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yaImpute (version 1.0-23)

yaImpute: An R Package for k-NN Imputation

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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Version

Install

install.packages('yaImpute')

Monthly Downloads

4,669

Version

1.0-23

License

GPL (>= 2)

Maintainer

Nicholas Crookston

Last Published

December 1st, 2014

Functions in yaImpute (1.0-23)

correctBias

Correct bias by selecting different near neighbors
newtargets

Finds K nearest neighbors for new target observations
yaiVarImp

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

Plots the scaled root mean square differences between observed and predicted
predict.yai

Generic predict function for class yai
grmsd

Generalized Root Mean Square Distance Between Observed and Imputed Values
compare.yai

Compares different k-NN solutions
MoscowMtStJoe

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

Report a complete imputation
ensembleImpute

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

Approximate nearest neighbor search routines
AsciiGridImpute

Imputes/Predicts data for Ascii Grid maps
yai

Find K nearest neighbors
unionDataJoin

Combines data from several sources
mostused

Tabulate references most often used in imputation
print.yai

Print a summary of a yai object
buildConsensus

Finds the consensus imputations among a list of yai objects
plot.yai

Plot observed verses imputed data
impute.yai

Impute variables from references to targets
cor.yai

Correlation between observed and imputed
yaiRFsummary

Build Summary Data For Method RandomForest
bestVars

Computes the number of best X-variables
notablyDistant

Find notably distant targets
notablyDifferent

Finds obervations with large differences between observed and imputed values
varSelection

Select variables for imputation models
plot.compare.yai

Plots a compare.yai object
plot.varSel

Boxplot of mean Mahalanobis distances from varSelection()
applyMask

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

Compute error components of k-NN imputations
whatsMax

Find maximum column for each row
rmsd.yai

Root Mean Square Difference between observed and imputed
TallyLake

Tally Lake, Flathead National Forest, Montana, USA
vars

List variables in a yai object