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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Details
| 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 |
| suggests | ccaPP , fastICA , gam , gower , parallel , randomForest , vegan |
| imports | graphics , grDevices , stats , utils |
| depends | R (>= 3.0.0) |
| Contributors | Nicholas Crookston, Andrew Finley, John Coulston |
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