gaussianKernel: Kernel functions used for local imputation
In abshev/superMICE: SuperLearner Method for MICE
View source: R/kernel_functions.R
gaussianKernel R Documentation
Kernel functions used for local imputation
Description
Kernel functions used for local imputation
Usage
gaussianKernel(x, xcenter, bw = 1, lambda = NULL)
Arguments
x
numeric vector of values to weight.
xcenter
numeric value to center the kernel.
bw
bandwidth of the kernel.
lambda
kernel radius, function of bw.
Value
kernel values for x centered at xcenter.
abshev/superMICE documentation built on May 10, 2022, 11:27 a.m.
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View source: R/kernel_functions.R
| gaussianKernel | R Documentation |
Kernel functions used for local imputation
Description
Kernel functions used for local imputation
Usage
gaussianKernel(x, xcenter, bw = 1, lambda = NULL)
Arguments
x |
numeric vector of values to weight. |
xcenter |
numeric value to center the kernel. |
bw |
bandwidth of the kernel. |
lambda |
kernel radius, function of |
Value
kernel values for x centered at xcenter.
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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