Floodlight 2 by Continuous for a Logistic Regression
Conduct a floodlight analysis for a logistic regression with a 2 x Continuous design involving a binary dependent variable.
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_label_add = TRUE, 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 )
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_label_add: logical. Should the labels for Johnson-Neyman point labels be added to the plot? (default = TRUE)
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)
See the following reference(s): Spiller et al. (2013) tools:::Rd_expr_doi("10.1509/jmr.12.0420")
Kim (2023) https://jinkim.science/docs/floodlight.pdf
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)
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