oddsratio | R Documentation |

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", ...)

`x` |
a numeric vector or matrix. |

`y` |
a numeric vector; ignored if |

`ci` |
Confidence Interval (CI) level |

`alternative` |
a character string specifying the alternative hypothesis;
Controls the type of CI returned: |

`log` |
Take in or output the log of the ratio (such as in logistic models). |

`...` |
Ignored |

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`

).

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:
`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)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.