half.range.mode {genefilter} | R Documentation |
For data assumed to be drawn from a unimodal, continuous distribution, the mode is estimated by the “half-range” method. Bootstrap resampling for variance reduction may optionally be used.
half.range.mode(data, B, B.sample, beta = 0.5, diag = FALSE)
data |
A numeric vector of data from which to estimate the mode. |
B |
Optionally, the number of bootstrap resampling rounds to use. Note
that B = 1 resamples 1 time, whereas omitting B
uses data as is, without resampling.
|
B.sample |
If bootstrap resampling is requested, the size of the bootstrap
samples drawn from data . Default is to use a sample which is
the same size as data . For large data sets, this may be slow
and unnecessary.
|
beta |
The fraction of the remaining range to use at each iteration. |
diag |
Print extensive diagnostics. For internal testing only... best left
FALSE .
|
Briefly, the mode estimator is computed by iteratively identifying
densest half ranges. (Other fractions of the current range can be
requested by setting beta
to something other than 0.5.) A densest half
range is an interval whose width equals half the current range, and
which contains the maximal number of observations. The subset of
observations falling in the selected densest half range is then used to compute
a new range, and the procedure is iterated. See the references for
details.
If bootstrapping is requested, B
half-range mode estimates are
computed for B
bootstrap samples, and their average is returned
as the final estimate.
The mode estimate.
Richard Bourgon <bourgon@stat.berkeley.edu>
## A single normal-mixture data set x <- c( rnorm(10000), rnorm(2000, mean = 3) ) M <- half.range.mode( x ) M.bs <- half.range.mode( x, B = 100 ) if(interactive()){ hist( x, breaks = 40 ) abline( v = c( M, M.bs ), col = "red", lty = 1:2 ) legend( 1.5, par("usr")[4], c( "Half-range mode", "With bootstrapping (B = 100)" ), lwd = 1, lty = 1:2, cex = .8, col = "red" ) } # Sampling distribution, with and without bootstrapping X <- rbind( matrix( rnorm(1000 * 100), ncol = 100 ), matrix( rnorm(200 * 100, mean = 3), ncol = 100 ) ) M.list <- list( Simple = apply( X, 2, half.range.mode ), BS = apply( X, 2, half.range.mode, B = 100 ) ) if(interactive()){ boxplot( M.list, main = "Effect of bootstrapping" ) abline( h = 0, col = "red" ) }