Cohen's *g* is an effect size of asymmetry (or marginal heterogeneity) for
dependent (paired) contingency tables ranging between 0 (perfect symmetry)
and 0.5 (perfect asymmetry) (see `stats::mcnemar.test()`

). (Note this is not
*not* a measure of (dis)agreement between the pairs, but of (a)symmetry.)

## Arguments

- x
a numeric vector or matrix.

`x`

and`y`

can also both be factors.- y
a numeric vector; ignored if

`x`

is a matrix. If`x`

is a factor,`y`

should be a factor of the same length.- ci
Confidence Interval (CI) level

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

`"two.sided"`

(default, two-sided CI),`"greater"`

or`"less"`

(one-sided CI). Partial matching is allowed (e.g.,`"g"`

,`"l"`

,`"two"`

...). See*One-Sided CIs*in effectsize_CIs.- ...
Ignored

## Value

A data frame with the effect size (`Cohens_g`

, `Risk_ratio`

(possibly with the prefix `log_`

), `Cohens_h`

) and its CIs (`CI_low`

and
`CI_high`

).

## Confidence (Compatibility) Intervals (CIs)

Confidence intervals are based on the proportion (\(P = g + 0.5\))
confidence intervals returned by `stats::prop.test()`

(minus 0.5), which give
a good close approximation.

## CIs and Significance Tests

"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).

## Plotting with `see`

The `see`

package contains relevant plotting functions. See the plotting vignette in the `see`

package.

## References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd Ed.). New York: Routledge.

## Examples

```
data("screening_test")
phi(screening_test$Diagnosis, screening_test$Test1)
#> Phi (adj.) | 95% CI
#> -------------------------
#> 0.85 | [0.81, 1.00]
#>
#> - One-sided CIs: upper bound fixed at [1.00].
phi(screening_test$Diagnosis, screening_test$Test2)
#> Phi (adj.) | 95% CI
#> -------------------------
#> 0.85 | [0.81, 1.00]
#>
#> - One-sided CIs: upper bound fixed at [1.00].
# Both tests seem comparable - but are the tests actually different?
(tests <- table(Test1 = screening_test$Test1, Test2 = screening_test$Test2))
#> Test2
#> Test1 "Neg" "Pos"
#> "Neg" 794 86
#> "Pos" 150 570
mcnemar.test(tests)
#>
#> McNemar's Chi-squared test with continuity correction
#>
#> data: tests
#> McNemar's chi-squared = 16.818, df = 1, p-value = 4.115e-05
#>
cohens_g(tests)
#> Cohen's g | 95% CI
#> ------------------------
#> 0.14 | [0.07, 0.19]
# Test 2 gives a negative result more than test 1!
```