Description Usage Arguments Value Warning Author(s) References See Also Examples
This function can be used to perform a default Bayesian hypothesis test for partial correlation, using the Savage-Dickey method (Dickey & Lientz, 1970). The test uses a Jeffreys-Zellner-Siow prior set-up (Liang et al., 2008).
1 2 3 4 | jzs_partcorSD(V1, V2, control,
SDmethod = c("fit.st", "dnorm", "splinefun", "logspline"),
alternative = c("two.sided", "less", "greater"),
n.iter=10000,n.burnin=500,standardize=TRUE)
|
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. |
SDmethod |
specify the precise method with which the density of the posterior distribution will be estimated in order to compute the Savage-Dickey ratio. |
alternative |
specify the alternative hypothesis for the correlation coefficient: |
n.iter |
number of total iterations per chain (see the package |
n.burnin |
length of burn in, i.e. number of iterations to discard at the beginning(see the package |
standardize |
logical. Should the variables be standardized? Defaults to TRUE. |
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 correlation coefficient. A value greater than one indicates evidence in favor of correlation, a value smaller than one indicates evidence against correlation. |
PosteriorProbability |
The posterior probability for the existence of a correlation between V1 and V2. |
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. |
In some cases the SDmethod fit.st
will fail to converge. If so, another optimization method is used, using different starting values. If the other optimization method does not converge either or gives you a negative Bayes factor (which is meaningless), you could try one of the other SDmethod options or see jzs_partcor
.
Michele B. Nuijten <m.b.nuijten@uvt.nl>, Ruud Wetzels, Dora Matzke, Conor V. Dolan, and Eric-Jan Wagenmakers.
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, 19, 1057-1064.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # 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_partcorSD(X,Y,C))
# plot posterior samples
plot(res$beta_samples)
# plot traceplot
plot(res$jagssamples)
# where the first chain (theta[1]) is for tau' and the second chain (theta[2]) for beta
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.