`vignettes/anova-statistics.Rmd`

`anova-statistics.Rmd`

This vignettes demontrates those functions of the *sjstats*-package that deal with Anova tables. These functions report different effect size measures, which are useful beyond significance tests (p-values), because they estimate the magnitude of effects, independent from sample size. *sjstats* provides following functions:

Befor we start, we fit a simple model:

```
library(sjstats)
# load sample data
data(efc)
# fit linear model
fit <- aov(
c12hour ~ as.factor(e42dep) + as.factor(c172code) + c160age,
data = efc
)
```

All functions accept objects of class `aov`

or `anova`

, so you can also use model fits from the *car* package, which allows fitting Anova’s with different types of sum of squares. Other objects, like `lm`

, will be coerced to `anova`

internally.

The following functions return the effect size statistic as named numeric vector, using the model’s term names.

The eta-squared is the proportion of the total variability in the dependent variable that is accounted for by the variation in the independent variable. It is the ratio of the sum of squares for each group level to the total sum of squares. It can be interpreted as percentage of variance accounted for by a variable.

For variables with 1 degree of freedeom (in the numerator), the square root of eta-squared is equal to the correlation coefficient *r*. For variables with more than 1 degree of freedom, eta-squared equals *R2*. This makes eta-squared easily interpretable. Furthermore, these effect sizes can easily be converted into effect size measures that can be, for instance, further processed in meta-analyses.

Eta-squared can be computed simply with:

The partial eta-squared value is the ratio of the sum of squares for each group level to the sum of squares for each group level plus the residual sum of squares. It is more difficult to interpret, because its value strongly depends on the variability of the residuals. Partial eta-squared values should be reported with caution, and Levine and Hullett (2002) recommend reporting eta- or omega-squared rather than partial eta-squared.

Use the `partial`

-argument to compute partial eta-squared values:

While eta-squared estimates tend to be biased in certain situations, e.g. when the sample size is small or the independent variables have many group levels, omega-squared estimates are corrected for this bias.

Omega-squared can be simply computed with:

`omega_sq()`

also has a `partial`

-argument to compute partial omega-squared values. Computing the partial omega-squared statistics is based on bootstrapping. In this case, use `n`

to define the number of samples (1000 by default.)

```
omega_sq(fit, partial = TRUE, n = 100)
#> term partial.omegasq
#> 1 as.factor(e42dep) 0.278
#> 2 as.factor(c172code) 0.005
#> 3 c160age 0.065
```

Espilon-squared is a less common measure of effect size. It is sometimes considered as an “adjusted r-squared” value. You can compute this effect size using `epsilon_sq()`

.

```
epsilon_sq(fit)
#> term epsilonsq
#> 1 as.factor(e42dep) 0.264
#> 2 as.factor(c172code) 0.004
#> 3 c160age 0.048
```

When the `ci.lvl`

-argument is defined, bootstrapping is used to compute the confidence intervals.

```
epsilon_sq(fit, ci.lvl = .95, n = 100)
#> term epsilonsq conf.low conf.high
#> 1 as.factor(e42dep) 0.264 0.211 0.317
#> 2 as.factor(c172code) 0.004 -0.004 0.016
#> 3 c160age 0.048 0.025 0.071
```

Finally, `cohens_f()`

computes Cohen’s F effect size for all independent variables in the model:

The `anova_stats()`

function takes a model input and computes a comprehensive summary, including the above effect size measures, returned as tidy data frame:

```
anova_stats(fit)
#> term df sumsq meansq statistic p.value etasq partial.etasq omegasq partial.omegasq epsilonsq cohens.f power
#> 1 as.factor(e42dep) 3 577756.33 192585.444 108.786 0.000 0.266 0.281 0.263 0.278 0.264 0.626 1.00
#> 2 as.factor(c172code) 2 11722.05 5861.024 3.311 0.037 0.005 0.008 0.004 0.005 0.004 0.089 0.63
#> 3 c160age 1 105169.60 105169.595 59.408 0.000 0.048 0.066 0.048 0.065 0.048 0.267 1.00
#> 4 Residuals 834 1476436.34 1770.307 NA NA NA NA NA NA NA NA NA
```

Like the other functions, the input may also be an object of class `anova`

, so you can also use model fits from the *car* package, which allows fitting Anova’s with different types of sum of squares:

```
anova_stats(car::Anova(fit, type = 3))
#> term sumsq meansq df statistic p.value etasq partial.etasq omegasq partial.omegasq epsilonsq cohens.f power
#> 1 (Intercept) 26851.070 26851.070 1 15.167 0.000 0.013 0.018 0.012 0.017 0.012 0.135 0.973
#> 2 as.factor(e42dep) 426461.571 142153.857 3 80.299 0.000 0.209 0.224 0.206 0.220 0.206 0.537 1.000
#> 3 as.factor(c172code) 7352.049 3676.025 2 2.076 0.126 0.004 0.005 0.002 0.003 0.002 0.071 0.429
#> 4 c160age 105169.595 105169.595 1 59.408 0.000 0.051 0.066 0.051 0.065 0.051 0.267 1.000
#> 5 Residuals 1476436.343 1770.307 834 NA NA NA NA NA NA NA NA NA
```

`eta_sq()`

and `omega_sq()`

have a `ci.lvl`

-argument, which - if not `NULL`

- also computes a confidence interval.

For eta-squared, i.e. `eta_sq()`

with `partial = FALSE`

, due to non-symmetry, confidence intervals are based on bootstrap-methods. Confidence intervals for partial omega-squared, i.e. `omega_sq()`

with `partial = TRUE`

- is also based on bootstrapping. In these cases, `n`

indicates the number of bootstrap samples to be drawn to compute the confidence intervals.

```
eta_sq(fit, partial = TRUE, ci.lvl = .8)
#> term partial.etasq conf.low conf.high
#> 1 as.factor(e42dep) 0.281 0.247 0.310
#> 2 as.factor(c172code) 0.008 0.001 0.016
#> 3 c160age 0.066 0.047 0.089
# uses bootstrapping - here, for speed, just 100 samples
omega_sq(fit, partial = TRUE, ci.lvl = .9, n = 100)
#> term partial.omegasq conf.low conf.high
#> 1 as.factor(e42dep) 0.278 0.238 0.325
#> 2 as.factor(c172code) 0.005 -0.004 0.021
#> 3 c160age 0.065 0.035 0.095
```