Version: 1.4.0
Date: 2025-03-21
Title: The Generalized Pairs Plot
Imports: grid, barcode, lattice, vcd, MASS, colorspace, methods
Enhances: YaleToolkit
Description: Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <doi:10.1080/10618600.2012.694762>.
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Copyright: (C) 2025 John W. Emerson and Walton A. Green
NeedsCompilation: no
Packaged: 2025-03-21 12:46:15 UTC; jay
Author: John W. Emerson [aut, cre], Walton A. Green [aut], Liya Xiang [ctb]
Maintainer: John W. Emerson <john.emerson@yale.edu>
Repository: CRAN
Date/Publication: 2025-03-21 13:20:16 UTC

Morphological descriptions of leaf floras

Description

Measurements of the percentages of leaves in 31 morphological (or architectural) categories found in 245 leaf floras from 4 studies.

Usage

data(Leaves)

Format

A data frame with 245 observations on the following 33 variables.

Lobd

a numeric vector giving percentage Lobed leaves

Entr

a numeric vector giving percentage Entire leaves

TReg

a numeric vector giving percentage leaves with Regular Teeth

TCls

a numeric vector giving percentage leaves with Close Teeth

TRnd

a numeric vector giving percentage leaves with Round Teeth

TAcu

a numeric vector giving percentage leaves Acute Teeth

TCmp

a numeric vector giving percentage leaves with Compound Teeth

ZNan

a numeric vector giving percentage Nanophyll leaves

ZLe1

a numeric vector giving percentage Leptophyll1 leaves

ZLe2

a numeric vector giving percentage Leptophyll2 leaves

ZMi1

a numeric vector giving percentage Microphyll1 leaves

ZMi2

a numeric vector giving percentage Microphyll2 leaves

ZMi3

a numeric vector giving percentage Microphyll3 leaves

ZMe1

a numeric vector giving percentage Megaphyll1 leaves

ZMe2

a numeric vector giving percentage Megaphyll2 leaves

ZMe3

a numeric vector giving percentage Megaphyll3 leaves

AEmg

a numeric vector giving percentage leaves with Emarginate Apexes

ARnd

a numeric vector giving percentage leaves with Round Apexes

AAcu

a numeric vector giving percentage leaves with Acute Apexes

AAtn

a numeric vector giving percentage leaves with Attenuate Apexes

BCor

a numeric vector giving percentage leaves with Cordate Bases

BRnd

a numeric vector giving percentage leaves with Round Bases

BAcu

a numeric vector giving percentage leaves with Acute Bases

Rlt1

a numeric vector giving percentage leaves with aspect ratio less than 1:1 (i.e. wider than long)

Rb12

a numeric vector giving percentage leaves with aspect ratio between 1:1 and 1:2

Rb23

a numeric vector giving percentage leaves with aspect ratio between 1:2 and 1:3

Rb34

a numeric vector giving percentage leaves with aspect ratio between 1:3 and 1:4

Rgt4

a numeric vector giving percentage leaves with aspect ratio between greater than 1:4

SObo

a numeric vector giving percentage Obovate leaves

SElp

a numeric vector giving percentage Elliptical leaves

SOvt

a numeric vector giving percentage Ovate leaves

MAT

a numeric vector giving mean annual temperature in degrees Centigrade

Study

a factor with levels Wolfe173 Jacobs Gregory Kowalski

Details

Data consists of a data frame with 245 rows and 33 columns (variables). The rows represent floras (collections of plants from a defined locality); the first 31 variables are percentages of leaves in each flora in each of 31 morphological categories; the 32nd variable is mean annual temperature of the area from which the floras was collected in degrees C, and the 32nd is a factor indicating which of 4 published studies the floras come from. See cited publications for more details.

Source

Green, W. A. (2006) Loosening the CLAMP: An exploratory graphical approach to the Climate Leaf Analysis Multivariate Program Palaeontologia Electronica 9(2):9A.

References

Gregory-Wodzicki, K. M. (2000) Relationships between leaf morphology and climate, Bolivia: implications for estimating paleoclimate from fossil floras. Paleobiology 26(4):668–688.

