fdr.test {bayesclust}R Documentation

For Testing Multiple Hypotheses, Controlling for FDR

Description

Allows the experimenter to assess the significance of clusters in multiple datasets whilst controlling for the False Discovery Rate (FDR).

Usage

fdr.test(namelist, q=0.05)

Arguments

namelist The initial argument should be a character vector of names of objects of class ``emp2pval''. Objects of this class are returned by the function emp2pval.
q q should be a value between 0 and 1. It is the maximum FDR that the experimenter is willing to tolerate.

Details

This function implements the FDR controlling procedure described in Benjamini and Hochberg (1995). This routine will look for the largest i such that P_{(i)} <= (i/m) * q . Please refer to the original paper for the notation and details.

Value

The output simply lists the datasets for which significant clusters were found.

Author(s)

Gopal, V.

References

Benjamini, Y. and Hochberg, Y. (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. JRRS (B) Vol. 57, No. 1

See Also

emp2pval for details on objects of class emp2pval.

Examples

# Generate random 2-variate data
Y <- matrix(rnorm(24), nrow=12)
Z <- matrix(rnorm(24), nrow=12)

# Search for optimal partitioning of data into 2 clusters
test1 <- cluster.test(Y, p=2)
test2 <- cluster.test(Z, p=2)

# Generate corresponding null density object.
null1 <- nulldensity(nsim=100, n=12, p=2, k=2)

# Convert EPP to p-value
test1.pval <- emp2pval(test1, null1)
test2.pval <- emp2pval(test2, null1)

# Test for significance, controlling for FDR
fdr.test(c("test1.pval", "test2.pval"), q=0.3)

[Package bayesclust version 2.1 Index]