bIndpTest {BiCausality} | R Documentation |
bIndpTest function
Description
This function infers dependency for a pair of variables i,j with bootstrapping.
Usage
bIndpTest(
mat,
i,
j,
z = c(),
alpha = 0.05,
IndpThs = 0.05,
nboot = 100,
pflag = FALSE
)
Arguments
mat |
is a matrix n by d where n is a number of transactions or samples and d is a number of dimensions. |
i |
is an ith dimension in |
j |
is an jth dimension in |
z |
is a conditioning d-dimensional vector on |
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. |
nboot |
is a number of bootstrap replicates for bootstrapping deployed to infer confidence intervals and distributions for hypothesis tests. The default is 100. |
pflag |
is a flag for printing progress message (TRUE). The default is FALSE (no printing). |
Value
This function returns results of dependency inference between i and j.
bmean |
A mean of dependency degrees between variables i and j. |
confInv |
An |
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 |
Examples
bIndpTest(mat=mat,i=1,j=2)