BFSD: log Bayes factor for full mediation

In arashl1364/BFMediate: Mediation Analysis Using a Combination of Bayesian Estimation Methods, Latent Variable Models, and Bayes Factors

log Bayes factor for full mediation

Description

Computes log Bayes factors for the partial mediation model using Savage-Dickey ratio

Usage

BFSD(Post,Prior,burnin)

Arguments

Post	output from PartialMed or any of measurement models
Prior	prior variance of the direct effect
burnin	number of MCMC draws before the posterior is converged, default = R/5

Value

log(BF_01), which is the evidence in favor of/against full mediation model (see Laghaie and Otter (2020) for guidelines on how to interpret BF_01)

See Also

For simulating data from simple mediation model see PartialMed

Examples

simPartialMed = function(beta_M,beta_Y, sigma_M, sigma_Y,N,X) {
  eps_M = rnorm(N)*sigma_M # generate errors for M (independent)
  eps_Y = rnorm(N)*sigma_Y # generate errors for Y (independent)
  M = beta_M[1] + beta_M[2] * X + eps_M # generate latent mediator M
  Y = beta_Y[1] + beta_Y[2] * M + beta_Y[3] * X + eps_Y # generate dependent variable
  list(X = X, M = M, Y = Y)
}

# Set up data generating parameters
N = 1000 # number of observations
sigma_M = .2^.5 # error std M
sigma_Y = .2^.5 # error std Y
beta_M = c(1, .3) # beta_0M and beta_1
beta_Y = c(1, .5, 0) # beta_0Y, beta_2, beta_3
X = rnorm(N,mean = 1,sd = 1)# generate random X
# Generate data based on parameters
Data = simPartialMed(beta_M,beta_Y,sigma_M,sigma_Y,N,X)

#Estimation
A_M = c(100,100); #Prior variance for beta_0M, beta_1
A_Y = c(100,100,1) #Prior variance for beta_0Y, beta_2, beta_3
R = 2000
out = PartialMed(Data=Data, Prior = list(A_M=A_M, A_Y=A_Y), R = R)
#Computing Bayes factor
BFPartialMed = exp(BFSD(Post = out , Prior = A_Y[3], burnin = R/5))

arashl1364/BFMediate documentation built on Oct. 11, 2023, 5:54 p.m.