Description Usage Arguments Details Value Author(s) See Also Examples
Calculate the KLD by binning continuous data.
KL distance is calculated using the formula
KLD(f_1(x),f_2(x)) = ∑_{i=1}^N{ f_1(x_i)*\log\frac{f_1(x_i)}{f_2(x_i)}}
1 | KLdist.matrix(x, ...)
|
x |
n by p matrix or a list or an object of a class that extends eSet.
If x is an object of a class derived from eSet (ExpressionSet,SnpSet etc),
then the values returned by the |
... |
arguments passed to
|
The data are binned, and then the KL distance between the two discrete
distributions is computed and used. The distance is computed between
rows of the input matrix (except if the input is an object of a class
that extends eSet and sample
is TRUE
.
The presumption is that all samples have the same number of observations. The list method is meant for use when samples sizes are unequal.
An object of class dist
is returned.
Beiying Ding
cor.dist
, spearman.dist
,
tau.dist
,euc
,
man
,KLD.matrix
,mutualInfo
1 2 | x <- matrix(rnorm(100), nrow = 5)
KLdist.matrix(x, symmetrize = TRUE)
|
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
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Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
1 2 3 4
2 5.9919159
3 3.4899114 0.9365696
4 2.2514987 0.7356014 1.2848732
5 2.5332660 1.1916893 0.9661362 1.3517168
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