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Pairwise HE Plots

Source: R/pairs.mlm.R
pairs.mlm.Rd

The function (in the form of an mlm method for the generic pairs function) constructs a “matrix” of pairwise HE plots (see heplot) for a multivariate linear model.

Usage

# S3 method for class 'mlm'
pairs(
  x,
  variables,
  var.labels,
  var.cex = 2,
  type = c("II", "III", "2", "3"),
  idata = NULL,
  idesign = NULL,
  icontrasts = NULL,
  imatrix = NULL,
  iterm = NULL,
  manova,
  offset.axes = 0.05,
  digits = getOption("digits") - 1,
  fill = FALSE,
  fill.alpha = 0.3,
  ...
)

Arguments

x

an object of class mlm.

variables

indices or names of the three of more response variables to be plotted; defaults to all of the responses.

var.labels

labels for the variables plotted in the diagonal panels; defaults to names of the response variables.

var.cex

character expansion for the variable labels.

type

type of sum-of-squares-and-products matrices to compute; one of "II", "III", "2", or "3", where "II" is the default (and "2" is a synonym).

idata

an optional data frame giving a factor or factors defining the intra-subject model for multivariate repeated-measures data. See Details of Anova for an explanation of the intra-subject design and for further explanation of the other arguments relating to intra-subject factors.

idesign

a one-sided model formula using the “data” in idata and specifying the intra-subject design for repeated measure models.

icontrasts

names of contrast-generating functions to be applied by default to factors and ordered factors, respectively, in the within-subject “data”; the contrasts must produce an intra-subject model matrix in which different terms are orthogonal. The default is c("contr.sum", "contr.poly").

imatrix

In lieu of idata and idesign, you can specify the intra-subject design matrix directly via imatrix, in the form of list of named elements. Each element gives the columns of the within-subject model matrix for an intra-subject term to be tested, and must have as many rows as there are responses; the columns of the within-subject model matrix for different terms must be mutually orthogonal.

iterm

For repeated measures designs, you must specify one intra-subject term (a character string) to select the SSPE (E) matrix used in the HE plot. Hypothesis terms plotted include the iterm effect as well as all interactions of iterm with terms.

manova

optional Anova.mlm object for the model; if absent a MANOVA is computed. Specifying the argument can therefore save computation in repeated calls.

offset.axes

proportion to extend the axes in each direction; defaults to 0.05.

digits

number of significant digits in axis end-labels; taken from the "digits" option.

fill

A logical vector indicating whether each ellipse should be filled or not. The first value is used for the error ellipse, the rest — possibly recycled — for the hypothesis ellipses; a single fill value can be given. Defaults to FALSE for backward compatibility. See Details of heplot

fill.alpha

Alpha transparency for filled ellipses, a numeric scalar or vector of values within [0,1], where 0 means fully transparent and 1 means fully opaque. Defaults to 0.3.

...

arguments to pass down to heplot, which is used to draw each panel of the display.

References

Friendly, M. (2006). Data Ellipses, HE Plots and Reduced-Rank Displays for Multivariate Linear Models: SAS Software and Examples Journal of Statistical Software, 17(6), 1-42. https://www.jstatsoft.org/v17/i06/

Friendly, M. (2007). HE plots for Multivariate General Linear Models. Journal of Computational and Graphical Statistics, 16(2) 421-444. http://datavis.ca/papers/jcgs-heplots.pdf

See also

heplot, heplot3d

Author

Michael Friendly

Examples


# ANCOVA, assuming equal slopes
rohwer.mod <- lm(cbind(SAT, PPVT, Raven) ~ SES + n + s + ns + na + ss, data=Rohwer)

# View all pairs, with ellipse for all 5 regressors
pairs(rohwer.mod, hypotheses=list("Regr" = c("n", "s", "ns", "na", "ss")))