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yaImpute (version 1.0-20)

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

1.0-20

License

GPL (>= 2)

Maintainer

Nicholas Crookston

Last Published

November 27th, 2013

Functions in yaImpute (1.0-20)

applyMask

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

Imputes/Predicts data for Ascii Grid maps
mostused

Tabulate references most often used in imputation
notablyDistant

Find notably distant targets
plot.yai

Plot observed verses imputed data
predict.yai

Generic predict function for class yai
newtargets

Finds K nearest neighbors for new target observations
foruse

Report a complete imputation
yai

Find K nearest neighbors
plot.notablyDifferent

Plots the scaled root mean square differences between observed and predicted
bestVars

Computes the number of best X-variables
compare.yai

Compares different k-NN solutions
ensembleImpute

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

Correct bias by selecting different near neighbors
errorStats

Compute error components of k-NN imputations
plot.varSel

Boxplot of mean Mahalanobis distances from varSelection()
buildConsensus

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

Correlation between observed and imputed
plot.compare.yai

Plots a compare.yai object
rmsd.yai

Root Mean Square Difference between observed and imputed
unionDataJoin

Combines data from several sources
yaiVarImp

Reports or plots importance scores for yai method randomForest
whatsMax

Find maximum column for each row
notablyDifferent

Finds obervations with large differences between observed and imputed values
yaiRFsummary

Build Summary Data For Method RandomForest
varSelection

Select variables for imputation models
TallyLake

Tally Lake, Flathead National Forest, Montana, USA
grmsd

Generalized Root Mean Square Distance Between Observed and Imputed Values
MoscowMtStJoe

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

Approximate nearest neighbor search routines
impute.yai

Impute variables from references to targets
print.yai

Print a summary of a yai object
vars

List variables in a yai object