model_parameters.htest: Parameters from hypothesis tests

View source: R/methods_htest.R

model_parameters.htestR Documentation

Parameters from hypothesis tests


Parameters of h-tests (correlations, t-tests, chi-squared, ...).


## S3 method for class 'htest'
  ci = 0.95,
  alternative = NULL,
  bootstrap = FALSE,
  effectsize_type = NULL,
  verbose = TRUE,
  cramers_v = NULL,
  phi = NULL,
  standardized_d = NULL,
  hedges_g = NULL,
  omega_squared = NULL,
  eta_squared = NULL,
  epsilon_squared = NULL,
  cohens_g = NULL,
  rank_biserial = NULL,
  rank_epsilon_squared = NULL,
  kendalls_w = NULL,

## S3 method for class 'pairwise.htest'
model_parameters(model, verbose = TRUE, ...)

## S3 method for class 'coeftest'
model_parameters(model, ci = 0.95, ci_method = "wald", verbose = TRUE, ...)



Object of class htest or pairwise.htest.


Level of confidence intervals for effect size statistic. Currently only applies to objects from chisq.test() or oneway.test().


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.


Should estimates be bootstrapped?


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.


Toggle warnings and messages.

cramers_v, phi, cohens_g, standardized_d, hedges_g, omega_squared, eta_squared, epsilon_squared, rank_biserial, rank_epsilon_squared, kendalls_w

Deprecated. Please use effectsize_type.


Arguments passed to or from other methods. For instance, when bootstrap = TRUE, arguments like type or parallel are passed down to bootstrap_model().


Method for computing degrees of freedom for confidence intervals (CI) and the related p-values. Allowed are following options (which vary depending on the model class): "residual", "normal", "likelihood", "satterthwaite", "kenward", "wald", "profile", "boot", "uniroot", "ml1", "betwithin", "hdi", "quantile", "ci", "eti", "si", "bci", or "bcai". See section Confidence intervals and approximation of degrees of freedom in model_parameters() for further details. When ci_method=NULL, in most cases "wald" is used then.


  • 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".

    • 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", or "riskratio".

    • 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".

    • 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.


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


model <- cor.test(mtcars$mpg, mtcars$cyl, method = "pearson")

model <- t.test(iris$Sepal.Width, iris$Sepal.Length)
model_parameters(model, effectsize_type = "hedges_g")

model <- t.test(mtcars$mpg ~ mtcars$vs)
model_parameters(model, effectsize_type = "hedges_g")

model <- t.test(iris$Sepal.Width, mu = 1)
model_parameters(model, effectsize_type = "cohens_d")

airquality$Month <- factor(airquality$Month, labels =[5:9])
model <- pairwise.t.test(airquality$Ozone, airquality$Month)

smokers <- c(83, 90, 129, 70)
patients <- c(86, 93, 136, 82)
model <- pairwise.prop.test(smokers, patients)

model <- stats::chisq.test(table(mtcars$am, mtcars$cyl))
model_parameters(model, effectsize_type = "cramers_v")

parameters documentation built on Jan. 11, 2023, 5:16 p.m.