Parameters from ANOVAs — model_parameters.aov • parameters

Parameters from ANOVAs

# S3 method for aov
model_parameters(
  model,
  omega_squared = NULL,
  eta_squared = NULL,
  epsilon_squared = NULL,
  df_error = NULL,
  type = NULL,
  ci = NULL,
  test = NULL,
  power = FALSE,
  keep = NULL,
  drop = NULL,
  parameters = keep,
  table_wide = FALSE,
  verbose = TRUE,
  ...
)

Arguments

model	Object of class `aov()`, `anova()`, `aovlist`, `Gam`, `manova()`, `Anova.mlm`, `afex_aov` or `maov`.
omega_squared	Compute omega squared as index of effect size. Can be `"partial"` (the default, adjusted for effect size) or `"raw"`.
eta_squared	Compute eta squared as index of effect size. Can be `"partial"` (the default, adjusted for effect size), `"raw"` or `"adjusted"` (the latter option only for ANOVA-tables from mixed models).
epsilon_squared	Compute epsilon squared as index of effect size. Can be `"partial"` (the default, adjusted for effect size) or `"raw"`.
df_error	Denominator degrees of freedom (or degrees of freedom of the error estimate, i.e., the residuals). This is used to compute effect sizes for ANOVA-tables from mixed models. See 'Examples'. (Ignored for `afex_aov`.)
type	Numeric, type of sums of squares. May be 1, 2 or 3. If 2 or 3, ANOVA-tables using `car::Anova()` will be returned. (Ignored for `afex_aov`.)
ci	Confidence Interval (CI) level for effect sizes `omega_squared`, `eta_squared` etc. The default, `NULL`, will compute no confidence intervals. `ci` should be a scalar between 0 and 1.
test	String, indicating the type of test for `Anova.mlm` to be returned. If `"multivariate"` (or `NULL`), returns the summary of the multivariate test (that is also given by the `print`-method). If `test = "univariate"`, returns the summary of the univariate test.
power	Logical, if `TRUE`, adds a column with power for each parameter.
keep	Character containing a regular expression pattern that describes the parameters that should be included in the returned data frame (for `keep`), resp. parameters to exclude (`drop`). `keep` may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If `keep` is a character vector, every parameter name in the "Parameter" column that matches the regular expression in `parameters` will be selected from the returned data frame (and vice versa, all parameter names matching `drop` will be excluded). Furthermore, if `keep` has more than one element, these will be merged with an `OR` operator into a regular expression pattern like this: `"(one\|two\|three)"`. If `keep` is a named list of regular expression patterns, the names of the list-element should equal the column name where selection should be applied. This is useful for model objects where `model_parameters()` returns multiple columns with parameter components, like in `model_parameters.lavaan()`. Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the `$Parameter` column of the parameters table to get the exact parameter names.
drop	Character containing a regular expression pattern that describes the parameters that should be included in the returned data frame (for `keep`), resp. parameters to exclude (`drop`). `keep` may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If `keep` is a character vector, every parameter name in the "Parameter" column that matches the regular expression in `parameters` will be selected from the returned data frame (and vice versa, all parameter names matching `drop` will be excluded). Furthermore, if `keep` has more than one element, these will be merged with an `OR` operator into a regular expression pattern like this: `"(one\|two\|three)"`. If `keep` is a named list of regular expression patterns, the names of the list-element should equal the column name where selection should be applied. This is useful for model objects where `model_parameters()` returns multiple columns with parameter components, like in `model_parameters.lavaan()`. Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the `$Parameter` column of the parameters table to get the exact parameter names.
parameters	Deprecated, alias for `keep`.
table_wide	Logical that decides whether the ANOVA table should be in wide format, i.e. should the numerator and denominator degrees of freedom be in the same row. Default: `FALSE`.
verbose	Toggle warnings and messages.
...	Arguments passed to or from other methods.

Value

A data frame of indices related to the model's parameters.

Note

For ANOVA-tables from mixed models (i.e. anova(lmer())), only partial or adjusted effect sizes can be computed. Note that type 3 ANOVAs with interactions involved only give sensible and informative results when covariates are mean-centred 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).

Examples

if (requireNamespace("effectsize", quietly = TRUE)) {
  df <- iris
  df$Sepal.Big <- ifelse(df$Sepal.Width >= 3, "Yes", "No")

  model <- aov(Sepal.Length ~ Sepal.Big, data = df)
  model_parameters(
    model,
    omega_squared = "partial",
    eta_squared = "partial",
    epsilon_squared = "partial"
  )

  model_parameters(
    model,
    omega_squared = "partial",
    eta_squared = "partial",
    ci = .9
  )

  model <- anova(lm(Sepal.Length ~ Sepal.Big, data = df))
  model_parameters(model)
  model_parameters(
    model,
    omega_squared = "partial",
    eta_squared = "partial",
    epsilon_squared = "partial"
  )

  model <- aov(Sepal.Length ~ Sepal.Big + Error(Species), data = df)
  model_parameters(model)

  if (FALSE) {
    if (require("lme4")) {
      mm <- lmer(Sepal.Length ~ Sepal.Big + Petal.Width + (1 | Species),
        data = df
      )
      model <- anova(mm)

      # simple parameters table
      model_parameters(model)

      # parameters table including effect sizes
      model_parameters(
        model,
        eta_squared = "partial",
        ci = .9,
        df_error = dof_satterthwaite(mm)[2:3]
      )
    }
  }
}