visualize_c_map: Visualization for Estimating c Bias + Estimates

In ViSe: Visualizing Sensitivity

Visualization for Estimating c Bias + Estimates

Description

This function displays a visualization of the possible bias c that allows for a non-zero effect in sensitivity. This function includes the ability to add values of effect size and correlation to see how they map onto the proposed c value.

Usage

visualize_c_map(
  dlow,
  r_values,
  d_values = NULL,
  f_values = NULL,
  f2_values = NULL,
  nnt_values = NULL,
  prob_values = NULL,
  prop_u1_values = NULL,
  prop_u2_values = NULL,
  prop_u3_values = NULL,
  prop_overlap_values = NULL,
  point_colors = c("red", "green", "blue"),
  size = 2,
  shape_1 = 2,
  shape_2 = 3,
  ribbon_color = "lightblue",
  lower = TRUE
)

Arguments

dlow	The lower limit of the possible effect size (required).
r_values	A vector of correlation values that are possible (required).
d_values	A vector of effect size values that are possible.
f_values	A vector of f effect size values that are possible.
f2_values	A vector of f2 effect size values that are possible.
nnt_values	A vector of number needed to treat effect size values that are possible.
prob_values	A vector of probability of superiority effect size values that are possible.
prop_u1_values	A vector of proportion of overlap u1 effect size values that are possible.
prop_u2_values	A vector of proportion of overlap u2 effect size values that are possible.
prop_u3_values	A vector of proportion of overlap u3 effect size values that are possible.
prop_overlap_values	A vector of proportion of distribution overlap effect size values that are possible.
point_colors	A vector of color names or codes to plot the effect sizes on the graph. You should use as many color names/codes as you have max of an effect size (i.e, if r has 4, d has 3, and prob has 5, then use 5 as the max number of colors).
size	The size of the symbols on the chart.
shape_1	a numeric value of one of the ggplot2 shapes
shape_2	a numeric value of one of the ggplot2 shapes - if you use different numbers, the two shapes are overlaid, as we found this effect made it easier to read with many effect sizes plotted on the same graph.
ribbon_color	a color name or code to shade the area that shows a non-zero effect in sensitivity.
lower	Use this to indicate if you want the lower or upper bound of d for one sided confidence intervals. If d is positive, you generally want lower = TRUE, while negative d values should enter lower = FALSE for the upper bound that is closer to zero (required).

Value

Returns a pretty graph of the possible effect size and correlation combinations with the region of effect colored in. Note that all effect sizes are converted to d for the graph.

graph

The graph of possible values for c

Examples

visualize_c_map(dlow = .25,
  d_values = c(.2, .3, .8),
  r_values = c(.1, .4, .3),
  lower = TRUE)

ViSe documentation built on Oct. 10, 2024, 9:07 a.m.