statsExpressions (version 1.0.1)

one_sample_test: One-sample tests

Description

A dataframe containing results from a one-sample test. The exact test and the effect size details contained will depend on the type argument.

For more details, see- https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html

Usage

one_sample_test(
  data,
  x,
  type = "parametric",
  test.value = 0,
  k = 2L,
  conf.level = 0.95,
  tr = 0.2,
  bf.prior = 0.707,
  effsize.type = "g",
  nboot = 100L,
  top.text = NULL,
  ...
)

Arguments

data

A dataframe (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted.

x

A numeric variable from the dataframe data.

type

A character specifying the type of statistical approach. Four possible options:

• "parametric"

• "nonparametric"

• "robust"

• "bayes"

Corresponding abbreviations are also accepted: "p" (for parametric), "np" (for nonparametric), "r" (for robust), or "bf" (for Bayesian).

test.value

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

k

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

conf.level

Confidence/Credible Interval (CI) level. Default to 0.95 (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.

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).

nboot

Number of bootstrap samples for computing confidence interval for the effect size (Default: 100).

top.text

Text to display on top of the Bayes Factor message. This is mostly relevant in the context of ggstatsplot functions.

...

Currently ignored.

Examples

# NOT RUN {
# 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
)
# }