ev.combo {BayesCombo}  R Documentation 
The function takes multiple effect sizes and a their standard errors and calculates the posterior probability for each hypothesis (H<: the effect size is less than 0, H0: the effect size is zero, or H>: the effect size is greater than zero).
ev.combo(beta, se.beta, beta0 = 0, ci = 99, H0 = c(0, 0), scale = FALSE,
H.priors = rep(1/3, 3), se.mult = 1, adjust = FALSE, epsilon = 1e06,
adj.factor = 1e04, ...)
beta 
Effect size. 
se.beta 
Standard error for the effect. 
beta0 
A prior value for the effect size. Default is zero. 
ci 
Is used to calculate the prior standard error if 
H0 
A vector of length two that defines the null hypothesis. If the
values are identical (e.g. 
scale 
Logical. Whether to scale the effect size by its standard error. Standardising has no effect on the calculations but standardised effect sizes may be easier to compare in a forest plot. 
H.priors 
Prior hypothesis probabilities; default is an equal
probability of 1/3, and they are specified in the following order:

se.mult 
Standard error multiplier used to increase or decrease the
prior SE and used in conjunction with 
adjust 
Logical. Whether to adjust very small posterior hypothesis probabilities. Adjusting prevents a single study from having too much influence on the results when combining multiple studies. For example, if the probability for a hypothesis from one study is zero, then additional studies cannot alter this probability (multiplying anything by zero is still zero). 
epsilon 
A small value that a posterior hypothesis probability must
fall below before an adjustment is made. Ignored if 
adj.factor 
A small number added to each posterior hypothesis
probability if 
... 
Options to be passed to 
This function calls pph()
once for each study to be combined,
where the posterior probabilities for one study are used as the priors for
the next study. One exeption is that values for se0
are ignored as
they are calculated automatically.
Object of class EV
which contains a matrix of posterior
probabilities for each updated step and other calculated values.
x < ev.combo(beta = c(0.0126, 5.0052, 1.2976, 0.0005),
se.beta = c(0.050, 2.581, 2.054, 0.003) )
x
plot(x)