summary.BEST: Extract summary statistics from an object of class BEST.

In BEST: Bayesian Estimation Supersedes the t-Test

Description

Provides summary statistics for each of the parameters (mean and standard deviation) of the group(s) of observations and their differences.

Usage

## S3 method for class 'BEST'
summary(object, credMass = 0.95,
  ROPEm = NULL, ROPEsd = NULL, ROPEeff = NULL,
  compValm = 0, compValsd = NULL, compValeff = 0, ...)

Arguments

object	an object of class BEST, as produced by the function BESTmcmc.
credMass	the probability mass to include in credible intervals.
ROPEm	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.
ROPEsd	a two element vector, such as c(-1, 1), specifying the limit of the ROPE on the (difference of) standard deviations.
ROPEeff	a two element vector, such as c(-1, 1), specifying the limit of the ROPE on the effect size.
compValm	a value for comparison with the (difference of) means.
compValsd	a value for comparison with the (difference of) standard deviations.
compValeff	a value for comparison with the effect size.
...	additional arguments for the summary or print function.

Value

Returns a matrix with the parameters in rows and the following columns:

mean, median, mode	the mean, median and mode of the MCMC samples for the corresponding parameter.
hdi%, hdiLow, hdiHigh	the percentage of posterior probability mass included in the highest density interval and the lower and upper limits.
compVal, %>compVal	the value for comparison and the percentage of the posterior probability mass above that value.
ROPElow, ROPEhigh, %InROPE	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:

mu1, mu2, muDiff	the means of each group and the difference in means
sigma1, sigma2, sigmaDiff	the standard deviations of each group and the difference in standard deviations
nu, log10nu	the normality parameter and its log
effSz	the effect size; d[a] from Macmillan & Creelman (1991).

For a single group, the rows will be:

mu	the mean
sigma	the standard deviation
nu, log10nu	the normality parameter and its log
effSz	the effect size.

Many of the elements of the matrix will be NA. The print method for the summary attempts to print this nicely.

Author(s)

Mike Meredith, based on code by John K. Kruschke.

References

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

See Also

Use the plotAll function for a graphical display of these same values.

Examples

## see "BEST-package"

BEST documentation built on Oct. 13, 2021, 9:08 a.m.