Easily compute planned contrast analyses (pairwise comparisons similar to t-tests but more powerful when more than 2 groups), and format in publication-ready format. Supports only three groups for the moment. In this particular case, the confidence intervals are bootstraped on chosen effect size (default to Cohen's d).
Usage
nice_contrasts(
response,
group,
covariates = NULL,
data,
effect.type = "cohens.d",
bootstraps = 2000,
...
)Arguments
- response
The dependent variable.
- group
The group for the comparison.
- covariates
The desired covariates in the model.
- data
The data frame.
- effect.type
What effect size type to use. One of "cohens.d" (default), "akp.robust.d", "unstandardized", "hedges.g", "cohens.d.sigma", or "r".
- bootstraps
The number of bootstraps to use for the confidence interval
- ...
Arguments passed to bootES::bootES.
Value
A dataframe, with the selected dependent variable(s), comparisons of interest, degrees of freedom, t-values, p-values, robust Cohen's d (dR), and the lower and upper 95% confidence intervals of the effect size (i.e., dR).
Details
Statistical power is lower with the standard t test compared than it is with the planned contrast version for two reasons: a) the sample size is smaller with the t test, because only the cases in the two groups are selected; and b) in the planned contrast the error term is smaller than it is with the standard t test because it is based on all the cases (source).
The effect size and confidence interval are calculated via bootES::bootES.
For the easystats equivalent, see:
modelbased::estimate_contrasts().
Examples
# Basic example
nice_contrasts(
data = mtcars,
response = "mpg",
group = "cyl",
bootstraps = 200
)
#> Warning: extreme order statistics used as endpoints
#> Dependent Variable Comparison df t p d CI_lower
#> 1 mpg 4 - 8 29 8.904534 8.568209e-10 3.587739 2.6170603
#> 2 mpg 6 - 8 29 3.111825 4.152209e-03 1.440495 0.7846083
#> 3 mpg 4 - 6 29 4.441099 1.194696e-04 2.147244 1.3637508
#> CI_upper
#> 1 4.480989
#> 2 2.098532
#> 3 3.065156
# \donttest{
nice_contrasts(
data = mtcars,
response = "disp",
group = "gear"
)
#> Dependent Variable Comparison df t p d CI_lower
#> 1 disp 3 - 5 29 2.916870 6.759287e-03 1.5062653 -0.1232237
#> 2 disp 4 - 5 29 -1.816053 7.971420e-02 -0.9666682 -2.2905942
#> 3 disp 3 - 4 29 6.385087 5.569503e-07 2.4729335 1.3680617
#> CI_upper
#> 1 3.0910469
#> 2 0.1865743
#> 3 3.2884629
# Multiple dependent variables
nice_contrasts(
data = mtcars,
response = c("mpg", "disp", "hp"),
group = "cyl"
)
#> Dependent Variable Comparison df t p d CI_lower
#> 1 mpg 4 - 8 29 8.904534 8.568209e-10 3.587739 2.6834278
#> 2 mpg 6 - 8 29 3.111825 4.152209e-03 1.440495 0.8007661
#> 3 mpg 4 - 6 29 4.441099 1.194696e-04 2.147244 1.4106856
#> 4 disp 4 - 8 29 -11.920787 1.064054e-12 -4.803022 -5.7635634
#> 5 disp 6 - 8 29 -7.104461 8.117219e-08 -3.288726 -4.3221240
#> 6 disp 4 - 6 29 -3.131986 3.945539e-03 -1.514296 -2.2317520
#> 7 hp 4 - 8 29 -8.285112 3.915144e-09 -3.338167 -4.2961984
#> 8 hp 6 - 8 29 -4.952403 2.895434e-05 -2.292517 -3.1803660
#> 9 hp 4 - 6 29 -2.162695 3.894886e-02 -1.045650 -1.6830577
#> CI_upper
#> 1 4.5494177
#> 2 2.0020817
#> 3 3.0968329
#> 4 -3.8558702
#> 5 -2.2777025
#> 6 -0.9210477
#> 7 -2.3388883
#> 8 -1.3040170
#> 9 -0.3922928
# Adding covariates
nice_contrasts(
data = mtcars,
response = "mpg",
group = "cyl",
covariates = c("disp", "hp")
)
#> Dependent Variable Comparison df t p d CI_lower
#> 1 mpg 4 - 8 27 0.7506447 0.45935889 3.587739 2.6442455
#> 2 mpg 6 - 8 27 -0.6550186 0.51799786 1.440495 0.8311059
#> 3 mpg 4 - 6 27 2.3955766 0.02379338 2.147244 1.3657347
#> CI_upper
#> 1 4.458875
#> 2 2.003721
#> 3 3.072860
# }