HDMT (version 1.0.5)

null_estimation: A function to estimate the proportions of the three component nulls

Description

This is a function to estimate the proportions of the three component nulls involved in mediation testing. We developed a three component-mixture model method to estimate the proportions of nulls and provide much more accurate control of the family-wise error rate (FWER) and the false discovery rate (FDR), when compared to the standard approach using the uniform null distribution.

Usage

null_estimation(input_pvalues)

Arguments

input_pvalues

A matrix contains two columns of p-values for candidate mediators. Column 1 is the p-value of testing if the exposure is associated with the candidate mediator (alpha!=0). Column 2 is the p-value of testing if the candidate mediator is associated with the outcome adjusted for the exposure (beta!=0).

Value

A list contains five elements.

alpha00

A numeric number represents the proportion of null \(H_{00}\)

alpha01

A numeric number represents the proportion of null \(H_{01}\)

alpha10

A numeric number represents the proportion of null \(H_{10}\)

alpha1

A numeric number represents the proportion of null alpha==0

alpha2

A numeric number represents the proportion of null beta==0

Details

A function to estimate the proportions of the three types of component null hypotheses:

\(H_{00}\): alpha==0 and beta==0

\(H_{01}\): alpha==0 and beta!=0

\(H_{10}\): alpha!=0 and beta==0

The methodology detail can be found in Dai et al (2020).

References

James Y. Dai, Janet L. Stanford, Michael LeBlanc. A multiple-testing procedure for high-dimensional mediation hypotheses, Journal of the American Statistical Association, 2020, DOI: 10.1080/01621459.2020.1765785.

Examples

# NOT RUN {
data(snp_input)
input_pvalues <- snp_input
#To save computing time for illustration, we use 10 percent of rows (p-values)
input_pvalues <- input_pvalues[sample(1:nrow(input_pvalues),
size <- ceiling(nrow(input_pvalues)/10)),]

nullprop <- null_estimation(input_pvalues)
# }