Package {apastats2}

Type:	Package
Title:	Statistical Results in APA Style
Version:	1.1.0
Description:	Proper formatting of statistical results in accordance with the American Psychological Association (APA) Manual (sixth edition), along with convenient helpers for summaries, confidence intervals, and plotting. Development repository: https://github.com/achetverikov/APAstats.
License:	GPL-3
Encoding:	UTF-8
Language:	en-US
URL:	https://achetverikov.github.io/APAstats/, https://github.com/achetverikov/APAstats
BugReports:	https://github.com/achetverikov/APAstats/issues
Imports:	stringr, utils, Hmisc, plyr, labeling, scales
Suggests:	superb, knitr, rmarkdown, lsr, MASS, pROC, afex, lme4, ez, diptest, BayesFactor, brms, car, emmeans, lmerTest, bayestestR, rockchalk, carData, testthat (≥ 3.0.0)
Depends:	stats, ggplot2, data.table, R (≥ 4.1.0)
LazyData:	TRUE
VignetteBuilder:	knitr
Config/testthat/edition:	3
Config/roxygen2/version:	8.1.1
NeedsCompilation:	no
Packaged:	2026-10-23 15:18:18 UTC; andre
Author:	Andrey Chetverikov [aut, cre]
Maintainer:	Andrey Chetverikov <andrey.chetverikov@uib.no>
Repository:	CRAN
Date/Publication:	2026-11-07 15:00:02 UTC

apastats2: Statistical Results in APA Style

Description

Proper formatting of statistical results in accordance with the American Psychological Association (APA) Manual (sixth edition), along with convenient helpers for summaries, confidence intervals, and plotting. Development repository: https://github.com/achetverikov/APAstats.

Author(s)

Maintainer: Andrey Chetverikov andrey.chetverikov@uib.no (ORCID)

Authors:

• Andrey Chetverikov andrey.chetverikov@uib.no (ORCID)

See Also

Useful links:

• https://achetverikov.github.io/APAstats/

• https://github.com/achetverikov/APAstats

• Report bugs at https://github.com/achetverikov/APAstats/issues

Cut a numeric variable into groups (bins) with advanced options

Description

Cut a numeric variable into groups (bins) with advanced options

Usage

adv_cut(
  x,
  ncuts = NULL,
  eq_groups = FALSE,
  cuts = NULL,
  num_labels = FALSE,
  labels = NULL,
  include_oob = TRUE,
  labels_at_means = TRUE,
  label_pairs_format = "[%.2f, %.2f]",
  ...
)

Arguments

Value

A factor assigning each element of x to a bin, or a numeric vector of bin labels when num_labels = TRUE. The result has the same length as x.

Examples

set.seed(1)
x <- sample(1:100, 20)
sort(x)

adv_cut(x, ncuts = 5)
adv_cut(x, ncuts = 5, eq_groups = TRUE)
adv_cut(x, ncuts = 5, eq_groups = TRUE, num_labels = TRUE)
adv_cut(x, ncuts = 5, eq_groups = TRUE, labels_at_means = FALSE)
adv_cut(x, cuts = seq(0, 100, by = 20))
adv_cut(x, cuts = seq(0, 100, by = 20), labels_at_means = FALSE)
adv_cut(x, cuts = seq(0, 100, by = 20), 
           labels_at_means = FALSE, label_pairs_format = "[%i, %i]")

Double aggregation

Description

Aggregates value twice providing mean of means, SD of SDs, etc.

Usage

aggr2(x, by, fun, ...)

Arguments

Value

The object returned by fun when applied to the group summaries. Its class depends on fun; scalar numeric summary functions such as base::mean return one numeric value.

Examples

x <- rnorm(100)
id <- rep(1:10, each = 10)

aggregate(x ~ id, FUN = mean)
aggr2(x, id, mean)

Format statistical results in APA style

Description

The apa function provides a convenient way to format statistical results according to American Psychological Association (APA) style guidelines. It dispatches to various S3 methods depending on the class of the input object.

Usage

apa(obj, ...)

Arguments

Details

This function supports various statistical objects directly:

• apa.ttest for stats::t.test and apa.r for stats::cor.test (both with class htest)

• apa.aov for stats::aov, apa.anova for car::Anova, and apa.afex_aov for afex::aov_ez, afex::aov_car, or afex::aov_4

• apa.glm for stats::glm, stats::lm, lme4::lmer, lme4::glmer, and lmerTest::lmer; apa.lmert remains available for summary objects from lmerTest::lmer

• apa.brmsfit for brms::brm and apa.BFBayesFactor for BayesFactor::anovaBF

• apa.chisq.test for stats::chisq.test

• apa.dip.test for diptest::dip.test

• apa.roc.test for pROC::roc.test

In addition, there are standalone utility functions for formatting:

• apa_mean_sd: Format means and standard deviations

• apa_mean_conf: Format means and confidence intervals

• apa_binom_mean_conf: Format binomial means and confidence intervals

• apa_mean_and_t: Compare means with t-tests and format results

• table_mean_conf: Get formatted means and CIs (for tables)

• table_mean_conf_by: Get formatted means and CIs by group (for tables)

Value

The result of the method for obj: usually a character vector of APA-formatted statistical results. Regression methods can return a data.frame of coefficient summaries, and ANOVA methods can return a data.frame with formatted text appended when requested. See each method's documentation for its columns and selection options.

Examples

# t-test example
t_res <- t.test(rnorm(30), rnorm(30, 0.5))
apa(t_res)

# Correlation example
cor_res <- cor.test(mtcars$mpg, mtcars$wt)
apa(cor_res)

Describe BayesFactor results

Description

Describe BayesFactor results

Usage

## S3 method for class 'BFBayesFactor'
apa(obj, digits = 2, top_limit = 10000, convert_to_power = TRUE, ...)

Arguments

Value

A character result reporting the Bayes factor, optionally in scientific notation for very large or small values.

Note

Code for converting to exponential notation is based on https://dankelley.github.io/r/2015/03/22/scinot.html

Examples

if (requireNamespace("BayesFactor", quietly = TRUE)) {
  bf <- BayesFactor::ttestBF(x = c(0.8, 1.1, 0.9, 1.5, 1.2, 0.7))
  print(apa(bf))
  print(apa(bf, top_limit = 10))
}

Describe afex ANOVA results

Description

Provides formatted string like F(num.Df, den.Df) = ..., p ..., eta2 = ... based on afex::aov_ez, afex::aov_car, or afex::aov_4 results.

