yaImpute v1.0-32


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Nearest Neighbor Observation Imputation and Evaluation Tools

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.

Functions in yaImpute

Name Description
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
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Last month downloads


Date 2020-02-13
Copyright ANN library is copyright University of Maryland and Sunil Arya and David Mount. See file COPYRIGHTS for details.
NeedsCompilation yes
License GPL (>= 2)
Repository CRAN
Repository/R-Forge/Project yaimpute
Repository/R-Forge/Revision 105
Repository/R-Forge/DateTimeStamp 2020-02-13 20:41:20
Date/Publication 2020-02-17 18:00:02 UTC
Packaged 2020-02-13 20:50:10 UTC; rforge

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