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Perform Frequentist and Bayesian t-tests with Effect Size Estimation from Tidy Data

Source: R/t_test_all_tidy.r
t_test_all_tidy.Rd

A wrapper around t_test_all that accepts tidy-format data frames with exactly three columns (participant ID, independent variable, dependent variable) or two columns only for one-sample design (participant ID, dependent variable). The function automatically reshapes the data (long \(\rightarrow\) wide for paired designs), checks for consistency, and then calls t_test_all() with the extracted vectors.

Usage

t_test_all_tidy(
  dataset,
  paired = F,
  var.equal = FALSE,
  onesample = FALSE,
  mu = 0,
  ci = c("freq", "bayes_central", "bayes_hdi"),
  alternative = c("two.sided", "less", "greater"),
  conf.level = 0.95,
  alpha = 0.05,
  pd = FALSE,
  bf = FALSE,
  cor = TRUE,
  mean_x_EAP = FALSE,
  mean_x_MAP = FALSE,
  mean_x_MED = FALSE,
  diff_EAP = FALSE,
  diff_MAP = FALSE,
  diff_MED = FALSE,
  cohens_d = NULL,
  cohens_d_EAP = FALSE,
  cohens_d_MAP = FALSE,
  cohens_d_MED = FALSE,
  cohens_dz = TRUE,
  cohens_dz_EAP = FALSE,
  cohens_dz_MAP = FALSE,
  cohens_dz_MED = FALSE,
  rscale_est = Inf,
  rscale_bf = "medium",
  iterations = 10000,
  map_density_n = 512,
  verbose = FALSE,
  show_design = TRUE,
  detailed = FALSE,
  fullbayes = FALSE
)

Arguments

dataset

A data frame with exactly three columns, in this order:

  1. Participant ID

  2. Independent variable (must have exactly two levels)

  3. Dependent variable (numeric)

paired

Logical. If TRUE, a paired t-test is performed (default FALSE).

var.equal

Logical. If TRUE, the two-sample t-test assumes equal variances (default FALSE).

onesample

Logical. If TRUE, a one-sample t-test is performed (default FALSE).

mu

Numeric. Null hypothesis value for the mean difference (default 0).

ci

Character vector specifying interval type(s): "freq", "bayes_central", or "bayes_hdi". Default is c("freq","bayes_central","bayes_hdi"). Passed to t_test_all.

alternative

Character. Alternative hypothesis: "two.sided", "less", or "greater". Default is "two.sided". Passed to t_test_all.

conf.level

Numeric. Confidence/credibility level (default 0.95). Passed to t_test_all.

alpha

Numeric. Significance level. Defaults to 0.05.

pd, bf, cor

Logical flags. Whether to compute probability of direction (pd), Bayes factors, or correlation (for paired samples). Passed to t_test_all.

mean_x_EAP, mean_x_MAP, mean_x_MED

Logical. Report posterior summaries for group means. Passed to t_test_all.

diff_EAP, diff_MAP, diff_MED

Logical. Report posterior summaries for the mean difference. Passed to t_test_all.

cohens_d

Logical or character. Type/request for Cohen's d in independent samples. Passed to t_test_all.

cohens_d_EAP, cohens_d_MAP, cohens_d_MED

Logical. Posterior summaries for Cohen's d. Passed to t_test_all.

cohens_dz

Logical. Request Cohen's dz for paired samples. Passed to t_test_all.

cohens_dz_EAP, cohens_dz_MAP, cohens_dz_MED

Logical. Posterior summaries for Cohen's dz. Passed to t_test_all.

rscale_est, rscale_bf

Numeric or character. Cauchy prior scales for estimation and Bayes factors (e.g., "ultrawide", "wide", "medium", or a positive number). Defaults are Inf and "medium", respectively. Passed to t_test_all.

iterations, map_density_n

Integer. MCMC iterations and grid size for MAP density. Passed to t_test_all.

verbose

Logical. If TRUE, print additional messages (default FALSE).

show_design

Logical. If TRUE, show message of design (default TRUE).

detailed

Logical. Whether to return detailed results (TRUE) or minimal output (FALSE, default).

fullbayes

Logical. Whether to show only Bayesian results (TRUE) or both frequentist and Bayesian results (FALSE, default).

Value

The object returned by t_test_all (test statistics, effect sizes, confidence/credible intervals, and Bayesian estimates).

Details

This function enforces the following:

  • dataset must have exactly three columns in the order: ID, independent variable, dependent variable.

  • The independent variable must have exactly two levels.

  • The dependent variable must be numeric.

  • If paired = TRUE, each participant must contribute exactly one observation in each condition.

If any requirement is violated, an informative error is raised.

See also

t_test_all

Examples

set.seed(610)
dat <- data.frame(
  id   = rep(1:10, each = 2),
  cond = rep(c("A","B"), times = 10),
  y    = rnorm(20)
)
# Independent-samples t-test
t_test_all_tidy(dat, paired = FALSE)
#> design: two samples (unequal variance)
#>        diff    t    df    p alpha sig cohens_d
#> 1 0.5569292 1.14 16.71 0.27  0.05  ns     0.51
# Paired-samples t-test
t_test_all_tidy(dat, paired = TRUE)
#> design: paired
#>        diff    t df     p alpha sig cohens_dz
#> 1 0.5569292 1.21  9 0.256  0.05  ns     0.384