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yaImpute (version 1.0-32)

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

4,235

Version

1.0-32

License

GPL (>= 2)

Last Published

February 17th, 2020

Functions in yaImpute (1.0-32)

cor.yai

Correlation between observed and imputed
bestVars

Computes the number of best X-variables
ann

Approximate nearest neighbor search routines
TallyLake

Tally Lake, Flathead National Forest, Montana, USA
ensembleImpute

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

Impute variables from references to targets
AsciiGridImpute

Imputes/Predicts data for Ascii Grid maps
compare.yai

Compares different k-NN solutions
applyMask

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

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

Finds the consensus imputations among a list of yai objects
correctBias

Correct bias by selecting different near neighbors
newtargets

Finds K nearest neighbors for new target observations
errorStats

Compute error components of k-NN imputations
rmsd.yai

Root Mean Square Difference between observed and imputed
foruse

Report a complete imputation
predict.yai

Generic predict function for class yai
print.yai

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

Plots a compare.yai object
vars

List variables in a yai object
unionDataJoin

Combines data from several sources
notablyDistant

Find notably distant targets
plot.yai

Plot observed verses imputed data
plot.varSel

Boxplot of mean Mahalanobis distances from varSelection()
notablyDifferent

Finds obervations with large differences between observed and imputed values
yaiRFsummary

Build Summary Data For Method RandomForest
whatsMax

Find maximum column for each row
grmsd

Generalized Root Mean Square Distance Between Observed and Imputed Values
yai

Find K nearest neighbors
plot.notablyDifferent

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

Select variables for imputation models
mostused

Tabulate references most often used in imputation
yaiVarImp

Reports or plots importance scores for yai method randomForest