uncertainties_coda

A data set of 200 simulated uncertainties with three variables and two groups, compositional

datasets

The qda() function from package 'MASS' is extended to calculate a weighted linear (LDA) and quadratic discriminant analysis (QDA) by changing the group variances and group means based on cell-wise uncertainties.
The uncertainties can be derived e.g. through relative errors for each individual measurement (cell), not only row-wise or column-wise uncertainties.
The method can be applied compositional data (e.g. portions of substances, concentrations) and non-compositional data.

Solveig Pospiech

vdar

Discriminant Analysis Incorporating Individual Uncertainties

uncertainties_coda function

A data frame with 200 rows and 4 columns<dl class="dl-horizontal">
 <dt>Var1</dt><dd>simulated observed variable, compositional</dd>
 <dt>Var2</dt><dd>simulated observed variable, compositional</dd>
 <dt>Var3</dt><dd>simulated observed variable, compositional</dd>
 <dt>Group</dt><dd>Factor with levels 'Group 1' and 'Group 2'</dd>
</dl>

Format

Simulated observation uncertainties of compositional data — uncertainties_coda

A data frame with 200 rows and 4 columns<dl class='dl-horizontal'>
 <dt>Var1</dt><dd>simulated observed variable, compositional</dd>
 <dt>Var2</dt><dd>simulated observed variable, compositional</dd>
 <dt>Var3</dt><dd>simulated observed variable, compositional</dd>
 <dt>Group</dt><dd>Factor with levels 'Group 1' and 'Group 2'</dd>
</dl>

uncertainties_coda: Simulated observation uncertainties of compositional data

Description

Usage

Arguments

Format