model_parameters.htest: Parameters from hypothesis tests

In easystats/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 'coeftest'
model_parameters(
  model,
  ci = 0.95,
  ci_method = "wald",
  keep = NULL,
  drop = NULL,
  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.
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.

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.

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 <- suppressWarnings(pairwise.prop.test(smokers, patients))
model_parameters(model)

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

easystats/parameters documentation built on April 12, 2024, 9:33 a.m.