model_parameters.htest: Parameters from hypothesis tests

In parameters: Processing of Model Parameters

Parameters from hypothesis tests

Description

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

Usage

## S3 method for class 'htest'
model_parameters(
  model,
  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, ...)

Arguments

model	Object of class htest or pairwise.htest.
ci	Level of confidence intervals for effect size statistic. Currently only applies to objects from chisq.test() or oneway.test().
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.
bootstrap	Should estimates be bootstrapped?
effectsize_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.
verbose	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().
ci_method	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.

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".

  • 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.

Value

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

Examples

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

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")

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

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

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.