kpcalg (version 1.0.1)

kernelCItest: Kernel Conditional Independence test

Description

Test to check the (conditional) dependence between two variables x and y given a set of variables S, using independence criteria. The kernelCItest() function, uses Distance Covariance or Hilbert-Schmidt Independence Criterion to test for the (conditional) independence between random variables, with an interface that can easily by used in skeleton, pc or kpc.

Usage

kernelCItest(x, y, S = NULL, suffStat, verbose = FALSE, data, ic.method = NULL, p = NULL, index = NULL, sig = NULL, numCol = NULL, numCluster = NULL, eps = NULL, paral = NULL)

Arguments

x, y, S

It is tested, whether x and y are conditionally independent given the subset S of the remaining nodes. x, y, S all are integers, corresponding to variable or node numbers.

suffStat

a list of parameters consisting of data, ic.method, p, index, sig, numCol, numCluster, eps, paral

verbose

a logical parameter, if TRUE, detailed output is provided.

data

numeric matrix witch collumns representing variables and rows representing samples

ic.method

Method for the (conditional) independence test: Distance Covariance (permutation or gamma test), HSIC (permutation or gamma test) or HSIC cluster

p

Number of permutations for Distance Covariance, HSIC permutation and HSIC cluster tests. Default is Distance Covariance

index

Number in (0,2] the power of the distance in the Distance Covariance

sig

Gaussian kernel width for HSIC tests. Default is 1

numCol

Number of columns used in the incomplete Cholesky decomposition. Default is 50

numCluster

Number of clusters for kPC clust algorithm

eps

Normalization parameter for kPC clust. Default is 0.1

paral

Number of cores to use for parallel calculations.

Value

kernelCItest() returns the p-value of the test.

References

G. Szekely, M. Rizzo and N. Bakirov (2007). Measuring and Testing Dependence by Correlation of Distances. The Annals of Statistics 2007, Vol. 35, No. 6, 2769-2794.

A. Gretton et al. (2005). Kernel Methods for Measuring Independence. JMLR 6 (2005) 2075-2129.

R. Tillman, A. Gretton and P. Spirtes (2009). Nonlinear directed acyclic structure learning with weakly additive noise model. NIPS 22, Vancouver.

Examples

set.seed(10)
library(pcalg)
z <- 10*runif(300)
w <- 10*runif(300)
x <- sin(z) + runif(300)
y <- cos(z) + runif(300)

data <- cbind(x,y,z,w)

#conditionally independent
test1a <- kernelCItest(x=1,y=2,S=c(3),suffStat = list(data=data,ic.method="dcc.gamma"))
test2a <- kernelCItest(x=1,y=2,S=c(3),suffStat = list(data=data,ic.method="dcc.perm"))
test3a <- kernelCItest(x=1,y=2,S=c(3),suffStat = list(data=data,ic.method="hsic.gamma"))
test4a <- kernelCItest(x=1,y=2,S=c(3),suffStat = list(data=data,ic.method="hsic.perm"))
test5a <- kernelCItest(x=1,y=2,S=c(3),suffStat = list(data=data,ic.method="hsic.clust"))
test6a <- gaussCItest( x=1,y=2,S=c(3),suffStat = list(C=cor(data),n=4))

test1a
test2a
test3a
test4a
test5a
test6a

#dependent
test1b <- kernelCItest(x=1,y=2,S=c(4),suffStat = list(data=data,ic.method="dcc.gamma"))
test2b <- kernelCItest(x=1,y=2,S=c(4),suffStat = list(data=data,ic.method="dcc.perm"))
test3b <- kernelCItest(x=1,y=2,S=c(4),suffStat = list(data=data,ic.method="hsic.gamma"))
test4b <- kernelCItest(x=1,y=2,S=c(4),suffStat = list(data=data,ic.method="hsic.perm"))
test5b <- kernelCItest(x=1,y=2,S=c(4),suffStat = list(data=data,ic.method="hsic.clust"))
test6b <- gaussCItest( x=1,y=2,S=c(4),suffStat = list(C=cor(data),n=4))

test1b
test2b
test3b
test4b
test5b
test6b