Usage

## S3 method for class 'afex_aov'
apa(
  obj,
  term,
  include_eta = TRUE,
  eta_digits = 2,
  f_digits = 2,
  df_digits = 0,
  append_to_table = FALSE,
  ...
)

Arguments

Value

A character vector reporting F statistics, degrees of freedom, p-values, and optional generalized eta-squared. With append_to_table = TRUE, a data.frame of selected ANOVA rows with an added res column containing the formatted results.

Examples

if (requireNamespace("afex", quietly = TRUE)) {
  data(stroop, package = "afex")

  afex_res <- afex::aov_ez(
    id = "pno",
    dv = "rt",
    within = "condition",
    data = stroop[!is.na(stroop$rt), ],
    fun_aggregate = mean
  )

  print(apa(afex_res, "condition"))
  print(apa(afex_res, "condition", eta_digits = 3))
  print(apa(afex_res, "condition", include_eta = FALSE))
}

Describe the results of ANOVA models or model comparisons

Description

Describe the results of ANOVA models or model comparisons

Usage

## S3 method for class 'anova'
apa(obj, term = 2, f.digits = 2, ...)

Arguments

Details

When using model comparison version, term can only be a number.

Value

Formatted string with F (or chi2), df, and p

Examples

if (requireNamespace("carData", quietly = TRUE)) {
  # Model comparison version
  mod1 <- lm(conformity ~ 1, data = carData::Moore)
  mod2 <- lm(conformity ~ fcategory, data = carData::Moore,
             contrasts = list(fcategory = contr.sum))
  mod3 <- lm(conformity ~ fcategory * partner.status, data = carData::Moore,
             contrasts = list(fcategory = contr.sum, partner.status = contr.sum))
  anova_obj <- anova(mod1, mod2, mod3)

  print(apa(anova_obj))
  print(apa(anova_obj, 3))

  # car::Anova version
  if (requireNamespace("car", quietly = TRUE)) {
    mod <- lm(conformity ~ fcategory * partner.status, data = carData::Moore,
              contrasts = list(fcategory = contr.sum, partner.status = contr.sum))
    afit <- car::Anova(mod)

    print(apa(afit, "fcategory"))
    print(apa(afit, 2, 4))
  }
}

Describe lmerTest anova results

Description

Describe lmerTest anova results

Usage

## S3 method for class 'anova.merMod'
apa(obj, term, f.digits = 2, ...)

Arguments

Value

formatted string describing the results of anova

Examples

if (requireNamespace("lmerTest", quietly = TRUE)) {
  # Sample data
  data(sleepstudy, package = "lme4")
  
  # Fit a mixed-effects model
  fit <- lmerTest::lmer(Reaction ~ Days + (1 + Days | Subject), sleepstudy)
  
  # ANOVA table
  afit <- anova(fit)
  
  # Format results in APA style
  print(apa(afit, "Days"))
  print(apa(afit, "Days", f.digits = 3))
}

Describe stats::aov results

Description

stats::aov() models containing Error() return an aovlist object, which cannot be passed to car::Anova(). This method extracts the ANOVA tables from each error stratum and reports effects using the residual degrees of freedom from the same stratum.

Usage

## S3 method for class 'aov'
apa(obj, term, sstype = 2, ...)

## S3 method for class 'aovlist'
apa(obj, term = NULL, f.digits = 2, ...)

Arguments

Value

formatted string with F(df_numerator, df_denominator) = F_value, p =/< p_value

one or more formatted strings with F, numerator and denominator df, and p values

Examples

if (requireNamespace("car", quietly = TRUE)) {
  # Using the mtcars dataset
  fit <- aov(mpg ~ cyl * am, data = mtcars)
  print(apa(fit, 'cyl'))
  print(apa(fit, 'am'))
  print(apa(fit, 'cyl:am'))

  # Using a different SS type
  print(apa(fit, 'cyl', sstype = 3))
}
fit_rm <- aov(yield ~ N * P * K + Error(block), data = npk)
apa(fit_rm, "N")
apa(fit_rm)

Describe brms model results

Description

Describe brms model results

Usage

## S3 method for class 'brmsfit'
apa(
  obj,
  term,
  trans = NULL,
  digits = 2,
  eff.size = FALSE,
  eff.size.type = "r",
  nsamples = 100,
  ci.type = "HPDI",
  ...
)

Arguments

Value

A character result reporting the posterior mean coefficient, its 95% interval, and optionally an effect-size estimate.

Examples

if (requireNamespace("brms", quietly = TRUE) &&
    requireNamespace("bayestestR", quietly = TRUE)) {
  # Generate sample data
  set.seed(42)
  x <- seq(-1, 1, length.out = 30)
  y <- 1 + 2 * x + rnorm(30, sd = 0.5)
  
  # Fit a Bayesian regression model
  fit <- brms::brm(y ~ x, data = data.frame(x, y), backend = "rstan",
                   chains = 1, cores = 1, iter = 500, seed = 42, refresh = 0)
  print(apa(fit, "x"))
  
  # Convert x to another scale for output
  print(apa(fit, "x", trans = function(val) val + 5))
}

Format chi-square test results in APA style

Description

Format chi-square test results in APA style

Usage

## S3 method for class 'chisq.test'
apa(obj, v = TRUE, addN = TRUE, ...)

Arguments

Value

Formatted results string

Examples

# Create a contingency table
M <- as.table(rbind(c(762, 327, 468), c(484, 239, 477)))
dimnames(M) <- list(
  gender = c("F", "M"),
  party = c("Democrat", "Independent", "Republican")
)
chi_result <- chisq.test(M)
apa(chi_result)

Describe Hartigans' dip test results

Description

Describe Hartigans' dip test results

Usage

## S3 method for class 'dip.test'
apa(obj, ...)

Arguments

Value

formatted results string

Examples

if (requireNamespace("diptest", quietly = TRUE)) {
  # Generate bimodal data
  bimodal_data <- c(rnorm(100, -2, 1), rnorm(100, 2, 1))
  
  # Perform dip test
  dip_result <- diptest::dip.test(bimodal_data)
  
  # Format results in APA style
  apa(dip_result)
}

Describe ezANOVA results

Description

Provides formatted string like F(DFn, DFd) = ..., p ..., eta2 = ... based on ezANOVA results

Usage

## S3 method for class 'ezANOVA'
apa(
  obj,
  term,
  include_eta = TRUE,
  spher_corr = TRUE,
  eta_digits = 2,
  f_digits = 2,
  df_digits = 0,
  append_to_table = FALSE,
  ...
)

Arguments

Value

A character vector reporting F statistics, degrees of freedom, p-values, and optional generalized eta-squared. With append_to_table = TRUE, a data.frame of selected ANOVA rows with an added res column containing the formatted results.

