Title:	Bayesian Deming Regression for Method Comparison
Version:	0.0.3
Maintainer:	Giorgio Pioda <gfwp@ticino.com>
Description:	Regression methods to quantify the relation between two measurement methods are provided by this package. The focus is on a Bayesian Deming regressions family. With a Bayesian method the Deming regression can be run in a traditional fashion or can be run in a robust way just decreasing the degree of freedom d.f. of the sampling distribution. With d.f. = 1 an extremely robust Cauchy distribution can be sampled. Moreover, models for dealing with heteroscedastic data are also provided. For reference see G. Pioda (2024) https://piodag.github.io/bd1/.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.3.1
Biarch:	true
Depends:	R (≥ 3.5.0)
Imports:	methods, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), rstantools (≥ 2.4.0), rrcov, mixtools, bayestestR, KernSmooth
LinkingTo:	BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
SystemRequirements:	GNU make
NeedsCompilation:	yes
Packaged:	2024-07-26 13:45:42 UTC; gfwp
Author:	Giorgio Pioda [aut, cre]
Repository:	CRAN
Date/Publication:	2024-07-26 14:30:07 UTC

The 'rstanbdp' package.

Description

Bayesian Deming regression for Method Comparison

References

Stan Development Team (NA). RStan: the R interface to Stan. R package version 2.32.5. https://mc-stan.org _PATH

Plot the calculated Y response with CI from the full Bayesian posterior distribution

Description

Plot the calculated Y response with CI from the full Bayesian posterior distribution

Usage

bdpCalcResponse(bdpreg, Xval, ci = 0.95, ...)

Arguments

Value

no return

Extract relevant data from the Bayesian Deming regression

Description

Extract relevant data from the Bayesian Deming regression

Usage

bdpExtract(bdpreg)

Arguments

Value

A data frame with extracted data

Pairs plot of the full posterior predictors

Description

Pairs plot of the full posterior predictors

Usage

bdpPairs(bdpreg, ...)

Arguments

Value

Pairs plot of the stanfit object

Plot Bayesian Deming regression and the confidence intervals from the full posterior distribution

Description

Plot Bayesian Deming regression and the confidence intervals from the full posterior distribution

Usage

bdpPlot(bdpreg, ci = 0.95, ...)

Arguments

Value

no return

Plot regression posterior pairs with CI Box and MD ellipses

Description

Plot regression posterior pairs with CI Box and MD ellipses

Usage

bdpPlotBE(bdpreg, cov.method = "MCD", ci = 0.95, ...)

Arguments

Value

no return

Plot studentized residuals from the Bayesian Deming regression

Description

Plot studentized residuals from the Bayesian Deming regression

Usage

bdpPlotResiduals(bdpreg, legend.pos = "bottomleft", legend.horizon = TRUE, ...)

Arguments

Value

no return

Print summary of sampled data

Description

Print summary of sampled data

Usage

bdpPrint(bdpreg, digits_summary = 4, ...)

Arguments

Value

Print of the stanfit object

Print summary of sampled data

Description

Print summary of sampled data

Usage

bdpSummary(bdpreg, ...)

Arguments

Value

Summary list of the stanfit object

Plot a traceplot of the sampled chains

Description

Plot a traceplot of the sampled chains

Usage

bdpTraceplot(bdpreg, ...)

Arguments

Value

Traceplot of the stanfit object

Bayesian Deming Pioda Regression for two method comparison with Rstan

Description

bdpreg is used to compare two measurement methods by means of a Bayesian regression analysis.

Usage

bdpreg(
  X,
  Y,
  ErrorRatio = 1,
  df = NULL,
  trunc = TRUE,
  heteroscedastic = c("homo", "linear"),
  slopeMu = 1,
  slopeSigma = 0.3,
  slopeTruncMin = 0.3333,
  slopeTruncMax = 10,
  interceptMu = 0,
  interceptSigma = 30,
  sigmaLambda = 0.3,
  AlphaMu = 1,
  AlphaSigma = 10,
  BetaMu = 0.1,
  BetaSigma = 0.5,
  BetaTruncMin = -1,
  BetaTruncMax = 1,
  ...
)

Arguments

Details

The Bayesian Deming regression can be run in a traditional fashion. In this case the error term is sampled from a T distribution with N-2 degree of freedom (N sample size).

The Bayesian Deming regression can be run as a robust regression specifying a decreased df parameter. It is possible to set df = 1 and perform the sampling from an extremely robust Cauchy distribution to suppress leveraged outliers. For moderate robustness a reasonably low value of df in the interval [6;10] can be an appropriated choice.

ErrorRatio can be set as usual for classical Deming regression. Default is 1. Strong ErrorRatio can lead to instability in the chains that may not converge after the burn in. For this purpose the trunc parameter can be used. In this way the normal distribution for the slope gets truncated at a minimum of 0.3333 (default). The parameter slopeTruncMin can override this value.

With the parameter heteroschedastic it is possible to use an alternative regression which models the heteroscedasticity with a linear growing variance. Alpha and Beta are the intercept and the slope for the variance variation. Alpha must be > 0. Beta is usually zero if no real heteroscedasticity is detected. Alternatively Beta shows low positive values, typically below 0.5 if heteroscedasticity is successfully modeled. The CI of Beta could indeed act as a test for heteroscedasticity. According to these empiric observations, Beta is also truncated to avoid erratic behavior of the Hamiltonian sampler.

The Bayesian Deming regression is recommended in many cases where traditional and non parametric method fail. It is particularly convenient with very small data set and/or with data set with low digit precision. In fact Bayesian Deming regression has no problem with ties.

The method with linear heteroscedastic fitting can be a meaningful answer to heteroscedastic data set. The CI are much narrower and the trade off between robustness and power can find a natural solution. It must be considered as highly experimental but also highly promising method. Users are advised to carefully check the sampled output for undesirable correlation between Alpha and/or Beta vs the slope and/or intercept. A plot with pairs() highly recommended.

Stan is usually good enough that init values for the chains must not be specified. In extreme cases it is anyway possible to set init values as a list of list.

Value

An object of class bdpreg which contains out a stanfit object returned by rstan::sampling and standata as list of input parameters.

References

G. Pioda (2014) https://piodag.github.io/bd1/

Examples

library(rstanbdp)
data(glycHem)

# Bayesian Deming Regression, for example with  df=10
fit.1 <-bdpreg(glycHem$Method1,glycHem$Method2,heteroscedastic="homo",
              df=10,chain=1,iter=1000)

# Print results
bdpPrint(fit.1,digits_summary = 4)

# Plot 2D intercepts /slopes pairs with CI and MD distance
bdpPlotBE(fit.1,cov.method="MCD",ci=0.95)

# Plot regression with CI
bdpPlot(fit.1,ci=0.95)

# Calculate response, plot histogram and CI
bdpCalcResponse(fit.1,Xval = 6)

# Extract Xhat, Yhat and Residuals
bdpExtract(fit.1)

# Plot a traceplot of the sampled chains
bdpTraceplot(fit.1)

# Plot standardized residuals
bdpPlotResiduals(fit.1)

# Plot posterior samples pairwise
bdpPairs(fit.1)

Mahalanobis distance for the posterior pairs

Description

Mahalanobis distance for the posterior pairs

Usage

bmpMD(stanRegr, cov.method)

Arguments

Value

Chi squared probability of the MD

Method comparison of glycated hemoglobin measurement

Description

This data gives the glycated hemoglobin measured in 20 patients.

Usage

data(glycHem)

Format

A data frame with 20 observations on the following 2 variables.