Provides summary statistics for each of the parameters (mean and standard deviation) of the group(s) of observations and their differences.
# S3 method for BEST
summary(object, credMass = 0.95,
ROPEm = NULL, ROPEsd = NULL, ROPEeff = NULL,
compValm = 0, compValsd = NULL, compValeff = 0, ...)an object of class BEST, as produced by the function BESTmcmc.
the probability mass to include in credible intervals.
a two element vector, such as c(-1, 1), specifying the limit of the ROPE on the difference of means (for 2 groups) or the mean (for 1 group). See plot.BEST for an explanation of ROPE.
a two element vector, such as c(-1, 1), specifying the limit of the ROPE on the (difference of) standard deviations.
a two element vector, such as c(-1, 1), specifying the limit of the ROPE on the effect size.
a value for comparison with the (difference of) means.
a value for comparison with the (difference of) standard deviations.
a value for comparison with the effect size.
additional arguments for the summary or print function.
Returns a matrix with the parameters in rows and the following columns:
the mean, median and mode of the MCMC samples for the corresponding parameter.
the percentage of posterior probability mass included in the highest density interval and the lower and upper limits.
the value for comparison and the percentage of the posterior probability mass above that value.
the lower and upper limits of the Region Of Practical Equivalence (ROPE) and the percentage of the posterior probability mass within the region.
If the analysis concerns a comparison of two groups, the matrix will have rows for:
the means of each group and the difference in means
the standard deviations of each group and the difference in standard deviations
the normality parameter and its log
the effect size; \(d[a]\) from Macmillan & Creelman (1991).
For a single group, the rows will be:
the mean
the standard deviation
the normality parameter and its log
the effect size.
Many of the elements of the matrix will be NA. The print method for the summary attempts to print this nicely.
Kruschke, J K. 2013. Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General 142(2):573-603. doi: 10.1037/a0029146
Macmillan, N. A., & Creelman, C. D. (1991). Detection Theory: A User's Guide. New York, Cambridge University Press
Use the plotAll function for a graphical display of these same values.
# NOT RUN {
## see "BEST-package"
# }
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