print.jzs_med: Print jzs_med output.

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

Description

Print the output of a jzs_med object.

Usage

## S3 method for class 'jzs_med'
print(x,...)

Arguments

x	a jzs_med object.
...	further arguments passed to or from other methods.

Value

The function returns a list with the following items:

EvidenceMediation	The posterior probability that the relation between the independent and the dependent variable is mediated by the specified mediator.
EvidenceFullMediation	The posterior probability that the relation between the independent and the dependent variable is fully mediated by the specified mediator and the direct effect of the independent variable on the dependent variable disappears after introducing the mediator.
BF_Mediation	The Bayes factor for mediation compared to no mediation. A value greater than one indicates evidence in favor of mediation, a value smaller than one indicates evidence against mediation.
BF_FullMediation	The Bayes factor for full mediation compared to no mediation. A value greater than one indicates evidence in favor of full mediation, a value smaller than one indicates evidence against full mediation.
BF_alpha	The Bayes factor for the existence of path alpha. A value greater than one indicates evidence that alpha exists, a value smaller than one indicates evidence that alpha does not exist.
BF_beta	The Bayes factor for the existence of path beta. A value greater than one indicates evidence that beta exists, a value smaller than one indicates evidence that beta does not exist.
BF_tau_accent	The Bayes factor for the existence of path tau_accent. A value greater than one indicates evidence that tau_accent exists, a value smaller than one indicates evidence that tau_accent does not exist.

Author(s)

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

Examples

## Not run: 
# simulate mediational data
a <- .5 
b <- .6
t_prime <- .3

X <- rnorm(50,0,1)
M <- a*X + rnorm(50,0,1)
Y <- t_prime*X + b*M + rnorm(50,0,1)

# run jzs_med
result <- jzs_med(independent=X,dependent=Y,mediator=M,SDmethod='dnorm')

# print result
result

## End(Not run)

BayesMed documentation built on May 2, 2019, 9:27 a.m.