heatmap.qad | R Documentation |
The pairwise computed dependence measures (output of the function pairwise.qad()
) are illustrated by a heatmap.
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 = "" )
pw_qad |
output of the function |
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 |
If the output of pairwise.qad
() contains p-values, significant values can be highlighted by stars by setting significance=TRUE.
a heatmap
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)
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