Change the neighbor selections in a yai object such that
bias (if any) in the average value of an expression
of one or more variables is reduced to be within a defined
confidence interval.
correctBias(object,trgVal,trgValCI=NULL,nStdev=1.5,excludeRefIds=NULL,trace=FALSE)An object of class yai where k = 1 and the neighbor selections
have been changed as described above. In addition, the call element is changed
to show both the original call to yai and the call to this function.
A new list called biasParameters is added to the yai object
with these tags:
the target CI.
the value of the bias that was achieved.
the number of passes through the data taken to achieve the result.
the old value of k.
an object of class yai with k > 1.
an expression defining a variable or combination of
variables that is applied to each member of the population (see details). If
passed as a character string it is coerced into an expression. The expression can
refer to one or more X- and Y-variables defined for the reference observations.
The confidence interval that should contain the mean(trgVal).
If the mean falls within this interval, the problem is solved. If NULL, the
interval is based on nStdev.
the number of standard deviations in the vector of values used to
compute the confidence interval when one is computed, ignored if trgValCI
is not NULL.
identities of reference observations to exclude from the population, if coded as "all" then all references are excluded (see details).
if TRUE, detailed output is produced.
Nicholas L. Crookston ncrookston.fs@gmail.com
Imputation as it is defined in yaImpute can yield biased
results. Lets say that you have a collection of reference
observations that happen to be selected in a non-biased way among a
population. In this discussion, population is a finite set of all
individual sample units of interest; the reference plus target observations
often represent this population (but this need not be true, see below).
If the average of a measured attribute is computed from
this random sample, it is an unbiased estimate of the true mean.
Using yai, while setting k=1, values for each of several
attributes are imputed from a single reference observation to a target
observation. Once the imputation is done over all the target observations, an
average of any one measured attribute can be computed over all the observations
in the population. There is no guarantee that this average will be within a
pre-specified confidence interval.
Experience shows that despite any lack of guarantee, the results are accurate
(not biased). This tends to hold true when the reference data contains
samples that cover the variation of the targets, even when they are not a
random sample, and even if some of the reference observations are from sample
units that are outside the target population.
Because there is no guarantee, and because the reference observations might
profitably come from sample units beyond those in the population
(so as to insure all kinds of targets have a matching reference), it is necessary
to test the imputation results for bias. If bias is found, it would be helpful to
do something to correct it.
The correctBias() function is designed to check for, and correct
discovered bias by selecting alternative nearby reference observations
to be imputed to targets that contribute to the bias. The idea is that even if
one reference is closest to a target, its attribute(s) of interest might
be greater (or less) than the mean. An alternative neighbor, one that may be
almost as close, might reduce the overall bias if it were used instead. If this is
the case, correctBias() switches the neighbor selections.
It makes as many switches as it can until the mean among the population
targets falls within the specified confidence interval.
There is no guarantee that the goal will be met.
The details of the method are:
1. An attribute of interest is established by naming one in the call with
argument tarVal. Note that this can be a simple variable name enclosed
in quotations marks or it can be an expression of one or more variables.
If the former, it is converted into an expression that is executed in the
environment of the reference observations (both the X- and Y-variables).
A confidence interval is computed for this value under the assumption that the
reference observations are an unbiased sample of the target population. This may
not be the case. Regardless, a confidence interval is necessary and it can
alternatively be supplied using trgValCI.
2. One of several possible passes through the data are taken to find neighbor switches
that will result in the bias being corrected. A pass includes computing the
attribute of interest by applying the expression to values imputed to all
the targets, under the assumption that the next neighbor is used
in place of the currently used neighbor. This computation results in a
vector with one element for each target observation that
measures the contribution toward reducing the bias that would be made if
a switch were made. The target observations are then ordered
into increasing order of how much the distance from the currently selected
reference would increase if the switch were to take place. Enough switches are
made in this order to correct the bias.
If the bias is not corrected by the first pass, another pass is done
using the next neighbor(s).
The number of possible passes is equal to k-1 where k is
set in the original call to yai. Note that switches are made
among targets only, and never among reference observations that may make up the
population. That is, reference observations are always left
to represent themselves with k=1.
3. Here are details of the argument excludeRefIds. When computing the mean
of the attribute of interest (using the expression),
correctBias() must know which observations represent the
population. Normally, all the target observations would be in this set, but perhaps
not all of the reference observations. When excludeRefIds
is left NULL, the population is made of all reference and all
target observations. Reference observations that should be left out
of the calculations because they are not part of the population can be specified
using the excludeRefIds argument as a vector of character strings identifing
the rownames to leave out, or a vector of row numbers that identify the row numbers to
leave out. If excludeRefIds="all", all reference observations are excluded.
yai
data(iris)
set.seed(12345)
# 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 an msn run, first build dummy variables for species.
sp1 <- as.integer(iris$Species=="setosa")
sp2 <- as.integer(iris$Species=="versicolor")
y2 <- data.frame(cbind(iris[,4],sp1,sp2),row.names=rownames(iris))
y2 <- y2[refs,]
names(y2) <- c("Petal.Width","Sp1","Sp2")
# find 5 refernece neighbors for each target
msn <- yai(x=x,y=y2,method="msn",k=5)
# check for and correct for bias in mean "Petal.Width". Neighbor
# selections will be changed as needed to bring the imputed values
# into line with the CI. In this case, no changes are made (npasses
# returns as zero).
msnCorr = correctBias(msn,trgVal="Petal.Width")
msnCorr$biasParameters
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