| microHIMA {HIMA} | R Documentation | 
High-dimensional mediation analysis for compositional microbiome data
Description
microHIMA is used to estimate and test high-dimensional mediation effects for compositional microbiome data.
Usage
microHIMA(X, Y, OTU, COV = NULL, FDRcut = 0.05, scale = TRUE, verbose = FALSE)
Arguments
X | 
 a vector of exposure.  | 
Y | 
 a vector of outcome.  | 
OTU | 
 a   | 
COV | 
 a   | 
FDRcut | 
 FDR cutoff applied to define and select significant mediators. Default =   | 
scale | 
 logical. Should the function scale the data? Default =   | 
verbose | 
 logical. Should the function be verbose? Default =   | 
Value
A data.frame containing mediation testing results of selected mediators (FDR < FDRcut). 
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.
FDR: false discovery rate of selected significant mediator.
References
Zhang H, Chen J, Feng Y, Wang C, Li H, Liu L. Mediation effect selection in high-dimensional and compositional microbiome data. Stat Med. 2021. DOI: 10.1002/sim.8808. PMID: 33205470; PMCID: PMC7855955.
Zhang H, Chen J, Li Z, Liu L. Testing for mediation effect with application to human microbiome data. Stat Biosci. 2021. DOI: 10.1007/s12561-019-09253-3. PMID: 34093887; PMCID: PMC8177450.
Examples
## Not run: 
# Note: In the following example, M1, M2, and M3 are true mediators.
data(himaDat)
head(himaDat$Example4$PhenoData)
microHIMA.fit <- microHIMA(X = himaDat$Example4$PhenoData$Treatment, 
                           Y = himaDat$Example4$PhenoData$Outcome, 
                           OTU = himaDat$Example4$Mediator, 
                           COV = himaDat$Example4$PhenoData[, c("Sex", "Age")],
                           scale = FALSE)
microHIMA.fit
## End(Not run)