ppool: p-values pooling and adjustment

In JumpTest: Financial Jump Detection

Description

Pooling input p-values and perfrom FDR adjustments

Usage

ppool(pmat, method = "SD")

Arguments

pmat	p-values matrix stored by columns
method	pooling methods, see details

Details

for p-values poolings, we provided six methods. "FI" for Fisher's method, "FD" for Fisher's with correlation adjustments, "SI" for Stouffer's method, "SD" for Stouffer's method with correlation adjustments, "MI" for minimum p-value methods, and "MA" for maximum p-value method

Value

stat	pooled test statistcs
pvalue	pooled p-values
adjp	pooled p-values via "BH" adjustments

References

Benjamini, Y. and Y. Hochberg (1995). "Controlling the false discovery rate: a practical and powerful approach to multiple testing." Journal of the Royal Statistical Society. Series B (Methodological): 289-300.

Chang, L.-C., et al. (2013). "Meta-analysis methods for combining multiple expression profiles: comparisons, statistical characterization and an application guideline." BMC bioinformatics 14(1): 368.

Won, S., et al. (2009). "Choosing an optimal method to combine P-values." Statistics in medicine 28(11): 1537-1553.

Alves, G., & Yu, Y. K. (2014). Accuracy evaluation of the unified P-value from combining correlated P-values. PloS one, 9(3), e91225.

Examples

orip <- matrix(runif(3000),1000,3)
pvobj <- ppool(orip)
pvalue <- pvobj@pvalue
padjust <- pvobj@adjp

Example output

Loading required package: MASS

JumpTest documentation built on May 1, 2019, 9:45 p.m.