find_bws | R Documentation |
This internal function can be used to estimate bandwidths based on the local Gaussian likelihood for the approach based on the one-free parameter bivariate Gaussian distribution.
find_bws(
data,
.kernel = c("normal", "uniform"),
use_boundaries = TRUE,
boundary_percentiles = c(0.1, 0.9),
tol = 1e-04
)
data |
A matrix containing the multivariate observations, one observation in each row. Note that the marginals of these observations must be normalised in order for the assumptions of this code to be satisfied. |
.kernel |
Specification of the kernel to use, either
|
use_boundaries |
Logic value, default |
boundary_percentiles |
A vector specifying two percentiles,
default |
tol |
Convergence tolerance, default value |
The defaults for this function has been selected after an
investigation of how different settings affected the final
values, with an emphasis on getting "good enough" values within
a shortest possible computational time. Keep in mind that the
data
must have normalised marginals.
The result of this function is a matrix that for each
bivariate combination from the points given in data
will
return the bandwidths obtained when the function
link{find_rho}
has been optimised with regard to the
selected bivariate subset of the data. In addition to the
bandwidths, there will also be a convergence
-column that
should contain 0
if the numerical combination of the
bandwidths succeeded without any problems.
The results based on the one-free parameter bivariate Gaussian distributions can differ quite a bit from the results based on the five-free parameter bivariate Gaussian distributions. It is thus not necessarily reasonable to use a bandwidth based on the one-free parameter for the five-free parameter.
The authors opinion is that the local Gaussian correlation extracted from the one-free parameter bivariate Gaussian density function is a complete waste of computational resources. Do not use it! Use the local Gaussian correlation based on the five-free parameter approach instead.
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