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. 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, Cohen's d, 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, and correct for contrasts but not for covariates and
other predictors. Because this method uses bootstrapping, it is recommended
to set a seed before using for reproducibility reasons (e.g.,
sed.seet(100)).
Does not for the moment support nested comparisons for marginal means,
only a comparison of all groups. For nested comparisons, please use
emmeans::contrast() directly, or for the easystats equivalent,
modelbased::estimate_contrasts().
When using nice_lm_contrasts(), please use as.factor() outside the
lm() formula, or it will lead to an error.
Examples
# Basic example
set.seed(100)
nice_contrasts(
data = mtcars,
response = "mpg",
group = "cyl",
bootstraps = 200
)
#> Warning: extreme order statistics used as endpoints
#> Warning: extreme order statistics used as endpoints
#> Dependent Variable Comparison df t p d CI_lower
#> 1 mpg cyl4 - cyl6 29 4.441099 1.194696e-04 2.147244 1.4350410
#> 2 mpg cyl4 - cyl8 29 8.904534 8.568209e-10 3.587739 2.7592293
#> 3 mpg cyl6 - cyl8 29 3.111825 4.152209e-03 1.440495 0.7548555
#> CI_upper
#> 1 3.111226
#> 2 4.585134
#> 3 1.965225
# \donttest{
set.seed(100)
nice_contrasts(
data = mtcars,
response = "disp",
group = "gear"
)
#> Warning: extreme order statistics used as endpoints
#> Dependent Variable Comparison df t p d
#> 1 disp gear3 - gear4 29 6.385087 5.569503e-07 2.4729335
#> 2 disp gear3 - gear5 29 2.916870 6.759287e-03 1.5062653
#> 3 disp gear4 - gear5 29 -1.816053 7.971420e-02 -0.9666682
#> CI_lower CI_upper
#> 1 1.28321568 3.2388008
#> 2 -0.02637868 3.0140308
#> 3 -2.36542748 0.1651726
# Multiple dependent variables
set.seed(100)
nice_contrasts(
data = mtcars,
response = c("mpg", "disp", "hp"),
group = "cyl"
)
#> Dependent Variable Comparison df t p d
#> 1 mpg cyl4 - cyl6 29 4.441099 1.194696e-04 2.147244
#> 2 mpg cyl4 - cyl8 29 8.904534 8.568209e-10 3.587739
#> 3 mpg cyl6 - cyl8 29 3.111825 4.152209e-03 1.440495
#> 4 disp cyl4 - cyl6 29 -3.131986 3.945539e-03 -1.514296
#> 5 disp cyl4 - cyl8 29 -11.920787 1.064054e-12 -4.803022
#> 6 disp cyl6 - cyl8 29 -7.104461 8.117219e-08 -3.288726
#> 7 hp cyl4 - cyl6 29 -2.162695 3.894886e-02 -1.045650
#> 8 hp cyl4 - cyl8 29 -8.285112 3.915144e-09 -3.338167
#> 9 hp cyl6 - cyl8 29 -4.952403 2.895434e-05 -2.292517
#> CI_lower CI_upper
#> 1 1.3531871 3.1223071
#> 2 2.7031569 4.5420741
#> 3 0.8485919 1.9643088
#> 4 -2.2636521 -0.8826532
#> 5 -5.7975938 -3.8538264
#> 6 -4.3208965 -2.2293026
#> 7 -1.7374299 -0.4355318
#> 8 -4.3451476 -2.3268987
#> 9 -3.1628173 -1.2854720
# Adding covariates
set.seed(100)
nice_contrasts(
data = mtcars,
response = "mpg",
group = "cyl",
covariates = c("disp", "hp")
)
#> Dependent Variable Comparison df t p d CI_lower
#> 1 mpg cyl4 - cyl6 27 2.3955766 0.02379338 2.147244 1.3531871
#> 2 mpg cyl4 - cyl8 27 0.7506447 0.45935889 3.587739 2.6274380
#> 3 mpg cyl6 - cyl8 27 -0.6550186 0.51799786 1.440495 0.8271079
#> CI_upper
#> 1 3.122307
#> 2 4.464881
#> 3 1.969248
# Now supports more than 3 levels
mtcars2 <- mtcars
mtcars2$carb <- as.factor(mtcars2$carb)
set.seed(100)
nice_contrasts(
data = mtcars,
response = "mpg",
group = "carb",
bootstraps = 200
)
#> Warning: extreme order statistics used as endpoints
#> Warning: extreme order statistics used as endpoints
#> Warning: extreme order statistics used as endpoints
#> Warning: extreme order statistics used as endpoints
#> Dependent Variable Comparison df t p d
#> 1 mpg carb1 - carb2 26 1.2175073 0.2343463275 0.5999941
#> 2 mpg carb1 - carb3 26 2.6717336 0.0128499280 1.8436713
#> 3 mpg carb1 - carb4 26 3.9521705 0.0005295477 1.9476509
#> 4 mpg carb1 - carb6 26 1.0761701 0.2917362452 1.1504742
#> 5 mpg carb1 - carb8 26 1.9725244 0.0592726906 2.1087173
#> 6 mpg carb2 - carb3 26 1.8892812 0.0700557269 1.2436772
#> 7 mpg carb2 - carb4 26 3.0134521 0.0056962070 1.3476567
#> 8 mpg carb2 - carb6 26 0.5248621 0.6041261067 0.5504801
#> 9 mpg carb2 - carb8 26 1.4385111 0.1622188105 1.5087231
#> 10 mpg carb3 - carb4 26 0.1579563 0.8757116294 0.1039796
#> 11 mpg carb3 - carb6 26 -0.6003263 0.5534859747 -0.6931971
#> 12 mpg carb3 - carb8 26 0.2295365 0.8202480949 0.2650460
#> 13 mpg carb4 - carb6 26 -0.7600781 0.4540454635 -0.7971767
#> 14 mpg carb4 - carb8 26 0.1535708 0.8791339283 0.1610664
#> 15 mpg carb6 - carb8 26 0.6775802 0.5040233386 0.9582431
#> CI_lower CI_upper
#> 1 -0.58753635 1.8832370
#> 2 0.91323442 2.5895929
#> 3 1.17473694 3.1782768
#> 4 0.46551758 2.0441176
#> 5 1.37871803 2.9378103
#> 6 0.53074348 1.9087905
#> 7 0.24747918 1.9406856
#> 8 -0.29651218 1.2234544
#> 9 0.78319092 2.2808378
#> 10 -0.39581626 0.6764844
#> 11 -0.88770641 -0.4704188
#> 12 0.03915433 0.4548119
#> 13 -1.33713781 -0.2526394
#> 14 -0.35723602 0.6891545
#> 15 0.81322595 1.1278374
# }