floodlight_2_by_continuous_logistic: Floodlight 2 by Continuous for a Logistic Regression

In kim: A Toolkit for Behavioral Scientists

Floodlight 2 by Continuous for a Logistic Regression

Description

Conduct a floodlight analysis for a logistic regression with a 2 x Continuous design involving a binary dependent variable.

Usage

floodlight_2_by_continuous_logistic(
  data = NULL,
  iv_name = NULL,
  dv_name = NULL,
  mod_name = NULL,
  interaction_p_include = TRUE,
  iv_level_order = NULL,
  dv_level_order = NULL,
  jn_points_disregard_threshold = NULL,
  output = "reg_lines_plot",
  num_of_spotlights = 20,
  jitter_x_percent = 0,
  jitter_y_percent = 5,
  dot_alpha = 0.3,
  dot_size = 6,
  interaction_p_value_font_size = 8,
  jn_point_font_size = 8,
  jn_point_label_hjust = NULL,
  interaction_p_vjust = -3,
  plot_margin = ggplot2::unit(c(75, 7, 7, 7), "pt"),
  legend_position = "right",
  line_types_for_pred_values = c("solid", "dashed"),
  line_thickness_for_pred_values = 2.5,
  jn_line_types = c("solid", "solid"),
  jn_line_thickness = 1.5,
  sig_region_color = "green",
  sig_region_alpha = 0.08,
  nonsig_region_color = "gray",
  nonsig_region_alpha = 0.08,
  x_axis_title = NULL,
  y_axis_title = NULL,
  legend_title = NULL,
  round_decimals_int_p_value = 3,
  round_jn_point_labels = 2
)

Arguments

data	a data object (a data frame or a data.table)
iv_name	name of the binary independent variable
dv_name	name of the binary dependent variable
mod_name	name of the continuous moderator variable
interaction_p_include	logical. Should the plot include a p-value for the interaction term?
iv_level_order	order of levels in the independent variable for legend. By default, it will be set as levels of the independent variable ordered using R's base function sort.
dv_level_order	order of levels in the dependent variable. By default, it will be set as levels of the dependent variable ordered using R's base function sort.
jn_points_disregard_threshold	the Minimum Distance in the unit of the moderator variable that will be used for various purposes, such as (1) to disregard the second Johnson-Neyman point that is different from the first Johnson-Neyman (JN) point by less than the Minimum Distance; (2) to determine regions of significance, which will calculate the p-value of the IV's effect (the focal dummy variable's effect) on DV at a candidate JN point + / - the Minimum Distance. This input is hard to explain, but a user can enter a really low value for this argument (e.g., jn_points_disregard_threshold = 0.1 for a moderator measured on a 100-point scale) or use the default. By default, jn_points_disregard_threshold = range of the moderator / 10000 For example, if the observed moderator values range from 1 to 7 (because it is a 7-point scale), then jn_points_disregard_threshold = (7 - 1) / 10000 = 0.0006
output	type of output (default = "reg_lines_plot"). Possible inputs: "interactions_pkg_results", "simple_effects_plot", "jn_points", "regions", "reg_lines_plot"
num_of_spotlights	How many spotlight analyses should be conducted to plot the predicted values at various values of the moderator? (default = 20)
jitter_x_percent	horizontally jitter dots by a percentage of the range of x values (default = 0)
jitter_y_percent	vertically jitter dots by a percentage of the range of y values (default = 5)
dot_alpha	opacity of the dots (0 = completely transparent, 1 = completely opaque). By default, dot_alpha = 0.3
dot_size	size of the dots (default = 6)
interaction_p_value_font_size	font size for the interaction p value (default = 8)
jn_point_font_size	font size for Johnson-Neyman point labels (default = 8)
jn_point_label_hjust	a vector of hjust values for Johnson-Neyman point labels. By default, the hjust value will be 0.5 for all the points.
interaction_p_vjust	By how much should the label for the interaction p-value be adjusted vertically? By default, interaction_p_vjust = -3)
plot_margin	margin for the plot By default plot_margin = ggplot2::unit(c(75, 7, 7, 7), "pt")
legend_position	position of the legend (default = "right"). If legend_position = "none", the legend will be removed.
line_types_for_pred_values	types of the lines for plotting the predicted values By default, line_types_for_pred_values = c("solid", "dashed")
line_thickness_for_pred_values	thickness of the lines for plotting the predicted values (default = 2.5)
jn_line_types	types of the lines for Johnson-Neyman points. By default, jn_line_types = c("solid", "solid")
jn_line_thickness	thickness of the lines at Johnson-Neyman points (default = 1.5)
sig_region_color	color of the significant region, i.e., range(s) of the moderator variable for which simple effect of the independent variable on the dependent variable is statistically significant.
sig_region_alpha	opacity for sig_region_color. (0 = completely transparent, 1 = completely opaque). By default, sig_region_alpha = 0.08
nonsig_region_color	color of the non-significant region, i.e., range(s) of the moderator variable for which simple effect of the independent variable on the dependent variable is not statistically significant.
nonsig_region_alpha	opacity for nonsig_region_color. (0 = completely transparent, 1 = completely opaque). By default, nonsig_region_alpha = 0.08
x_axis_title	title of the x axis. By default, it will be set as input for mod_name. If x_axis_title = FALSE, it will be removed.
y_axis_title	title of the y axis. By default, it will be set as input for dv_name. If y_axis_title = FALSE, it will be removed.
legend_title	title of the legend. By default, it will be set as input for iv_name. If legend_title = FALSE, it will be removed.
round_decimals_int_p_value	To how many digits after the decimal point should the p value for the interaction term be rounded? (default = 3)
round_jn_point_labels	To how many digits after the decimal point should the jn point labels be rounded? (default = 2)

Details

See the following reference(s): Spiller et al. (2013) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1509/jmr.12.0420")} Kim (2023) https://jinkim.science/docs/floodlight.pdf

Examples

floodlight_2_by_continuous_logistic(
data = mtcars,
iv_name = "am",
dv_name = "vs",
mod_name = "mpg")
# adjust the number of spotlights
# (i.e., predict values at only 4 values of the moderator)
floodlight_2_by_continuous_logistic(
data = mtcars,
iv_name = "am",
dv_name = "vs",
mod_name = "mpg",
num_of_spotlights = 4)

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