LS_simulateDifferencesDS: Computes the gradient and function value of least-squares...

View source: R/LS_simulateDifferencesDS.R

LS_simulateDifferencesDSR Documentation

Computes the gradient and function value of least-squares loss based on the removal of one data point from the original data. Used for sensitivity analyses required for the implementation of differential privacy.

Description

Calculate the gradient and function value of least-squares loss of a given point based on the removal of one data point from the original data. Used for sensitivity analyses required for the implementation of differential privacy.

Usage

LS_simulateDifferencesDS(w, x, y)

Arguments

w

The current estimate of coefficient vector

x

The design matrix

y

The outcome vector

Details

The current estimate of w was sent to target server and applied on x,y.

Value

The gradient and function value of least-squares loss based on the removal of one data point from the original data

Author(s)

Roman Schefzik


transbioZI/dsMTLBase documentation built on Jan. 20, 2025, 8:18 p.m.