Report with any
Note that these are computed with each column representing the different groups, and the first column representing the treatment group and the second column baseline (or control). Effects are given as
treatment / control. If you wish you use rows as groups you must pass a transposed
table, or switch the
oddsratio(x, y = NULL, ci = 0.95, alternative = "two.sided", log = FALSE, ...) riskratio(x, y = NULL, ci = 0.95, alternative = "two.sided", log = FALSE, ...) cohens_h(x, y = NULL, ci = 0.95, alternative = "two.sided", ...)
a numeric vector or matrix.
a numeric vector; ignored if
Confidence Interval (CI) level
a character string specifying the alternative hypothesis;
Controls the type of CI returned:
Take in or output the log of the ratio (such as in logistic models).
A data frame with the effect size (
(possibly with the prefix
Cohens_h) and its CIs (
For Odds ratios, Risk ratios and Cohen's h, confidence intervals are estimated using the standard normal parametric method (see Katz et al., 1978; Szumilas, 2010).
"Confidence intervals on measures of effect size convey all the information
in a hypothesis test, and more." (Steiger, 2004). Confidence (compatibility)
intervals and p values are complementary summaries of parameter uncertainty
given the observed data. A dichotomous hypothesis test could be performed
with either a CI or a p value. The 100 (1 - α)% confidence
interval contains all of the parameter values for which p > α
for the current data and model. For example, a 95% confidence interval
contains all of the values for which p > .05.
Note that a confidence interval including 0 does not indicate that the null (no effect) is true. Rather, it suggests that the observed data together with the model and its assumptions combined do not provided clear evidence against a parameter value of 0 (same as with any other value in the interval), with the level of this evidence defined by the chosen α level (Rafi & Greenland, 2020; Schweder & Hjort, 2016; Xie & Singh, 2013). To infer no effect, additional judgments about what parameter values are "close enough" to 0 to be negligible are needed ("equivalence testing"; Bauer & Kiesser, 1996).
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd Ed.). New York: Routledge.
Katz, D. J. S. M., Baptista, J., Azen, S. P., & Pike, M. C. (1978). Obtaining confidence intervals for the risk ratio in cohort studies. Biometrics, 469-474.
Szumilas, M. (2010). Explaining odds ratios. Journal of the Canadian academy of child and adolescent psychiatry, 19(3), 227.
Other effect sizes for contingency table:
data("RCT_table") RCT_table # note groups are COLUMNS oddsratio(RCT_table) oddsratio(RCT_table, alternative = "greater") riskratio(RCT_table) cohens_h(RCT_table)
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