statsExpressions (version 1.0.1)

two_sample_test: Two-sample tests

Description

A dataframe containing details from results of a two-sample test and effect size plus confidence intervals.

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

Usage

two_sample_test(
  data,
  x,
  y,
  subject.id = NULL,
  type = "parametric",
  paired = FALSE,
  k = 2L,
  conf.level = 0.95,
  effsize.type = "g",
  var.equal = FALSE,
  bf.prior = 0.707,
  tr = 0.2,
  nboot = 100,
  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

The grouping (or independent) variable from the dataframe data.

y

The response (or outcome or dependent) variable from the dataframe data.

subject.id

Relevant in case of repeated measures or within-subjects design (paired = TRUE, i.e.), it specifies the subject or repeated measures identifier. Important: Note that if this argument is NULL (which is the default), the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted and you leave this argument unspecified, the results can be inaccurate.

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).

paired

Logical that decides whether the experimental design is repeated measures/within-subjects or between-subjects. The default is FALSE.

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%).

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).

var.equal

a logical variable indicating whether to treat the two variances as being equal. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.

bf.prior

A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors and posterior estimates.

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.

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 -------------------------------------

# between-subjects design
two_sample_test(
  data = sleep,
  x = group,
  y = extra,
  type = "p"
)

# within-subjects design
two_sample_test(
  data = VR_dilemma,
  x = modality,
  y = score,
  paired = TRUE,
  subject.id = id,
  type = "p"
)

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

# between-subjects design
two_sample_test(
  data = sleep,
  x = group,
  y = extra,
  type = "np"
)

# within-subjects design
two_sample_test(
  data = VR_dilemma,
  x = modality,
  y = score,
  paired = TRUE,
  subject.id = id,
  type = "np"
)

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

# between-subjects design
two_sample_test(
  data = sleep,
  x = group,
  y = extra,
  type = "r"
)

# within-subjects design
two_sample_test(
  data = VR_dilemma,
  x = modality,
  y = score,
  paired = TRUE,
  subject.id = id,
  type = "r"
)

#' # ------------------------------ Bayesian ------------------------------

# between-subjects design
two_sample_test(
  data = sleep,
  x = group,
  y = extra,
  type = "bayes"
)

# within-subjects design
two_sample_test(
  data = VR_dilemma,
  x = modality,
  y = score,
  paired = TRUE,
  subject.id = id,
  type = "bayes"
)
# }