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https://github.com/mbsohn/cmmb
Sparse compositional mediation model for binary outcomes
https://github.com/mbsohn/cmmb
Last synced: 11 days ago
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Sparse compositional mediation model for binary outcomes
- Host: GitHub
- URL: https://github.com/mbsohn/cmmb
- Owner: mbsohn
- Created: 2021-07-04T22:53:13.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-07-05T20:14:39.000Z (over 3 years ago)
- Last Synced: 2024-08-01T16:18:41.098Z (3 months ago)
- Language: R
- Size: 152 KB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-rm-omics - **CMMB** - Sohn - [A compositional mediation model for a binary outcome: Application to microbiome studies](https://doi.org/10.1093/bioinformatics/btab605) (Software packages and methods / Causal Effect Estimation)
README
README
================
Michael B. Sohn
7/05/2021
## CMMB: Compositional Mediation Model for Binary Outcomes
The cmmb function estimates direct and indirect effects of treatment on
binary outcomes mediated through a compositional mediator that consists
of multiple components. For detailed information about the arguments,
please see the documentation for *cmmb()*.
Note: the number of components can be high-dimensional. However, it will
be computationally intensive to run it with high-dimensional components
as it tests the indirect effect using debiased bootstrap estimates based
on 2,000 (in default) random samplings.
### Installation
install.packages(“devtools”)
devtools::install\_github(“mbsohn/cmmb”)
### Example: estimate direct and total indirect effects
``` r
### load functions used in data generation and performance comparison
library(cmmb)
source("comparators.R")
### Generate a simulated dataset of 50 samples and 5 taxa
set.seed(2021)
### The gen.cmm.sim.data function simulates data based on the logistic normal distribution.
### The first argument takes the number of samples and the second takes the number of components
### (taxa). By default, both direct and indirect effects are significantly different from zero.
sim.dat <- gen.cmm.sim.data(50, 5)
### Run CMM for binary outcomes
rslt <- cmmb(Y=sim.dat$Y, M=sim.dat$M, tr=sim.dat$tr, X=sim.dat$X)
rslt
```
## $total
## Estimate Lower Limit Upper Limit
## DE 0.1831665 0.1206513 0.2885970
## TIDE 0.2585639 0.1525723 0.3321615
##
## $cwprod
## Estimate Lower Limit Upper Limit
## taxon1 0.45250088 0.0643407 0.7435297
## taxon2 0.00179244 -0.2030688 0.1593904
## taxon3 -0.20841877 -0.5068055 0.1638699
## taxon4 0.61865737 -0.1058526 1.1229078
## taxon5 0.51405582 -0.4161137 1.1703481
##
## attr(,"class")
## [1] "cmmb"
``` r
### Plot products of component-wise path coefficients
plot_cw_ide(rslt)
```
### Example: sensitivity analysis for the total indirect effect
``` r
rslt.sa <- cmmb(Y=sim.dat$Y, M=sim.dat$M, tr=sim.dat$tr, X=sim.dat$X, ForSA=TRUE)
### Plot sensitivity of the estimated total indirect effect
plot_cmmb_sa(rslt.sa)
```
### Performance comparison
Note: By default, the *gen.cmm.sim.data* function simulates data such
that both direct and indirect effects are significantly different from
zero.
``` r
sim.dat <- gen.cmm.sim.data(50, 5)
PCS(Y=sim.dat$Y, M=sim.dat$M, tr=sim.dat$tr, X=sim.dat$X)
```
## Estimate Lower Limit Upper Limit
## DE 0.8585578 -1.011952 1.807630
## TIDE 0.5737664 -2.550865 2.263679
``` r
PCP(Y=sim.dat$Y, M=sim.dat$M, tr=sim.dat$tr, X=sim.dat$X)
```
## Estimate Lower Limit Upper Limit
## DE 0.16603048 0.09844733 0.34177689
## TIDE 0.01797279 -0.15262002 0.07936442
``` r
cmmb(Y=sim.dat$Y, M=sim.dat$M, tr=sim.dat$tr, X=sim.dat$X)$total
```
## Estimate Lower Limit Upper Limit
## DE 0.1615200 0.11897322 0.2372022
## TIDE 0.1003837 0.02902102 0.1425433