summary.hdbma {hdbma}

Summary for hdbma results

Description

This function summarize the results from hdbma objects with estimations, standard errors, and confidence intervals.

Usage

## S3 method for class 'hdbma'
summary(object, ..., plot = TRUE, RE = TRUE,
quant = c(0.025, 0.25, 0.5, 0.75, 0.975), digit = 4, method = 1)

Arguments

object

the hdbma object from the hdbma function.

...

further arguments passed to or from other methods.

plot

if true, plot the estimation summaries. Default is True.

RE

if true, present results for relative effects. Default is True.

quant

the set of quantiles to be shown in the sumamry results. Default is c(0.025, 0.25, 0.5, 0.75, 0.975).

digit

the number of digit to be shown.

method

the method to be shown. See the reference.

Value

result1, ..., result4

the inference results for estimated mediation effects from methods 1 to 4.

result1.re, ..., result4.re

the inference results for estimated relative effects from methods 1 to 4.

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

data("weight_behavior")
#for all the examples, needs to remove or increase n.iter and n.burnin

#binary predictor
test.b.c<- hdbma(pred=weight_behavior[,3], m=weight_behavior[,c(4,14,12,13)],
                 y=weight_behavior[,1],n.iter=10,n.burnin = 1)
summary(test.b.c)

##use covariate for y
test.b.c.2<- hdbma(pred=weight_behavior[,3], m=weight_behavior[,12:14],
                     y=weight_behavior[,1],cova=weight_behavior[,2],n.iter=10,n.burnin = 1)
summary(test.b.c.2)

#categorical predictor
test.ca.c<- hdbma(pred=weight_behavior[,4], m=weight_behavior[,12:14],
                     y=weight_behavior[,1],n.iter=10,n.burnin = 1)
summary(test.ca.c)

#use covariate for mediators
test.b.c.3<- hdbma(pred=weight_behavior[,3], m=weight_behavior[,c(9,12:14)],
                       y=weight_behavior[,1],mcov=weight_behavior[,c(2,5)],
                       mclist = list(1,2),n.iter=10,n.burnin = 1)
summary(test.b.c.3)

#use continuous predictor
test.c.c<- hdbma(pred=weight_behavior[,2], m=weight_behavior[,12:14],
                       y=weight_behavior[,1],n.iter=10,n.burnin = 1)
summary(test.c.c,method=3)

#use transfered continuous predictor
test.c.c.2<- hdbma(pred=weight_behavior[,2], m=weight_behavior[,12:14],
                     y=weight_behavior[,1],fpy=list(1,c("x","x^2")),
                     n.iter=10,n.burnin = 1)
summary(test.c.c.2,method=1)

#multiple predictors
test.m.c<- hdbma(pred=weight_behavior[,2:4], m=weight_behavior[,12:14],
                 y=weight_behavior[,1],n.iter=10,n.burnin = 1)
summary(test.m.c,RE=FALSE)

##binary outcome
test.m.b<- hdbma(pred=weight_behavior[,2:4], m=weight_behavior[,12:14],
                     y=weight_behavior[,15],cova=weight_behavior[,5],
                     n.iter=10,n.burnin = 1)
summary(test.m.b,method=2)

##categorical outcome
weight_behavior[,14]=as.factor(weight_behavior[,14])
test.m.c<- hdbma(pred=weight_behavior[,2:4], m=weight_behavior[,12:13],
                     y=weight_behavior[,14],cova=weight_behavior[,5],
                     n.iter=10,n.burnin = 1)
summary(test.m.c,method=2)
summary(test.m.c,method=1)

##time-to-event outcome
##Surv class outcome (survival analysis)
#use a simulation
set.seed(1)
N=100

alpha=0.5
x=rnorm(N,0,1)
x=ifelse(x>0,1,0)
e1=rnorm(N,0,1)
M=alpha*x+e1
lambda=0.01
rho=1
beta=1.2
c=-1
rateC=0.001
v=runif(n=N)
Tlat =(- log(v) / (lambda * exp(c*x+M*beta)))^(1 / rho)
C=rexp(n=N, rate=rateC)
time=pmin(Tlat, C)
status <- as.numeric(Tlat <= C)

test.m.t.1<- hdbma(pred=x, m=M,y=Surv(time,status),inits=function(){
  list(r=1,lambda=0.01)
},n.iter=10,n.burnin = 1)
summary(test.m.t.1,RE=FALSE)