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.
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.
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".
- warning
Whether to display the Welch test warning or not.
- ...
Further arguments to be passed to the
t.testfunction (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.
Examples
# Make the basic table
nice_t_test(
data = mtcars,
response = "mpg",
group = "am"
)
#> 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 -0.6705686
# Multiple dependent variables at once
nice_t_test(
data = mtcars,
response = names(mtcars)[1:7],
group = "am"
)
#> 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 -2.2659731
#> 2 cyl 3.354114 25.85363 2.464713e-03 1.2084550 0.4315896
#> 3 disp 4.197727 29.25845 2.300413e-04 1.4452210 0.6417834
#> 4 hp 1.266189 18.71541 2.209796e-01 0.4943081 -0.2260466
#> 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 -0.6705686
#> 2 1.9683146
#> 3 2.2295592
#> 4 1.2066992
#> 5 -1.1245498
#> 6 2.7329218
#> 7 1.1770176
# 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.
#>
#> Dependent Variable t df p d CI_lower CI_upper
#> 1 mpg -4.106127 30 0.0002850207 -1.477947 -2.265973 -0.6705686
# One-sided instead of two-sided
nice_t_test(
data = mtcars,
response = "mpg",
group = "am",
alternative = "less"
)
#> 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 -0.6705686
# One-sample t-test
nice_t_test(
data = mtcars,
response = "mpg",
mu = 10
)
#> 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
# Paired t-test instead of independent samples
nice_t_test(
data = ToothGrowth,
response = "len",
group = "supp",
paired = TRUE
)
#> Using paired t-test.
#>
#> Dependent Variable t df p d CI_lower CI_upper
#> 1 len 3.302585 29 0.002549842 0.6029668 0.2088153 0.9883436
# Make sure cases appear in the same order for
# both levels of the grouping factor