RrT.mad: Robust estimator for a generalized ratio model with Tukey...

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

Robust estimator for a generalized ratio model with Tukey biweight function and MAD scale by iteratively re-weighted least squares (IRLS) algorithm for M-estimation

Description

Robust estimator for a generalized ratio model with Tukey biweight function and MAD scale by iteratively re-weighted least squares (IRLS) algorithm for M-estimation

Usage

RrT.mad(x1, y1, g1 = 0.5, c1 = 10.03, 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 5.01 to 10.03 (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 of the scale (AAD or MAD)
efg	error flag. 1: acalculia (all weights become zero) 0: successful termination

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