yaImpute (version 1.0-21)

newtargets: Finds K nearest neighbors for new target observations

Description

Finds nearest neighbor reference observations for a given set of target observations using an established (see yai) object. Intended use is to facilitate breaking up large imputation problems (see AsciiGridImpute).

Usage

newtargets(object,newdata,k=NULL,ann=NULL)

Arguments

object

an object of class yai.

newdata

a data frame or matrix of new targets for which neighbors are are found. Must include at least the X-variables used in the original call to yai.

k

if NULL, the value is taken from object, otherwise the number of nearest neighbors to find.

ann

if NULL, the value is taken from object. When TRUE ann is used to find neighbors, and when FALSE a slow exact search is used.

Value

• An object of class yai, that is a copy of the first argument with the following elements replaced:
• call
  the call.
• obsDropped
  a list of the row names for observations dropped for various reasons (missing data).
• trgRows
  a list of the row names for target observations as a subset of all observations.
• xall
  the X-variables for all observations.
• neiDstTrgs
  a matrix of distances between a target (identified by its row name) and the k references. There are k columns.
• neiIdsTrgs
  a matrix of reference identifications that correspond to neiDstTrgs.
• neiDstRefs
  set NULL as if noRefs=TRUE in the original call to yai.
• neiIdsRefs
  set NULL as if noRefs=TRUE in the original call to yai.
• k
  the value of k, replaced if changed.
• ann
  the value of the ann argument.

See Also

yai

Examples

require (yaImpute)

data(iris)

# set the random number seed so that example results are consistant
# normally, leave out this command
set.seed(12345)

# form some test data
refs=sample(rownames(iris),50) # just the reference observations
x <- iris[refs,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")

# get imputations for the target observations (not references)
malNew <- newtargets(mal,iris[!(rownames(iris) %in% rownames(x)),])

# output a data frame of observed and imputed values for
# the observations that are not in the original yai object

impute(malNew,vars=yvars(malNew))

# in this example, Y is not specified (not required for mahalanobis).
mal2 <- yai(x=x,method="mahalanobis")
identical(foruse(mal),foruse(mal2))

# here, method randomForest's unsupervised classification is used (no Y).
rf <- yai(x=x,method="randomForest")
# now get imputations for the targets in the iris data (those that are
# not references).
rfNew <- newtargets(rf,iris[!(rownames(iris) %in% rownames(x)),])