Jacobs, B. F. (1999) Estimation of rainfall variables from leaf characters in tropical Africa. Palaeogeography, Palaeoclimatology, Palaeoecology 145:231–250.

Jacobs, B. F. (2002) Estimation of low-latitude paleoclimates using fossil angiosperm leaves: examples from the Miocene Tugen Hills, Kenya. Paleobiology 28(3):399–421.

Kowalski, E. A. (2002) Mean annual temperature estimation base on leaf morphology: a test from tropical South America. Palaeogeography, Palaeoclimatology, Palaeoecology 188:141–165.

Wolfe, J.A., (1993), A method of obtaining climatic parameters from leaf assemblages. U.S. Geological Survey Bulletin 2040, 73 pp.

Examples

data(Leaves)
## maybe str(Leaves) ; plot(Leaves) ...

Generalized Pairs Plots

Description

Produces a matrix of plots showing pairwise relationships between quantitative and categorical variables in a complex data set.

Usage

gpairs(x,
       upper.pars = list(scatter = "points",
                         conditional = "barcode",
                         mosaic = "mosaic"),
       lower.pars = list(scatter = "points",
                         conditional = "boxplot",
                         mosaic = "mosaic"),
       diagonal = "default",
       outer.margins = list(bottom = unit(2, "lines"), 
                            left = unit(2, "lines"), 
                            top = unit(2, "lines"), 
                            right = unit(2, "lines")), 
       xylim = NULL,
       outer.labels = NULL, outer.rot = c(0, 90), gap = 0.05, 
       buffer = 0.02, reorder = NULL, cluster.pars = NULL, 
       stat.pars = NULL, scatter.pars = NULL, 
       bwplot.pars = NULL, stripplot.pars = NULL, barcode.pars=NULL,
       mosaic.pars = NULL, axis.pars = NULL, diag.pars = NULL, 
       whatis = FALSE)

corrgram(x)

Arguments

x

a data frame (or matrix the relationships between whose columns are to be examined). Any combination of quantitative and categorical variables is acceptable.

upper.pars

see Details

lower.pars

see Details

diagonal

by default, the diagonal from the top left to the bottom right is used for displaying the variable names (and, in our version, the marginal distributions of the variables); diagonal="other" will place the diagonal running from the top right down to the bottom left.

outer.margins

a list of length 4 with units as components named bottom, left, top, and right, giving the outer margins; the default uses two lines of text. A vector of length 4 with units (ordered properly) will work, as will a vector of length 4 with numeric values (interpreted as lines).

xylim

optionally specify a single range to be used as xlim and ylim where appropriate. Note that if this option causes cropping, it will fail to work in barcode panels.

outer.labels

the default is NULL, for alternating axis labels around the perimeter. If "all", all labels are printed, and if "none" no labels are printed.

outer.rot

a 2-vector (x, y) rotating the top/bottom outer labels x degrees and the left/right outer labels y degrees. Only works for categorical labels of boxplot and mosaic panels.

gap

the gap between the tiles; defaulting to 0.05 of the width of a tile.

buffer

the fraction by which to expand the range of quantitative variables to provide plots that will not truncate plotting symbols. Defaults to 0 percent of range currently.

reorder

currently only support for the string "cluster", which reorders the columns according to the output of hclust. Note that factors are coerced to numbers by replacing them with integers, which implicitly assumes what is probably an arbitrary ordering.

cluster.pars

a list with two elements named dist.method and hclust.method. These are passed respectively to dist and hclust. NULL is equivalent to list(dist.method = "euclidean", hclust.method = "complete").

stat.pars

NULL is equivalent to list(fontsize = 7, signif = 0.05, verbose = FALSE, use.color = TRUE, missing = 'missing', just = 'centre'); stat.pars\$verbose can be TRUE (providing 5 statistics), FALSE (providing 2 statistics), or NA (nothing). The string missing is used in summaries where there are missing values; fontsize and just control the size and justification of the text summaries (see grid.text and gpar. The use.color=FALSE option provides an alternative summary of the strength of the correlation (see Green and Hickey (2006)). This is only used with scatter="stats") in upper.pars and lower.pars.

