hdbma {hdbma}

High-Dimensional Bayesian Mediation Analysis

Description

We use the adaptive lasso priors for the Bayesian mediation analysis. Significant exposure variables, mediators are identified and their effects infered.

Usage

hdbma(pred, m, y, refy = rep(NA, ncol(data.frame(y))),
predref = rep(NA, ncol(data.frame(pred))), fpy = NULL,
deltap = rep(0.001, ncol(data.frame(pred))), fmy = NULL,
deltam = rep(0.001, ncol(data.frame(m))), fpm = NULL,
mref = rep(NA, ncol(data.frame(m))), cova = NULL, mcov = NULL, mclist = NULL,
inits = NULL, n.chains = 1, n.iter = 1100, n.burnin = 100, n.thin = 1,
mucv = NULL, Omegacv = NULL, mu0.1 = NULL, Omega0.1 = NULL, mu1.1 = NULL,
Omega1.1 = NULL, mu0.a = NULL, Omega0.a = NULL, mu1.a = NULL, Omega1.a = NULL,
mu0.b = NULL, Omega0.b = NULL, mu1.b = NULL, Omega1.b = NULL, mu0.c = NULL,
Omega0.c = NULL, mu1.c = NULL, Omega1.c = NULL, preci = 1e-06, tmax = Inf,
multi = NULL, filename = NULL, deltax = 1, r1 = 1, partial = FALSE)

Arguments

pred

the vector/matrix of exposure(s)/predictor(s).

m

a data frame contains all potential mediators and covariates.

y

the vector/matrix of outcome(s).

refy

the reference group of y if the outcome is categorical.

predref

the reference group of pred if the exposure/predictor is categorical.

fpy

the transformation function of predictor(s) (pred) to explain y. [[1]] list all continuous predictors to be transformed, then following items list the transformation functions for each predictor in list [[1]] in that order.

deltap

the vector of changing amount in predictors.

fmy

the transformation functions of mediators (m) to explain y, [[1]] list all continuous mediators in m to be transformed, then following items list the transformation functions for each mediator in list [[1]] in that order.

deltam

the vector of changing amount in mediators.

fpm

the transformation functions of predictors (pred) to explain mediators (m), [[1]] is a matrix, the first column indicator the mediators to be explained, the second column are the continuous predictors to be transformed; then transformation functions are listed in the following items by the row order of [[1]].

mref

the reference group of m if any of them is categorical. By default, the reference group is the first one in alphebetic order.

cova

the covariates for the outcome.

mcov

the data frame with all covariates for mediators

mclist

the list of all covariates for mediators. If mclist is NULL but mcov is not, use all covariates in mcov for all mediators. Otherwise the first item of mclist lists all mediators that are using different covariates, the following items give the columns of covariates in mcov for the mediators in order of mclist[[1]]. Use NA is no covariates are to be used. If a mediator is not listed in mclist[[1]], use all covariates in mcov.

inits

to specify the starting values of parameters. Default is NULL. See R2jags:jags.

n.chains

number of Markov chains (default: 1). See R2jags:jags.

n.iter

number of total iterations per chain (including burn in; default: 1100). See R2jags:jags.

n.burnin

length of burn in, i.e. number of iterations to discard at the beginning. Default is 100. If n.burnin is 0, jags() will run 100 iterations for adaption. See R2jags:jags.

n.thin

thinning rate. Must be a positive integer. Default is 1. See R2jags:jags.

mucv

the prior mean for the variables in cova. Default is 0.

Omegacv

the prior precision for the variables in cova. Default is 0 preci.

mu0.1

the prior mean for the intercept in the prediction model for all mediators. Default is 0.

Omega0.1

the prior precision for the intercept in the prediction model for all mediators. Default is preci.

mu1.1

a vector of the size of meditators include the prior mean for the slope of the exposures in the prediction model for all mediators. Default is rep(0,P).

Omega1.1

the prior precision matrix (P*P) for the slope of the exposures in the prediction model for all mediators. Default is a diagoal matrix with preci.

mu0.a

the prior mean for the intercept in the prediction model for all continuous mediators. Default is rep(0,p1), p1 is the number of continuous mediators.

Omega0.a

the prior precision for the intercept in the prediction model for all continuous mediators. Default is diag(preci).

mu1.a

the prior mean for the slope of the exposures in the prediction model for all continuous mediators. Default is rep(0,p1), p1 is the number of continuous mediators.

Omega1.a

the prior precision for the slope of the exposures in the prediction model for all continuous mediators. Default is diag(preci).

mu0.b

the prior mean for the intercept in the prediction logit model for all binary mediators. Default is rep(0,p2), p2 is the number of bianry mediators.

Omega0.b

the prior precision matrix for the intercept in the prediction logit model for all binary mediators. Default is diag(preci).

mu1.b

the prior mean for the slope of exposure(s) in the prediction logit model for all binary mediators. Default is rep(0,p2).

Omega1.b

the prior precision matrix for the slope of exposure(s) in the prediction logit model for all binary mediators. Default is diag(preci).

mu0.c

the prior mean for the intercept in the prediction logit model for all categorical mediators. Default is 0 for all (array(0,p3,cat1,nmc), where p3 is the number of categorical mediators, cat1 is the maximum number of categories of all categorical mediators, and nmc is the number of mcov.

Omega0.c

the prior precision matrix for intercept in the prediction logit model for all categorical mediators. Default is preci at the diagnal matrix of nmc*nmc dimension.

mu1.c

the prior mean for the slope of exposures in the prediction logit model for all categorical mediators. Default is 0 for all (array(0,p3,cat1,c1), where p3 is the number of categorical mediators, cat1 is the maximum number of categories of all categorical mediators, and c1 is the number of exposures.

Omega1.c

the prior precision matrix for the slope of exposures in the prediction logit model for all categorical mediators. Default is preci at the diagnal matrix of c1*c1 dimension.

preci

the prior precision level. Default is 0.000001.

tmax

the maximum time to event for survival analysis. Default is Inf.

multi

in the survival analysis only. If true, calculate the multiplicative effect of survival time.

filename

the directory and filename for the bugs model. If is NULL, the function will generate the bugs model using default functions.

deltax

the change unit in the exposures to calculate the mediation effects. Default is 1.

r1

the penalty parameter is

\lambda^*=\frac{\lambda}{(\alpha\hat{\beta})^r}.

. Default is 1.

partial

if true, do the partial lasso,

\lambda^*=\frac{\lambda}{\alpha^r}.

Default is FALSE.

Details

The function will automatically catch the types of the outcome and compile the bugs model. Results will be summarized use the summary function. Please see examples under summary.

Value

A hdbma object is returned with the following items. Results are summarized using the summary.hdbma function.

aie1, ade1, ate1, ..., ate4

the average indirect effect (ie), direct effect (de) and total effect (te) from the four different methods. See the references.

sims.list

the simulation results from the bugs model.

data0

the organized data set that was analyzed.

omu3, omu4

the total effects from method 3 or 4 for survival outcome only.

Author(s)

Qingzhao Yu and Bin Li

References

Yu, Q., Hagan, J., Wu, X., Richmond-Bryant, J., and Li, B., 2023, High-Dimensional Bayesian Mediation Analysis with Adaptive Laplace Priors. Submitted.

Examples

#Check summary.hdbma.