Examples

if (requireNamespace("ez", quietly = TRUE) && requireNamespace("afex", quietly = TRUE)) {
  # Using the Stroop dataset from afex package
  data(stroop, package = "afex")
  
  # Run ezANOVA
  ez_res <- ez::ezANOVA(
    data = stroop[!is.na(stroop$rt),],
    dv = rt,
    wid = pno,
    within = condition,
    detailed = TRUE
  )
  
  # Format results in APA style
  print(apa(ez_res, "condition"))
  print(apa(ez_res, "condition", eta_digits = 3))
  print(apa(ez_res, "condition", include_eta = FALSE))
}

Describe regression model (GLM, GLMer, lm, lm.circular, ...)

Description

Describe regression model (GLM, GLMer, lm, lm.circular, ...)

Usage

## S3 method for class 'glm'
apa(
  obj,
  term = NULL,
  dtype = 1,
  b.digits = 2,
  t.digits = 2,
  test.df = FALSE,
  p.as.number = FALSE,
  term.pattern = NULL,
  eff.size = FALSE,
  adj.digits = FALSE,
  ...
)

## S3 method for class 'lm'
apa(
  obj,
  term = NULL,
  dtype = 1,
  b.digits = 2,
  t.digits = 2,
  test.df = FALSE,
  p.as.number = FALSE,
  term.pattern = NULL,
  eff.size = FALSE,
  adj.digits = FALSE,
  ...
)

## S3 method for class 'glmerMod'
apa(
  obj,
  term = NULL,
  dtype = 1,
  b.digits = 2,
  t.digits = 2,
  test.df = FALSE,
  p.as.number = FALSE,
  term.pattern = NULL,
  eff.size = FALSE,
  adj.digits = FALSE,
  ...
)

## S3 method for class 'lmerModLmerTest'
apa(
  obj,
  term = NULL,
  dtype = 1,
  b.digits = 2,
  t.digits = 2,
  test.df = FALSE,
  p.as.number = FALSE,
  term.pattern = NULL,
  eff.size = FALSE,
  adj.digits = FALSE,
  ...
)

Arguments

Value

A character result for a selected term. Otherwise, a data.frame with columns B (coefficient estimate), SE (standard error), Stat (test statistic), p (p-value), eff (term name), and str (complete APA-formatted result). The summary columns are character vectors except that p is numeric when p.as.number = TRUE. term.pattern filters the rows of this data frame.

Examples

# Using the Animals dataset from MASS package
Animals <- MASS::Animals
Animals$body <- log(Animals$body)
Animals$brain <- log(Animals$brain)

# Fit a linear model
fit <- lm(brain ~ body, Animals)

# Format results in different styles
apa(fit, "body")
apa(fit, "(Intercept)")
apa(fit, "body", 2)   # B(SE), p format
apa(fit, "body", 3)   # B, SE, t, p format
apa(fit, "body", 4)   # B(SE), t format
apa(fit, "body", 3, test.df = TRUE)  # Include df in the output

# Full model summary for all coefficients
apa(fit)

# With effect size (requires rockchalk package)
if (requireNamespace("rockchalk", quietly = TRUE)) {
  print(apa(fit, "body", 4, eff.size = TRUE))
}

if (requireNamespace("lmerTest", quietly = TRUE)) {
  fm <- lmerTest::lmer(Reaction ~ Days + (Days | Subject), lme4::sleepstudy)
  print(apa(fm, "Days", test.df = TRUE))
}

if (requireNamespace("lme4", quietly = TRUE)) {
  gm <- lme4::glmer(
    cbind(incidence, size - incidence) ~ period + (1 | herd),
    data = lme4::cbpp,
    family = binomial
  )
  print(apa(gm, "period2"))
}

Format t-test results in APA style

Description

Format t-test results in APA style

Usage

## S3 method for class 'htest'
apa(obj, show.mean = FALSE, abs = FALSE, ...)

Arguments

Value

A string with t-test value, degrees of freedom, p-value, and optionally the mean with CI

Examples

# One-sample t-test
t_res <- t.test(rnorm(20, mean = -10, sd = 2))
apa(t_res)
apa(t_res, show.mean = TRUE)

# Two-sample t-test
t_two <- t.test(rnorm(20), rnorm(20, mean = 0.8))
apa(t_two)

Describe linearHypothesis test results

Description

Describe linearHypothesis test results

Usage

## S3 method for class 'linearHypothesis'
apa(obj, ...)

Arguments

Value

A character result reporting the chi-squared or F statistic, degrees of freedom, and p-value for the linear-hypothesis test.

Examples

if (requireNamespace("car", quietly = TRUE) &&
    requireNamespace("carData", quietly = TRUE)) {
  # Create a linear model
  mod.davis <- lm(weight ~ repwt, data = carData::Davis)
  
  # Test hypotheses
  res <- car::linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"))
  print(apa(res))
  
  # Test using Chi-square
  res.chi <- car::linearHypothesis(mod.davis, c("(Intercept) = 0", "repwt = 1"), test = "Chisq")
  print(apa(res.chi))
}

Format results from list objects in APA style

Description

This function detects the type of statistical results stored in a list object and redirects to the appropriate APA formatting method. Currently supports ezANOVA results (apa.ezANOVA).

Usage

## S3 method for class 'list'
apa(obj, ...)

Arguments

Value

A character vector of formatted ANOVA results, or a data.frame containing selected ANOVA rows and a res column when append_to_table = TRUE. See apa.ezANOVA.

Format correlation test results in APA style

Description

Format correlation test results in APA style

Usage

## S3 method for class 'r'
apa(obj, ...)

Arguments

Value

A string with correlation coefficient, degrees of freedom, and p-value

Examples

# Pearson correlation
x <- rnorm(40)
y <- x * 0.6 + rnorm(40, 0, 0.8)
rc <- cor.test(x, y)
apa(rc)

Describe differences between ROC curves

Description

Describe differences between ROC curves

Usage

## S3 method for class 'roc.test'
apa(obj, ...)

## S3 method for class 'roc.diff'
apa(obj, ...)

Arguments

Value

A character result reporting the test statistic and p-value for the comparison between ROC curves.

