Description Usage Arguments Value Author(s) References Examples
Estimate the total indirect effect (TIDE) given a correlation coefficient
1 | ccmm.sensitivity(rh, y, M, tr, x = NULL, w = NULL)
|
rh |
Correlation coefficient |
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 |
Estimated TIDE given a correlation coefficient
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 | # 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]);
ccmm.sensitivity(rh=0, outcome, mediators, treatment, covariates);
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