statsExpressions (version 0.7.0)

expr_t_twosample: Expression and dataframe for two-sample t-test

Description

Expression and dataframe for two-sample t-test

Usage

expr_t_twosample(
  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 = 0.1,
  nboot = 100,
  top.text = NULL,
  output = "expression",
  ...
)

Arguments

data

A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted.

x

The grouping variable from the dataframe data.

y

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

subject.id

In case of repeated measures design (paired = TRUE, i.e.), this argument specifies the subject or repeated measures id. 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

Type of statistic expected ("parametric" or "nonparametric" or "robust" or "bayes").Corresponding abbreviations are also accepted: "p" (for parametric), "np" (nonparametric), "r" (robust), or "bf"resp.

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

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

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.

tr

Trim level for the mean when carrying out robust tests. If you get error stating "Standard error cannot be computed because of Winsorized variance of 0 (e.g., due to ties). Try to decrease the trimming level.", try to play around with the value of tr, which is by default set to 0.1. 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.

output

If "expression", will return expression with statistical details, while "dataframe" will return a dataframe containing the results.

...

Currently ignored.

Value

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

References

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

Examples

# NOT RUN {
# for reproducibility
set.seed(123)
library(statsExpressions)

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

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

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

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

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

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

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

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

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

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

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

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