dcsis: Performs distance correlation sure independence screening...

In dcortools: Providing Fast and Flexible Functions for Distance Correlation Analysis

Performs distance correlation sure independence screening \insertCiteli2012featuredcortools with some additional options (such as calculating corresponding tests).

Description

Performs distance correlation sure independence screening \insertCiteli2012featuredcortools with some additional options (such as calculating corresponding tests).

Usage

dcsis(
  X,
  Y,
  k = floor(nrow(X)/log(nrow(X))),
  threshold = NULL,
  calc.cor = "spearman",
  calc.pvalue.cor = FALSE,
  return.data = FALSE,
  test = "none",
  adjustp = "none",
  b = 499,
  bias.corr = FALSE,
  use = "all",
  algorithm = "auto"
)

Arguments

X	A dataframe or matrix.
Y	A vector-valued response having the same length as the number of rows of X.
k	Number of variables that are selected (only used when threshold is not provided).
threshold	If provided, variables with a distance correlation larger than threshold are selected.
calc.cor	If set as "pearson", "spearman" or "kendall", a corresponding correlation matrix is additionally calculated.
calc.pvalue.cor	logical; IF TRUE, a p-value based on the Pearson or Spearman correlation matrix is calculated (not implemented for calc.cor = "kendall") using Hmisc::rcorr.
return.data	logical; specifies if the dcmatrix object should contain the original data.
test	Allows for additionally calculating a test based on distance Covariance. Specifies the type of test that is performed, "permutation" performs a Monte Carlo Permutation test. "gamma" performs a test based on a gamma approximation of the test statistic under the null. "conservative" performs a conservative two-moment approximation. "bb3" performs a quite precise three-moment approximation and is recommended when computation time is not an issue.
adjustp	If setting this parameter to "holm", "hochberg", "hommel", "bonferroni", "BH", "BY" or "fdr", corresponding adjusted p-values are additionally returned for the distance covariance test.
b	specifies the number of random permutations used for the permutation test. Ignored for all other tests.
bias.corr	logical; specifies if the bias corrected version of the sample distance covariance \insertCitehuo2016fastdcortools should be calculated.
use	"all" uses all observations, "complete.obs" excludes NAs, "pairwise.complete.obs" uses pairwise complete observations for each comparison.
algorithm	specifies the algorithm used for calculating the distance covariance. "fast" uses an O(n log n) algorithm if the observations are one-dimensional and metr.X and metr.Y are either "euclidean" or "discrete", see also \insertCitehuo2016fast;textualdcortools. "memsave" uses a memory saving version of the standard algorithm with computational complexity O(n^2) but requiring only O(n) memory. "standard" uses the classical algorithm. User-specified metrics always use the classical algorithm. "auto" chooses the best algorithm for the specific setting using a rule of thumb. "memsave" is typically very inefficient for dcsis and should only be applied in exceptional cases.

Value

dcmatrix object with the following two additional slots:

name selected	description indices of selected variables.
name dcor.selected	distance correlation of the selected variables and the response Y.

References

\insertRef

berschneider2018complexdcortools \insertRefdueck2014affinelydcortools

\insertRef

huang2017statisticallydcortools

\insertRef

huo2016fastdcortools

\insertRef

li2012featuredcortools

\insertRef

szekely2007dcortools

\insertRef

szekely2009browniandcortools

Examples

X <- matrix(rnorm(1e5), ncol = 1000)
Y <- sapply(1:100, function(u) sum(X[u, 1:50])) + rnorm(100)
a <- dcsis(X, Y)

dcortools documentation built on Dec. 10, 2022, 1:09 a.m.