Examples

if (requireNamespace("pROC", quietly = TRUE)) {
  # Create sample data
  set.seed(42)
  n <- 100
  group <- factor(sample(c(0, 1), n, replace = TRUE))
  test1 <- rnorm(n, mean = 1 * (as.numeric(group) - 1))
  test2 <- rnorm(n, mean = 0.7 * (as.numeric(group) - 1))
  
  # Create ROC curves
  roc1 <- pROC::roc(group, test1)
  roc2 <- pROC::roc(group, test2)
  
  # Test difference between ROC curves
  roc_diff <- pROC::roc.test(roc1, roc2)
  
  # Format results in APA style
  apa(roc_diff)
}

Describe lmerTest results

Description

Note that this function uses summary object from lmerTest::lmer model and not the model itself (see examples). Otherwise p-values will be computed during the call and everything will be very slow.

Usage

## S3 method for class 'summary.merMod'
apa(obj, factor, dtype = "t", ...)

## S3 method for class 'lmert'
apa(obj, factor, dtype = "t", ...)

Arguments

Value

A character vector reporting the selected fixed-effect estimate, standard error, test statistic, degrees of freedom when available, and p-value.

Examples

if (requireNamespace("lmerTest", quietly = TRUE)) {
  # Fit a mixed-effects model with p-values
  fm <- lmerTest::lmer(Reaction ~ Days + (Days | Subject), lme4::sleepstudy)
  fms <- summary(fm)
  
  # Format results in APA style
  print(apa(fms, "Days"))
  print(apa(fms, "(Intercept)"))
  print(apa(fms, 2))
  print(apa(fms, "Days", "B"))
}

Describe contrasts created by emmeans

Description

Describe contrasts created by emmeans

Usage

## S3 method for class 'summary_emm'
apa(obj, term, dtype = "B", df = FALSE, ...)

## S3 method for class 'emmeans'
apa(obj, term, dtype = "B", df = FALSE, ...)

Arguments

Value

string with formatted results

Examples

if (requireNamespace("emmeans", quietly = TRUE)) {
  # Create a model
  warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)
  
  # Create estimated marginal means
  warp.emm <- emmeans::emmeans(warp.lm, ~ tension | wool)
  
  # Create contrasts
  sum_contr <- summary(emmeans::contrast(warp.emm, "trt.vs.ctrl"))
  
  # Format results in APA style
  print(apa(sum_contr, 1))
  print(apa(sum_contr, 3))
  print(apa(sum_contr, 3, dtype = "t"))
  print(apa(sum_contr, 3, dtype = "c"))
  print(apa(sum_contr, 3, dtype = "c", df = TRUE))
}

Format t-test results in APA style

Description

Format t-test results in APA style

Usage

## S3 method for class 'ttest'
apa(obj, show.mean = FALSE, abs = FALSE, ...)

Arguments

Value

A string with t-test value, degrees of freedom, p-value, and optionally the mean with CI

Examples

# One-sample t-test
t_res <- t.test(rnorm(20, mean = -10, sd = 2))
apa(t_res)
apa(t_res, show.mean = TRUE)
apa(t_res, show.mean = TRUE, abs = TRUE)

# Two-sample t-test
t_two <- t.test(rnorm(20), rnorm(20, mean = 0.8))
apa(t_two)

Format binomial proportions and confidence intervals in APA style

Description

Format binomial proportions and confidence intervals in APA style

Usage

apa_binom_mean_conf(x, digits = 2, ...)

Arguments

Value

A string with the mean and confidence interval in square brackets

Examples

# Generate binomial data
set.seed(123)
x <- rbinom(500, 1, prob = 0.7)

# Format results in APA style
apa_binom_mean_conf(x)

Describe ezStats results

Description

Returns formatted string with mean and SD from ezStats object

Usage

apa_ezStats(obj, term = 1, ...)

Arguments

Value

string with formatted results

Examples

if (requireNamespace("ez", quietly = TRUE) && requireNamespace("afex", quietly = TRUE)) {
  # Using the Stroop dataset from afex package
  data(stroop, package = "afex")
  # Get descriptive statistics

  ezstats_res <- ez::ezStats(
    data = stroop[!is.na(stroop$rt),],
    dv = rt,
    wid = pno,
    within = .(congruency)
  )
  
  # Format results in APA style
  print(apa_ezStats(ezstats_res, 1))
  print(apa_ezStats(ezstats_res, "incongruent"))
  print(apa_ezStats(ezstats_res, 1, digits = 3))
}

Format mean and confidence interval values into APA-style string

Description

Format mean and confidence interval values into APA-style string

Usage

apa_format_mean_conf(
  mean_val,
  lower_ci,
  upper_ci,
  addCI = FALSE,
  digits = 2,
  ...
)

Arguments

Value

A formatted string with mean and confidence intervals

Examples

# Example usage
mean_val <- 5.67
lower_ci <- 4.56
upper_ci <- 6.78
# Format mean and CI in APA style
apa_format_mean_conf(mean_val, lower_ci, upper_ci)
# Format mean and CI with additional CI prefix
apa_format_mean_conf(mean_val, lower_ci, upper_ci, addCI = TRUE)
# Format mean and CI with custom number of digits
apa_format_mean_conf(mean_val, lower_ci, upper_ci, digits = 3)

Compare means with t-tests and format in APA style

Description

Compare means with t-tests and format in APA style

Usage

apa_mean_and_t(
  x,
  by,
  which.mean = 1,
  digits = 2,
  paired = FALSE,
  eff.size = FALSE,
  abs = FALSE,
  aggregate_by = NULL,
  transform.means = NULL,
  ...
)

Arguments

Value

A formatted string with t-test results in APA style

Examples

# Using simulated data
rt <- rnorm(100)
gr <- factor(rep(c("A", "B"), each = 50))
apa_mean_and_t(rt, gr)
apa_mean_and_t(rt, gr, which.mean = 3)
apa_mean_and_t(rt, gr, eff.size = TRUE)

Format means and confidence intervals in APA style

Description

Format means and confidence intervals in APA style

Usage

apa_mean_conf(
  x,
  bootCI = TRUE,
  addCI = FALSE,
  digits = 2,
  transform.means = NULL,
  ...
)

