shadings: Shading-generating Functions for Residual-based Shadings

In vcd: Visualizing Categorical Data

Shading-generating Functions for Residual-based Shadings

Description

Shading-generating functions for computing residual-based shadings for mosaic and association plots.

Usage

shading_hcl(observed, residuals = NULL, expected = NULL, df = NULL,
  h = NULL, c = NULL, l = NULL, interpolate = c(2, 4), lty = 1,
  eps = NULL, line_col = "black", p.value = NULL, level = 0.95, ...)

shading_hsv(observed, residuals = NULL, expected = NULL, df = NULL,
  h = c(2/3, 0), s = c(1, 0), v = c(1, 0.5),
  interpolate = c(2, 4), lty = 1, eps = NULL, line_col = "black",
  p.value = NULL, level = 0.95, ...)

shading_max(observed = NULL, residuals = NULL, expected = NULL, df = NULL,
  h = NULL, c = NULL, l = NULL, lty = 1, eps = NULL, line_col = "black",
  level = c(0.9, 0.99), n = 1000, ...)

shading_Friendly(observed = NULL, residuals = NULL, expected = NULL, df = NULL,
  h = c(2/3, 0), lty = 1:2, interpolate = c(2, 4),
  eps = 0.01, line_col = "black", ...)

shading_Friendly2(observed = NULL, residuals = NULL,
  expected = NULL, df = NULL, lty = 1:2, interpolate = c(2, 4), eps =
  0.01, line_col = "black", ...)

shading_sieve(observed = NULL, residuals = NULL, expected = NULL, df = NULL,
  h = c(260, 0), lty = 1:2, interpolate = c(2, 4),
  eps = 0.01, line_col = "black", ...)

shading_binary(observed = NULL, residuals = NULL, expected = NULL, df = NULL,
  col = NULL)

shading_Marimekko(x, fill = NULL, byrow = FALSE)

shading_diagonal(x, fill = NULL)

hcl2hex(h = 0, c = 35, l = 85, fixup = TRUE)

Arguments

observed	contingency table of observed values
residuals	contingency table of residuals
expected	contingency table of expected values
df	degrees of freedom of the associated independence model.
h	hue value in the HCL or HSV color description, has to be in [0, 360] for HCL and in [0, 1] for HSV colors. The default is to use blue and red for positive and negative residuals respectively. In the HCL specification it is c(260, 0) by default and for HSV c(2/3, 0).
c	chroma value in the HCL color description. This controls the maximum chroma for significant and non-significant results respectively and defaults to c(100, 20).
l	luminance value in the HCL color description. Defaults to c(90, 50) for small and large residuals respectively.
s	saturation value in the HSV color description. Defaults to c(1, 0) for large and small residuals respectively.
v	saturation value in the HSV color description. Defaults to c(1, 0.5) for significant and non-significant results respectively.
interpolate	a specification for mapping the absolute size of the residuals to a value in [0, 1]. This can be either a function or a numeric vector. In the latter case, a step function with steps of equal size going from 0 to 1 is used.
lty	a vector of two line types for positive and negative residuals respectively. Recycled if necessary.
eps	numeric tolerance value below which absolute residuals are considered to be zero, which is used for coding the border color and line type. If set to NULL (default), all borders have the default color specified by line\_col. If set to a numeric value, all border colors corresponding to residuals with a larger absolute value are set to the full positive or negative color, respectively; borders corresponding to smaller residuals are are drawn with line\_col and lty[1]

. This is used principally in shading\_Friendly.

line_col	default border color (for shading_sieve: default sieve color).
p.value	the p value associated with the independence model. By default, this is computed from a Chi-squared distribution with df degrees of freedom. p.value can be either a scalar or a function(observed, residuals, expected, df) that computes the p value from the data. If set to NA no inference is performed.
level	confidence level of the test used. If p.value is smaller than 1 - level, bright colors are used, otherwise dark colors are employed. For shading_max a vector of levels can be supplied. The corresponding critical values are then used as interpolate cut-offs.
n	number of permutations used in the call to coindep_test.
col	a vector of two colors for positive and negative residuals respectively.
fixup	logical. Should the color be corrected to a valid RGB value before correction?
x	object of class table used to determine the dimension.
fill	Either a character vector of color codes, or a palette function that generates such a vector. Defaults to rainbow_hcl
byrow	logical; shall tiles be filled by row or by column?
...	Other arguments passed to hcl2hex or hsv, respectively.

Details

These shading-generating functions can be passed to strucplot to generate residual-based shadings for contingency tables. strucplot calls these functions with the arguments observed, residuals, expected, df which give the observed values, residuals, expected values and associated degrees of freedom for a particular contingency table and associated independence model.

