Description Usage Arguments Value Author(s) References Examples
Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional.
1 2 |
y |
Vector of continuous outcomes |
M |
Matrix of compositional data |
tr |
Vector of continuous or binary treatments |
x |
Matrix of covariates |
w |
Vector of weights on samples |
method.est.cov |
One of two options ("bootstrap", "normal") to estimate the variance of indirect effects |
n.boot |
Number of bootstrap samples |
sig.level |
Significance level to estimate bootstrap confidence intervals for direct and indirect effects of treatment |
tol |
Error tolerance |
max.iter |
Maximum number of iteration in a debias procedure |
If method.est.cov is "bootstrap",
DE |
Direct effect of treatment on an outcome |
DE.CI |
Bootstrap confidence interval for the direct effect |
TIDE |
Total indirect effect of treatment on an outcome |
TIDE.CI |
Bootstrap confidence interval for the indirect effect |
IDEs |
Component-wise indirect effects of treatment on an outcome |
IDE.CIs |
Bootstrap confidence intervals for the component-wise indirect effects |
If method.est.cov is "normal",
DE |
Direct effect of treatment on an outcome |
Var.DE |
Variance of the direct effect |
TIDE |
Total indirect effect of treatment on an outcome |
Var.TIDE |
Variance of the indirect effect |
IDEs |
Component-wise indirect effects of treatment on an outcome |
Var.IDEs |
Variances of the component-wise indirect effects |
Michael B. Sohn
Maintainer: Michael B. Sohn <msohn@mail.med.upenn.edu>
Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies (AOAS: In revision)
1 2 3 4 5 6 7 8 9 | # Load test data
data(ccmm_test_data);
outcome <- ccmm_test_data[,1];
treatment <- ccmm_test_data[,2];
mediators <- as.matrix(ccmm_test_data[,3:22]);
covariates <- as.matrix(ccmm_test_data[,23:24]);
# Run CCMM
rslt.ccmm <- ccmm(outcome, mediators, treatment, covariates);
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