Nearest Neighbor Observation Imputation and Evaluation Tools
Approximate nearest neighbor search routines
Removes neighbors that share (or not) group membership with targets.
Imputes/Predicts data for Ascii Grid maps
Computes the number of best X-variables
Finds the consensus imputations among a list of yai objects
Compares different k-NN solutions
Correlation between observed and imputed
Correct bias by selecting different near neighbors
Computes the mean, median, or mode among a list of impute.yai objects
Compute error components of k-NN imputations
Report a complete imputation
Generalized Root Mean Square Distance Between Observed and Imputed Val...
Impute variables from references to targets
Tabulate references most often used in imputation
Finds K nearest neighbors for new target observations
Finds observations with large differences between observed and imputed...
Find notably distant targets
Plots a compare.yai object
Plots the scaled root mean square differences between observed and pre...
Boxplot of mean Mahalanobis distances from varSelection()
Plot observed verses imputed data
Generic predict function for class yai
Print a summary of a yai object
Root Mean Square Difference between observed and imputed
Combines data from several sources
List variables in a yai object
Select variables for imputation models
Find maximum column for each row
Find K nearest neighbors
Build Summary Data For Method RandomForest
Reports or plots importance scores for yai method randomForest
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.
Useful links