R: bSCMDepndentGraphFunc function

bSCMDepndentGraphFunc {BiCausality}

R Documentation

bSCMDepndentGraphFunc function

This function infers dependencies for all pairs of variables with bootstrapping.

bSCMDepndentGraphFunc(
  mat,
  nboot = 100,
  alpha = 0.05,
  IndpThs = 0.05,
  pflag = FALSE
)

`mat`	is a matrix n by d where n is a number of transactions or samples and d is a number of dimensions.
`nboot`	is a number of bootstrap replicates for bootstrapping deployed to infer confidence intervals and distributions for hypothesis tests. The default is 100.
`alpha`	is a significance threshold for hypothesis tests (Mann Whitney) that deploys for testing degrees of dependency, association direction, and causal direction. The default is 0.5.
`IndpThs`	is a threshold for the degree of dependency. In the independence test, to claim that any variables are dependent, the dependency degree must greater than this value significantly. The default is 0.05.
`pflag`	is a flag for printing progress message (TRUE). The default is FALSE (no printing).

This function returns results of dependency inference among variables.

`E0`	An adjacency matrix of undirected graph where there is an edge between any pair of variables if they are dependent.
`E0pval`	A matrix of p-values from independence test of pairs of variables.
`E0mean`	A matrix of means of dependency degrees between variables.
`E0lowbound`	A matrix of lower bounds of dependency-degree confidence intervals between variables.
`depInfo$'i`, `j'$bmean`	A mean of dependency degrees between variables i and j.
`depInfo$'i`, `j'$confInv`	An `alpha`*100th percentile confidence interval of dependency degrees between variables i and j.
`depInfo$'i`, `j'$testRes`	A Mann-Whitney hypothesis test result for an independence test between variables i and j. The null hypothesis is that the distributions of dependency degrees of i,j differ by a location shift of `IndpThs` and the alternative is that distributions of dependency degrees of i,j is shifted greater than `IndpThs`.
`depInfo$'i`, `j'$indices`	A pair of indices of i and j in a numeric vector.
`Dboot`	A list of `D`s (aligned list of transactions) that are generated from sampling with replacement on input samples (`mat`) `nboot` times.

bSCMDepndentGraphFunc(mat, nboot=50)

[Package BiCausality version 0.1.4 Index]