Package {chiplot}

Chi-Plots for Assessing Bivariate and Multivariate Dependence

Description

The chi-plot (Fisher and Switzer, 1985, 2001) is a graphical aid to interpreting a scatterplot of two variables: under independence the points form a horizontal band, and different forms of association produce characteristically different patterns. Fisher and Lee (2026) extend the idea to multivariate data, to explore whether a multivariate distribution factorizes into independent blocks of variables.

Details

The main functions are:

chiplot

a chi-plot for two variables;

chipairs

a scatterplot matrix with the corresponding chi-plots above the diagonal;

chimult

chi- and lambda-values, permutation tests and the multivariate chi-plot for a given partition of the variables into blocks;

chipart

a search over all partitions of the variables, keeping those that show a significant departure from independence;

chidepth

the data depth functions used for the lambda-values of multivariate chi-plots.

The calculations of chi-values are carried out in compiled C code. All other code uses base R only.

Author(s)

Nicholas I. Fisher and Alan J. Lee. Maintainer: Nicholas I. Fisher <nif@valuemetrics.com.au>

References

Fisher, N. I. and Switzer, P. (1985). Chi-plots for assessing dependence. Biometrika, 72, 253–265. doi:10.1093/biomet/72.2.253

Fisher, N. I. and Switzer, P. (2001). Graphical assessment of dependence: is a picture worth a hundred tests? The American Statistician, 55, 233–239. doi:10.1198/000313001317098248

Fisher, N. I. and Lee, A. J. (2026). Chi-plots for assessing multivariate dependence. Submitted to Journal of Computational and Graphical Statistics.

Data Depth for Multivariate Chi-Plots

Description

Calculates the data depth of each row of a data matrix with respect to the whole sample. These depths are the basis of the lambda-values in chimult.

Usage

chidepth(X, depth = c("RP", "TD", "PD", "MhD"), Nsim = 100)

Arguments

Details

All the depth functions are implemented in base R:

"RP"

random projection depth: the average, over Nsim random directions, of the univariate halfspace depth of the projected data. The directions are generated as in fda.usc::mdepth.RP, so the results are identical for the same random number seed. This is the default.

"TD"

the average of the univariate halfspace (Tukey) depths of the coordinates, as fda.usc::mdepth.TD. Note that this is not the exact multivariate Tukey depth.

"PD"

projection depth (Zuo and Serfling, 2000), 1 / (1 + O(x)) with outlyingness O(x) = \max_u |u^T x - \mathrm{med}(u^T X)| / \mathrm{MAD}(u^T X), the maximum being taken over Nsim random directions.

"MhD"

Mahalanobis depth, 1 / (1 + d^2) with d the Mahalanobis distance from the mean, as fda.usc::mdepth.MhD. This is very fast but parametric.

The names "mdepth.RP", "mdepth.TD", "mdepth.MhD" and "Projection" are also accepted.

A user-specified depth must be a function of the data matrix that returns one depth per row, either as a numeric vector or as a list with component dep. For example, function(X) fda.usc::mdepth.RP(X, proj = 500)$dep, or a function calling depth() in package DepthProc.

Value

A numeric vector of depths, one for each row of X.

References

Zuo, Y. and Serfling, R. (2000). General notions of statistical depth function. The Annals of Statistics, 28, 461–482.

See Also

chimult

Examples

set.seed(1)
X <- matrix(rnorm(200), 100, 2)
d <- cbind(RP = chidepth(X, "RP"), TD = chidepth(X, "TD"),
           PD = chidepth(X, "PD"), MhD = chidepth(X, "MhD"))
round(cor(d, method = "spearman"), 2)

## a user-supplied depth: inverse distance from the coordinatewise median
chidepth(X[1:5, ], function(X) 1 / (1 + sqrt(rowSums(t(t(X) - apply(X, 2, median))^2))))

Multivariate Chi-Plot for a Partition of the Variables

Description

For a multivariate sample and a partition of the variables into blocks, chimult calculates the chi- and lambda-values of Fisher and Lee (2026), permutation tests of independence of the blocks, and the material for the multivariate chi-plot, which is drawn by plot.

Usage

chimult(Data, D, nperm = 100, Nsim = 100, depth = "RP", jitter = NULL,
        jitter.amount = 0.25, plot = FALSE, verbose = TRUE, ...)

## S3 method for class 'chimult'
plot(x, pc.level = 0.95, main = "", col1 = "red",
     col2 = "blue", pt = 12, box = TRUE, cex = NULL, mar = c(4, 4, 2, 2),
     guide.option = 1, del = 0.2, ...)

## S3 method for class 'chimult'
print(x, ...)

Arguments

Details

Observations with missing values are removed. Each column is centred at its median and divided by its interquartile range.

