Easily compute t-test analyses, with effect sizes,
and format in publication-ready format. The 95% confidence interval
is for the effect size, Cohen's d, both provided by the effectsize package.
Usage
nice_t_test(
data,
response,
group = NULL,
correction = "none",
paired = FALSE,
verbose = TRUE,
...
)Arguments
- data
The data frame.
- response
The dependent variable.
- group
The group for the comparison.
- correction
What correction for multiple comparison to apply, if any. Default is "none" and the only other option (for now) is "bonferroni".
- paired
Whether to use a paired t-test.
- verbose
Whether to display the Welch test warning or not.
- ...
Further arguments to be passed to the
t.test()function (e.g., to use Student instead of Welch test, to change from two-tail to one-tail, or to do a paired-sample t-test instead of independent samples).
Value
A formatted dataframe of the specified model, with DV, degrees of freedom, t-value, p-value, the effect size, Cohen's d, and its 95% confidence interval lower and upper bounds.
Details
This function relies on the base R t.test() function, which
uses the Welch t-test per default (see why here:
https://daniellakens.blogspot.com/2015/01/always-use-welchs-t-test-instead-of.html).
To use the Student t-test, simply add the following
argument: var.equal = TRUE.
Note that for paired t tests, you need to use paired = TRUE, and
you also need data in "long" format rather than wide format (like for
the ToothGrowth data set). In this case, the group argument refers
to the participant ID for example, so the same group/participant is
measured several times, and thus has several rows. Note also that R >= 4.4.0
has stopped supporting the paired argument for the formula method used
internally here.
For the easystats equivalent, use: report::report() on the
t.test() object.
Examples
# Make the basic table
nice_t_test(
data = mtcars,
response = "mpg",
group = "am"
)
#> Using independent samples t-test.
#>
#> Using Welch t-test (base R's default; cf. https://doi.org/10.5334/irsp.82).
#> For the Student t-test, use `var.equal = TRUE`.
#>
#> Dependent Variable t df p d CI_lower
#> 1 mpg -3.767123 18.33225 0.001373638 -1.477947 -2.265973
#> CI_upper
#> 1 -0.6705684
# Multiple dependent variables at once
nice_t_test(
data = mtcars,
response = names(mtcars)[1:7],
group = "am"
)
#> Using independent samples t-test.
#>
#> Using Welch t-test (base R's default; cf. https://doi.org/10.5334/irsp.82).
#> For the Student t-test, use `var.equal = TRUE`.
#>
#> Dependent Variable t df p d CI_lower
#> 1 mpg -3.767123 18.33225 1.373638e-03 -1.4779471 -2.2659732
#> 2 cyl 3.354114 25.85363 2.464713e-03 1.2084550 0.4315895
#> 3 disp 4.197727 29.25845 2.300413e-04 1.4452210 0.6417836
#> 4 hp 1.266189 18.71541 2.209796e-01 0.4943081 -0.2260463
#> 5 drat -5.646088 27.19780 5.266742e-06 -2.0030843 -2.8592770
#> 6 wt 5.493905 29.23352 6.272020e-06 1.8924060 1.0300224
#> 7 qsec 1.287845 25.53421 2.093498e-01 0.4656285 -0.2532864
#> CI_upper
#> 1 -0.6705684
#> 2 1.9683142
#> 3 2.2295594
#> 4 1.2066995
#> 5 -1.1245499
#> 6 2.7329219
#> 7 1.1770177
# Can be passed some of the regular arguments
# of base [t.test()]
# Student t-test (instead of Welch)
nice_t_test(
data = mtcars,
response = "mpg",
group = "am",
var.equal = TRUE
)
#> Using Student t-test.
#>
#> Using independent samples t-test.
#>
#> Dependent Variable t df p d CI_lower CI_upper
#> 1 mpg -4.106127 30 0.0002850207 -1.477947 -2.265973 -0.6705684
# One-sided instead of two-sided
nice_t_test(
data = mtcars,
response = "mpg",
group = "am",
alternative = "less"
)
#> Using independent samples t-test.
#>
#> Using Welch t-test (base R's default; cf. https://doi.org/10.5334/irsp.82).
#> For the Student t-test, use `var.equal = TRUE`.
#>
#> Dependent Variable t df p d CI_lower
#> 1 mpg -3.767123 18.33225 0.0006868192 -1.477947 -2.265973
#> CI_upper
#> 1 -0.6705684
# One-sample t-test
nice_t_test(
data = mtcars,
response = "mpg",
mu = 10
)
#> Using independent samples t-test.
#>
#> Using Welch t-test (base R's default; cf. https://doi.org/10.5334/irsp.82).
#> For the Student t-test, use `var.equal = TRUE`.
#>
#> Using one-sample t-test.
#>
#> Dependent Variable t df p d CI_lower CI_upper
#> 1 mpg 9.470995 31 1.154598e-10 1.674251 1.12797 2.208995
# Make sure cases appear in the same order for
# both levels of the grouping factor