report() assigning effect size to intercept in model

[RenyBB]

For example, we want to do a type III ANOVA, so we fit a linear model with categorical predictors and use the car::Anova function:

some_linear_model <- lm(mpg ~ as.factor(cyl)*as.factor(am), data=mtcars) 
some_anova <- car::Anova(some_linear_model, type = "III")

Then, we use report() and report_table() to output the results:

report::report(some_anova)
report::report_table(some_anova)

The effect sizes using repor_tablet() are correct, but the effect sizes using report() don't match up with the correct effects:

• The main effect of (Intercept) is statistically significant and large (F(1, 26) =
  171.10, p < .001; Eta2 (partial) = 0.41, 95% CI [0.15, 1.00])
• The main effect of as.factor(cyl) is statistically significant and large (F(2, 26)
  = 9.12, p < .001; Eta2 (partial) = 0.20, 95% CI [0.02, 1.00])
• The main effect of as.factor(am) is statistically significant and medium (F(1, 26)
  = 6.35, p = 0.018; Eta2 (partial) = 0.10, 95% CI [0.00, 1.00])
• The interaction between as.factor(cyl) and as.factor(am) is statistically not
  significant and large (F(2, 26) = 1.38, p = 0.269; Eta2 (partial) = 0.41, 95% CI
  [0.15, 1.00])

Compare these to the results obtained with report_table():

[mattansb]

It seems silly, but for type 3 ANOVA tables we do get the intercept term, and it does have a meaning:
It is the proportional reduction in error accounted for by the inclusion of the intercept. So in a sense, this is the "variance explained" by the intercept:

library(performance)

m0 <- lm(mpg ~ 0, data = mtcars)
m1 <- lm(mpg ~ 1, data = mtcars)

car::Anova(m1, type = 3)
#> Anova Table (Type III tests)
#> 
#> Response: mpg
#>             Sum Sq Df F value    Pr(>F)    
#> (Intercept)  12916  1  355.58 < 2.2e-16 ***
#> Residuals     1126 31                      
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

effectsize::F_to_eta2(f = 355.58, df = 1, df_error = 31, ci = NULL) |> 
  print(digits = 6)
#> Eta2 (partial)
#> --------------
#> 0.919810 

1 - (rmse(m1) ^ 2) / (rmse(m0) ^ 2)
#> [1] 0.9198104

[RenyBB]

Sorry, I can see how the title I chose is completely uninformative. I've edited the post to describe the issue in more detail - the values for the effect sizes don't match the correct effects when using report(). For example, the effect size for the interaction term should be 0.1 but report() returns 0.41.

[mattansb]

Ah, I see.

@IndrajeetPatil @rempsyc wasn't this recycling issue resolved in #198 ?

[rempsyc]

The effect sizes are misaligned (probably because it is NA for the intercept instead of an empty string). Reprex:

packageVersion("report")
#> [1] '0.5.8.5'

some_linear_model <- lm(mpg ~ as.factor(cyl)*as.factor(am), data=mtcars) 
some_anova <- car::Anova(some_linear_model, type = "III")

