View source: R/posterior_distributions.R
posterior_r | R Documentation |
Samples from the posterior distribution of the correlation coefficient. The posterior distribution is generated using Fisher's fiducial approach and corresponds exactly to the use of a Jeffrey's prior.
posterior_r(r, df, filter = 0, upper_null = 0, fixed = FALSE, ndraws = 2e+05)
r |
The correlation of the previous study. |
df |
The degrees of freedom in the previous study. |
filter |
The filter value reflects the probability of nonsignificant results being filtered. filter = 0 means that there is no filtering and you would have observed nonsignificant results. filter = 1 means that only significant results are observed and you would never have seen nonsigificant results if they had occurred. Filtering is based on alpha = .05 and assumes that are have observed a significant result. Filtering is conducted by weighting (actually filtering) the posterior distribution. For instance, if filter = 1, then the posterior of the null (i.e., the noncentrality parameter is 0) is up to 20 times more likely than when the noncentrality parameter is very large. Setting filter > 0 slows estimation. |
upper_null |
Specifies the upper value of the composite null hypothesis in units of the correlation. The default value of upper_null = 0 keeps the point null hypothesis. A value of, for instance, upper_null = .05 would remove all posterior values between -.05 and .05. |
fixed |
Specifies whether the predictors in the model are either fixed or random. The default is FALSE for random predictors. |
ndraws |
Specifies the number of initial samples from the posterior. For small effect sizes and when filter or upper_null > 0 the number of returned samples from the posterior distribution will be lower than ndraws. |
A vector of samples from the posterior distribution in units of the correlation parameter ρ.
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