# Equivalence Testing for F-tests

#### Aaron R. Caldwell

#### 2025-02-06

For an open access tutorial paper explaining how to set equivalence bounds, and how to perform and report equivalence for ANOVA models see Campbell and Lakens (2021). These functions are meant to be omnibus tests, and additional testing may be necessary1.

# F-test Calculations

Statistical equivalence testing (or “omnibus non-inferiority testing” as stated by Campbell and Lakens (2021)) for F-tests are special use case of the cumulative distribution function of the non-central F distribution.

As Campbell and Lakens (2021) state, these type of questions answer the question: “Can we reject the hypothesis that the total proportion of variance in outcome Y attributable to X is greater than or equal to the equivalence bound \(\Delta\)?”

\[ H_0: \space 1 > \eta^2_p \geq \Delta \\ H_1: \space 0 \geq \eta^2_p < \Delta \]

In `TOSTER` we go a tad farther and calculate a more generalization of the non-centrality parameter to allow for the equivalence test for F-tests to be applied to variety of designs.

Campbell and Lakens (2021) calculate the p-value as:

\[ p = p_f(F; J-1, N-J, \frac{N \cdot \Delta}{1-\Delta}) \]

However, this approach could not be applied to factorial ANOVA and the paper only outlines how to apply this approach to a one-way ANOVA and an extension to Welch’s one-way ANOVA.

However, the non-centrality parameter (ncp = \(\lambda\)) can be calculated with the equivalence bound and the degrees of freedom:

\[ \lambda_{eq} = \frac{\Delta}{1-\Delta} \cdot(df_1 + df_2 +1) \]

The p-value for the equivalence test (\(p_{eq}\)) could then be calculated from traditional ANOVA results and the distribution function:

\[ p_{eq} = p_f(F; df_1, df_2, \lambda_{eq}) \]

## An Example

Using the `InsectSprays` data set in R and the base R `aov` function we can demonstrate how this omnibus equivalence testing can be applied with `TOSTER`.

From the initial analysis we an see a clear “significant” effect (the p-value listed is zero but it just very small) of the factor spray. However, we may be interested in testing if the effect is practically equivalent. I will arbitrarily set the equivalence bound to a partial eta-squared of 0.35 (\(H_0: \eta^2_p > 0.35\)).

    library(TOSTER)
    # Get Data
    data("InsectSprays")
    # Build ANOVA
    aovtest = aov(count ~ spray,
                  data = InsectSprays)

    # Display overall results
    knitr::kable(broom::tidy(aovtest),
                caption = "Traditional ANOVA Test")

Traditional ANOVA Test
term | df | sumsq | meansq | statistic | p.value
--- | --- | --- | --- | --- | ---
spray | 5 | 2668.833 | 533.76667 | 34.70228 | 0
Residuals | 66 | 1015.167 | 15.38131 | NA | NA

We can then use the information in the table above to perform an equivalence test using the `equ_ftest` function. This function returns an object of the S3 class `htest` and the output will look very familiar to the the t-test. The main difference is the estimates, and confidence interval, are for partial \(\eta^2_p\).

    equ_ftest(Fstat = 34.70228,
              df1 = 5,
              df2 = 66,
              eqbound = 0.35)

    ##
    ##  Equivalence Test from F-test
    ##
    ## data:  Summary Statistics
    ## F = 34.702, df1 = 5, df2 = 66, p-value = 1
    ## 95 percent confidence interval:
    ##  0.5806263 0.7804439
    ## sample estimates:
    ## [1] 0.724439

Based on the results above we would conclude there is a significant effect of “spray” and the differences due to spray are not statistically equivalent. In essence, we reject the traditional null hypothesis of “no effect” but accept the null hypothesis of the equivalence test.

The `equ_ftest` is very useful because all you need is very basic summary statistics. However, if you are doing all your analyses in R then you can use the `equ_anova` function. This function accepts objects produced from `stats::aov`, `car::Anova` and `afex::aov_car` (or any anova from `afex`).

    equ_anova(aovtest,
              eqbound = 0.35)

    ##        effect df1 df2  F.value       p.null      pes eqbound     p.equ
    ## 1 spray         5  66 34.70228 3.182584e-17 0.724439    0.35 0.9999965

### Visualize partial \(\eta^2\)

Just like the standardized mean differences, `TOSTER` also has a function to visualize \(\eta^2_p\).

The function, `plot_pes`, operates in a fashion very similar to `equ_ftest`. In essence, all you have to do is provide the F-statistic, numerator degrees of freedom, and denominator degrees of freedom. We can also select the type of plot with the `type` argument. Users have the option of producing a consonance plot (`type = "c"`), a consonance density plot (`type = "cd"`), or both (`type = c("cd","c")`). By default, `plot_pes` will produce both plots.

    plot_pes(Fstat = 34.70228,
             df1 = 5,
             df2 = 66)

# Power Analysis for F-tests

Power for an equivalence F-test can be calculated with the same equations supplied by Campbell and Lakens (2021). I have included these within the `power_eq_f` function.

    power_eq_f(df1 = 2,
                df2 = 60,
                eqbound = .15)

    ## Note: equ_anova only validated for one-way ANOVA; use with caution

    ##
    ##      Power for Non-Inferiority F-test
    ##
    ##             df1 = 2
    ##             df2 = 60
    ##         eqbound = 0.15
    ##       sig.level = 0.05
    ##           power = 0.8188512

# References

Campbell, Harlan, and Daniël Lakens. 2021. “Can We Disregard the Whole Model? Omnibus Non-Inferiority Testing for R2 in Multi-Variable Linear Regression and in ANOVA.” British Journal of Mathematical and Statistical Psychology 74 (1): e12201. https://doi.org/10.1111/bmsp.12201.

1. Russ Lenth’s emmeans R package has some capacity for equivalence testing on the marginal means (i.e., a form of pairwise testing). See the emmeans package vignettes for details↩︎