Arguments

Value

A string with a mean followed by confidence intervals in square brackets

Examples

x <- runif(100, 0, 50)
apa_mean_conf(x)
apa_mean_conf(x, bootCI = FALSE)  # Normal approximation instead of bootstrap

Format means and standard deviations in APA style

Description

Format means and standard deviations in APA style

Usage

apa_mean_sd(
  x = NULL,
  m = NULL,
  sd = NULL,
  digits = 2,
  dtype = "p",
  m_units = "",
  sd_units = "",
  ...
)

Arguments

Value

A formatted string with mean and standard deviation in APA style

Examples

x <- rnorm(1000, 50, 25)
apa_mean_sd(x)
apa_mean_sd(m = 50.2, sd = 9.8)  # With pre-computed values
apa_mean_sd(x, dtype = "c")  # Comma format instead of parentheses

Deprecated functions in the apastats2 package

Description

These functions are deprecated and will be removed in a future version. Please use their replacements as indicated.

• describe.ttest -> apa (for t.test objects)

• describe.r -> apa (for cor.test objects)

• describe.chi -> apa (for chisq.test objects)

• describe.mean.sd -> apa_mean_sd

• describe.mean.conf -> apa_mean_conf

• describe.binom.mean.conf -> apa_binom_mean_conf

• describe.mean.and.t -> apa_mean_and_t

• describe.Anova -> apa (for Anova objects)

• describe.aov -> apa (for aov objects)

• describe.anova -> apa (for anova objects)

• describe.afex -> apa (for afex_aov objects)

• describe.bf -> apa (for BFBayesFactor objects)

• describe.brm -> apa (for brmsfit objects)

• describe.dip.test -> apa (for dip.test objects)

• describe.emmeans -> apa (for emmeans objects)

• describe.ezanova -> apa (for ezanova objects)

• describe.ezstats -> apa (for ezstats objects)

• describe.glm -> apa (for glm/lm objects)

• describe.lht -> apa (for linearHypothesis objects)

• describe.lmer -> apa (for lmer objects)

• describe.lmert -> apa (for lmerTest objects)

• describe.lmtaov -> apa (for lmerTest anova objects)

• describe.lsmeans -> apa (for lsmeans objects)

• describe.roc.diff -> apa (for ROC difference objects)

• mean.nn -> mean_nn

• sd.nn -> sd_nn

• sum.nn -> sum_nn

• drop.empty.cols -> drop_empty_cols

• binom.ci -> binom_ci

• mean.round -> mean_round

• sd.round -> sd_round

• load.libs -> load_libs

• lmer.fixef -> lmer_fixef

• omit.zeroes -> omit_zeroes

• f.round -> f_round

• base.breaks -> base_breaks

• base.breaks.x -> base_breaks_x

• base.breaks.y -> base_breaks_y

• ⁠plot.pointrange()⁠ -> plot_pointrange

• mymean -> mean_nn

• mysum -> sum_nn

• mysd -> sd_nn

Usage

describe.ttest(t, ...)

describe.r(rc, ...)

describe.chi(tbl, ...)

describe.Anova(afit, term, f.digits = 2, ...)

describe.aov(fit, term = NULL, sstype = 2, ...)

describe.anova(anova_res, rown = 2, f.digits = 2, ...)

describe.afex(
  afex_fit,
  term,
  include_eta = TRUE,
  eta_digits = 2,
  f_digits = 2,
  df_digits = 0,
  append_to_table = FALSE,
  ...
)

describe.bf(bf, digits = 2, top_limit = 10000, convert_to_power = TRUE, ...)

describe.brm(
  mod,
  term,
  trans = NULL,
  digits = 2,
  eff.size = FALSE,
  eff.size.type = "r",
  nsamples = 100,
  ci.type = "HPDI",
  ...
)

describe.dip.test(x, ...)

describe.emmeans(obj, term, dtype = "B", df = FALSE, ...)

describe.ezanova(
  ezfit,
  term,
  include_eta = TRUE,
  spher_corr = TRUE,
  eta_digits = 2,
  f_digits = 2,
  df_digits = 0,
  append_to_table = FALSE,
  ...
)

describe.ezstats(ezstats_res, term = 1, ...)

describe.glm(
  fit,
  term = NULL,
  dtype = 1,
  b.digits = 2,
  t.digits = 2,
  test.df = FALSE,
  p.as.number = FALSE,
  term.pattern = NULL,
  eff.size = FALSE,
  adj.digits = FALSE,
  ...
)

describe.lht(hyp, ...)

describe.lmer(
  fm,
  pv,
  digits = c(2, 2, 2),
  incl.rel = 0,
  dtype = "B",
  incl.p = TRUE
)

describe.lmert(sfit, factor, dtype = "t", ...)

describe.lmtaov(afit, term, f.digits = 2, ...)

describe.lsmeans(obj, term, dtype = "B", df = FALSE, ...)

describe.roc.diff(roc_diff)

describe.mean.sd(
  x = NULL,
  m = NULL,
  sd = NULL,
  digits = 2,
  dtype = "p",
  m_units = "",
  sd_units = "",
  ...
)

describe.mean.conf(
  x,
  bootCI = TRUE,
  addCI = FALSE,
  digits = 2,
  transform.means = NULL,
  ...
)

describe.binom.mean.conf(x, digits = 2, ...)

describe.mean.and.t(
  x,
  by,
  which.mean = 1,
  digits = 2,
  paired = FALSE,
  eff.size = FALSE,
  abs = FALSE,
  aggregate_by = NULL,
  transform.means = NULL,
  ...
)

mymean(...)

mysum(...)

mysd(...)

## S3 method for class 'nn'
mean(x, ...)

sd.nn(x, ...)

## S3 method for class 'nn'
sum(x, ...)

drop.empty.cols(df)

binom.ci(x)

## S3 method for class 'round'
mean(x, ...)

sd.round(x, ...)

load.libs(libs)

lmer.fixef(fit.lmer)

omit.zeroes(x, digits = 2)

f.round(x, digits = 2, strip.lead.zeros = FALSE)

base.breaks(x, scale = "x", addSegment = TRUE, ...)

base.breaks.x(x, addSegment = TRUE, ...)

base.breaks.y(x, addSegment = TRUE, ...)

