statsExpressions (version 1.3.2)

one_sample_test: One-sample tests

Description

The table below provides summary about:

• statistical test carried out for inferential statistics

• type of effect size estimate and a measure of uncertainty for this estimate

• functions used internally to compute these details

Hypothesis testing

Type	Test	Function used
Parametric	One-sample Student's t-test	stats::t.test
Non-parametric	One-sample Wilcoxon test	stats::wilcox.test
Robust	Bootstrap-t method for one-sample test	WRS2::trimcibt
Bayesian	One-sample Student's t-test	BayesFactor::ttestBF

Effect size estimation

Type	Effect size	CI available?	Function used
Parametric	Cohen's d, Hedge's g	Yes	effectsize::cohens_d(), effectsize::hedges_g()
Non-parametric	r (rank-biserial correlation)	Yes	effectsize::rank_biserial()
Robust	trimmed mean	Yes	WRS2::trimcibt()
Bayes Factor	difference	Yes	bayestestR::describe_posterior()

Usage

one_sample_test(
  data,
  x,
  type = "parametric",
  test.value = 0,
  alternative = "two.sided",
  k = 2L,
  conf.level = 0.95,
  tr = 0.2,
  bf.prior = 0.707,
  effsize.type = "g",
  ...
)

Value

The returned tibble data frame can contain some or all of the following columns (the exact columns will depend on the statistical test):

• statistic: the numeric value of a statistic

• df: the numeric value of a parameter being modeled (often degrees of freedom for the test)

• df.error and df: relevant only if the statistic in question has two degrees of freedom (e.g. anova)

• p.value: the two-sided p-value associated with the observed statistic

• method: the name of the inferential statistical test

• estimate: estimated value of the effect size

• conf.low: lower bound for the effect size estimate

• conf.high: upper bound for the effect size estimate

• conf.level: width of the confidence interval

• conf.method: method used to compute confidence interval

• conf.distribution: statistical distribution for the effect

• effectsize: the name of the effect size

• n.obs: number of observations

• expression: pre-formatted expression containing statistical details

For examples of dataframe outputs, see examples and this vignette.

Note that all examples are preceded by set.seed() calls for reproducibility.

Arguments

data

A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.

x

A numeric variable from the dataframe data.

type

A character specifying the type of statistical approach:

• "parametric"

• "nonparametric"

• "robust"

• "bayes"

You can specify just the initial letter.

test.value

A number indicating the true value of the mean (Default: 0).

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

k

Number of digits after decimal point (should be an integer) (Default: k = 2L).

conf.level

Scalar between 0 and 1. If unspecified, the defaults return 95% confidence/credible intervals (0.95).

tr

Trim level for the mean when carrying out robust tests. In case of an error, try reducing the value of tr, which is by default set to 0.2. Lowering the value might help.

bf.prior

A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors and posterior estimates. In addition to numeric arguments, several named values are also recognized: "medium", "wide", and "ultrawide", corresponding to r scale values of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this value corresponds to scale for fixed effects.

effsize.type

Type of effect size needed for parametric tests. The argument can be "d" (for Cohen's d) or "g" (for Hedge's g).

...

Currently ignored.

Examples

# \donttest{
# for reproducibility
set.seed(123)
library(statsExpressions)
options(tibble.width = Inf, pillar.bold = TRUE, pillar.neg = TRUE)

# ----------------------- parametric ---------------------------------------

one_sample_test(
  data       = ggplot2::msleep,
  x          = brainwt,
  test.value = 0.275,
  type       = "parametric"
)

# ----------------------- non-parametric -----------------------------------

one_sample_test(
  data       = ggplot2::msleep,
  x          = brainwt,
  test.value = 0.275,
  type       = "nonparametric"
)

# ----------------------- robust --------------------------------------------

one_sample_test(
  data       = ggplot2::msleep,
  x          = brainwt,
  test.value = 0.275,
  type       = "robust"
)

# ---------------------------- Bayesian -----------------------------------

one_sample_test(
  data       = ggplot2::msleep,
  x          = brainwt,
  test.value = 0.275,
  type       = "bayes",
  bf.prior   = 0.8
)
# }