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yaImpute (version 1.0-34)

Nearest Neighbor Observation Imputation and Evaluation Tools

Description

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.

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Install

install.packages('yaImpute')

Monthly Downloads

3,086

Version

1.0-34

License

GPL (>= 2)

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Maintainer

Jeffrey S Evans

Last Published

December 12th, 2023

Functions in yaImpute (1.0-34)

MoscowMtStJoe

Moscow Mountain and St. Joe Woodlands (Idaho, USA) Tree and LiDAR Data
notablyDistant

Find notably distant targets
ensembleImpute

Computes the mean, median, or mode among a list of impute.yai objects
errorStats

Compute error components of k-NN imputations
foruse

Report a complete imputation
predict.yai

Generic predict function for class yai
plot.yai

Plot observed verses imputed data
grmsd

Generalized Root Mean Square Distance Between Observed and Imputed Values
yai

Find K nearest neighbors
impute.yai

Impute variables from references to targets
newtargets

Finds K nearest neighbors for new target observations
print.yai

Print a summary of a yai object
plot.compare.yai

Plots a compare.yai object
mostused

Tabulate references most often used in imputation
rmsd.yai

Root Mean Square Difference between observed and imputed
vars

List variables in a yai object
yaiRFsummary

Build Summary Data For Method RandomForest
whatsMax

Find maximum column for each row
varSelection

Select variables for imputation models
unionDataJoin

Combines data from several sources
notablyDifferent

Finds observations with large differences between observed and imputed values
yaiVarImp

Reports or plots importance scores for yai method randomForest
plot.notablyDifferent

Plots the scaled root mean square differences between observed and predicted
plot.varSel

Boxplot of mean Mahalanobis distances from varSelection()
ann

Approximate nearest neighbor search routines
buildConsensus

Finds the consensus imputations among a list of yai objects
applyMask

Removes neighbors that share (or not) group membership with targets.
compare.yai

Compares different k-NN solutions
bestVars

Computes the number of best X-variables
TallyLake

Tally Lake, Flathead National Forest, Montana, USA
correctBias

Correct bias by selecting different near neighbors
cor.yai

Correlation between observed and imputed
AsciiGridImpute

Imputes/Predicts data for Ascii Grid maps