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.

1 |

`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 |

`n.grid` |
the number of divisions per dimension to using in forming
the default grid when the |

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`

.

- eda.plot
extended data analysis plot showing the original data along with a perspective and contour plot of the resulting KDE. In the case that the primary input

`x`

is a single variable (a time series), only the KDE is plotted.- plot
plot the KDE or original (training) data. Options are:

- style
a character string denoting the type of plot to produce. Choices are

`"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"`

.- dimensions
a two-element integer vector denoting the dimensions/variables/columns to select from the training data and resulting multidimensional KDE for perspective and contour plotting. 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:`1:2`

for multivariate training data, 1 for univariate training data.- xlab
character string defining the x-axis label. Default:

`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.- ylab
character string defining the y-axis label. Default:

`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"`

.- zlab
character string defining the z-axis label for perspective plots. Default:

`"KDE"`

.- grid
a logical flag. If

`TRUE`

, a grid is plotted for the`"original"`

style plot. Default:`"FALSE"`

.- ...
Optional arguments to be passed directly to the specified plotting routine.

a summary of the KDE object is printed.. Available options are:

- justify
text justification ala

`prettPrintList`

. Default:`"left"`

.- sep
header separator ala

`prettyPrintList`

. Default:`":"`

.- ...
Additional print arguments sent directly to the

`prettyPrintList`

function).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## 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")
``` |

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