Package: yaImpute 1.0-35

yaImpute: Nearest Neighbor Observation Imputation and Evaluation Tools

Performs nearest neighbor-based imputation using one or more alternative approaches to processing multivariate data. These include methods based on canonical correlation: analysis, canonical correspondence analysis, and a multivariate adaptation of the random forest classification and regression techniques of Leo Breiman and Adele Cutler. Additional methods are also offered. The package includes functions for comparing the results from running alternative techniques, detecting imputation targets that are notably distant from reference observations, detecting and correcting for bias, bootstrapping and building ensemble imputations, and mapping results.

Authors:Jeffrey S. Evans [aut, cre], Nicholas L. Crookston [aut], Andrew O. Finley [aut], John Coulston [ctb, com]

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yaImpute.pdf |yaImpute.html
yaImpute/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/jeffreyevans/yaimpute/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • MoscowMtStJoe - Moscow Mountain and St. Joe Woodlands (Idaho, USA) Tree and LiDAR Data
  • TallyLake - Tally Lake, Flathead National Forest, Montana, USA

On CRAN:

imputation

7.68 score 3 stars 14 packages 91 scripts 4.1k downloads 4 mentions 33 exports 0 dependencies

Last updated 2 months agofrom:07f316a264. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 08 2024
R-4.5-win-x86_64OKNov 08 2024
R-4.5-linux-x86_64OKNov 08 2024
R-4.4-win-x86_64OKNov 08 2024
R-4.4-mac-x86_64OKNov 08 2024
R-4.4-mac-aarch64OKNov 08 2024
R-4.3-win-x86_64OKNov 08 2024
R-4.3-mac-x86_64OKNov 08 2024
R-4.3-mac-aarch64OKNov 08 2024

Exports:annapplyMaskAsciiGridImputeAsciiGridPredictbestVarsbuildConsensuscompare.yaicor.yaicorrectBiasensembleImputeerrorStatsforusegrmsdimputeimpute.yaimostusednewtargetsnotablyDifferentnotablyDistantplot.notablyDifferentplot.varSelpredict.yairmsdrmsd.yaiunionDataJoinvarsvarSelectionwhatsMaxxvarsyaiyaiRFsummaryyaiVarImpyvars

Dependencies:

Readme and manuals

Help Manual

Help pageTopics
Approximate nearest neighbor search routinesann
Removes neighbors that share (or not) group membership with targets.applyMask
Imputes/Predicts data for Ascii Grid mapsAsciiGridImpute AsciiGridPredict
Computes the number of _best_ X-variablesbestVars
Finds the consensus imputations among a list of yai objectsbuildConsensus
Compares different k-NN solutionscompare.yai
Correlation between observed and imputedcor.yai
Correct bias by selecting different near neighborscorrectBias
Computes the mean, median, or mode among a list of impute.yai objectsensembleImpute
Compute error components of k-NN imputationserrorStats
Report a complete imputationforuse
Generalized Root Mean Square Distance Between Observed and Imputed Valuesgrmsd
Impute variables from references to targetsimpute impute.yai
Moscow Mountain and St. Joe Woodlands (Idaho, USA) Tree and LiDAR DataMoscowMtStJoe
Tabulate references most often used in imputationmostused
Finds K nearest neighbors for new target observationsnewtargets
Finds obervations with large differences between observed and imputed valuesnotablyDifferent
Find notably distant targetsnotablyDistant
Plots a compare.yai objectplot.compare.yai
Plots the scaled root mean square differences between observed and predictedplot.notablyDifferent
Boxplot of mean Mahalanobis distances from varSelection()plot.varSel
Plot observed verses imputed dataplot.impute.yai plot.yai
Generic predict function for class yaipredict.yai
Print a summary of a yai objectprint.yai summary.yai
Root Mean Square Difference between observed and imputedrmsd rmsd.yai
Tally Lake, Flathead National Forest, Montana, USATallyLake
Combines data from several sourcesunionDataJoin
List variables in a yai objectvars xvars yvars
Select variables for imputation modelsvarSelection
Find maximum column for each rowwhatsMax
Find K nearest neighborsyai yaImpute
Build Summary Data For Method RandomForestyaiRFsummary
Reports or plots importance scores for yai method randomForestyaiVarImp