Nice formatting of simple slopes for lm models

Extracts simple slopes from lm() model object and format for a publication-ready format.

Usage

nice_lm_slopes(
  model,
  predictor,
  moderator,
  b.label = "b",
  standardize = FALSE,
  mod.id = TRUE,
  ci.alternative = "two.sided",
  ...
)

Arguments

model

The model to be formatted.

predictor

The independent variable.

moderator

The moderating variable.

b.label

What to rename the default "b" column (e.g., to capital B if using standardized data for it to be converted to the Greek beta symbol in the nice_table() function). Now attempts to automatically detect whether the variables were standardized, and if so, sets b.label = "B" automatically. Factor variables or dummy variables (only two numeric values) are ignored when checking for standardization. This argument is now deprecated, please use argument standardize directly instead.

standardize

Logical, whether to standardize the data before refitting the model. If TRUE, automatically sets b.label = "B". Defaults to FALSE. Note that if you have factor variables, these will be pseudo-betas, so these coefficients could be interpreted more like Cohen's d.

mod.id

Logical. Whether to display the model number, when there is more than one model.

ci.alternative

Alternative for the confidence interval of the sr2. It can be either "two.sided (the default in this package), "greater", or "less".

...

Further arguments to be passed to the lm() function for the models.

Value

A formatted dataframe of the simple slopes of the specified lm model, with DV, levels of IV, degrees of freedom, regression coefficient, t-value, p-value, and the effect size, the semi-partial correlation squared, and its confidence interval.

Details

The effect size, sr2 (semi-partial correlation squared, also known as delta R2), is computed through effectsize::r2_semipartial. Please read the documentation for that function, especially regarding the interpretation of the confidence interval. In rempsyc, instead of using the default one-sided alternative ("greater"), we use the two-sided alternative.

To interpret the sr2, use effectsize::interpret_r2_semipartial().

For the easystats equivalent, use report::report() on the lm() model object.

See also

Checking for moderation before checking simple slopes: nice_lm, nice_mod, nice_slopes. Tutorial: https://rempsyc.remi-theriault.com/articles/moderation

Examples

# Make and format model
model <- lm(mpg ~ gear * wt, mtcars)
nice_lm_slopes(model, predictor = "gear", moderator = "wt")
#>   Dependent Variable Predictor (+/-1 SD) df        b        t          p
#> 1                mpg       gear (LOW-wt) 28 7.540509 2.010656 0.05408136
#> 2                mpg      gear (MEAN-wt) 28 5.615951 1.943711 0.06204275
#> 3                mpg      gear (HIGH-wt) 28 3.691393 1.795568 0.08336403
#>          sr2 CI_lower   CI_upper
#> 1 0.03048448        0 0.08823243
#> 2 0.02848830        0 0.08418650
#> 3 0.02431123        0 0.07551496

# Make and format multiple models
model2 <- lm(qsec ~ gear * wt, mtcars)
my.models <- list(model, model2)
x <- nice_lm_slopes(my.models, predictor = "gear", moderator = "wt")
x
#>   Model Number Dependent Variable Predictor (+/-1 SD) df         b          t
#> 1            1                mpg       gear (LOW-wt) 28  7.540509  2.0106560
#> 2            1                mpg      gear (MEAN-wt) 28  5.615951  1.9437108
#> 3            1                mpg      gear (HIGH-wt) 28  3.691393  1.7955678
#> 4            2               qsec       gear (LOW-wt) 28 -1.933515 -0.8847558
#> 5            2               qsec      gear (MEAN-wt) 28 -1.742853 -1.0351610
#> 6            2               qsec      gear (HIGH-wt) 28 -1.552191 -1.2956736
#>            p        sr2 CI_lower   CI_upper
#> 1 0.05408136 0.03048448        0 0.08823243
#> 2 0.06204275 0.02848830        0 0.08418650
#> 3 0.08336403 0.02431123        0 0.07551496
#> 4 0.38382325 0.02280057        0 0.11642689
#> 5 0.30945179 0.03121151        0 0.14035904
#> 6 0.20566798 0.04889790        0 0.18442862
# Get interpretations
cbind(x, Interpretation = effectsize::interpret_r2_semipartial(x$sr2))
#>   Model Number Dependent Variable Predictor (+/-1 SD) df         b          t
#> 1            1                mpg       gear (LOW-wt) 28  7.540509  2.0106560
#> 2            1                mpg      gear (MEAN-wt) 28  5.615951  1.9437108
#> 3            1                mpg      gear (HIGH-wt) 28  3.691393  1.7955678
#> 4            2               qsec       gear (LOW-wt) 28 -1.933515 -0.8847558
#> 5            2               qsec      gear (MEAN-wt) 28 -1.742853 -1.0351610
#> 6            2               qsec      gear (HIGH-wt) 28 -1.552191 -1.2956736
#>            p        sr2 CI_lower   CI_upper Interpretation
#> 1 0.05408136 0.03048448        0 0.08823243          small
#> 2 0.06204275 0.02848830        0 0.08418650          small
#> 3 0.08336403 0.02431123        0 0.07551496          small
#> 4 0.38382325 0.02280057        0 0.11642689          small
#> 5 0.30945179 0.03121151        0 0.14035904          small
#> 6 0.20566798 0.04889790        0 0.18442862          small