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:
vdar_0.1.3-2.tar.gz
vdar_0.1.3-2.zip(r-4.7-any)vdar_0.1.3-2.zip(r-4.6-any)
vdar_0.1.3-2.tar.gz(r-4.7-any)vdar_0.1.3-2.tar.gz(r-4.6-any)
vdar_0.1.3-2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
vdar/json (API)
| # Install 'vdar' in R: |
| install.packages('vdar', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- dataobs - Simulated observation data
- dataobs_coda - Simulated observation of compositional data
- datatrue - Simulated true data
- datatrue_coda - Simulated true compositional data
- uncertainties - Simulated observation uncertainties
- uncertainties_coda - Simulated observation uncertainties of compositional data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:f1e6e9efd5. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 105 | ||
| source / vignettes | OK | 161 | ||
| linux-release | OK | 106 | ||
| windows-devel | OK | 70 | ||
| windows-release | OK | 77 | ||
| wasm-release | OK | 92 |
Exports:calc_estimate_true_vargeneralized_meanvldavqda
Dependencies:bayesmcompositionsDEoptimRMASSRcppRcppArmadillorobustbasetensorA
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Estimate true group variance | calc_estimate_true_var calc_estimate_true_var.default calc_estimate_true_var.rmult |
| Simulated observation data | dataobs |
| Simulated observation of compositional data | dataobs_coda |
| Simulated true data | datatrue |
| Simulated true compositional data | datatrue_coda |
| Force positive definiteness | force_posdef |
| Generalized mean | generalized_mean generalized_mean.default generalized_mean.rmult |
| predict.vqda | predict.vlda predict.vqda |
| Simulated observation uncertainties | uncertainties |
| Simulated observation uncertainties of compositional data | uncertainties_coda |
| Weighted Linear Discriminant Analysis | vlda |
| Weighted Quadratic Discriminant Analysis | vqda |