The effect sizes and their confidence intervals are computed using effectsize::eta_squared and effectsize::omega_squared functions.

  y, = NULL,
  paired = FALSE,
  k = 2L,
  conf.level = 0.95,
  effsize.type = "omega",
  var.equal = FALSE,
  output = "expression",



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


The grouping variable from the dataframe data.


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

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.


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


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


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


Type of effect size needed for parametric tests. The argument can be "biased" (equivalent to "d" for Cohen's d for t-test; "eta" for partial eta-squared for anova) or "unbiased" (equivalent to "g" Hedge's g for t-test; "omega" for partial omega-squared for anova)).


a logical variable indicating whether to treat the variances in the samples as equal. If TRUE, then a simple F test for the equality of means in a one-way analysis of variance is performed. If FALSE, an approximate method of Welch (1951) is used, which generalizes the commonly known 2-sample Welch test to the case of arbitrarily many samples.


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


Additional arguments (currently ignored).


For more details, see-


# for reproducibility set.seed(123) library(statsExpressions) # -------------------- between-subjects ------------------------------ # to get expression expr_anova_parametric( data = ggplot2::msleep, x = vore, y = sleep_rem )
#> paste(italic("F")["Welch"], "(", "3", ",", "11.10", ") = ", "2.63", #> ", ", italic("p"), " = ", "0.102", ", ", widehat(omega["p"]^2), #> " = ", "0.14", ", CI"["95%"], " [", "0.00", ", ", "0.30", #> "]", ", ", italic("n")["obs"], " = ", 56L)
# -------------------- repeated measures ------------------------------ # to get dataframe expr_anova_parametric( data = iris_long, x = condition, y = value, = id, paired = TRUE, output = "dataframe" )
#> # A tibble: 1 x 10 #> statistic parameter1 parameter2 p.value group term estimate #> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> #> 1 776. 1.15 171. 1.32e-69 rowid:condition condition 0.707 #> ci.width conf.low conf.high #> <dbl> <dbl> <dbl> #> 1 0.95 0.666 0.740