parameters (version 0.10.1)

model_parameters.aov: Parameters from ANOVAs

Description

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,
  verbose = TRUE,
  ...
)

Arguments

model

Object of class aov, anova, aovlist, Gam, manova, 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.

verbose

Toggle warnings and messages.

...

Arguments passed to or from other methods.

Value

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

Examples

# NOT RUN {
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 (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)
    )
  }
}
# }