The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

COMBO Notation Guide

Kim Hochstedler

Notation

This guide is designed to summarize key notation and quantities used the COMBO R Package and associated publications.

Term Definition Description
\(X\) Predictor matrix for the true outcome.
\(Z\) Predictor matrix for the observed outcome, conditional on the true outcome.
\(Y\) \(Y \in \{1, 2\}\) True binary outcome. Reference category is 2.
\(y_{ij}\) \(\mathbb{I}\{Y_i = j\}\) Indicator for the true binary outcome.
\(Y^*\) \(Y^* \in \{1, 2\}\) Observed binary outcome. Reference category is 2.
\(y^*_{ik}\) \(\mathbb{I}\{Y^*_i = k\}\) Indicator for the observed binary outcome.
True Outcome Mechanism \(\text{logit} \{ P(Y = j | X ; \beta) \} = \beta_{j0} + \beta_{jX} X\) Relationship between \(X\) and the true outcome, \(Y\).
Observation Mechanism \(\text{logit}\{ P(Y^* = k | Y = j, Z ; \gamma) \} = \gamma_{kj0} + \gamma_{kjZ} Z\) Relationship between \(Z\) and the observed outcome, \(Y^*\), given the true outcome \(Y\).
\(\pi_{ij}\) \(P(Y_i = j | X ; \beta) = \frac{\text{exp}\{\beta_{j0} + \beta_{jX} X_i\}}{1 + \text{exp}\{\beta_{j0} + \beta_{jX} X_i\}}\) Response probability for individual \(i\)’s true outcome category.
\(\pi^*_{ikj}\) \(P(Y^*_i = k | Y_i = j, Z ; \gamma) = \frac{\text{exp}\{\gamma_{kj0} + \gamma_{kjZ} Z_i\}}{1 + \text{exp}\{\gamma_{kj0} + \gamma_{kjZ} Z_i\}}\) Response probability for individual \(i\)’s observed outcome category, conditional on the true outcome.
\(\pi^*_{ik}\) \(P(Y^*_i = k | Y_i, X, Z ; \gamma) = \sum_{j = 1}^2 \pi^*_{ikj} \pi_{ij}\) Response probability for individual \(i\)’s observed outcome cateogry.
\(\pi^*_{jj}\) \(P(Y^* = j | Y = j, Z ; \gamma) = \sum_{i = 1}^N \pi^*_{ijj}\) Average probability of correct classification for category \(j\).
Sensitivity \(P(Y^* = 1 | Y = 1, Z ; \gamma) = \sum_{i = 1}^N \pi^*_{i11}\) True positive rate. Average probability of observing outcome \(k = 1\), given the true outcome \(j = 1\).
Specificity \(P(Y^* = 2 | Y = 2, Z ; \gamma) = \sum_{i = 1}^N \pi^*_{i22}\) True negative rate. Average probability of observing outcome \(k = 2\), given the true outcome \(j = 2\).
\(\beta_X\) Association parameter of interest in the true outcome mechanism.
\(\gamma_{11Z}\) Association parameter of interest in the observation mechanism, given \(j=1\).
\(\gamma_{12Z}\) Association parameter of interest in the observation mechanism, given \(j=2\).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.