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yaImpute (version 1.0-29)

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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Version

Install

install.packages('yaImpute')

Monthly Downloads

3,048

Version

1.0-29

License

GPL (>= 2)

Maintainer

Nicholas Crookston

Last Published

December 10th, 2017

Functions in yaImpute (1.0-29)

applyMask

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

Correct bias by selecting different near neighbors
cor.yai

Correlation between observed and imputed
ann

Approximate nearest neighbor search routines
buildConsensus

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

Compares different k-NN solutions
AsciiGridImpute

Imputes/Predicts data for Ascii Grid maps
MoscowMtStJoe

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

Tally Lake, Flathead National Forest, Montana, USA
bestVars

Computes the number of best X-variables
newtargets

Finds K nearest neighbors for new target observations
impute.yai

Impute variables from references to targets
ensembleImpute

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

Compute error components of k-NN imputations
notablyDistant

Find notably distant targets
mostused

Tabulate references most often used in imputation
plot.compare.yai

Plots a compare.yai object
foruse

Report a complete imputation
print.yai

Print a summary of a yai object
notablyDifferent

Finds obervations with large differences between observed and imputed values
unionDataJoin

Combines data from several sources
grmsd

Generalized Root Mean Square Distance Between Observed and Imputed Values
varSelection

Select variables for imputation models
vars

List variables in a yai object
yai

Find K nearest neighbors
yaiRFsummary

Build Summary Data For Method RandomForest
whatsMax

Find maximum column for each row
plot.yai

Plot observed verses imputed data
rmsd.yai

Root Mean Square Difference between observed and imputed
predict.yai

Generic predict function for class yai
plot.notablyDifferent

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

Boxplot of mean Mahalanobis distances from varSelection()
yaiVarImp

Reports or plots importance scores for yai method randomForest