Extracts simple slopes from lm() model
object and format for a publication-ready format.
nice_lm_slopes(
model,
predictor,
moderator,
b.label = "b",
standardize = FALSE,
mod.id = TRUE,
ci.alternative = "two.sided",
...
)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.
The model to be formatted.
The independent variable.
The moderating variable.
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.
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.
Logical. Whether to display the model number, when there is more than one model.
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.
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.
Checking for moderation before checking simple slopes:
nice_lm, nice_mod,
nice_slopes. Tutorial:
https://rempsyc.remi-theriault.com/articles/moderation