bmpMD

Mahalanobis distance for the posterior pairs

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/>.

Giorgio Pioda

rstanbdp

Bayesian Deming Regression for Method Comparison

bmpMD function

<dl><dt>stanRegr</dt>
<dd>Rstan rstanbdp object</dd>
<dt>cov.method</dt>
<dd>rrcov covariance method ("SDe", "MCD", or "Classical"). Default MCD.</dd></dl>

Arguments

Mahalanobis distance for the posterior pairs — bmpMD

<dl>

<dt>stanRegr</dt>
<dd>Rstan rstanbdp object</dd>


<dt>cov.method</dt>
<dd>rrcov covariance method ("SDe", "MCD", or "Classical"). Default MCD.</dd>

</dl>

bmpMD: Mahalanobis distance for the posterior pairs

Description

Usage

Value

Arguments