RrH.mad: Robust estimator for a generalized ratio model with Huber's...

In robRatio: M-Estimators for Generalized Ratio and Linear Regression Models

Robust estimator for a generalized ratio model with Huber's weight function and MAD scal by iteratively re-weighted least squares (IRLS) algorithm for M-estimation

Description

Robust estimator for a generalized ratio model with Huber's weight function and MAD scal by iteratively re-weighted least squares (IRLS) algorithm for M-estimation

Usage

RrH.mad(x1, y1, g1 = 0.5, c1 = 2.88, rp.max = 100, cg.rt = 0.01)

Arguments

x1	single explanatory variable
y1	objective variable
g1	power (default: g1=0.5(conventional ratio model))
c1	tuning parameter usually from 1.44 to 2.88 (equivalent to those for AAD scale)
rp.max	maximum number of iteration
cg.rt	convergence condition to stop iteration (default: cg1=0.001)

Value

a list with the following elements

par	robustly estimated ratio of y1 to x1
res	homoscedastic quasi-residuals
wt	robust weights
rp	total number of iteration
s1	changes in scale through iterative calculation
efg	error flag. 1: acalculia (all weights become zero) 0: successful termination

robRatio documentation built on Nov. 5, 2025, 5:25 p.m.