Interpret a value based on a set of rules. See rules().
Usage
interpret(x, ...)
# S3 method for numeric
interpret(x, rules, name = attr(rules, "rule_name"), ...)
# S3 method for effectsize_table
interpret(x, rules, ...)Arguments
- x
Vector of value break points (edges defining categories), or a data frame of class
effectsize_table.- ...
Currently not used.
- rules
Set of
rules(). Whenxis a data frame, can be a name of an established set of rules.- name
Name of the set of rules (will be printed).
Value
For numeric input: A character vector of interpretations.
For data frames: the
xinput with an additionalInterpretationcolumn.
Examples
rules_grid <- rules(c(0.01, 0.05), c("very significant", "significant", "not significant"))
interpret(0.001, rules_grid)
#> [1] "very significant"
#> (Rules: Custom rules)
#>
interpret(0.021, rules_grid)
#> [1] "significant"
#> (Rules: Custom rules)
#>
interpret(0.08, rules_grid)
#> [1] "not significant"
#> (Rules: Custom rules)
#>
interpret(c(0.01, 0.005, 0.08), rules_grid)
#> [1] "very significant" "very significant" "not significant"
#> (Rules: Custom rules)
#>
interpret(c(0.35, 0.15), c("small" = 0.2, "large" = 0.4), name = "Cohen's Rules")
#> [1] "large" "small"
#> (Rules: Cohen's Rules)
#>
interpret(c(0.35, 0.15), rules(c(0.2, 0.4), c("small", "medium", "large")))
#> [1] "medium" "small"
#> (Rules: Custom rules)
#>
# ----------
d <- cohens_d(mpg ~ am, data = mtcars)
interpret(d, rules = "cohen1988")
#> Cohen's d | 95% CI | Interpretation
#> -------------------------------------------
#> -1.48 | [-2.27, -0.67] | large
#>
#> - Estimated using pooled SD.
#> - Interpretation rule: cohen1988
d <- glass_delta(mpg ~ am, data = mtcars)
interpret(d, rules = "gignac2016")
#> Glass' delta (adj.) | 95% CI
#> ------------------------------------
#> -1.10 | [-1.80, -0.37]
interpret(d, rules = rules(1, c("tiny", "yeah okay")))
#> Glass' delta (adj.) | 95% CI
#> ------------------------------------
#> -1.10 | [-1.80, -0.37]
m <- lm(formula = wt ~ am * cyl, data = mtcars)
eta2 <- eta_squared(m)
interpret(eta2, rules = "field2013")
#> # Effect Size for ANOVA (Type I)
#>
#> Parameter | Eta2 (partial) | 95% CI | Interpretation
#> ----------------------------------------------------------
#> am | 0.63 | [0.44, 1.00] | large
#> cyl | 0.47 | [0.24, 1.00] | large
#> am:cyl | 2.26e-03 | [0.00, 1.00] | very small
#>
#> - One-sided CIs: upper bound fixed at [1.00].
#> - Interpretation rule: field2013
X <- chisq.test(mtcars$am, mtcars$cyl == 8)
interpret(oddsratio(X), rules = "chen2010")
#> Odds ratio | 95% CI | Interpretation
#> ------------------------------------------
#> 0.11 | [0.02, 0.62] | large
#>
#> - Interpretation rule: chen2010
interpret(cramers_v(X), "lovakov2021")
#> Cramer's V (adj.) | 95% CI | Interpretation
#> -------------------------------------------------
#> 0.44 | [0.03, 1.00] | large
#>
#> - One-sided CIs: upper bound fixed at [1.00].
#> - Interpretation rule: lovakov2021