Description Usage Arguments Value Warning Author(s) References See Also Examples

View source: R/jzs_partcorSD.R

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("dnorm", "splinefun", "logspline", "fit.st"),
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
``` |

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