Association Plots

Association plots in R. An association plot draws the results of an association test by charting the Pearson Residuals.

Association plots in R are drawn using assocplot()

assocplot(x, col = c("black", "red"), space = 0.3,
          main = NULL, xlab = NULL, ylab = NULL)

Parameter	Explanation
x	the data, usually a numeric matrix.
col	colors for positive and negative associations.
space	amount of space between the bars, as a fraction of average bar height and width (default = 0.3).
main, xlab, ylab	title annotations.

 

Essentially you need a 2-dimensional matrix to use assocplot():

VADeaths

      Rural Male Rural Female Urban Male Urban Female
50-54       11.7          8.7       15.4          8.4
55-59       18.1         11.7       24.3         13.6
60-64       26.9         20.3       37.0         19.3
65-69       41.0         30.9       54.6         35.1
70-74       66.0         54.3       71.1         50.0

Apart from the titles, the only graphical parameter you can alter directly is col, to alter the positive and negative bar colors:

assocplot(VADeaths, col = c("lightblue", "pink"),
	  xlab = "Age class", ylab = "Driver actegory")

Graphical parameters

If you want to alter the general appearance of your association plot you’ll need to set the appropriate graphical parameters using par() before using assocplot():

opar <- par(las = 1, cex = 0.8, mar = c(5,7,2,1))

assocplot(VADeaths, col = c("blue", "tomato"),
          space = 0.05, xlab = "Age class")
title(ylab = "Driver category", line = 6)

par(opar)

In the preceding example the margins were widened to allow the labels to “fit”. Note also how title() was used to place the y-axis annotation on an outer line.

Data layout

Essentially you need a 2D matrix for assocplot() to make an association plot in R. If you have something else you need to coerce it to the correct form.

Here are some options:

• data.frame use as.matrix() to alter the form.
• table use x[r, c, n, ...] to “pick out” the appropriate 2D sub-table or..
• table use margin.table to “collapse” a table and combine across the margins you want.

# 3D table
HairEyeColor

, , Sex = Male

       Eye
Hair    Brown Blue Hazel Green
  Black    32   11    10     3
  Brown    53   50    25    15
  Red      10   10     7     7
  Blond     3   30     5     8

, , Sex = Female

       Eye
Hair    Brown Blue Hazel Green
  Black    36    9     5     2
  Brown    66   34    29    14
  Red      16    7     7     7
  Blond     4   64     5     8

# Choose "Male"
HairEyeColor[,,1]

       Eye
Hair    Brown Blue Hazel Green
  Black    32   11    10     3
  Brown    53   50    25    15
  Red      10   10     7     7
  Blond     3   30     5     8

# Combine "Male" and "Female"
margin.table(HairEyeColor, margin = c(1,2))

       Eye
Hair    Brown Blue Hazel Green
  Black    68   20    15     5
  Brown   119   84    54    29
  Red      26   17    14    14
  Blond     7   94    10    16

# Combine "Eye"
margin.table(HairEyeColor, margin = c(1,3))

       Sex
Hair    Male Female
  Black   56     52
  Brown  143    143
  Red     34     37
  Blond   46     81

Alternatives to assocplot()

The assocplot() function is not the only was to draw an association plot using R. You could run a chisq.test() and extract the Pearson residuals $residuals, which you then plot using barplot().

X <- chisq.test(VADeaths)
X$residuals

         Rural Male Rural Female Urban Male Urban Female
50-54 -0.0001229145  -0.09956533  0.2454344  -0.21106734
55-59  0.0422284686  -0.56107962  0.4550546  -0.06391943
60-64 -0.0951496863  -0.16808112  0.5368919  -0.40335827
65-69 -0.2718462679  -0.34870589  0.2349807   0.36003546
70-74  0.2624133483   0.73510055 -0.8898149   0.09370444

barplot(X$residuals, beside = TRUE, col = cm.colors(5),
        ylim = c(-1,1), legend = TRUE,
	args.legend = list(x = "top", bty = "n", ncol = 5))
title(ylab = "Pearson residuals", xlab = "Category")

To get multiple rows, with a separate mini-plot for each row you would need to set-up par(mfrow = c(rows, cols)).

There are potential advantages to this method, for example you can add horizontal lines at +/- 2 to show the “significance band”. However, it is also somewhat more involved!

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