multiple_regression {kim} | R Documentation |
Multiple regression
Description
Conduct multiple regression analysis and summarize the results in a data.table.
Usage
multiple_regression(
data = NULL,
formula = NULL,
vars_to_mean_center = NULL,
mean_center_vars = NULL,
sigfigs = NULL,
round_digits_after_decimal = NULL,
round_p = NULL,
pretty_round_p_value = TRUE,
return_table_upper_half = FALSE,
round_r_squared = 3,
round_f_stat = 2,
prettify_reg_table_col_names = TRUE,
silent = FALSE
)
Arguments
data |
a data object (a data frame or a data.table) |
formula |
a formula object for the regression equation |
vars_to_mean_center |
(deprecated) a character vector specifying names of variables that will be mean-centered before the regression model is estimated |
mean_center_vars |
a character vector specifying names of variables that will be mean-centered before the regression model is estimated |
sigfigs |
number of significant digits to round to |
round_digits_after_decimal |
round to nth digit after decimal
(alternative to |
round_p |
number of decimal places to round p values (overrides all other rounding arguments) |
pretty_round_p_value |
logical. Should the p-values be rounded
in a pretty format (i.e., lower threshold: "<.001").
By default, |
return_table_upper_half |
logical. Should only the upper part
of the table be returned?
By default, |
round_r_squared |
number of digits after the decimal both r-squared and adjusted r-squared values should be rounded to (default 3) |
round_f_stat |
number of digits after the decimal the f statistic of the regression model should be rounded to (default 2) |
prettify_reg_table_col_names |
logical. Should the column names
of the regression table be made pretty (e.g., change "std_beta" to
"Std. Beta")? (Default = |
silent |
If |
Details
To include standardized beta(s) in the regression results table, the following package(s) must be installed prior to running the function: Package 'lm.beta' v1.5-1 (or possibly a higher version) by Stefan Behrendt (2014), https://cran.r-project.org/package=lm.beta
Value
the output will be a data.table showing multiple regression results.
Examples
multiple_regression(data = mtcars, formula = mpg ~ gear * cyl)
multiple_regression(
data = mtcars, formula = mpg ~ gear * cyl,
mean_center_vars = "gear",
round_digits_after_decimal = 2)