ccmm: Causal Compositional Mediation Model

In ccmm: 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

Author(s)

Michael B. Sohn

Maintainer: Michael B. Sohn <[e-mail]>

References

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

Examples

# 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);

Example output

Warning message:
In est.param$intercept * rep(1, n) :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

ccmm documentation built on May 2, 2019, 3:29 p.m.