impute.yai: Impute variables from references to targets In yaImpute: Nearest Neighbor Observation Imputation and Evaluation Tools

Description

Imputes the observation for variables from a reference observation to a target observation. Also, imputes a value for a reference from other references. This practice is useful for validation (see `yai`). Variables not available in the original data may be imputed using argument `ancillaryData`.

Usage

 ```1 2 3 4``` ```## S3 method for class 'yai' impute(object,ancillaryData=NULL,method="closest", method.factor=method,k=NULL,vars=NULL, observed=TRUE,...) ```

Arguments

 `object` an object of class `yai`. `ancillaryData` a data frame of variables that may not have been used in the original call to `yai`. There must be one row for each reference observation, no missing data, and row names must match those used in the reference observations. `method` the method used to compute the imputed values for continuous variables, as follows: `closest`: use the single neighbor that is closest (this is the default and is always used when k=1); `mean`: the mean of the k neighbors is taken; `median`: the median of the k neighbors is taken; `dstWeighted`: a weighted mean is taken over the k neighbors where the weights are 1/(1+d). `method.factor` the method used to compute the imputed values for factors, as follows: `closest`: use the single neighbor that is closest (this is the default and is always used when k=1); `mean or median`: actually is the mode\-\-it is the factor level that occurs the most often among the k neighbors; `dstWeighted`: a mode where the count is the sum of the weights (1/(1+d)) rather than each having a weight of 1. `k` the number neighbors to use in averages, when NULL all present are used. `vars` a character vector of variables to impute, when NULL, the behaviour depends on the value of `ancillaryData`: when it is NULL, the Y-variables are imputed and otherwise all present in `ancillaryData` are imputed. `observed` when TRUE, columns are created for observed values (those from the target observations) as well as imputed values (those from the reference observations. `...` passed to other methods, currently not used.

Value

An object of class `c("impute.yai","data.frame")`, with rownames identifying observations and column names identifying variables. When observed=TRUE additional columns are created with a suffix of .o.

NA's fill columns of observed values when no corresponding value is known, as in the case for Y-variables from target observations.

Scale factors for each variable are returned as an attribute (see `attributes`).

Author(s)

Nicholas L. Crookston [email protected]
Andrew O. Finley [email protected]
Emilie Henderson [email protected]

`yai`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```require(yaImpute) data(iris) # form some test data refs=sample(rownames(iris),50) x <- iris[,1:3] # Sepal.Length Sepal.Width Petal.Length y <- iris[refs,4:5] # Petal.Width Species # build a yai object using mahalanobis mal <- yai(x=x,y=y,method="mahalanobis") # output a data frame of observed and imputed values # of all variables and observations. impute(mal) malImp=impute(mal,ancillaryData=iris) plot(malImp) ```