Report with any stats::chisq.test() or stats::fisher.test().
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 x and y arguments.
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 data frame with the effect size (Odds_ratio, Risk_ratio
(possibly with the prefix log_), Cohens_h) and its CIs (CI_low and
CI_high).
a numeric vector or matrix. x and y can also
both be factors.
a numeric vector; ignored if x is a matrix. If
x is a factor, y should be a factor of the same length.
Confidence Interval (CI) level
a character string specifying the alternative hypothesis;
Controls the type of CI returned: "two.sided" (two-sided CI; default),
"greater" (one-sided CI) or "less" (one-sided CI). Partial matching is
allowed (e.g., "g", "l", "two"...). See One-Sided CIs in
effectsize_CIs.
Take in or output the log of the ratio (such as in logistic models).
Ignored
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 - \(\alpha\))% confidence
interval contains all of the parameter values for which p > \(\alpha\)
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 \(\alpha\) 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:
cohens_g(),
phi()
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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