In particular, for a given response, the 1-D representations of H and E matrices correspond to line segments. The E ``ellipse'' is shown as a filled rectangle whose width equals the mean squared error for that response. The H ``ellipse'' for each model term is shown as a line segment whose length represents either the size of the effect or the evidence for that effect.
This version is an initial sketch. Details of the implementation are subject to change.
heplot1d(mod, ...)
"heplot1d"(mod, terms, hypotheses, term.labels = TRUE, hyp.labels = TRUE,
variables = 1, error.ellipse = !add, factor.means = !add, grand.mean = !add,
remove.intercept = TRUE, type = c("II", "III", "2", "3"),
idata=NULL, idesign=NULL, icontrasts=c("contr.sum", "contr.poly"),
imatrix=NULL, iterm=NULL, manova,
size = c("evidence", "effect.size"), level = 0.68, alpha = 0.05,
center.pch = "|", col = getOption("heplot.colors", c("red", "blue", "black", "darkgreen", "darkcyan","magenta", "brown","darkgray")),
lty = 2:1, lwd = 1:2, xlab, main = "",
xlim, axes = TRUE, offset.axes, add = FALSE, verbose = FALSE, ...)"mlm".TRUE, defaults to all terms;
if FALSE, no terms are plotted.linearHypothesis function in the car package;
the list elements can be named, in which case the names are used.TRUE (the default) the names of the terms are used;
if FALSE, term labels are not plotted.TRUE (the default) the names of components of the list of
hypotheses are used; if FALSE, hypothesis labels are not plotted.1:2.TRUE, plot the error ellipse; defaults to TRUE,
if the argument add is FALSE (see below).TRUE or FALSE; defaults to TRUE,
if the argument add is FALSE (see below).TRUE, plot the centroid for all of the data;
defaults to TRUE,
if the argument add is FALSE (see below).TRUE (the default), do not plot the
ellipse for the intercept even if it is in the MANOVA table."II", "III", "2", or "3", where "II" is
the default (and "2" is a synomym).Anova for an explanation of the intra-subject design and for further explanation
of the other arguments relating to intra-subject factors.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.
This functionality
requires car version 2.0 or later.iterm effect as well as all interactions
of iterm with terms.Anova.mlm object for the model; if absent a
MANOVA is computed. Specifying the argument can therefore save
computation in repeated calls."evidence", the default, the scaling is done so that
a ``significant'' hypothesis ellipse extends outside of the error
ellipse; if "effect.size", the hypothesis ellipse is on the same
scale as the error ellipse.0.68.size="evidence", then the
hypothesis ellipse is scaled so that it just touches the error ellipse at the
specified alpha level; a larger hypothesis elllipse therefore indicates
statistical significance; defaults to 0.05."|".options(heplot.colors =c(...). Otherwise,
the default colors are c("red", "blue", "black", "darkgreen", "darkcyan", "magenta", "brown", "darkgray").
2:1.1:2."".TRUETRUE, add to the current plot; the default is FALSE.
If TRUE, the error ellipse is not plotted.TRUE, print the MANOVA table and details of hypothesis
tests; the default is FALSE.plot, text,
and points."heplot1d", with
coordinates for the various hypothesis ellipses and the error ellipse, and
the limits of the horizontal and vertical axes.
(No methods for manipulating these objects are currently available.)The components are:
Anova, linearHypothesis for hypothesis tests in mlms
heplot, heplot3d, pairs.mlm for other HE plot methods
## Plastics data
plastic.mod <- lm(cbind(tear, gloss, opacity) ~ rate*additive, data=Plastic)
heplot1d(plastic.mod, col=c("pink","blue"))
heplot1d(plastic.mod, col=c("pink","blue"),variables=2)
heplot1d(plastic.mod, col=c("pink","blue"),variables=3)
## Bees data
bees.mod <- lm(cbind(Iz,Iy) ~ caste*treat*time, data=Bees)
heplot1d(bees.mod)
heplot1d(bees.mod, variables=2)
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