nice_lm {rempsyc}

Nice formatting of lm models

Description

Formats output of lm() model object for a publication-ready format.

Usage

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

Arguments

model

The model to be formatted.

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 effectsize::r2_semipartial function for the effect size.

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.

Value

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

See Also

Checking simple slopes after testing for moderation: nice_lm_slopes, nice_mod, nice_slopes. Tutorial: https://rempsyc.remi-theriault.com/articles/moderation

Examples

# Make and format model
model <- lm(mpg ~ cyl + wt * hp, mtcars)
nice_lm(model)

# Make and format multiple models
model2 <- lm(qsec ~ disp + drat * carb, mtcars)
my.models <- list(model, model2)
x <- nice_lm(my.models)
x


# Get interpretations
cbind(x, Interpretation = effectsize::interpret_r2_semipartial(x$sr2))