scatter.pars

NULL is equivalent to list(pch = 1, size = unit(0.25, "char"), col = "black", frame.fill = NULL, border.col = "black").

bwplot.pars

NULL, passed to bwplot for producing boxplots.

stripplot.pars

NULL is equivalent to list(pch = 1, size = unit(0.5, 'char'), col = 'black', jitter = FALSE).

barcode.pars

NULL is equivalent to list(nint = 0, ptsize = unit(0.25, "char"), ptpch = 1, bcspace = NULL, use.points = FALSE).

mosaic.pars

NULL. Currently shade, gp_labels, gp, and gp_args are passed through to strucplot for producing mosaic tiles.

axis.pars

NULL is equivalent to list(n.ticks = 5, fontsize = 9).

diag.pars

NULL is equivalent to list(fontsize = 9, show.hist = TRUE, hist.color = 'black').

whatis

default is FALSE; TRUE returns whatis(x).

Details

In some cases, the graphics device can not be resized after production of the plot because of the way rotation of barcodes is performed.

upper.pars and lower.pars are lists possibly containing named elements 'scatter', 'conditional' and 'mosaic'. Each element of the list is a string implementing the following options: scatter = exactly one of ('points', 'lm', 'ci', 'symlm', 'loess', 'corrgram', 'stats', 'qqplot'); 'conditional' = exactly one of ('boxplot', 'stripplot', 'barcode'); mosaic='mosaic' (only option currently implemented).

corrgram() is just a wrapper to gpairs() producing a ‘corrgram’ in the style of Michael Friendly.

Value

If whatis=TRUE, the value is a data frame containing variable names, types, numbers of missing values, numbers of distinct values, precisions, maxima and minima.

Author(s)

John W. Emerson, Walton Green; thanks to Michael Friendly for augmenting the functionality with arguments to strucplot and to Liya Xiang for the September 2024 fixes related to grid graphics rotation (via package barcode) and the display problems with boxplot tiles from package lattice that plagued some environments.

References

Emerson, John W. (1998) "Mosaic Displays in S-PLUS: A General Implementation and a Case Study." Statistical Computing and Graphics Newsletter Vol. 9,No. 1, 1998.

Basford, K. E. and J. W. Tukey (1999) Graphical Analysis of Multiresponse Data: Illustrated with a Plant Breeding Trial.

Friendly, M. (2000). Visualizing Categorical Data. SAS Press.

Friendly, M., 2002, "Corrgrams: Exploratory displays for correlation matrices." American Statistician 56(4), 316–324.

Green, W. A. (2006) "Loosening the CLAMP: An exploratory graphical approach to the Climate Leaf Analysis Multivariate Program." Palaeontologia Electronica 9(2):9A.

See Also

pairs, splom, mosaicplot, strucplot, bwplot, barcode, stripplot.

Examples


allexamples <- FALSE

y <- data.frame(A=c(rep("red", 100), rep("blue", 100)),
                B=c(rnorm(100),round(rnorm(100,5,1),1)), C=runif(200),
                D=c(rep("big", 150), rep("small", 50)),
                E=rnorm(200), stringsAsFactors=TRUE)
gpairs(y)

data(iris)
gpairs(iris)
if (allexamples) {
  gpairs(iris, upper.pars = list(scatter = 'stats'),
         scatter.pars = list(pch = substr(as.character(iris$Species), 1, 1),
                             col = as.numeric(iris$Species)),
         stat.pars = list(verbose = FALSE))
  gpairs(iris, lower.pars = list(scatter = 'corrgram'),
         upper.pars = list(conditional = 'boxplot', scatter = 'loess'),
         scatter.pars = list(pch = 20))
}

data(Leaves)
gpairs(Leaves[1:10], lower.pars = list(scatter = 'loess'))
if (allexamples) {
  gpairs(Leaves[1:10], upper.pars = list(scatter = 'stats'),
         lower.pars = list(scatter = 'corrgram'),
         stat.pars = list(verbose = FALSE), gap = 0)
  corrgram(Leaves[,-33])
}

runexample <- FALSE
if (runexample) {
  data(NewHavenResidential)
  gpairs(NewHavenResidential)
}