ccmm (version 1.0)

ccmm: Causal Compositional Mediation Model

Description

Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional.

Usage

ccmm(y, M, tr, x = NULL, w = NULL, method.est.cov = "bootstrap", n.boot = 2000,
	sig.level = 0.05, tol = 1e-06, max.iter = 5000)

Arguments

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

Value

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

References

Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies (AOAS: In revision)

Examples

# NOT RUN {
# 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);
# }