# jzs_partcor: A default Bayesian hypothesis test for partial correlation... In MicheleNuijten/BayesMed: Default Bayesian Hypothesis Tests for Correlation, Partial Correlation, and Mediation

## Description

This function can be used to perform a default Bayesian hypothesis test for partial correlation, using a Jeffreys-Zellner-Siow prior set-up (Liang et al., 2008).

## Usage

 ```1 2``` ```jzs_partcor(V1, V2, control, alternative = c("two.sided", "less", "greater"), n.iter=10000,n.burnin=500,standardize=TRUE) ```

## Arguments

 `V1` a numeric vector. `V2` a numeric vector of the same length as V1. `control` a numeric vector of the same length as V1 and V2. This variable is partialled out of the correlation between V1 and V2. `alternative` specify the alternative hypothesis for the correlation coefficient: `two.sided`, `greater` than zero, or `less` than zero. `n.iter` number of total iterations per chain (see the package `R2jags`). Defaults to 10000. `n.burnin` length of burn in, i.e. number of iterations to discard at the beginning(see the package `R2jags`). Defaults to 500. `standardize` logical. Should the variables be standardized? Defaults to TRUE.

## Details

See Wetzels & Wagenmakers, 2012.

## Value

The function returns a list with the following items:

 `PartCoef` Mean of the posterior samples of the unstandardized partial correlation (the regression coefficient beta in the equation V2 = intercept + alpha*control + beta*V1). `BayesFactor` The Bayes factor for the existence of a partial correlation between V1 and V2, controlled for the control variable. A value greater than one indicates evidence in favor of partial correlation, a value smaller than one indicates evidence against partial correlation. `PosteriorProbability` The posterior probability for the existence of a partial correlation between V1 and V2, controlled for the control variable. `beta` The posterior samples for the regression coefficient beta. This is the unstandardized partial correlation. `jagssamples` The JAGS output for the MCMC estimation of the path. This object can be used to construct a traceplot.

## Author(s)

Michele B. Nuijten <m.b.nuijten@uvt.nl>, Ruud Wetzels, Dora Matzke, Conor V. Dolan, and Eric-Jan Wagenmakers.

## References

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, 19, 1057-1064.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```## Not run: # simulate partially correlated data X <- rnorm(50,0,1) C <- .5*X + rnorm(50,0,1) Y <- .3*X + .6*C + rnorm(50,0,1) # run jzs_partcor res <- jzs_partcor(X,Y,C) # plot posterior samples plot(res\$beta_samples) # plot traceplot plot(res\$jagssamples) # where the first chain (theta) is for tau' and the second chain (theta) for beta ## End(Not run) ```

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