logistic_regression_table: Logistic regression table

In kim: A Toolkit for Behavioral Scientists

Logistic regression table

Description

Construct a table of logistic regression results from the given glm object estimating a logistic regression model.

Usage

logistic_regression_table(
  logistic_reg_glm_object = NULL,
  z_values_keep = FALSE,
  constant_row_clean = TRUE,
  odds_ratio_cols_combine = TRUE,
  round_b_and_se = 3,
  round_z = 3,
  round_p = 3,
  round_odds_ratio = 3,
  round_r_sq = 3,
  round_model_chi_sq = 3,
  pretty_round_p_value = TRUE
)

Arguments

logistic_reg_glm_object	a glm object estimating a logistic regression model
z_values_keep	logical. Should the z values be kept in the table? (default = FALSE)
constant_row_clean	logical. Should the row for the constant be cleared except for b and standard error of b? (default = TRUE)
odds_ratio_cols_combine	logical. Should the odds ratio columns be combined? (default = TRUE)
round_b_and_se	number of decimal places to which to round b and standard error of b (default = 3)
round_z	number of decimal places to which to round z values (default = 3)
round_p	number of decimal places to which to round p-values (default = 3)
round_odds_ratio	number of decimal places to which to round odds ratios (default = 3)
round_r_sq	number of decimal places to which to round R-squared values (default = 3)
round_model_chi_sq	number of decimal places to which to round model chi-squared values (default = 3)
pretty_round_p_value	logical. Should the p-values be rounded in a pretty format (i.e., lower threshold: "<.001"). By default, pretty_round_p_value = TRUE.

Value

the output will be a summary of logistic regression results.

Examples

logistic_regression_table(logistic_reg_glm_object =
glm(formula = am ~ mpg, family = binomial(), data = mtcars))
logistic_regression_table(logistic_reg_glm_object =
glm(formula = am ~ mpg, family = binomial(), data = mtcars),
z_values_keep = TRUE, constant_row_clean = FALSE,
odds_ratio_cols_combine = FALSE)

kim documentation built on Oct. 9, 2023, 5:08 p.m.