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Parameters from ANOVAs

Source: R/methods_aov.R
model_parameters.aov.Rd

Parameters from ANOVAs

Usage

# S3 method for class 'aov'
model_parameters(
  model,
  type = NULL,
  df_error = NULL,
  ci = NULL,
  alternative = NULL,
  test = NULL,
  power = FALSE,
  es_type = NULL,
  keep = NULL,
  drop = NULL,
  table_wide = FALSE,
  verbose = TRUE,
  ...
)

# S3 method for class 'afex_aov'
model_parameters(
  model,
  es_type = NULL,
  df_error = NULL,
  type = NULL,
  keep = NULL,
  drop = NULL,
  verbose = TRUE,
  ...
)

Arguments

model

Object of class aov(), anova(), aovlist, Gam, manova(), Anova.mlm, afex_aov or maov.

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.)

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.)

ci

Confidence Interval (CI) level for effect sizes specified in es_type. 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.

es_type

The effect size of interest. Not that possibly not all effect sizes are applicable to the model object. See 'Details'. For Anova models, can also be a character vector with multiple effect size names.

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.

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 effectsize::effectsize(). For example, to calculate partial effect sizes types, use partial = TRUE. For objects of class htest or BFBayesFactor, adjust = TRUE can be used to return bias-corrected effect sizes, which is advisable for small samples and large tables. See also ?effectsize::eta_squared for arguments partial and generalized; ?effectsize::phi for adjust; and ?effectsize::oddratio for log.

Value

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

Details

  • For an object of class htest, data is extracted via insight::get_data(), and passed to the relevant function according to:

    • A t-test depending on type: "cohens_d" (default), "hedges_g", or one of "p_superiority", "u1", "u2", "u3", "overlap".

      • For a Paired t-test: depending on type: "rm_rm", "rm_av", "rm_b", "rm_d", "rm_z".

    • A Chi-squared tests of independence or Fisher's Exact Test, depending on type: "cramers_v" (default), "tschuprows_t", "phi", "cohens_w", "pearsons_c", "cohens_h", "oddsratio", "riskratio", "arr", or "nnt".

    • A Chi-squared tests of goodness-of-fit, depending on type: "fei" (default) "cohens_w", "pearsons_c"

    • A One-way ANOVA test, depending on type: "eta" (default), "omega" or "epsilon" -squared, "f", or "f2".

    • A McNemar test returns Cohen's g.

    • A Wilcoxon test depending on type: returns "rank_biserial" correlation (default) or one of "p_superiority", "vda", "u2", "u3", "overlap".

    • A Kruskal-Wallis test depending on type: "epsilon" (default) or "eta".

    • A Friedman test returns Kendall's W. (Where applicable, ci and alternative are taken from the htest if not otherwise provided.)

  • For an object of class BFBayesFactor, using bayestestR::describe_posterior(),

    • A t-test depending on type: "cohens_d" (default) or one of "p_superiority", "u1", "u2", "u3", "overlap".

    • A correlation test returns r.

    • A contingency table test, depending on type: "cramers_v" (default), "phi", "tschuprows_t", "cohens_w", "pearsons_c", "cohens_h", "oddsratio", or "riskratio", "arr", or "nnt".

    • A proportion test returns p.

  • Objects of class anova, aov, aovlist or afex_aov, depending on type: "eta" (default), "omega" or "epsilon" -squared, "f", or "f2".

  • Other objects are passed to parameters::standardize_parameters().

For statistical models it is recommended to directly use the listed functions, for the full range of options they provide.

