Description Usage Arguments Details Value Note Author(s) References See Also Examples
This function computes the distance correlation between every pair of columns of the input data matrix.
1 | dcor.matrix(Data)
|
Data |
A matrix containing the data |
Using for loops, all pairs of columns are passed to link[energy]{dcor}
function from link[energy]{energy-package}
.
A numeric square matrix. The number of rows and columns is equal to the number
of columns of Data
and they are named accordingly.
This function uses for loops, which are not efficient for an input matrix with too many columns.
Habil Zare
Szekely, G.J., Rizzo, M.L., and Bakirov, N.K. (2007), Measuring and Testing Dependence by Correlation of Distances, _Annals of Statistics_, Vol. 35 No. 6, pp. 2769-2794.
<URL: http://dx.doi.org/10.1214/009053607000000505>
Szekely, G.J. and Rizzo, M.L. (2009), Brownian Distance Covariance, _Annals of Applied Statistics_, Vol. 3, No. 4, 1236-1265.
<URL: http://dx.doi.org/10.1214/09-AOAS312>
Szekely, G.J. and Rizzo, M.L. (2009), Rejoinder: Brownian Distance Covariance, _Annals of Applied Statistics_, Vol. 3, No. 4, 1303-1308.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## Data:
data(aml)
dcor1 <- dcor.matrix(Data=aml[,1:5])
dcor1
## Comparison with Pearson:
cor1 <- abs(cor(aml[,1:5]))
## With 202 samples, distance and Pearson correlations do not differ much:
dcor1-cor1
dcor2 <- dcor.matrix(Data=aml[1:20,1:5])
cor2 <- abs(cor(aml[1:20,1:5]))
## Distance correlation is more robust if fewer samples are available:
dcor2-cor2
plot(dcor2-cor1,cor1-cor2,xlim=c(-0.5,0.5),ylim=c(-0.5,0.5))
|
Loading required package: graph
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, basename, cbind, colMeans, colSums, colnames,
dirname, 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
Loading required package: energy
7180_at 272_at 2769_at 4267_at 79183_at
7180_at 1.0000000 0.4584777 0.2736519 0.2514875 0.3069972
272_at 0.4584777 1.0000000 0.4053482 0.3518125 0.4546532
2769_at 0.2736519 0.4053482 1.0000000 0.4762997 0.2476616
4267_at 0.2514875 0.3518125 0.4762997 1.0000000 0.3024571
79183_at 0.3069972 0.4546532 0.2476616 0.3024571 1.0000000
7180_at 272_at 2769_at 4267_at 79183_at
7180_at 0.00000000 -0.025513852 -0.04091352 0.02042267 0.030629233
272_at -0.02551385 0.000000000 -0.02544705 -0.01264602 -0.006530137
2769_at -0.04091352 -0.025447053 0.00000000 -0.02538237 0.022567588
4267_at 0.02042267 -0.012646020 -0.02538237 0.00000000 0.034186756
79183_at 0.03062923 -0.006530137 0.02256759 0.03418676 0.000000000
7180_at 272_at 2769_at 4267_at 79183_at
7180_at 0.00000000 0.08104044 0.248990015 0.256060600 0.05784615
272_at 0.08104044 0.00000000 0.137269242 0.386519574 -0.02561272
2769_at 0.24899001 0.13726924 0.000000000 0.009810655 -0.03975300
4267_at 0.25606060 0.38651957 0.009810655 0.000000000 0.13708974
79183_at 0.05784615 -0.02561272 -0.039753000 0.137089742 0.00000000
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