Package: classmap 1.2.3

classmap: Visualizing Classification Results

Tools to visualize the results of a classification of cases. The graphical displays include stacked plots, silhouette plots, quasi residual plots, and class maps. Implements the techniques described and illustrated in Raymaekers, Rousseeuw and Hubert (2021), Class maps for visualizing classification results, Technometrics, appeared online. <doi:10.1080/00401706.2021.1927849> (open access) and Raymaekers and Rousseeuw (2021), Silhouettes and quasi residual plots for neural nets and tree-based classifiers, <arxiv:2106.08814>. Examples can be found in the vignettes: "Discriminant_analysis_examples","K_nearest_neighbors_examples", "Support_vector_machine_examples", "Rpart_examples", "Random_forest_examples", and "Neural_net_examples".

Authors:Jakob Raymaekers [aut, cre], Peter Rousseeuw [aut]

classmap_1.2.3.tar.gz
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classmap.pdf |classmap.html✨
classmap/json (API)

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

Peer review:

Datasets:

On CRAN:

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

3.08 score 20 scripts 202 downloads 19 exports 51 dependencies

Last updated 2 years agofrom:0a687b2e1f. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 16 2024
R-4.5-winNOTENov 16 2024
R-4.5-linuxNOTENov 16 2024
R-4.4-winNOTENov 16 2024
R-4.4-macNOTENov 16 2024
R-4.3-winNOTENov 16 2024
R-4.3-macNOTENov 16 2024

Exports:classmapconfmat.vcrmakeFVmakeKernelqresplotsilplotstackedplotvcr.da.newdatavcr.da.trainvcr.forest.newdatavcr.forest.trainvcr.knn.newdatavcr.knn.trainvcr.neural.newdatavcr.neural.trainvcr.rpart.newdatavcr.rpart.trainvcr.svm.newdatavcr.svm.train

Dependencies:cellWiseclasscliclustercolorspaceDEoptimRe1071fansifarverggplot2gluegridExtragtableisobandkernlablabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnlmepcaPPpillarpkgconfigplyrproxyR6randomForestRColorBrewerRcppRcppArmadilloreshape2rlangrobustbaserpartrrcovscalesshapestringistringrsvdtibbleutf8vctrsviridisLitewithr

Discriminant_analysis_examples

Raymaekers, J. and Rousseeuw, P.J.

Rendered fromDiscriminant_analysis_examples.Rmdusingknitr::knitron Nov 16 2024.

Last update: 2022-01-09
Started: 2021-05-10

K_nearest_neighbors_examples

Raymaekers, J. and Rousseeuw, P.J.

Rendered fromK_nearest_neighbors_examples.Rmdusingknitr::knitron Nov 16 2024.

Last update: 2022-01-09
Started: 2021-05-10

Neural_net_examples

Raymaekers, J. and Rousseeuw, P.J.

Rendered fromNeural_net_examples.Rmdusingknitr::knitron Nov 16 2024.

Last update: 2022-01-09
Started: 2021-06-27

Random_forest_examples

Raymaekers, J. and Rousseeuw, P.J.

Rendered fromRandom_forest_examples.Rmdusingknitr::knitron Nov 16 2024.

Last update: 2022-01-09
Started: 2021-06-27

Rpart_examples

Raymaekers, J. and Rousseeuw, P.J.

Rendered fromRpart_examples.Rmdusingknitr::knitron Nov 16 2024.

Last update: 2022-01-09
Started: 2021-06-27

Support_vector_machine_examples

Raymaekers, J. and Rousseeuw, P.J.

Rendered fromSupport_vector_machine_examples.Rmdusingknitr::knitron Nov 16 2024.

Last update: 2022-01-09
Started: 2021-05-10

Readme and manuals

Help Manual

Help pageTopics
Draw the class map to visualize classification results.classmap
Build a confusion matrix from the output of a function 'vcr.*.*'.confmat.vcr
Amazon book reviews datadata_bookReviews
Floral buds datadata_floralbuds
Instagram datadata_instagram
Titanic datadata_titanic
Constructs feature vectors from a kernel matrix.makeFV
Compute kernel matrixmakeKernel
Draw a quasi residual plot of PAC versus a data featureqresplot
Draw the silhouette plot of a classificationsilplot
Make a vertically stacked mosaic plot of class predictions.stackedplot
Carry out discriminant analysis on new data, and prepare to visualize its results.vcr.da.newdata
Carry out discriminant analysis on training data, and prepare to visualize its results.vcr.da.train
Prepare for visualization of a random forest classification on new data.vcr.forest.newdata
Prepare for visualization of a random forest classification on training datavcr.forest.train
Carry out a k-nearest neighbor classification on new data, and prepare to visualize its results.vcr.knn.newdata
Carry out a k-nearest neighbor classification on training data, and prepare to visualize its results.vcr.knn.train
Prepare for visualization of a neural network classification on new data.vcr.neural.newdata
Prepare for visualization of a neural network classification on training data.vcr.neural.train
Prepare for visualization of an rpart classification on new data.vcr.rpart.newdata
Prepare for visualization of an rpart classification on training data.vcr.rpart.train
Prepare for visualization of a support vector machine classification on new data.vcr.svm.newdata
Prepare for visualization of a support vector machine classification on training data.vcr.svm.train