spmBCA is an abbreviation for scatterplotMatrixBCA.
This function just sets up a call to pairs with custom panel functions.
scatterplotMatrixBCA(x, ...)
"scatterplotMatrixBCA"(x, data=NULL, subset, labels, ...)
"scatterplotMatrixBCA"(x, var.labels = colnames(x), diagonal = c("density", "boxplot", "histogram", "oned", "qqplot", "none"), adjust = 1, nclass, plot.points = TRUE, smooth = TRUE, spread = smooth && !by.groups, span = 0.5, loess.threshold = 2, reg.line = lm, transform = FALSE, family = c("bcPower", "yjPower"), ellipse = FALSE, levels = c(0.5, 0.95), robust = TRUE, groups = NULL, by.groups = FALSE, labels, id.method = "mahal", id.n = 0, id.cex = 1, id.col = palette()[1], col = if (n.groups == 1) palette()[c(2, 1, 3)] else rep(palette(), length = n.groups), pch = 1:n.groups, lwd = 2, lwd.smooth = lwd, lwd.spread = lwd, lty = 1, lty.smooth = lty, lty.spread = 2, cex = par("cex"), cex.axis = par("cex.axis"), cex.labels = NULL, cex.main = par("cex.main"), legend.plot = length(levels(groups)) > 1, row1attop = TRUE, ...)
spmBCA(x, ...) ~ x1 + x2 + ... + xk or ~ x1 + x2 + ... + xk | z where z
evaluates to a factor or other variable to divide the data into groups.scatterplotMatrix.formula,
a data frame within which to evaluate the formula.id.n=0 for labeling no points. See
showLabels for details of these arguments. If the plot uses
different colors for groups, then the id.col argument is ignored and
label colors are determined by the col argument.
density function.hist
function.TRUE the points are plotted in each
off-diagonal panel.TRUE a loess smooth is plotted in each
off-diagonal panel.TRUE (the default when not smoothing by groups), a smoother is applied to
the root-mean-square positive and negative residuals from the loess line to display conditional spread and
asymmetry.loess.threshold unique
values (default, 5) of the variable on the vertical axis.FALSE a line is plotted using the
function given by this argument; e.g., using rlm in
package MASS plots a robust-regression line.TRUE, multivariate normalizing power transformations
are computed with powerTransform, rounding the estimated powers to `nice' values for plotting;
if a vector of powers, one for each variable, these are applied prior to plotting. If there are groups
and by.groups is TRUE, then the transformations are estimated conditional on the
groups factor."bcPower" for the Box-Cox family or
"yjPower" for the Yeo-Johnson family (see powerTransform).TRUE data-concentration ellipses are plotted in
the off-diagonal panels.c(.5, .9).TRUE use the cov.trob function in the MASS package
to calculate the center and covariance matrix for the data ellipses.TRUE, regression lines are fit by groups.par).palette()[2] for nonparametric regression lines and palette()[1] for
linear regression lines and points if there are
no groups, and successive colors for the groups if there are groups.1).lwd).lwd).1, solid line).lty).2, broken line).par).TRUE then a legend for the groups is plotted
in the first diagonal cell.TRUE (the default) the first row is at the top, as in a matrix, as
opposed to at the bottom, as in graph (argument suggested by Richard Heiberger).NULL. This function is used for its side effect: producing
a plot.
scatterplotMatrix