report::report(some_anova)
#> Type 3 ANOVAs only give sensible and informative results when covariates
#>   are mean-centered and factors are coded with orthogonal contrasts (such
#>   as those produced by `contr.sum`, `contr.poly`, or `contr.helmert`, but
#>   *not* by the default `contr.treatment`).
#> The ANOVA suggests that:
#> 
#>   - The main effect of (Intercept) is statistically significant and large (F(1,
#> 26) = 171.10, p < .001; Eta2 (partial) = 0.41, 95% CI [0.15, 1.00])
#>   - The main effect of as.factor(cyl) is statistically significant and large
#> (F(2, 26) = 9.12, p < .001; Eta2 (partial) = 0.20, 95% CI [0.02, 1.00])
#>   - The main effect of as.factor(am) is statistically significant and medium
#> (F(1, 26) = 6.35, p = 0.018; Eta2 (partial) = 0.10, 95% CI [0.00, 1.00])
#>   - The interaction between as.factor(cyl) and as.factor(am) is statistically not
#> significant and large (F(2, 26) = 1.38, p = 0.269; Eta2 (partial) = 0.41, 95%
#> CI [0.15, 1.00])
#> 
#> Effect sizes were labelled following Field's (2013) recommendations.
report::report_table(some_anova)
#> Type 3 ANOVAs only give sensible and informative results when covariates
#>   are mean-centered and factors are coded with orthogonal contrasts (such
#>   as those produced by `contr.sum`, `contr.poly`, or `contr.helmert`, but
#>   *not* by the default `contr.treatment`).
#> Parameter                    | Sum_Squares | df | Mean_Square |      F |      p | Eta2 (partial) | Eta2_partial 95% CI
#> ----------------------------------------------------------------------------------------------------------------------
#> (Intercept)                  |     1573.23 |  1 |     1573.23 | 171.10 | < .001 |                |                    
#> as.factor(cyl)               |      167.71 |  2 |       83.85 |   9.12 | < .001 |           0.41 |        [0.15, 1.00]
#> as.factor(am)                |       58.43 |  1 |       58.43 |   6.35 | 0.018  |           0.20 |        [0.02, 1.00]
#> as.factor(cyl):as.factor(am) |       25.44 |  2 |       12.72 |   1.38 | 0.269  |           0.10 |        [0.00, 1.00]
#> Residuals                    |      239.06 | 26 |        9.19 |        |        |                |

^{Created on 2024-07-10 with reprex v2.1.1}

So yes, just like in #198, it seems like indeed it wasn't properly fixed since we have the same issue with the old example:

packageVersion("report")
#> [1] '0.5.8.5'

m <- lm(mpg ~ factor(am) * factor(cyl), mtcars)
a <- car::Anova(m, type = 3)

report::report(a)
#> Type 3 ANOVAs only give sensible and informative results when covariates
#>   are mean-centered and factors are coded with orthogonal contrasts (such
#>   as those produced by `contr.sum`, `contr.poly`, or `contr.helmert`, but
#>   *not* by the default `contr.treatment`).
#> The ANOVA suggests that:
#> 
#>   - The main effect of (Intercept) is statistically significant and large (F(1,
#> 26) = 171.10, p < .001; Eta2 (partial) = 0.20, 95% CI [0.02, 1.00])
#>   - The main effect of factor(am) is statistically significant and large (F(1,
#> 26) = 6.35, p = 0.018; Eta2 (partial) = 0.41, 95% CI [0.15, 1.00])
#>   - The main effect of factor(cyl) is statistically significant and medium (F(2,
#> 26) = 9.12, p < .001; Eta2 (partial) = 0.10, 95% CI [0.00, 1.00])
#>   - The interaction between factor(am) and factor(cyl) is statistically not
#> significant and large (F(2, 26) = 1.38, p = 0.269; Eta2 (partial) = 0.20, 95%
#> CI [0.02, 1.00])
#> 
#> Effect sizes were labelled following Field's (2013) recommendations.
report::report_table(a)
#> Type 3 ANOVAs only give sensible and informative results when covariates
#>   are mean-centered and factors are coded with orthogonal contrasts (such
#>   as those produced by `contr.sum`, `contr.poly`, or `contr.helmert`, but
#>   *not* by the default `contr.treatment`).
#> Parameter              | Sum_Squares | df | Mean_Square |      F |      p | Eta2 (partial) | Eta2_partial 95% CI
#> ----------------------------------------------------------------------------------------------------------------
#> (Intercept)            |     1573.23 |  1 |     1573.23 | 171.10 | < .001 |                |                    
#> factor(am)             |       58.43 |  1 |       58.43 |   6.35 | 0.018  |           0.20 |        [0.02, 1.00]
#> factor(cyl)            |      167.71 |  2 |       83.85 |   9.12 | < .001 |           0.41 |        [0.15, 1.00]
#> factor(am):factor(cyl) |       25.44 |  2 |       12.72 |   1.38 | 0.269  |           0.10 |        [0.00, 1.00]
#> Residuals              |      239.06 | 26 |        9.19 |        |        |                |

^{Created on 2024-07-10 with reprex v2.1.1}