## S3 method for class 'pointrange'
plot(
  data,
  mapping,
  pos = position_dodge(0.3),
  pointsize = I(3),
  linesize = I(1),
  pointfill = I("white"),
  pointshape = NULL,
  within_subj = FALSE,
  wid = "uid",
  bars = "ci",
  withinvars = NULL,
  betweenvars = NULL,
  x_as_numeric = FALSE,
  custom_geom_before = NULL,
  connecting_line = FALSE,
  pretty_breaks_y = FALSE,
  pretty_y_axis = FALSE,
  exp_y = FALSE,
  print_aggregated_data = FALSE,
  do_aggregate = FALSE,
  add_margin = FALSE,
  margin_label = "all",
  margin_x_vals = NULL,
  bars_instead_of_points = FALSE,
  geom_bar_params = list(),
  add_jitter = FALSE,
  individual_points_params = list(),
  drop_NA_subj = FALSE,
  design = "between",
  debug = FALSE
)

Value

Each function returns the result of its replacement and signals a deprecation warning. The returned value depends on the function:

• The describe.* functions return character vectors containing APA-formatted statistical results, such as test statistics, degrees of freedom, p-values, effect sizes, means, or confidence intervals. describe.glm and describe.lmer instead return a data frame when no term is selected, with formatted coefficient summaries in columns B, SE, Stat, p, eff, and str; see apa.glm for the column meanings. With append_to_table = TRUE, describe.afex and describe.ezanova instead return a data frame containing the selected ANOVA results and an additional res column with formatted text.

• mymean, mean.nn, mysum, sum.nn, mysd, and sd.nn return numeric summaries (mean, sum, or standard deviation) with missing values removed.

• drop.empty.cols returns the input data frame with columns containing only missing values removed.

• binom.ci returns a named numeric vector: y is the estimated proportion, ymin and ymax are its confidence limits, and len is the number of observations.

• mean.round, sd.round, omit.zeroes, and f.round return character vectors containing formatted numbers.

• load.libs invisibly returns a list of logical values, one per requested package, indicating whether loading succeeded. It is called for the side effect of loading and attaching packages.

• lmer.fixef returns a data frame with columns Estimate, Std.Err, and t, giving fixed-effect estimates, standard errors, and estimate-to-standard-error ratios.

• base.breaks, base.breaks.x, and base.breaks.y return a continuous ggplot2 scale (class ScaleContinuousPosition) with axis breaks; when addSegment = TRUE, they return a list containing a segment layer and that scale.

• plot.pointrange returns a ggplot object showing group summaries with confidence intervals or standard errors.

Tufte-like breaks for axes

Description

Tufte-like breaks for axes

Usage

base_breaks(x, scale = "x", addSegment = TRUE, ...)

base_breaks_x(x, addSegment = TRUE, ...)

base_breaks_y(x, addSegment = TRUE, ...)

Arguments

Value

A continuous position scale (class ScaleContinuousPosition) with breaks spanning x. When addSegment = TRUE, a list containing an axis-segment layer and the scale, suitable for adding to a ggplot2 plot.

Functions

• base_breaks_x(): Tufte-like breaks for X axis

• base_breaks_y(): Tufte-like breaks for Y axis

Examples

p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  theme_minimal()

p
p + base_breaks(mtcars$wt, scale = "x") +
  base_breaks(mtcars$mpg, scale = "y")

Binomial confidence interval as a vector

Description

Binomial confidence interval as a vector

Usage

binom_ci(x)

Arguments

Value

A named numeric vector: y is the estimated proportion, ymin and ymax are its 95% confidence limits, and len is the sample size.

Examples

binom_ci(rbinom(500, 1, prob = 0.7))

Drop empty columns from a data.frame

Description

Drop empty (consisting of NA only) columns from a data.frame. Based on https://stackoverflow.com/a/2644009/1344028

Usage

drop_empty_cols(df)

Arguments

Value

data.frame without empty columns

Examples

df <- data.frame(x = rnorm(20), y = rep("A", 20), z = rep(NA, 20))
str(df)
df <- drop_empty_cols(df)
str(df)

Formatted rounding

Description

Formatted rounding

Usage

f_round(x, digits = 2, strip.lead.zeros = FALSE)

Arguments

Value

A character vector containing x rounded to the specified number of decimal places, optionally omitting zeros before decimal points.

Examples

f_round(5.8242)
f_round(5.8251)
f_round(5.82999, digits = 3)
f_round(5.82999, digits = 4)

Format results

Description

Internal function used to convert latex-formatted results to pandoc style.

Usage

format_results(res_str, type = "pandoc")

Arguments

Value

A character vector containing res_str in the requested notation: LaTeX markup, Pandoc emphasis, or text for plotmath expressions.

Get adjusted confidence intervals or standard errors

Description

Computes grouped summary statistics and precision intervals for between- subject, within-subject, and mixed designs. With no within-subject factors, ordinary independent-observation intervals are computed. Supplying a subject identifier in a between-subject design first aggregates repeated rows within subject-by-group cells.

get_superb_ci() is retained for backward compatibility. New code should use get_adjusted_ci(). Its within-subject results are intended to remain numerically compatible with the former implementation backed by superb.

Usage

get_adjusted_ci(
  data,
  value_var,
  within = NULL,
  between = NULL,
  wid = NULL,
  adjustments = list(purpose = "single", decorrelation = NULL),
  errorbar = "CI",
  gamma = 0.95,
  drop_NA_subj = FALSE,
  drop_missing_levels = TRUE,
  aggr_fun = mean,
  debug = FALSE,
  ...
)

get_superb_ci(
  data,
  wid,
  within,
  value_var,
  between = NULL,
  adjustments = list(purpose = "single", decorrelation = "CM"),
  errorbar = "CI",
  drop_NA_subj = FALSE,
  drop_missing_levels = TRUE,
  aggr_fun = mean,
  debug = FALSE,
  ...
)

Arguments

Details

The within-subject implementation and statistic-specific precision conventions are adapted from the superb framework and its R implementation (Cousineau, Goulet, & Harding, 2021). In particular, the Cousineau-Morey path follows the two-step centering and bias-correction algorithm used by superb. The code is implemented locally; apastats2 does not depend on or call superb.

Value

A data.frame with one row per group and the grouping variables, center (summary statistic), lowerwidth and upperwidth (signed interval offsets from the center), lower_ci and upper_ci (interval limits), and descr (formatted summary and interval text). With errorbar = "none", both offsets are zero and both limits equal the center.