For each observation u_i, the generalized orthant is the set of v with v_j \le u_{ij} if u_{ij} > 0 and v_j > u_{ij} otherwise. With H_i the proportion of the sample in this orthant, and H_{1i}, \dots, H_{ki} the proportions in the corresponding orthants for the variables of each block, the chi-value is

\chi_i = \sqrt{n}\,(H_i - P_i) / \{P_i (1 - P_i S_i)\}^{1/2},

where P_i = \prod_l H_{li} and S_i = 1 + \sum_l (1 - H_{li}) / H_{li}. Under independence of the blocks the chi-values are approximately standard normal. The lambda-value of each observation is its data depth, transformed by the empirical distribution function of the depths.

The null distributions are estimated by permuting the rows of blocks 1, \dots, k-1, keeping the rows within each block together. sigP.chi is the proportion of permutations whose mean squared chi-value is at least that of the data; sigP.lam is the p-value of a two-sample Kolmogorov–Smirnov test comparing the data depths with those of the permuted data.

The plot shows the variables in each group, the chi-plot, and, if sigP.lam <= 1 - pc.level, a Q-Q plot of the data depths against their permutation distribution.

With the same random number seed, chimult gives the same results as the authors' original chimult.lam function with fda.usc::mdepth.RP.

Value

An object of class "chimult", a list with components

References

Fisher, N. I. and Lee, A. J. (2026). Chi-plots for assessing multivariate dependence. Submitted to Journal of Computational and Graphical Statistics.

See Also

chipart to search over partitions; chidepth; chiplot.

Examples

## Two independent blocks of two correlated normal variables
set.seed(161225)
n <- 200
z1 <- matrix(rnorm(2 * n), n) %*% chol(matrix(c(1, 0.5, 0.5, 1), 2))
z2 <- matrix(rnorm(2 * n), n) %*% chol(matrix(c(1, 0.7, 0.7, 1), 2))
Data <- data.frame(z1, z2)
colnames(Data) <- paste0("Var.", 1:4)
res <- chimult(Data, D = c(2, 2), nperm = 100)
res
plot(res)

## Dependence between the blocks
Data$Var.3 <- Data$Var.3 + 0.8 * Data$Var.1
res2 <- chimult(Data, D = c(2, 2), nperm = 100)
plot(res2, guide.option = 3)


## Five variables, mutually uncorrelated but with a hole in the data
set.seed(280926)
z <- matrix(rnorm(20000 * 5), ncol = 5)
z <- z[sqrt(rowSums((z - 1)^2)) >= 2.5, ][1:200, ]
res3 <- chimult(z, D = rep(1, 5), nperm = 500)
plot(res3, guide.option = 4, pt = 10)

Scatterplot Matrix with Chi-Plots

Description

A scatterplot matrix, like pairs, with the scatterplots below the diagonal and the corresponding chi-plots above the diagonal.

Usage

chipairs(Data, labels = NULL, ctrl.lines = 2, jitter = NULL,
         jitter.amount = 0.25, cex.points = NULL, pch.type = 19,
         dot.col = "black", cex.label = NULL, cex.axis = 0.7,
         plgap = 0.2, axes = TRUE, main = NULL, ...)

Arguments

Details

As in pairs, the panel in row i and column j has variable j on the horizontal axis and variable i on the vertical axis. Each pair of variables uses all the observations for which both are present. The chi-plot is symmetric in the two variables.

Value

NULL, invisibly.

See Also

chiplot, pairs

Examples

chipairs(geochem[, 1:5], main = "geochem")

## a smaller sample, with the points emphasised
chipairs(geochem[1:300, c("Cr2O3", "MgO", "CaO", "Ni")], cex.points = 0.5)

Search All Partitions of the Variables with Multivariate Chi-Plots

Description

chipart enumerates the partitions of the p variables into two or more blocks, runs chimult for each, and keeps the results for partitions where either test statistic has p-value less than SigProb. chi.partitions lists the partitions examined.

Usage

chipart(Data, kfix = 0, SigProb = 0.05, nperm = 100, Nsim = 100,
        depth = "RP", nblocks = NULL, nperm.screen = NULL,
        screen.level = 0.2, jitter = NULL, jitter.amount = 0.25,
        verbose = TRUE)

chi.partitions(p, kfix = 0, nblocks = NULL)

## S3 method for class 'chipart'
plot(x, which = NULL, ask = NULL, ...)

## S3 method for class 'chipart'
print(x, ...)

Arguments

Details

The number of partitions grows quickly with p: the Bell numbers are 1, 2, 5, 15, 52, 203, 877, 4140, 21147, 115975, .... The options kfix, nblocks, nperm.screen and a fast depth such as depth = "MhD" reduce the computing time.

For each partition the variables are reordered so that the blocks are contiguous, keeping the original order within each block, and chimult is called. A partition is “interesting” if sigP.chi or sigP.lam is less than SigProb. No allowance is made for multiple testing.

Value

chipart returns an object of class "chipart", a list with components

chi.partitions returns the matrix of partitions.

