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yaImpute (version 1.0-34.1)

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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Install

install.packages('yaImpute')

Monthly Downloads

4,235

Version

1.0-34.1

License

GPL (>= 2)

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Last Published

September 21st, 2024

Functions in yaImpute (1.0-34.1)

buildConsensus

Finds the consensus imputations among a list of yai objects
MoscowMtStJoe

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

Tally Lake, Flathead National Forest, Montana, USA
plot.compare.yai

Plots a compare.yai object
notablyDistant

Find notably distant targets
compare.yai

Compares different k-NN solutions
AsciiGridImpute

Imputes/Predicts data for Ascii Grid maps
applyMask

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

Root Mean Square Difference between observed and imputed
ann

Approximate nearest neighbor search routines
print.yai

Print a summary of a yai object
bestVars

Computes the number of best X-variables
foruse

Report a complete imputation
ensembleImpute

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

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

Generic predict function for class yai
plot.yai

Plot observed verses imputed data
correctBias

Correct bias by selecting different near neighbors
cor.yai

Correlation between observed and imputed
newtargets

Finds K nearest neighbors for new target observations
errorStats

Compute error components of k-NN imputations
notablyDifferent

Finds observations with large differences between observed and imputed values
impute.yai

Impute variables from references to targets
unionDataJoin

Combines data from several sources
varSelection

Select variables for imputation models
vars

List variables in a yai object
whatsMax

Find maximum column for each row
yai

Find K nearest neighbors
yaiRFsummary

Build Summary Data For Method RandomForest
yaiVarImp

Reports or plots importance scores for yai method randomForest
mostused

Tabulate references most often used in imputation
plot.varSel

Boxplot of mean Mahalanobis distances from varSelection()
plot.notablyDifferent

Plots the scaled root mean square differences between observed and predicted