References

Cousineau, D., Goulet, M.-A., & Harding, B. (2021). Summary plots with adjusted error bars: The superb framework with an implementation in R. Advances in Methods and Practices in Psychological Science, 4(3). doi:10.1177/25152459211035109

Cousineau, D. (2005). Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method. The Quantitative Methods for Psychology, 1(1), 42-45. doi:10.20982/tqmp.01.1.p042

Morey, R. D. (2008). Confidence intervals from normalized data: A correction to Cousineau (2005). The Quantitative Methods for Psychology, 4(2), 61-64. doi:10.20982/tqmp.04.2.p061

Examples

data(memory_noise)

# Between-subject intervals: Exp. 1A vs Exp. 1B
target_higher <- memory_noise[
  memory_noise$relative_noise == "target more noisy",
]
get_adjusted_ci(
  target_higher,
  value_var = "bias_percent",
  between = "experiment",
  wid = "participant"
)

# Within-subject Cousineau-Morey intervals for relative noise
get_adjusted_ci(
  memory_noise[memory_noise$experiment == "Exp. 1A", ],
  value_var = "bias_percent",
  within = "relative_noise",
  wid = "participant"
)

# Correlation-adjusted intervals
get_adjusted_ci(
  memory_noise[memory_noise$experiment == "Exp. 1A", ],
  value_var = "bias_percent", within = "relative_noise", wid = "participant",
  adjustments = list(decorrelation = "CA")
)

# Mixed design: experiment is between, relative noise is within
get_adjusted_ci(
  memory_noise,
  value_var = "bias_percent",
  within = "relative_noise",
  between = "experiment",
  wid = "participant"
)

Describe lmer in-text

Description

A shortcut for apa.glm(..., dtype=4)

Usage

ins_lmer(obj, term = NULL, digits = 2, adj.digits = TRUE)

Arguments

Value

A character result for a selected term, or a data.frame of coefficient summaries when term = NULL. The table has the columns documented in apa.glm; str contains estimates, standard errors, and test statistics formatted for in-text reporting.

Examples

if (requireNamespace("lme4", quietly = TRUE)) {
  # Fit a mixed-effects model
  fm <- lme4::lmer(Reaction ~ Days + (Days | Subject), lme4::sleepstudy)
  
  # Concise in-text reporting
  print(ins_lmer(fm, "Days"))
  print(ins_lmer(fm, "(Intercept)", digits = 1))
}

Length of unique values

Description

Counts unique values

Usage

lengthu(x)

Arguments

Value

A numeric scalar giving the number of distinct values in x.

Examples

x <- c(5, 7, 8, 9, 5, 7)
length(x)
lengthu(x)

Get nice matrix of fixed effects from lmer

Description

Get nice matrix of fixed effects from lmer

Usage

lmer_fixef(fit.lmer)

Arguments

Value

A data.frame with one row per fixed-effect coefficient and columns Estimate (coefficient), Std.Err (standard error), and t (the ratio of the estimate to its standard error). Row names identify model terms.

Quietly load libraries

Description

Quietly load libraries

Usage

load_libs(libs)

Arguments

Value

Invisibly, a list of logical scalars, one per package in libs, indicating whether loading succeeded. Called for the side effect of loading and attaching the requested packages.

Examples

load_libs(c("ggplot2", "apastats2", "Hmisc"))

Mean with na.rm=TRUE

Description

Mean with na.rm=TRUE

Usage

mean_nn(x, ...)

Arguments

Value

A numeric or complex scalar giving the mean of x, with missing values removed.

Examples

x <- c(NA, 10, 90)
mean(x)
mean_nn(x)

Rounded mean

Description

Mean rounded to the specified number of digits

Usage

mean_round(x, ...)

Arguments

Value

A character string containing the mean rounded to the specified number of decimal places.

Examples

mean_round(c(10, 99))
mean_round(c(10, 99, NA))
mean_round(c(10, 99), 2)

Memory bias under relative-noise manipulations

Description

A compact participant-level dataset derived from Experiments 1A and 1B of Chetverikov and Hansmann-Roth (2026). In Exp. 1A the high-noise stimulus had SD = 20 degrees; in Exp. 1B it had SD = 45 degrees. Low-noise stimuli had SD = 5 degrees in both samples.

Usage

data(memory_noise)

Format

A data.frame with 70 observations and 6 variables.

Details

Only unequal-noise trials are included. Bias is estimated with the same weighted probability-density asymmetry approach used in the paper and averaged over target/non-target dissimilarities from 1 to 44 degrees, the range in which Experiments 1A and 1B showed the relative-noise interaction. Positive values indicate attraction toward the competing non-target and negative values indicate repulsion.

• participant. An anonymized participant identifier, unique across Experiments 1A and 1B.

• experiment. Between-subject sample: "Exp. 1A" or "Exp. 1B".

• high_noise_sd. High stimulus-noise standard deviation used in the sample (20 or 45 degrees).

• relative_noise. Ordered within-subject factor: "target less noisy" or "target more noisy".

• bias_percent. Density-asymmetry bias in percentage points. Positive values indicate attraction toward the non-target; negative values indicate repulsion.

• n_trials. Number of non-outlier unequal-noise trials contributing to the weighted bias curve.

Source

Derived from the public behavioral dataset at https://osf.io/kqb8t/. The package subset is based on the canonical CHR2026 representation used for model comparison.

References

Chetverikov, A., & Hansmann-Roth, S. (2026). Noise in Competing Representations Determines the Direction of Memory Biases. eLife, 15, RP111380. doi:10.7554/eLife.111380.1

Omit leading zero from number

Description

Omit leading zero from number

Usage

omit_zeroes(x, digits = 2)

Arguments

Value

A character vector containing formatted values with the leading character removed, intended to omit the zero before a decimal point.

Examples

omit_zeroes(0.2312)
omit_zeroes(0.2312, digits = 3)
omit_zeroes("000.2312", digits = 1)

Paste several strings, add 'and' before last

Description

Paste several strings, add 'and' before last

Usage

paste_and(x, sep = ", ", suffix = "")

Arguments

Value

A character string listing the values in x, with the requested separators, suffix, and final conjunction.

