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.
|Author||Nicholas L. Crookston, Andrew O. Finley, John Coulston (Sunil Arya and David Mount for ANN)|
|Date of publication||2015-07-20 18:36:55|
|Maintainer||Nicholas L. Crookston <firstname.lastname@example.org>|
|License||GPL (>= 2)|
ann: Approximate nearest neighbor search routines
applyMask: Removes neighbors that share (or not) group membership with...
asciigridimpute: Imputes/Predicts data for Ascii Grid maps
bestVars: Computes the number of _best_ X-variables
buildConsensus: Finds the consensus imputations among a list of yai objects
compare.yai: Compares different k-NN solutions
correctbias: Correct bias by selecting different near neighbors
cor.yai: Correlation between observed and imputed
ensembleImpute: Computes the mean, median, or mode among a list of impute.yai...
errorStats: Compute error components of k-NN imputations
foruse: Report a complete imputation
grmsd: Generalized Root Mean Square Distance Between Observed and...
impute.yai: Impute variables from references to targets
MoscowMtStJoe: Moscow Mountain and St. Joe Woodlands (Idaho, USA) Tree and...
mostused: Tabulate references most often used in imputation
newtargets: Finds K nearest neighbors for new target observations
notablydifferent: Finds obervations with large differences between observed and...
notablydistant: Find notably distant targets
plot.compare.yai: Plots a compare.yai object
plot.notablydifferent: Plots the scaled root mean square differences between...
plot.varSel: Boxplot of mean Mahalanobis distances from varSelection()
plot.yai: Plot observed verses imputed data
predict.yai: Generic predict function for class yai
print: Print a summary of a yai object
rmsd.yai: Root Mean Square Difference between observed and imputed
TallyLake: Tally Lake, Flathead National Forest, Montana, USA
unionDataJoin: Combines data from several sources
vars: List variables in a yai object
varSelection: Select variables for imputation models
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...