dcsis | R Documentation |

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

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" )

`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. |

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. |

berschneider2018complexdcortools \insertRefdueck2014affinelydcortools

\insertRefhuang2017statisticallydcortools

\insertRefhuo2016fastdcortools

\insertRefli2012featuredcortools

\insertRefszekely2007dcortools

\insertRefszekely2009browniandcortools

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

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