SMAHP {SMAHP} | R Documentation |
Main function
Description
An approach for survival mediation analysis of high-dimensional proteogenomic data.
Usage
SMAHP(
X,
M,
C,
time,
status,
model_option = "MCP",
SIS_thres = "n/log(n)",
p_adjust_option = "BH",
p_thres = 0.05
)
Arguments
X |
An n by p matrix of exposures. |
M |
An n by p matrix of mediators. |
C |
An n by p matrix of covariates. If there are no covariates, set C = NULL. |
time |
A vector of survival time of samples. |
status |
A vector of status indicator: 0=alive, 1=dead. |
model_option |
The penalization method used when selecting X for M. Options include MCP, elastic net and lasso. Default is MCP. |
SIS_thres |
SIS thresholds. Default is "n/log(n)". Other options include "n-1", "n/2log(n)", "2n/log(n)", "3n/log(n)". |
p_adjust_option |
The method for multiple correction. Option include q-value, holm, hochberg, hommel, bonferroni, BH, BY, and fdr. Default is BH. |
p_thres |
Threshold for determining significance. |
Value
A list with the following components:
p_final_matrix |
raw p-value matrix before adjustment |
p_adjusted_matrix |
adjusted p-value matrix |
p_med_matrix |
mediation-exposure matrix |
outcome_model |
coefficient estimates from outcome model |
med_results |
coefficient estimates from mediation model |
Examples
data(example_dat)
surv_dat <- example_dat$surv_dat
SMAHP(example_dat$X, example_dat$M, example_dat$C, time = surv_dat$time, status = surv_dat$status)