eHIMA | R Documentation |
eHIMA
is used to estimate and test high-dimensional mediation effects using an efficient algorithm. It provides
higher statistical power than the standard hima
.
eHIMA(
X,
M,
Y,
COV = NULL,
Y.family = c("gaussian"),
topN = NULL,
scale = TRUE,
FDRcut = 0.05,
verbose = FALSE
)
X |
a vector of exposure. |
M |
a |
Y |
a vector of continuous outcome. Do not use data.frame or matrix. |
COV |
a matrix of adjusting covariates. Rows represent samples, columns represent variables. Can be |
Y.family |
currently |
topN |
an integer specifying the number of top markers from sure independent screening.
Default = |
scale |
logical. Should the function scale the data? Default = |
FDRcut |
Benjamini-Hochberg FDR cutoff applied to select significant mediators. Default = |
verbose |
logical. Should the function be verbose? Default = |
A data.frame containing mediation testing results of significant mediators (FDR <FDRcut
).
mediation name of selected significant mediator.
coefficient estimates of exposure (X) –> mediators (M).
standard error for alpha.
coefficient estimates of mediators (M) –> outcome (Y) (adjusted for exposure).
standard error for beta.
mediation (indirect) effect, i.e., alpha*beta.
relative importance of the mediator.
joint raw p-value of selected significant mediator (based on Benjamini-Hochberg FDR method).
Bai X, Zheng Y, Hou L, Zheng C, Liu L, Zhang H. An Efficient Testing Procedure for High-dimensional Mediators with FDR Control. Statistics in Biosciences. 2024. DOI: 10.1007/s12561-024-09447-4.
## Not run:
# Note: In the following example, M1, M2, and M3 are true mediators.
data(himaDat)
# Y is continuous and normally distributed
# Example 1 (continuous outcome):
head(himaDat$Example1$PhenoData)
eHIMA.fit <- eHIMA(X = himaDat$Example1$PhenoData$Treatment,
Y = himaDat$Example1$PhenoData$Outcome,
M = himaDat$Example1$Mediator,
COV = himaDat$Example1$PhenoData[, c("Sex", "Age")],
Y.family = 'gaussian',
scale = FALSE,
verbose = TRUE)
eHIMA.fit
## End(Not run)
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