BFe {BayesRep} | R Documentation |
Equality of effect size Bayes factor
Description
Computes the equality of effect size Bayes factor
Usage
BFe(to, so, tr, sr, tau, log = FALSE)
Arguments
to |
Original effect estimate |
so |
Standard error of the original effect estimate |
tr |
Replication effect estimate |
sr |
Standard error of the replication effect estimate |
tau |
The heterogeneity standard deviation |
log |
Logical indicating whether the natural logarithm of the Bayes
factor should be returned. Defaults to |
Details
The equality of effect size Bayes factor is the Bayes factor
contrasting the hypothesis of equal original and replication effect sizes
H_0: \theta_o = \theta_r
to the hypothesis
of unequal effect sizes H_1: \theta_o \neq \theta_r
. Under the hypothesis of unequal effect sizes H_1
the
study specific effect sizes are assumed to be normally distributed around
an overall effect size with heterogeneity standard deviation tau
.
Value
The equality of effect size Bayes factor
\mathrm{BF}_{01}
. \mathrm{BF}_{01} > 1
indicates that the data favour the hypothesis of equal effect sizes
H_0
(replication success), whereas \mathrm{BF}_{01} <
1
indicates that the data favour the hypothesis of unequal
effect sizes H_1
(replication failure).
Author(s)
Samuel Pawel
References
Bayarri, M. and Mayorall, A. (2002). Bayesian Design of "Successful" Replications. The American Statistician, 56(3): 207-214. doi:10.1198/000313002155
Verhagen, J. and Wagenmakers, E. J. (2014). Bayesian tests to quantify the result of a replication attempt. Journal of Experimental Psychology: General, 145:1457-1475. doi:10.1037/a0036731
Examples
## strong evidence for unequal effect sizes
BFe(to = 1, tr = 0.5, so = sqrt(1/100), sr = sqrt(1/100), tau = 0.3)
## some evidence for equal effect sizes
BFe(to = 1, tr = 1, so = sqrt(1/200), sr = sqrt(1/200), tau = 0.3)