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

# S3 method for htest
model_parameters(
model,
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,
ci = 0.95,
bootstrap = FALSE,
verbose = TRUE,
...
)

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

## Arguments

model Object of class htest or pairwise.htest. Compute Cramer's V or phi as index of effect size. Can be "raw" or "adjusted" (effect size will be bias-corrected). Only applies to objects from chisq.test(). If TRUE, compute standardized d as index of effect size. Only applies to objects from t.test(). Calculation of d is based on the t-value (see t_to_d) for details. If TRUE, compute Hedge's g as index of effect size. Only applies to objects from t.test(). Logical, if TRUE, returns the non-partial effect size Omega, Eta or Epsilon squared. Only applies to objects from oneway.test(). If TRUE, compute Cohen's g as index of effect size. Only applies to objects from mcnemar.test(). If TRUE, compute the rank-biserial correlation as effect size measure. Only applies to objects from wilcox.test(). If TRUE, compute the rank epsilon squared as effect size measure. Only applies to objects from kruskal.test(). If TRUE, compute the Kendall's coefficient of concordance as effect size measure. Only applies to objects from friedman.test(). Level of confidence intervals for effect size statistic. Currently only applies to objects from chisq.test() or oneway.test(). Should estimates be bootstrapped? Toggle warnings and messages. Arguments passed to or from other methods.

## 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)
#> Pearson's product-moment correlation
#>
#> Parameter1 | Parameter2 |     r |         95% CI | t(30) |      p
#> -----------------------------------------------------------------
#> mtcars$mpg | mtcars$cyl | -0.85 | [-0.93, -0.72] | -8.92 | < .001
#>
#> Alternative hypothesis: true correlation is not equal to 0
#>

model <- t.test(iris$Sepal.Width, iris$Sepal.Length)
model_parameters(model)
#> Welch Two Sample t-test
#>
#> Parameter1       |        Parameter2 | Mean_Parameter1 | Mean_Parameter2 | Difference |         95% CI | t(225.68) |      p
#> ---------------------------------------------------------------------------------------------------------------------------
#> iris$Sepal.Width | iris$Sepal.Length |            3.06 |            5.84 |      -2.79 | [-2.94, -2.64] |    -36.46 | < .001
#>
#> Alternative hypothesis: true difference in means is not equal to 0
#>

model <- t.test(mtcars$mpg ~ mtcars$vs)
model_parameters(model)
#> Welch Two Sample t-test
#>
#> Parameter  |     Group | mtcars$vs = 0 | mtcars$vs = 1 | Difference |          95% CI | t(22.72) |      p
#> ---------------------------------------------------------------------------------------------------------
#> mtcars$mpg | mtcars$vs |         16.62 |         24.56 |       7.94 | [-11.46, -4.42] |    -4.67 | < .001
#>
#> Alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
#>

model <- t.test(iris$Sepal.Width, mu = 1) model_parameters(model) #> One Sample t-test #> #> Parameter | Mean | mu | Difference | 95% CI | t(149) | p #> ---------------------------------------------------------------------------- #> iris$Sepal.Width | 3.06 | 1.00 |       2.06 | [2.99, 3.13] |  57.81 | < .001
#>
#> Alternative hypothesis: true mean is not equal to 1
#>

data(airquality)
airquality$Month <- factor(airquality$Month, labels = month.abb[5:9])
model <- pairwise.t.test(airquality$Ozone, airquality$Month)
model_parameters(model)
#> # Fixed Effects
#>
#> Group1 | Group2 |      p
#> ------------------------
#> Jun    |    May | > .999
#> Jul    |    May | < .001
#> Jul    |    Jun | 0.051
#> Aug    |    May | < .001
#> Aug    |    Jun | 0.050
#> Aug    |    Jul | > .999
#> Sep    |    May | > .999
#> Sep    |    Jun | > .999
#> Sep    |    Jul | 0.005
#> Sep    |    Aug | 0.004
#>
#> p-value adjustment method: Holm (1979)
#>

smokers <- c(83, 90, 129, 70)
patients <- c(86, 93, 136, 82)
model <- pairwise.prop.test(smokers, patients)
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
model_parameters(model)
#> # Fixed Effects
#>
#> Group1 | Group2 |      p
#> ------------------------
#> 2      |      1 | > .999
#> 3      |      1 | > .999
#> 3      |      2 | > .999
#> 4      |      1 | 0.119
#> 4      |      2 | 0.093
#> 4      |      3 | 0.124
#>
#> p-value adjustment method: Holm (1979)
#>