pph {BayesCombo}  R Documentation 
Calculates the posterior probability of hypotheses for one study
Description
The function takes a single effect size and its standard error and calculates the posterior probability of 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).
Usage
pph(beta, se.beta, beta0 = 0, se0 = NULL, ci = 99, se.mult = 1,
H0 = c(0, 0), H.priors = rep(1/3, 3), scale = FALSE, adjust = FALSE,
epsilon = 1e06, adj.factor = 1e04)
Arguments
beta 
Effect size. 
se.beta 
Standard error for the effect. 
beta0 
A prior value for the effect size. Default is zero. 
se0 
A prior standard error for the effect size. Default is 
ci 
Is used to calculate the prior standard error if 
se.mult 
Standard error multiplier used to increase or decrease the
prior SE and used in conjunction with 
H0 
A vector of length two that defines the null hypothesis. If the
values are identical (e.g. 
H.priors 
Prior hypothesis probabilities; default is an equal
probability of 1/3, and they are specified in the following order:

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. 
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 
Details
Effect sizes could be a mean difference between groups, regression slope, odds ratio, or other values provided by statistical models. The standard errors represent the uncertainty in the effect size and are provided by most statistical software.
Two types of priors need to be specified. The first is the prior for the effect size, which is given by a mean (usually zero) and standard error, which can be specified by the user or calculated automatically. Second, prior probabilities for each hypothesis need to be provided, and an equal probability of 1/3 is used by default.
The null hypothesis (usually effect size = 0) can be specified as either a point null or as a range. A point null tests if the effect size is exactly zero while the range null tests if the effect size close enough to zero for practical purposes. 'Close enough' is defined by the user as a range on either size of zero.
To combine multiple effect sizes use the ev.combo
function.
Value
Object of class pph
which contains the posterior hypothesis
probabilities and other calculated values.
See Also
Examples
# library(labstats) # need to install from CRAN
# plot(time.immob ~ dose, data=fluoxetine)
# summary(lm(time.immob ~ dose, data=fluoxetine))
x < pph(beta=0.25200, se.beta=0.09913) # dose effect from above output
x