Load all the definitions in a glossary file, usually for subsequent display using glossary_table
Usage
glossary_load_all(path = glossary_path())Arguments
- path
The path to the glossary file; set default with
glossary_path
Examples
demo_glossary <- system.file("glossary.yml", package = "glossary2")
glossary_load_all(demo_glossary)
glossary_table(FALSE) # get table as a data frame
#> term
#> SESOI SESOI
#> alpha alpha
#> alpha (graphics) alpha (graphics)
#> effect size effect size
#> html html
#> joins joins
#> p-value p-value
#> power power
#> definition
#> SESOI Smallest Effect Size of Interest: the smallest effect that is theoretically or practically meaningful\n\nSee [Equivalence Testing for Psychological Research](https://doi.org/10.1177/2515245918770963) for a tutorial on methods for choosing an SESOI.
#> alpha The threshold chosen in Neyman-Pearson hypothesis testing to distinguish test results that lead to the decision to reject the null hypothesis, or not, based on the desired upper bound of the Type 1 error rate. An alpha level of 5% is most commonly used, but other alpha levels can be used as long as they are determined and preregistered by the researcher before the data is analyzed.
#> alpha (graphics) A value between 0 and 1 used to control the levels of transparency in a plot
#> effect size 'quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest' (Kelley & Preacher, 2012)
#> html This is a paragraph with a [link](https://url.com).\n\nAnd another paragraph before a list:\n\n* Item 1\n* List 2
#> joins Ways to combine data from two tables
#> p-value The probability of the observed data, or more extreme data, if the null hypothesis is true. The lower the p-value, the higher the test statistic, and less likely it is to observe the data if the null hypothesis is true.
#> power The probability of rejecting the null hypothesis when it is false, for a specific analysis, effect size, sample size, and criteria for significance.