Package: treeDA 0.0.5

treeDA: Tree-Based Discriminant Analysis

Performs sparse discriminant analysis on a combination of node and leaf predictors when the predictor variables are structured according to a tree, as described in Fukuyama et al. (2017) <doi:10.1371/journal.pcbi.1005706>.

Authors:Julia Fukuyama [aut, cre]

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treeDA.pdf |treeDA.html✨
treeDA/json (API)

# Install 'treeDA' in R:

install.packages('treeDA', repos = c('https://jfukuyama.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jfukuyama/treeda/issues

Datasets:

treeda_example - Example dataset

On CRAN:

3.70 score 9 scripts 170 downloads 1 mentions 7 exports 80 dependencies

Last updated 4 years agofrom:86ae334c34. Checks:OK: 6 WARNING: 1. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 02 2024
R-4.5-win	OK	Nov 02 2024
R-4.5-linux	WARNING	Nov 02 2024
R-4.4-win	OK	Nov 02 2024
R-4.4-mac	OK	Nov 02 2024
R-4.3-win	OK	Nov 02 2024
R-4.3-mac	OK	Nov 02 2024

Exports:combine_plot_and_tree get_leaf_position makeNodeAndLeafPredictors nodeToLeafCoefficients plot_coefficients treeda treedacv

Dependencies:ade4 ape askpass Biobase BiocGenerics biomformat Biostrings class cli cluster codetools colorspace cpp11 crayon curl data.table digest elasticnet fansi farver foreach generics GenomeInfoDb GenomeInfoDbData ggplot2 glue gtable httr igraph IRanges isoband iterators jsonlite labeling lars lattice lifecycle magrittr MASS Matrix mda mgcv mime multtest munsell mvtnorm nlme openssl permute phyloseq pillar pixmap pkgconfig plyr R6 RColorBrewer Rcpp RcppArmadillo reshape2 rhdf5 rhdf5filters Rhdf5lib rlang S4Vectors scales sp sparseLDA stringi stringr survival sys tibble UCSC.utils utf8 vctrs vegan viridisLite withr XVector zlibbioc

treeDA vignette

Julia Fukuyama

Rendered fromtreeda-vignette.Rmdusingknitr::rmarkdownon Nov 02 2024.

Last update: 2020-06-24
Started: 2017-07-16

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Tree-based discriminant analysis	treeDA-package treeDA
Coefficients from treeda fit	coef.treeda
Method for combining two ggplots	combine_plot_and_tree
Get leaf positions from a tree layout	get_leaf_position
Make a matrix with predictors for each leaf and node	makeNodeAndLeafPredictors
Node coefficients to leaf coefficients	nodeToLeafCoefficients
Plot the discriminating axes from treeda	plot_coefficients
Plot a treedacv object	plot.treedacv
Predict using new data	predict.treeda
Print a treeda object	print.treeda
Print treedacv objects	print.treedacv
Tree-based sparse discriminant analysis	treeda
Example dataset	treeda_example
treeda cross validation	treedacv