References

Fisher, N. I. and Lee, A. J. (2026). Chi-plots for assessing multivariate dependence. Submitted to Journal of Computational and Graphical Statistics.

See Also

chimult

Examples

## the partitions examined for 4 variables, and with the first kept fixed
chi.partitions(4)
chi.partitions(4, kfix = 1)

## four variables, of which the second and fourth are dependent
set.seed(5)
W <- matrix(rnorm(100 * 4), 100, 4)
W[, 4] <- W[, 4] + W[, 2]
colnames(W) <- paste0("V", 1:4)
res <- chipart(W, nperm = 20, verbose = FALSE)
res
plot(res, which = 3)


## screening with fewer permutations
res2 <- chipart(W, nperm = 200, nperm.screen = 20)
res2$summary

Chi-Plot for Assessing Dependence of Two Variables

Description

chivals calculates the chi- and lambda-values of Fisher and Switzer (1985) for a bivariate sample; chiplot calculates them and draws the chi-plot.

Usage

chiplot(x, y = NULL, ctrl.lines = 2, jitter = NULL, jitter.amount = 0.25,
        cex.points = NULL, pch.type = 19, dot.col = "black", main = "",
        cex.axis = 1, cex.lam = 1.2, plot = TRUE, ...)

chivals(x, y = NULL, jitter = NULL, jitter.amount = 0.25)

## S3 method for class 'chiplot'
plot(x, ctrl.lines = 2, cex.points = NULL, pch.type = 19,
     dot.col = "black", main = "", cex.axis = 1, cex.lam = 1.2,
     xlab = expression(lambda), ylab = expression(chi),
     col.ctrl = "red", lty.ctrl = 1, ...)

## S3 method for class 'chiplot'
print(x, ...)

Arguments

Details

Observations with a missing value in either variable are removed. For each point (x_i, y_i), let F_i, G_i and H_i be the proportions of the other n-1 points with x_j \le x_i, y_j \le y_i, and both, respectively. Then

\chi_i = (H_i - F_i G_i) / \{F_i (1 - F_i) G_i (1 - G_i)\}^{1/2},

\lambda_i = 4\,\mathrm{sign}(\tilde F_i \tilde G_i)\, \max(\tilde F_i^2, \tilde G_i^2),

where \tilde F_i = F_i - 1/2 and \tilde G_i = G_i - 1/2. Points with |\lambda_i| \ge 4\{1/(n-1) - 1/2\}^2, at the edges of the data, are not plotted. Under independence the points lie in a horizontal band about \chi = 0, approximately the stated percentage of them between the control lines at \pm c_p / \sqrt{n}, with c_p = 1.54, 1.78 and 2.18 for 90%, 95% and 99%.

As in the original program, the sine transformation of the chi-values mentioned by Fisher and Switzer (1985) is not used. The chi- and lambda-values depend only on the ranks of the data.

Value

An object of class "chiplot" (returned invisibly by chiplot): a list with components

References

Fisher, N. I. and Switzer, P. (1985). Chi-plots for assessing dependence. Biometrika, 72, 253–265. doi:10.1093/biomet/72.2.253

Fisher, N. I. and Switzer, P. (2001). Graphical assessment of dependence: is a picture worth a hundred tests? The American Statistician, 55, 233–239. doi:10.1198/000313001317098248

See Also

chipairs for all pairs of variables; chimult for blocks of variables.

Examples

set.seed(1)
op <- par(mfrow = c(2, 2))
## Independent normal variables
x <- rnorm(100); y <- rnorm(100)
chiplot(x, y, main = "Independence")
## Bivariate normal, correlation 0.7
y <- 0.7 * x + sqrt(1 - 0.7^2) * rnorm(100)
chiplot(x, y, main = "Correlation 0.7")
## Non-monotone association: points near a half circle
x <- rnorm(2000); y <- rnorm(2000)
keep <- y >= 0 & abs(x^2 + y^2 - 1) <= 0.5
chiplot(x[keep][1:100], y[keep][1:100], main = "Half ring")
## Ordered categorical variable: jittered automatically
u <- rnorm(100)
grade <- cut(u + rnorm(100), c(-Inf, -1, 0, 1, Inf), ordered_result = TRUE)
res <- chiplot(u, grade, main = "Ordered categories")
par(op)
res

Geochemical Measurements

Description

Measurements of 11 geochemical constituents on 1000 samples, used to illustrate chipairs. The values are the ranks of the measurements within each variable, with tied values given their average rank.

Usage

geochem

Format

A data frame with 1000 rows and 11 numeric columns: TiO2, Cr2O3, FeO, MnO, MgO, CaO, Ni, Zn, Ga, Y and Zr.

Source

ValueMetrics Australia. Included with the permission of the copyright holder.

Examples

summary(geochem[, 1:4])
chipairs(geochem[, c("Cr2O3", "MgO", "Ni")])