ccmm {ccmm} | R Documentation |

## 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 |

### Author(s)

Michael B. Sohn

Maintainer: Michael B. Sohn <msohn@mail.med.upenn.edu>

### 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);
```

*ccmm*version 1.0 Index]