expr_anova_parametric(
data,
x,
y,
subject.id = NULL,
paired = FALSE,
k = 2L,
conf.level = 0.95,
effsize.type = "omega",
var.equal = FALSE,
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. |

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%` lower and upper confidence intervals (`0.95` ). |

effsize.type |
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**)). |

var.equal |
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. |

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

... |
Additional arguments (currently ignored). |

## Value

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

## Examples

#> 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,
subject.id = 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