heatmap.qad {qad}

Heatmap of dependence measures

Description

The pairwise computed dependence measures (output of the function pairwise.qad()) are illustrated by a heatmap.

Usage

heatmap.qad(
  pw_qad,
  select = c("dependence", "max.dependence", "asymmetry"),
  fontsize = 4,
  significance = FALSE,
  use_p.adjust = TRUE,
  sign.level = 0.05,
  scale = "abs",
  color = "plasma",
  white_font = 0.7,
  rb_values = c(10, 0.315, 0.15),
  title = ""
)

Arguments

pw_qad

output of the function pairwise.qad().

select

a character indicating which dependence value is plotted. Options are c("dependence", "max.dependence", "asymmetry").

fontsize

a numeric specifying the font size of the values.

significance

a logical indicating whether significant values with respect to the (adjusted) qad p.values are denoted by a star.

use_p.adjust

a logical indicating if the adjusted p.values are used (default = TRUE).

sign.level

numeric value indicating the significance level.

scale

character indicating whether the heatmap uses a relative or absolute scale. Options are 'rel' or 'abs' (default).

color

Select the color palette. Options are c("plasma" (default), "viridis", "inferno", "magma", "cividis", "rainbow").

white_font

numeric between 0 and 1 denoting the start value for white text font (default = 0.7)

rb_values

a vector of size 3 with number of values, start value and end value in the rainbow colors space (if color = 'rainbow').

title

The text for the title

Details

If the output of pairwise.qad() contains p-values, significant values can be highlighted by stars by setting significance=TRUE.

Value

a heatmap

Examples

n <- 100
x1 <- runif(n, 0, 1)
x2 <- x1^2 + rnorm(n, 0, 0.1)
x3 <- runif(n, 0, 1)
x4 <- x3 - x2 + rnorm(n, 0, 0.1)
sample_df <- data.frame(x1,x2,x3,x4)
#Fit qad
model <- pairwise.qad(sample_df, p.value = FALSE)
heatmap.qad(model, select = "dependence", fontsize = 6)