visualize_c_map {ViSe}

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,
  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.

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)