Examples

data(iris)
# get mean petal width and SD by group
res <- as.vector(by(iris$Sepal.Width, iris$Species, apa_mean_sd))
res
paste_and(res)
paste_and(res, sep = ";")

data(memory_noise)
# get bias and SD in the two experiment samples
res <- as.vector(by(
  memory_noise$bias_percent,
  memory_noise$experiment,
  apa_mean_sd
))
res
# no comma with two groups
paste_and(res)
paste_and(res, suffix = " percentage points")

Pointrange plot

Description

Pointrange plot

Usage

plot_pointrange(
  data,
  mapping,
  pos = position_dodge(0.3),
  pointsize = I(3),
  linesize = I(1),
  pointfill = I("white"),
  pointshape = NULL,
  within_subj = FALSE,
  wid = "uid",
  bars = "ci",
  withinvars = NULL,
  betweenvars = NULL,
  x_as_numeric = FALSE,
  custom_geom_before = NULL,
  connecting_line = FALSE,
  pretty_breaks_y = FALSE,
  pretty_y_axis = FALSE,
  exp_y = FALSE,
  print_aggregated_data = FALSE,
  do_aggregate = FALSE,
  add_margin = FALSE,
  margin_label = "all",
  margin_x_vals = NULL,
  bars_instead_of_points = FALSE,
  geom_bar_params = list(),
  add_jitter = FALSE,
  individual_points_params = list(),
  drop_NA_subj = FALSE,
  design = "between",
  debug = FALSE
)

Arguments

Details

For point and line properties (e.g., pointfill) passing NULL allows to avoid setting these values (useful when they are mapped to some variables).

Value

A ggplot object displaying group summaries and their error bars, with optional margin summaries, connecting lines, bars, or individual points.

Examples

data(memory_noise)

# Between-subject CI: compare Experiments 1A and 1B
target_higher <- memory_noise[
  memory_noise$relative_noise == "target more noisy",
]
plot_pointrange(
  target_higher,
  aes(x = experiment, y = bias_percent)
) + labs(
  x = "Experiment",
  y = "Bias toward non-target (%)"
)

# Mixed design: relative noise is within subjects, experiment is between
plot_pointrange(
  memory_noise,
  aes(x = relative_noise, color = experiment, y = bias_percent),
  wid = "participant",
  within_subj = TRUE,
  withinvars = "relative_noise",
  betweenvars = "experiment",
  connecting_line = TRUE
) +
  labs(
    x = "Relative noise",
    y = "Bias toward non-target (%)",
    color = "Experiment"
  ) +
  scale_x_discrete(labels = c(
    "target less noisy" = "Target less noisy",
    "target more noisy" = "Target more noisy"
  ))

# The same mixed design with standard errors
plot_pointrange(
  memory_noise,
  aes(x = relative_noise, color = experiment, y = bias_percent),
  wid = "participant",
  within_subj = TRUE,
  withinvars = "relative_noise",
  betweenvars = "experiment",
  bars = "se"
) +
  labs(
    x = "Relative noise",
    y = "Bias toward non-target (%)",
    color = "Experiment"
  ) +
  scale_x_discrete(labels = c(
    "target less noisy" = "Target less noisy",
    "target more noisy" = "Target more noisy"
  ))

Round p-value

Description

If p-value is <= 0.001, returns ".001" else returns p-value rounded to the specified number of digits, optionally including relation sign ("<" or "=").

Usage

round_p(
  values,
  include.rel = 1,
  digits = 3,
  strip.lead.zeros = TRUE,
  replace.very.small = 0.001
)

Arguments

Value

A character vector of formatted p-values, including the requested relation signs and small-value threshold notation.

Examples

p_values <- c(0.025, 0.0001, 0.001, 0.568)
round_p(p_values)
round_p(p_values, digits = 2)
round_p(p_values, include.rel = FALSE)
round_p(p_values, include.rel = FALSE, strip.lead.zeros = FALSE)
round_p(p_values, include.rel = FALSE, strip.lead.zeros = FALSE, replace.very.small = 0.01)

Exponentiate scale

Description

Helps to transform log-scale back to normal

Usage

scale_y_exp(digits = 0, ...)

Arguments

Value

A continuous y-position scale (class ScaleContinuousPosition) with breaks and labels transformed from logarithmic to exponentiated values.

SD with na.rm=TRUE

Description

SD with na.rm=TRUE

Usage

sd_nn(x, ...)

Arguments

Value

A numeric scalar giving the standard deviation of x, with missing values removed.

Examples

x <- c(NA, 10, 90)
sd(x)
sd_nn(x)

Rounded SD

Description

SD rounded to the specified number of digits

Usage

sd_round(x, ...)

Arguments

Value

A character string containing the standard deviation rounded to the specified number of decimal places.

Examples

sd_round(c(10, 99))
sd_round(c(10, 99, NA))
sd_round(c(10, 99), 2)

Get the means between the points of a sequence

Description

Get the means between the points of a sequence

Usage

seq_mean(x)

Arguments

Value

A numeric vector of midpoints between consecutive values in x.

Examples

seq_mean(c(1, 3, 5, 7, 10))

Sum with na.rm=TRUE

Description

Sum with na.rm=TRUE

Usage

sum_nn(x, ...)

Arguments

Value

A numeric or complex scalar giving the sum of x, with missing values removed.

Examples

x <- c(NA, 10, 90)
sum(x)
sum_nn(x)

Get formatted means and confidence intervals

Description

Get formatted means and confidence intervals

Usage

table_mean_conf(x, digits = 2, binom = FALSE, ...)

Arguments

Value

A character vector of length two: the formatted mean followed by the confidence limits in square brackets.

Examples

# For continuous data
x <- rnorm(100, mean = 50, sd = 10)
table_mean_conf(x)
table_mean_conf(x, digits = 3)

# For binary data
y <- rbinom(100, 1, prob = 0.7)
table_mean_conf(y, binom = TRUE)

Get formatted means and confidence intervals by group

Description

Get formatted means and confidence intervals by group

Usage

table_mean_conf_by(x, by, digits = 2, binom = FALSE)

Arguments

Value

An array of list entries, with dimensions and dimension names determined by the levels of the grouping variables in by. Each observed group contains the two-element character vector returned by table_mean_conf; unobserved combinations contain NULL.

Examples

# Group means for continuous data
x <- rnorm(100, mean = 50, sd = 10)
groups <- rep(c("A", "B", "C"), length.out = 100)
table_mean_conf_by(x, groups)

# Group means for binary data 
y <- rbinom(100, 1, prob = 0.7)
table_mean_conf_by(y, groups, binom = TRUE)