DP.RKHS: Releasing Differential Private RKHS smoothing mean of a...

In sxz155/PFDA:

Description

This function create a DP RKHS smoothing mean from an existing data set with known eigenvalues and eigenvectors

Usage

DP.RKHS(grid, Data, alpha = 1, beta = 0.1, kernel = "Gau", phi = 0.01,
  ro = 0.2, col.drop = TRUE)

Arguments

grid	grid (x-axis) for each curve, default is equally espaced between 0 and 1.
Data	a matrix which the of interest curves are located in columns
alpha, beta	Privacy parameters, real numbers
phi	real number, penalty parameter
ro	kernel range parameter in kernel, real number
drop.col	TRUE/FALSE for cleaning the Data, deleting Columns/Rows including missing values

Value

f.tilda: DP RKHS smoothing mean

delta: the coefficient of the noise, real number

f: RKHS smoothing mean

X: original data (X=Data)

sxz155/PFDA documentation built on May 30, 2019, 10:40 p.m.