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

df <- iris
df$Sepal.Big <- ifelse(df$Sepal.Width >= 3, "Yes", "No")

model <- aov(Sepal.Length ~ Sepal.Big, data = df)
model_parameters(model)
#> Parameter | Sum_Squares |  df | Mean_Square |    F |     p
#> ----------------------------------------------------------
#> Sepal.Big |        1.10 |   1 |        1.10 | 1.61 | 0.207
#> Residuals |      101.07 | 148 |        0.68 |      |      
#> 
#> Anova Table (Type 1 tests)

model_parameters(model, es_type = c("omega", "eta"), ci = 0.9)
#> Parameter | Sum_Squares |  df | Mean_Square |    F |     p |   Omega2
#> ---------------------------------------------------------------------
#> Sepal.Big |        1.10 |   1 |        1.10 | 1.61 | 0.207 | 4.04e-03
#> Residuals |      101.07 | 148 |        0.68 |      |       |         
#> 
#> Parameter | Omega2 90% CI | Eta2 |  Eta2 90% CI
#> -----------------------------------------------
#> Sepal.Big |  [0.00, 1.00] | 0.01 | [0.00, 1.00]
#> Residuals |               |      |             
#> 
#> Anova Table (Type 1 tests)

model <- anova(lm(Sepal.Length ~ Sepal.Big, data = df))
model_parameters(model)
#> Parameter | Sum_Squares |  df | Mean_Square |    F |     p
#> ----------------------------------------------------------
#> Sepal.Big |        1.10 |   1 |        1.10 | 1.61 | 0.207
#> Residuals |      101.07 | 148 |        0.68 |      |      
#> 
#> Anova Table (Type 1 tests)
model_parameters(
  model,
  es_type = c("omega", "eta", "epsilon"),
  alternative = "greater"
)
#> Parameter | Sum_Squares |  df | Mean_Square |    F |     p |   Omega2 | Eta2 | Epsilon2
#> ---------------------------------------------------------------------------------------
#> Sepal.Big |        1.10 |   1 |        1.10 | 1.61 | 0.207 | 4.04e-03 | 0.01 | 4.07e-03
#> Residuals |      101.07 | 148 |        0.68 |      |       |          |      |         
#> 
#> Anova Table (Type 1 tests)

model <- aov(Sepal.Length ~ Sepal.Big + Error(Species), data = df)
model_parameters(model)
#> # Species 
#> 
#> Parameter | Sum_Squares | df | Mean_Square |    F |     p
#> ---------------------------------------------------------
#> Sepal.Big |       28.27 |  1 |       28.27 | 0.81 | 0.534
#> Residuals |       34.94 |  1 |       34.94 |      |      
#> 
#> # Within 
#> 
#> Parameter | Sum_Squares |  df | Mean_Square |     F |      p
#> ------------------------------------------------------------
#> Sepal.Big |        4.74 |   1 |        4.74 | 20.24 | < .001
#> Residuals |       34.21 | 146 |        0.23 |       |       
#> 
#> Anova Table (Type 1 tests)
# \donttest{
df <- iris
df$Sepal.Big <- ifelse(df$Sepal.Width >= 3, "Yes", "No")
mm <- lme4::lmer(Sepal.Length ~ Sepal.Big + Petal.Width + (1 | Species), data = df)
#> boundary (singular) fit: see help('isSingular')
model <- anova(mm)

# simple parameters table
model_parameters(model)
#> Parameter   | Sum_Squares | df | Mean_Square |      F
#> -----------------------------------------------------
#> Sepal.Big   |        1.10 |  1 |        1.10 |   4.96
#> Petal.Width |       68.50 |  1 |       68.50 | 309.23
#> 
#> Anova Table (Type 1 tests)

# parameters table including effect sizes
model_parameters(
  model,
  es_type = "eta",
  ci = 0.9,
  df_error = dof_satterthwaite(mm)[2:3]
)
#> Parameter   | Sum_Squares | df | Mean_Square |      F | Eta2 (partial) |  Eta2 90% CI
#> -------------------------------------------------------------------------------------
#> Sepal.Big   |        1.10 |  1 |        1.10 |   4.96 |           0.03 | [0.01, 1.00]
#> Petal.Width |       68.50 |  1 |       68.50 | 309.23 |           0.68 | [0.63, 1.00]
#> 
#> Anova Table (Type 1 tests)
# }