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_lm_contrasts(
model,
group,
data,
p_adjust = "none",
effect.type = "cohens.d",
bootstraps = 2000,
...
)Arguments
- model
The model to be formatted.
- group
The group for the comparison.
- data
The data frame.
- p_adjust
Character: adjustment method (e.g., "bonferroni") – added to options
- 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
# Make and format model (group need to be a factor)
mtcars2 <- mtcars
mtcars2$cyl <- as.factor(mtcars2$cyl)
model <- lm(mpg ~ cyl + wt * hp, mtcars2)
set.seed(100)
nice_lm_contrasts(model, group = "cyl", data = mtcars, bootstraps = 500)
#> Dependent Variable Comparison df t p d CI_lower
#> 1 mpg cyl4 - cyl6 26 0.8452452 0.4056854 2.147244 1.2831781
#> 2 mpg cyl4 - cyl8 26 0.7047256 0.4872464 3.587739 2.7405544
#> 3 mpg cyl6 - cyl8 26 0.1302365 0.8973817 1.440495 0.7828333
#> CI_upper
#> 1 2.985932
#> 2 4.582310
#> 3 1.978447
# Several models at once
mtcars2$gear <- as.factor(mtcars2$gear)
model2 <- lm(qsec ~ cyl, data = mtcars2)
my.models <- list(model, model2)
set.seed(100)
nice_lm_contrasts(my.models, group = "cyl", data = mtcars, bootstraps = 500)
#> Dependent Variable Comparison df t p d CI_lower
#> 1 mpg cyl4 - cyl6 26 0.8452452 0.4056854062 2.1472439 1.2831781
#> 2 mpg cyl4 - cyl8 26 0.7047256 0.4872463657 3.5877388 2.6849327
#> 3 mpg cyl6 - cyl8 26 0.1302365 0.8973817161 1.4404949 0.7982678
#> 4 qsec cyl4 - cyl6 29 1.6103903 0.1181454384 0.7786137 -0.3761605
#> 5 qsec cyl4 - cyl8 29 3.9396785 0.0004711519 1.5873417 0.8656254
#> 6 qsec cyl6 - cyl8 29 1.7470521 0.0912125385 0.8087280 -0.4254810
#> CI_upper
#> 1 2.985932
#> 2 4.529380
#> 3 2.006210
#> 4 1.916876
#> 5 2.270826
#> 6 1.724369
# Now supports more than 3 levels
mtcars2$carb <- as.factor(mtcars2$carb)
model <- lm(mpg ~ carb + wt * hp, mtcars2)
set.seed(100)
nice_lm_contrasts(model, group = "carb", data = mtcars2, bootstraps = 500)
#> Warning: extreme order statistics used as endpoints
#> Dependent Variable Comparison df t p d
#> 1 mpg carb1 - carb2 23 -0.11167606 0.9120492 0.5999941
#> 2 mpg carb1 - carb3 23 -0.11169714 0.9120326 1.8436713
#> 3 mpg carb1 - carb4 23 0.60966169 0.5480622 1.9476509
#> 4 mpg carb1 - carb6 23 -0.09074550 0.9284805 1.1504742
#> 5 mpg carb1 - carb8 23 -0.43874537 0.6649399 2.1087173
#> 6 mpg carb2 - carb3 23 -0.04660405 0.9632311 1.2436772
#> 7 mpg carb2 - carb4 23 0.84056376 0.4092461 1.3476567
#> 8 mpg carb2 - carb6 23 -0.04379931 0.9654424 0.5504801
#> 9 mpg carb2 - carb8 23 -0.42623613 0.6738985 1.5087231
#> 10 mpg carb3 - carb4 23 0.73745888 0.4683007 0.1039796
#> 11 mpg carb3 - carb6 23 -0.01237567 0.9902326 -0.6931971
#> 12 mpg carb3 - carb8 23 -0.41044652 0.6852769 0.2650460
#> 13 mpg carb4 - carb6 23 -0.46023032 0.6496724 -0.7971767
#> 14 mpg carb4 - carb8 23 -0.81847254 0.4214869 0.1610664
#> 15 mpg carb6 - carb8 23 -0.35808238 0.7235472 0.9582431
#> CI_lower CI_upper
#> 1 -0.51694856 1.8254559
#> 2 1.02879860 2.7169111
#> 3 0.97780962 3.1942469
#> 4 0.39326638 2.2035687
#> 5 1.26010382 2.9683400
#> 6 0.46065533 1.9684140
#> 7 0.38715124 2.1885538
#> 8 -0.20539908 1.3147806
#> 9 0.67615602 2.1138626
#> 10 -0.45720063 0.5834472
#> 11 -0.89904526 -0.4788142
#> 12 0.04082518 0.4698009
#> 13 -1.34485228 -0.2904916
#> 14 -0.40209114 0.6084037
#> 15 0.80938200 1.1156342