., at points not necessarily members of the training set.
KDE(x, at=NULL, n.grid=100)
x |
a matrix whose columns contain the coordinates for each dimension. Each row represents the location of a single point in a multidimensional embedding. |
at |
the locations of the points over which the KDE is to be
calculated. Default: a multidimensional uniform grid of points spanning
the training data space (defined by x ). |
n.grid |
the number of divisions per dimension to using in forming
the default grid when the at input is unspecified. Default: 100 . |
The kernel bandwidth is constant (non-adaptive) and is
determined by first computing the minimum variance
of all dimensions (columns) of x
. This minimum variance
is then used in Scott's Rule to compute the final bandwidth.
This function is primarily used for estimating the mutual information of a time series and is included here for illustrative purposes.
an object of class KDE
.
x
is a single variable
(a time series), only the KDE is plotted."original"
, "perspective"
, and "contour"
for plotting the
original training data, a perspective plot of the KDE, or a contour plot of
the KDE over the specifed dimensions. In the case that the primary input x
is a single variable
(a time series), this parameter is automatically set to unity and a KDE is plotted.
Default: "original"
.x
is a single variable
(a time series), this parameter is automatically set to unity and a KDE is plotted.
Default: 1:2
for multivariate training data, 1 for univariate training data.dimnames
of the specified
dimensions
of the training data. If missing, "X"
is used. For univariate training data,
the x-axis label is set to the name of the original time series.dimnames
of the specified
dimensions
of the training data. If missing, "Y"
is used. For univariate training data,
the y-axis label is set to "KDE"
."KDE"
.TRUE
, a grid is plotted for the "original"
style plot.
Default: "FALSE"
.prettPrintList
. Default: "left"
.prettyPrintList
. Default: ":"
.prettyPrintList
function).
## create a mixture of 2-D Gaussian distributed ## RVs with different means, standard ## deviations, point density, and orientation. n.sample <- c(1000, 500, 300) ind <- rep(1:3, n.sample) x <- rmvnorm(sum(n.sample), mean = rbind(c(-10,-20), c(10,0), c(0,0))[ ind, ], sd = rbind(c(5,3), c(1,3) , c(0.3,1))[ ind, ], rho = c(0.5, 1, -0.4)[ind]) ## perform the KDE z <- KDE(x) print(z) ## plot a summary of the results eda.plot(z) ## form KDE of beamchaos series plot(KDE(beamchaos),type="l")