Parameters from ANOVAs — model_parameters.aov • parameters

Parameters from ANOVAs

Usage

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

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

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

# S3 method for afex_aov
model_parameters(
  model,
  omega_squared = NULL,
  eta_squared = NULL,
  epsilon_squared = NULL,
  df_error = NULL,
  type = NULL,
  keep = NULL,
  drop = NULL,
  parameters = keep,
  verbose = TRUE,
  ...
)

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

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

# S3 method for maov
model_parameters(
  model,
  omega_squared = NULL,
  eta_squared = NULL,
  epsilon_squared = NULL,
  df_error = NULL,
  type = NULL,
  ci = NULL,
  alternative = 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.
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 section One-Sided CIs in the effectsize_CIs vignette.
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 (for keep) or excluded (for drop) in the returned data frame. 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 keep 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: See keep.
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]
      )
    }
  }
}