Package: vdar 0.1.3-2

Solveig Pospiech

vdar: Discriminant Analysis Incorporating Individual Uncertainties

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.

Authors:Solveig Pospiech [aut, cre]

vdar_0.1.3-2.tar.gz
vdar_0.1.3-2.tar.gz(r-4.5-noble)vdar_0.1.3-2.tar.gz(r-4.4-noble)
vdar_0.1.3-2.tgz(r-4.4-emscripten)vdar_0.1.3-2.tgz(r-4.3-emscripten)
vdar.pdf |vdar.html
vdar/json (API)
NEWS

# Install 'vdar' in R:
install.packages('vdar', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 145 downloads 4 exports 8 dependencies

Last updated 3 years agofrom:f1e6e9efd5. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 15 2024
R-4.5-linuxOKDec 15 2024

Exports:calc_estimate_true_vargeneralized_meanvldavqda

Dependencies:bayesmcompositionsDEoptimRMASSRcppRcppArmadillorobustbasetensorA

Readme and manuals

Help Manual

Help pageTopics
Estimate true group variancecalc_estimate_true_var calc_estimate_true_var.default calc_estimate_true_var.rmult
Simulated observation datadataobs
Simulated observation of compositional datadataobs_coda
Simulated true datadatatrue
Simulated true compositional datadatatrue_coda
Force positive definitenessforce_posdef
Generalized meangeneralized_mean generalized_mean.default generalized_mean.rmult
predict.vqdapredict.vlda predict.vqda
Simulated observation uncertaintiesuncertainties
Simulated observation uncertainties of compositional datauncertainties_coda
Weighted Linear Discriminant Analysisvlda
Weighted Quadratic Discriminant Analysisvqda