The shadings shading_hcl and shading_hsv do the same thing conceptually, but use HCL or HSV colors respectively. The former is usually preferred because they are perceptually based. Both shadings visualize the sign of the residuals of an independence model using two hues (by default: blue and red). The absolute size of the residuals is visualized by the colorfulness and the amount of grey, by default in three categories: very colorful for large residuals (> 4), less colorful for medium sized residuals (< 4 and > 2), grey/white for small residuals (< 2). More categories or a continuous scale can be specified by setting interpolate. Furthermore, the result of a significance test can be visualized by the amount of grey in the colors. If significant, a colorful palette is used, if not, the amount of color is reduced. See Zeileis, Meyer, and Hornik (2007) and diverge_hcl for more details.

The shading shading_max is applicable in 2-way contingency tables and uses a similar strategy as shading_hcl. But instead of using the cut-offs 2 and 4, it employs the critical values for the maximum statistic (by default at 90% and 99%). Consequently, color in the plot signals a significant result at 90% or 99% significance level, respectively. The test is carried out by calling coindep_test.

The shading shading_Friendly is very similar to shading_hsv, but additionally codes the sign of the residuals by different line types. See Friendly (1994) for more details. shading_Friendly2 and shading_sieve are similar, but use HCL colors.

The shading shading_binary just visualizes the sign of the residuals by using two different colors (default: blue HCL(260, 50, 70) and red HCL(0, 50, 70)).

shading_Marimekko is a simple generating function for producing, in conjunction with mosaic, so-called Marimekko-charts, which paint the tiles of each columns of a mosaic display in the same color to better display departures from independence.

shading_diagonal generates a color shading for basically square matrices (or arrays having the first two dimensons of same length) visualizing the diagonal cells, and the off-diagonal cells 1, 2, ... steps removed.

The color implementations employed are hsv from base R and polarLUV from the colorspace package, respectively. To transform the HCL coordinates to a hexadecimal color string (as returned by hsv), the function hex is employed. A convenience wrapper hcl2hex is provided.

Value

A shading function which takes only a single argument, interpreted as a vector/table of residuals, and returns a "gpar" object with the corresponding vector(s)/table(s) of graphical parameter(s).

Author(s)

Achim Zeileis [e-mail]

References

Friendly M. (1994), Mosaic Displays for Multi-Way Contingency Tables. Journal of the American Statistical Association, 89, 190–200.

Meyer D., Zeileis A., and Hornik K. (2006), The Strucplot Framework: Visualizing Multi-Way Contingency Tables with vcd. Journal of Statistical Software, 17(3), 1–48. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v017.i03")}. See also vignette("strucplot", package = "vcd").

Zeileis A., Meyer D., Hornik K. (2007), Residual-Based Shadings for Visualizing (Conditional) Independence. Journal of Computational and Graphical Statistics, 16, 507–525.

Zeileis A., Hornik K. and Murrell P. (2008), Escaping RGBland: Selecting Colors for Statistical Graphics. Computational Statistics & Data Analysis, 53, 3259–3270. Preprint available from https://www.zeileis.org/papers/Zeileis+Hornik+Murrell-2009.pdf.

See Also

hex, polarLUV, hsv, mosaic, assoc, strucplot, diverge_hcl

Examples

## load Arthritis data
data("Arthritis")
art <- xtabs(~Treatment + Improved, data = Arthritis)

## plain mosaic display without shading
mosaic(art)

## with shading for independence model
mosaic(art, shade = TRUE)
## which uses the HCL shading
mosaic(art, gp = shading_hcl)
## the residuals are too small to have color,
## hence the cut-offs can be modified
mosaic(art, gp = shading_hcl, gp_args = list(interpolate = c(1, 1.8)))
## the same with the Friendly palette 
## (without significance testing)
mosaic(art, gp = shading_Friendly, gp_args = list(interpolate = c(1, 1.8)))

## assess independence using the maximum statistic
## cut-offs are now critical values for the test statistic
mosaic(art, gp = shading_max)

## association plot with shading as in base R
assoc(art, gp = shading_binary(col = c(1, 2)))

## Marimekko Chart
hec <- margin.table(HairEyeColor, 1:2)
mosaic(hec, gp = shading_Marimekko(hec))
mosaic(HairEyeColor, gp = shading_Marimekko(HairEyeColor))

## Diagonal cells shading
ac <- xtabs(VisualAcuity)
mosaic(ac, gp = shading_diagonal(ac))

vcd documentation built on Sept. 17, 2024, 1:08 a.m.