View source: R/methods_htest.R
model_parameters.htest | R Documentation |
Parameters of h-tests (correlations, t-tests, chi-squared, ...).
## 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, ...)
model |
Object of class |
ci |
Level of confidence intervals for effect size statistic. Currently
only applies to objects from |
alternative |
A character string specifying the alternative hypothesis;
Controls the type of CI returned: |
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 |
... |
Arguments passed to or from other methods. For instance, when
|
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): |
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_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")
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