bw_sampleSmoothing: Loftsgaarden-Quesenberry kth nearest neighbor bandwidths
In kcucchi/vdke: Variable Kernel Density Estimation methods
Description
Usage
Arguments
Details
Value
Examples
Description
bw_balloon_LQ looks for the bandwidth following the
Loftsgaarden-Quesenberry algorithm. The bandwidth depends on the point of
estimation.
Usage
1
bw_sampleSmoothing(tdat, k)
Arguments
tdat
a vector containing the training dataset.
k
specifying the order of the neighbor.
Details
These are the details of the function.
Value
a vector of bandwidths (one bandwidth per evaluation data point)
Examples
1
2
bw_sampleSmoothing(tdat = 1:10,k = 3)
bw_sampleSmoothing(tdat = (1:10)^2,k = 3)
kcucchi/vdke documentation built on May 20, 2019, 8:28 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Examples
Description
bw_balloon_LQ looks for the bandwidth following the
Loftsgaarden-Quesenberry algorithm. The bandwidth depends on the point of
estimation.
Usage
1 | bw_sampleSmoothing(tdat, k)
|
Arguments
tdat |
a vector containing the training dataset. |
k |
specifying the order of the neighbor. |
Details
These are the details of the function.
Value
a vector of bandwidths (one bandwidth per evaluation data point)
Examples
1 2 | bw_sampleSmoothing(tdat = 1:10,k = 3)
bw_sampleSmoothing(tdat = (1:10)^2,k = 3)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.