adjust_quantile {HDMT} | R Documentation |
A function to compute the quantiles of the estimated mixture null distribution for pmax using either the approximation or exact method
Description
A function to generate the quantiles of the estimated three-component mixture null distribution for pmax (the maximum of the two p-values for testing mediation) using either approximation or exact method
Usage
adjust_quantile(alpha00, alpha01, alpha10, alpha1, alpha2,
input_pvalues, exact = 0)
Arguments
alpha00 |
A numeric number represents the proportion of null |
alpha01 |
A numeric number represents the proportion of null |
alpha10 |
A numeric number represents the proportion of null |
alpha1 |
A numeric number represents the proportion of null alpha==0 (association between exposure and mediator) |
alpha2 |
A numeric number represents the proportion of null beta==0 (association between mediator and outcome adjusted for exposure) |
input_pvalues |
A matrix contains two columns of p-values for candidate mediators. Column 1 is the p-value of testing if a exposure is associated with the mediator (alpha!=0). Column 2 is the p-value of testing if a mediator is associated with the outcome adjusted for exposure(beta!=0) |
exact |
Use the option to choose from two methods. exact=0: the approximation method without estimating the CDFs when deriving the mixture null distribution; exact=1: the exact method to estimate the CDFs nonparametrically when deriving the mixture null distribution |
Details
The is a function to computed the expected quantiles for the observed p-max values based on the estimated mixture null distribution. The methodology detail can be found in Dai et al (2020).
Value
A vector contains the expected quantiles of p-values based on the estimated mixture null distribution. See Dai et al (2020) for details of how to compute quantiles using the approximation method (exact=0) or the exact method (exact=1).
Author(s)
James Y. Dai and X. Wang
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
data(snp_input)
input_pvalues <- snp_input
#To save time for illustration, we use 10 percent of rows
input_pvalues <- input_pvalues[sample(1:nrow(input_pvalues),size=ceiling(nrow(input_pvalues)/10)),]
nullprop <- null_estimation(input_pvalues)
pnull <- adjust_quantile(nullprop$alpha00,nullprop$alpha01,nullprop$alpha10,nullprop$alpha1,
nullprop$alpha2,input_pvalues,exact=0)