BayesMed-package: A default Bayesian hypothesis test for mediation,...

In MicheleNuijten/BayesMed: Default Bayesian Hypothesis Tests for Correlation, Partial Correlation, and Mediation

Description

This package can be used to perform a default Bayesian hypothesis test for mediation, correlation, and partial correlation, either analytically or through the Savage-Dickey method (Dickey & Lientz, 1970). All tests make use of a Jeffreys-Zellner-Siow prior set-up (Liang et al., 2008). This package is based on the paper by Nuijten, Wetzels, Matzke, Dolan, and Wagenmakers (under review).

Details

Package:	BayesMed
Type:	Package
Version:	1.2.2
Date:	2018-05-28
License:	GPL-2

The main functions jzs_med and jzs_medSD can be used to establish and test mediation in a data set. With jzs_cor and jzs_corSD you can establish and test correlation, and with jzs_partcor and jzs_partcorSD partial correlation.

Note

This function requires the program "JAGS" (Just Another Gibbs Sampler) to be in the PATH variable. This program can be obtained from http://mcmc-jags.sourceforge.net.

Author(s)

Michele B. Nuijten <[e-mail]>, Ruud Wetzels, Dora Matzke, Conor V. Dolan, and Eric-Jan Wagenmakers. Many thanks to Sacha Epskamp.

References

Dickey, J. M., & Lientz, B. P. (1970). The weighted likelihood ratio, sharp hypotheses about chances, the order of a Markov chain. The Annals of Mathematical Statistics, 214-226.

Liang, F., Paulo, R., Molina, G., Clyde, M. A., & Berger, J. O. (2008). Mixtures of g priors for Bayesian variable selection. Journal of the American Statistical Association, 103(481), 410-423.

Nuijten, M. B., Wetzels, R., Matzke, D., Dolan, C. V., & Wagenmakers, E.-J. (2014). A default Bayesian hypothesis test for mediation. Behavior Research Methods. doi: 10.3758/s13428-014-0470-2

Wetzels, R. & Wagenmakers, E.-J. (2012). A default Bayesian hypothesis test for correlations and partial correlations. Psychonomic Bulletin & Review.

Examples

## Not run: 
# simulate mediational data
X <- rnorm(50,0,1)
M <- .5*X + rnorm(50,0,1)
Y <- .3*X + .6*M + rnorm(50,0,1)

###########

# run jzs_med to perform the Bayesian hypothesis test for mediation

result <- jzs_med(independent=X,dependent=Y,mediator=M)
result

### NOTE ###
#Sometimes this error will pop up:
#
#Error in solve.default(nItheta) : 
#  system is computationally singular: reciprocal condition number = *some small number*
#Error in mydt2(0, mT, sT, dfT) : unused arguments (mT, sT, dfT)
#In addition: Warning message:
#In jzs_medSD(X, Y, M) :
#  fit.st did not converge. Alternative optimization method was used.
#  
#If this happens, just run jzs_medSD() again. 
#This usually solves the convergence problem. If it does not,
#try a different SD method. For instance: jzs_medSD(X,Y,M,SDmethod="dnorm").
#
#############

# plot results
plot(result$main_result)

# plot posterior samples including credible interval, mean, and median 
# of the indirect effect alpha*beta
plot(result$ab_samples)

# inspect separate posterior distributions of alpha, beta, and tau_prime
plot(result$alpha_samples)
plot(result$beta_samples)
plot(result$tau_prime_samples)

# print a traceplot of the chains
# where the first chain (theta[1]) is for tau' and the second chain (theta[2]) for beta
plot(result$jagssamplesA)
plot(result$jagssamplesTB) 

###########

# run jzs_medSD to perform the Savage-Dickey (SD) Bayesian hypothesis test for mediation

result_SD <- jzs_medSD(independent=X,dependent=Y,mediator=M)
result_SD

# plot(results)
plot(result_SD$main_result)

# plot posterior samples 
# including credible interval, mean, and median of the indirect effect alpha*beta
plot(result_SD$ab_samples)

# inspect separate posterior distributions of alpha, beta, and tau_prime
plot(result_SD$alpha_samples)
plot(result_SD$beta_samples)
plot(result_SD$tau_prime_samples)

# print a traceplot of the chains
# where the first chain (theta[1]) is for tau' and the second chain (theta[2]) for beta
plot(result_SD$jagssamplesA)
plot(result_SD$jagssamplesTB) 

## End(Not run)

MicheleNuijten/BayesMed documentation built on Jan. 31, 2020, 7:45 a.m.