yaImpute (version 1.0-15)

yai: Find K nearest neighbors

Description

Given a set of observations, yai 1) separates the observations into reference and target observations, 2) applies the specified method to project X-variables into a Euclidean space (not always, see argument method), and 3) finds the k-nearest neighbors within the referenece observations and between the reference and target observations. An alternative method using randomForest classification and regression trees is provided for steps 2 and 3. Target observations are those with values for X-variables and not for Y-variables, while reference observations are those with no missing values for X-and Y-variables (see Details for the exception).

Usage

yai(x=NULL,y=NULL,data=NULL,k=1,noTrgs=FALSE,noRefs=FALSE,
    nVec=NULL,pVal=.05,method="msn",ann=TRUE,mtry=NULL,ntree=500,
    rfMode="buildClasses")

Arguments

x

1) a matrix or data frame containing the X-variables for all observations with row names are the identification for the observations, or 2) a one-sided formula defining the X-variables as a linear formula. If a formula is coded for x

y

1) a matrix or data frame containing the Y-variables for the reference observations, or 2) a one-sided formula defining the Y-variables as a linear formula.

data

when x and y are formulas, then data is a data frame or matrix that contains all the variables with row names are the identification for the observations. The observations are split by yai into two sets.

k

the number of nearest neighbors; default is 1.

noTrgs

when TRUE, skip finding neighbors for target observations.

noRefs

when TRUE, skip finding neighbors for reference observations.

nVec

number of canonical vectors to use (methods msn and msn2), or number of independent of X-variables reference data when method mahalanobis. When NULL, the number is set by the function.

pVal

significant level for canonical vectors, used when method is msn or msn2.

method

is the strategy finding neighbors; the options are the quoted key words (see details):

• euclidean

{distance is computed in a normalized X space.} raw{like euclidean, except no normalizat

Value

• An object of class yai, which is a list with the following tags:
• call
  the call.
• yRefs, xRefs
  matrices of the X- and Y-variables for just the reference observations (unscaled). The scale factors are attached as attributes.
• 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.
• cancor
  returned from cancor function when method msn or msn2 is used (NULL otherwise).
• ccaVegan
  an object of class cca (from package vegan) when method gnn is used.
• ftest
  a list containing partial F statistics and a vector of Pr>F (pgf) corresponding to the canonical correlation coefficients when method msn or msn2 is used (NULL otherwise).
• yScale, xScale
  scale data used on yRefs and xRefs as needed.
• k
  the value of k.
• pVal
  as input; only used when method msn or msn2 is used.
• projector
  NULL when not used. For methods msn, msn2, gnn and mahalanobis, this is a matrix that projects normalized X-variables into a space suitable for doing Eculidian distances.
• nVec
  number of canonical vectors used (methods msn and msn2), or number of independent X-variables in the reference data when method mahalanobis is used.
• method
  as input, the method used.
• ranForest
  a list of the forests if method randomForest is used. There is one forest for each Y-variable, or just one forest when there are no Y-variables.
• ICA
  a list of information from fastICA when method ica is used.
• ann
  the value of ann, TRUE when ann is used, FALSE otherwise.
• xlevels
  NULL if no factors are used as predictors; otherwise a list of predictors that have factors and their levels (see lm).
• 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, neiIdsRefs
  counterparts for references.

item

• ann
• mtry
• ntree
• rfMode

code

4.5-18

Details

See the paper at http://www.jstatsoft.org/v23/i10 (it includes examples).

The following information is in addition to the content in the papers.

You need not have any Y-variables to run yai for the following methods: euclidean, raw, mahalanobis, ica, random, and randomForest (in which case unsupervised classification is performed). However, normally yai classifies reference observations as those with no missing values for X- and Y- variables and target observations are those with values for X- variables and missing data for Y-variables. When Y is NULL (there are no Y-variables), all the observations are considered references. See newtargets for an example of how to use yai in this situation.

Examples

require (yaImpute)

data(iris)

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

# form some test data, y's are defined only for reference 
# observations.
refs=sample(rownames(iris),50)
x <- iris[,1:2]      # Sepal.Length Sepal.Width
y <- iris[refs,3:4]  # Petal.Length Petal.Width

# build yai objects using 2 methods
msn <- yai(x=x,y=y)
mal <- yai(x=x,y=y,method="mahalanobis")

# running the following examples will load packages vegan 
# and randomForest, and is more complicated.

data(MoscowMtStJoe)

# convert polar slope and aspect measurements to cartesian
# (which is the same as Stage's (1976) transformation).

polar <- MoscowMtStJoe[,40:41]
polar[,1] <- polar[,1]*.01      # slope proportion
polar[,2] <- polar[,2]*(pi/180) # aspect radians
cartesian <- t(apply(polar,1,function (x)
               {return (c(x[1]*cos(x[2]),x[1]*sin(x[2]))) }))
colnames(cartesian) <- c("xSlAsp","ySlAsp")
x <- cbind(MoscowMtStJoe[,37:39],cartesian,MoscowMtStJoe[,42:64])
y <- MoscowMtStJoe[,1:35]

mal <- yai(x=x, y=y, method="mahalanobis", k=1)
gnn <- yai(x=x, y=y, method="gnn", k=1)
msn <- yai(x=x, y=y, method="msn", k=1)

plot(mal,vars=yvars(mal)[1:16])

# reduce the plant community data for randomForest.
yba  <- MoscowMtStJoe[,1:17]
ybaB <- whatsMax(yba,nbig=7)  # see help on whatsMax

rf <- yai(x=x, y=ybaB, method="randomForest", k=1)

# build the imputations for the original y's
rforig <- impute(rf,ancillaryData=y)

# compare the results
compare.yai(mal,gnn,msn,rforig)
plot(compare.yai(mal,gnn,msn,rforig))

# build another randomForest case forcing regression
# to be used for continuous variables. The answers differ
# but one is not clearly better than the other.

rf2 <- yai(x=x, y=ybaB, method="randomForest", rfMode="regression")
rforig2 <- impute(rf2,ancillaryData=y)
compare.yai(rforig2,rforig)