newtargets {yaImpute} | R Documentation |
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
.
newtargets(object,newdata,ann=NULL)
object |
an object of class yai . |
newdata |
a data frame or matrix of new targets for which neighbors are are found. |
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. |
An object of class yai
, which 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 data frame of distances between a target (identified by its row name) and the k references. There are k columns. |
neiIdsTrgs |
A data frame 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 . |
ann |
the value of the ann argument. |
Nicholas L. Crookston ncrookston@fs.fed.us
Andrew O. Finley finleya@msu.edu
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)),])