bw_sampleSmoothing: Loftsgaarden-Quesenberry kth nearest neighbor bandwidths
In kcucchi/vkde: Variable Kernel Density Estimation methods
Description
Usage
Arguments
Details
Value
Examples
View source: R/sampleSmoothing_h.R
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/vkde documentation built on May 29, 2019, 3:01 a.m.
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Description Usage Arguments Details Value Examples
View source: R/sampleSmoothing_h.R
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.
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