PWD_outlier: Weighted Deming Regression - Outlier scanning

In ppwdeming: Precision Profile Weighted Deming Regression

Weighted Deming Regression – Outlier scanning

Description

This function tests for outliers from the fitted regression, and refits on a sanitized data set (with outliers removed).

Usage

PWD_outlier(X, Y, K, lambda=1, Pcut=0.01, printem=FALSE)

Arguments

X	the vector of predicate readings,
Y	the vector of test readings,
K	the maximum number of outliers to seek,
lambda	optional the ratio of the X to the Y precision profile (defaults to 1),
Pcut	optional, default 0.01 (1%), cutoff for statistical significance of Bonferroni P,
printem	optional - if TRUE, routine will print out results as a message.

Details

The method is modeled on the Rosner sequential ESD outlier procedure and assumes the sample is large enough to assume normality of the predicted residuals.

Value

A list containing the following components:

ndrop	the number of significant outliers
drop	a vector of the indices of the outliers
cor	the Pearson correlation between X and Y
cleancor	the Pearson correlation between cleaned X and Y (after outliers removed)
scalr	the scaled residuals of all cases from the sanitized fit
keep	logical vector identifying which cases retained in sanitized data set
basepar	the sigma, kappa, alpha, beta of the full data set
lastpar	the sigma, kappa, alpha, beta of the sanitized data set

Author(s)

Douglas M. Hawkins, Jessica J. Kraker [e-mail]

References

Hawkins DM and Kraker JJ. Precision Profile Weighted Deming Regression for Methods Comparison, on Arxiv (2025) doi:10.48550/arXiv.2508.02888

Hawkins DM (2008). Outliers in Wiley Encyclopedia of Clinical Trials, eds R. D’Agostino, L. Sullivan, and J. Massaro. Wiley, New York.

Examples

# library
library(ppwdeming)

# parameter specifications
sigma <- 1
kappa <- 0.08
alpha <- 1
beta  <- 1.1
true  <- 8*10^((0:99)/99)
truey <- alpha+beta*true
# simulate single sample - set seed for reproducibility
set.seed(1039)
# specifications for predicate method
X     <- sigma*rnorm(100)+true *(1+kappa*rnorm(100))
# specifications for test method
Y     <- sigma*rnorm(100)+truey*(1+kappa*rnorm(100))
# add some outliers
Y[c(1,2,100)] <- Y[c(1,2,100)] + c(7,4,-45)

# check for outliers, re-fit, and store output
outliers_assess <- PWD_outlier(X, Y, K=5, printem=TRUE)

ppwdeming documentation built on Sept. 9, 2025, 5:37 p.m.