ccmm.sensitivity: Sensitivity analysis
In ccmm: Compositional Mediation Model

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)

# 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);