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Parameters from hypothesis tests

Source: R/methods_htest.R, R/methods_lmtest.R
model_parameters.htest.Rd

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,
  es_type = NULL,
  verbose = TRUE,
  ...
)

# 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?

es_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.

...

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

Further non-documented arguments are:

  • digits, p_digits, ci_digits and footer_digits to set the number of digits for the output. groups can be used to group coefficients. These arguments will be passed to the print-method, or can directly be used in print(), see documentation in print.parameters_model().

  • If s_value = TRUE, the p-value will be replaced by the S-value in the output (cf. Rafi and Greenland 2020).

  • pd adds an additional column with the probability of direction (see bayestestR::p_direction() for details). Furthermore, see 'Examples' for this function.

  • For developers, whose interest mainly is to get a "tidy" data frame of model summaries, it is recommended to set pretty_names = FALSE to speed up computation of the summary table.

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.

Value

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

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.

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, es_type = "hedges_g")
#> Welch Two Sample t-test
#> 
#> Parameter1       |        Parameter2 | Mean_Parameter1 | Mean_Parameter2
#> ------------------------------------------------------------------------
#> iris$Sepal.Width | iris$Sepal.Length |            3.06 |            5.84
#> 
#> Parameter1       | Difference |         95% CI | Hedges' g |       g 95% CI
#> ---------------------------------------------------------------------------
#> iris$Sepal.Width |      -2.79 | [-2.94, -2.64] |     -4.20 | [-4.64, -3.75]
#> 
#> Parameter1       | t(225.68) |      p
#> -------------------------------------
#> iris$Sepal.Width |    -36.46 | < .001
#> 
#> Alternative hypothesis: true difference in means is not equal to 0

model <- t.test(mtcars$mpg ~ mtcars$vs)
model_parameters(model, es_type = "hedges_g")
#> Welch Two Sample t-test
#> 
#> Parameter  |     Group | Mean_Group1 | Mean_Group2 | Difference
#> ---------------------------------------------------------------
#> mtcars$mpg | mtcars$vs |       16.62 |       24.56 |      -7.94
#> 
#> Parameter  |          95% CI | Hedges' g |       g 95% CI | t(22.72) |      p
#> -----------------------------------------------------------------------------
#> mtcars$mpg | [-11.46, -4.42] |     -1.64 | [-2.46, -0.79] |    -4.67 | < .001
#> 
#> Alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0

model <- t.test(iris$Sepal.Width, mu = 1)
model_parameters(model, es_type = "cohens_d")
#> One Sample t-test
#> 
#> Parameter        |   mu | Difference |       95% CI | Cohen's d |     d 95% CI
#> ------------------------------------------------------------------------------
#> iris$Sepal.Width | 1.00 |       2.06 | [2.99, 3.13] |      4.72 | [4.15, 5.27]
#> 
#> Parameter        | t(149) |      p
#> ----------------------------------
#> iris$Sepal.Width |  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 <- suppressWarnings(pairwise.prop.test(smokers, patients))
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)

model <- suppressWarnings(chisq.test(table(mtcars$am, mtcars$cyl)))
model_parameters(model, es_type = "cramers_v")
#> Pearson's Chi-squared test
#> 
#> Chi2(2) | Cramer's V (adj.) | Cramers 95% CI |     p
#> ----------------------------------------------------
#> 8.74    |              0.46 |   [0.00, 1.00] | 0.013