kernel-gaussian: Compute a Gaussian kernel
In pfocal: Fast Convolution of Matrices

gaussian_kernel_confidence

R Documentation

Compute a Gaussian kernel

Description

Functions to compute a Gaussian kernel.

Usage

gaussian_kernel_confidence(
  vertical_r0 = 0.05,
  vertical_sd = 1,
  horizontal_r0 = vertical_r0,
  horizontal_sd = vertical_sd,
  tail_included = TRUE
)

gaussian_kernel_radius(
  vertical_radius,
  vertical_sd = 1,
  horizontal_radius = vertical_radius,
  horizontal_sd = vertical_sd,
  tail_included = TRUE
)

Arguments

`vertical_r0`	[numeric] The kernel's r0 (exponential) in the vertical dimension.
`vertical_sd`	[numeric] The kernel's standard deviation in the vertical dimension.
`horizontal_r0`	[numeric] The kernel's r0 (exponential) in the horizontal dimension.
`horizontal_sd`	[numeric] The kernel's standard deviation in the horizontal dimension.
`tail_included`	[logical] Whether or not to include the kernel tail.
`vertical_radius`	[numeric] The kernel's radius in the vertical dimension.
`horizontal_radius`	[numeric] The kernel's radius in the horizontal dimension.

Value

A matrix corresponding to the kernel.

Examples


gaussian_kernel_confidence(vertical_r0 = 0.4, vertical_sd = 1, 
                           horizontal_r0 = 0.5, horizontal_sd = 2)
gaussian_kernel_confidence(vertical_r0 = 0.4, vertical_sd = 1, 
                           horizontal_r0 = 0.5, horizontal_sd = 2)

pfocal documentation built on June 17, 2022, 5:07 p.m.