qHIMA {HIMA}

High-dimensional quantile mediation analysis

Description

qHIMA is used to estimate and test high-dimensional quantile mediation effects.

Usage

qHIMA(
  X,
  M,
  Y,
  Z,
  penalty = c("MCP", "SCAD", "lasso"),
  topN = NULL,
  tau = 0.5,
  scale = TRUE,
  Bonfcut = 0.05,
  verbose = FALSE,
  ...
)

Arguments

X

a vector of exposure.

M

a data.frame or matrix of high-dimensional mediators. Rows represent samples, columns represent mediator variables.

Y

a vector of continuous outcome. Do not use data.frame or matrix.

Z

a matrix of adjusting covariates. Rows represent samples, columns represent variables. Can be NULL.

penalty

the penalty to be applied to the model (a parameter passed to function conquer.cv.reg in package conquer. Either 'MCP' (the default), 'SCAD', or 'lasso'.

topN

an integer specifying the number of top markers from sure independent screening. Default = NULL. If NULL, topN will be 2*ceiling(n/log(n)), where n is the sample size. If the sample size is greater than topN (pre-specified or calculated), all mediators will be included in the test (i.e. low-dimensional scenario).

tau

quantile level of outcome. Default = 0.5. A vector of tau is accepted.

scale

logical. Should the function scale the data? Default = TRUE.

Bonfcut

Bonferroni-corrected p value cutoff applied to define and select significant mediators. Default = 0.05.

verbose

logical. Should the function be verbose? Default = FALSE.

...

other arguments.

Value

A data.frame containing mediation testing results of selected mediators (Bonferroni-adjusted p value <Bonfcut).

• ID: index of selected significant mediator.

• alpha: coefficient estimates of exposure (X) –> mediators (M).

• alpha_se: standard error for alpha.

• beta: coefficient estimates of mediators (M) –> outcome (Y) (adjusted for exposure).

• beta_se: standard error for beta.

• Bonferroni.p: statistical significance of the mediator (Bonferroni-corrected p value).

References

Zhang H, Hong X, Zheng Y, Hou L, Zheng C, Wang X, Liu L. High-Dimensional Quantile Mediation Analysis with Application to a Birth Cohort Study of Mother–Newborn Pairs. Bioinformatics. 2023. (In press)

Examples

## Not run: 
# Note: In the following example, M1, M2, and M3 are true mediators.
data(himaDat)

head(himaDat$Example5$PhenoData)

qHIMA.fit <- qHIMA(X = himaDat$Example5$PhenoData$Treatment,
                M = himaDat$Example5$Mediator, 
                Y = himaDat$Example5$PhenoData$Outcome, 
                Z = himaDat$Example5$PhenoData[, c("Sex", "Age")], 
                Bonfcut = 0.05,
                tau = c(0.3, 0.5, 0.7),
                scale = FALSE, 
                verbose = TRUE)
qHIMA.fit

## End(Not run)