heplot(mod, ...)
"heplot"(mod, terms, hypotheses, term.labels = TRUE, hyp.labels = TRUE, err.label="Error", label.pos=NULL, variables = 1:2, 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, markH0=!is.null(iterm), manova, size = c("evidence", "effect.size"), level = 0.68, alpha = 0.05, segments = 60, center.pch = "+", center.cex=2, col = getOption("heplot.colors", c("red", "blue", "black", "darkgreen", "darkcyan","magenta", "brown","darkgray")), lty = 2:1, lwd = 1:2, fill=FALSE, fill.alpha=0.3, xlab, ylab, main = "", xlim, ylim, axes=TRUE, offset.axes, add = FALSE, verbose = FALSE, warn.rank = 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.0:4) or character strings
(in c("center", "bottom", "left", "top", "right"), or in
c("C", "S", "W", "N", "E") use in labeling ellipses,
recycled as necessary.
Values of 1, 2, 3 and 4, respectively indicate positions below, to the left of, above
and to the right of the max/min coordinates of the ellipse; the value 0 specifies
the centroid of the ellipse object.
The default, label.pos=NULL
uses the correlation of the ellipse to determine "top" (r>=0) or "bottom" (r<0). even="" more="" flexible="" options="" are="" described="" in="" 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.mark.H0) used to
draw cross-hairs and a point indicating the value of a point null hypothesis.
The default is TRUE if iterm is non-NULL.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 at level alpha extends outside of the error
ellipse; if "effect.size", the hypothesis ellipse is on the same
scale as the error ellipse.0.68, giving a standard 1 SD bivariate ellipse.size="evidence", then the
hypothesis ellipse is scaled so that it just touches the error ellipse at the
specified alpha level; a larger hypothesis ellipse somewhere in the space of
the response variables therefore indicates
statistical significance; defaults to 0.05.60."+".2.options(heplot.colors =c(...). Otherwise,
the default colors are c("red", "blue", "black", "darkgreen", "darkcyan", "magenta", "brown", "darkgray").
2:1.1:2.[0,1], where 0 means fully transparent and 1 means fully opaque."".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.TRUE, do not suppress warnings about the rank of the
hypothesis matrix when the ellipse collapses to a line; the default is
FALSE.plot, text,
and points."heplot", with
coordinates for the various hypothesis ellipses and the error ellipse, and
the limits of the horizontal and vertical axes. These may be useful for
adding additional annotations to the plot, using standard plotting functions.
(No methods for manipulating these objects are currently available.)The components are:
heplot function plots a representation of the covariance ellipses
for hypothesized model terms and linear hypotheses (H) and the corresponding
error (E) matrices for two response variables in a multivariate linear model (mlm).
The plot helps to visualize the nature and dimensionality
response variation on the two variables jointly
in relation to error variation that is summarized in the various multivariate
test statistics (Wilks' Lambda, Pillai trace, Hotelling-Lawley trace, Roy maximum
root). Roy's maximum root test has a particularly simple visual interpretation,
exploited in the size="evidence" version of the plot. See the description of
argument alpha.
For a 1 df hypothesis term (a quantitative regressor, a single contrast or
parameter test), the H matrix has rank 1 (one non-zero latent root of
$H E^{-1}$) and the H "ellipse" collapses to a degenerate line.
Typically, you fit a mlm with mymlm <- lm(cbind(y1, y2, y3, ...) ~ modelterms),
and plot some or all of the modelterms with heplot(mymlm, ...).
Arbitrary linear hypotheses related to the terms in the model (e.g., contrasts of
an effect) can be included in the plot using the hypotheses argument.
See linearHypothesis for details.
For repeated measure designs, where the response variables correspond to one or
more variates observed under a within-subject design, between-subject effects
and within-subject effects must be plotted separately, because the error terms
(E matrices) differ. When you specify an intra-subject term (iterm),
the analysis and HE plots
amount to analysis of the matrix Y of responses post-multiplied by a matrix
M determined by the intra-subject design for that term. See Friendly (2010)
or the vignette("repeated") in this package for an extended discussion and
examples.
The related candisc package provides functions for
visualizing a multivariate linear model in a low-dimensional view via a
generalized canonical discriminant analyses. heplot.candisc
and heplot3d.candisc provide a low-rank 2D (or 3D) view of the effects for
a given term in the space of maximum discrimination.
When an element of fill is TRUE, the ellipse outline is drawn using the corresponding
color in col, and the interior is filled with a transparent version of this color specified
in fill.alpha. To produce filled (non-degenerate) ellipses without the bounding outline, use
a value of lty=0 in the corresponding position.
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
Friendly, Michael (2010). HE Plots for Repeated Measures Designs. Journal of Statistical Software, 37(4), 1-40. URL http://www.jstatsoft.org/v37/i04/.
Fox, J., Friendly, M. & Weisberg, S. (2013). Hypothesis Tests for Multivariate Linear Models Using the car Package. The R Journal, 5(1), http://journal.r-project.org/archive/2013-1/fox-friendly-weisberg.pdf.
Friendly, M. & Sigal, M. (2014) Recent Advances in Visualizing Multivariate Linear Models. Revista Colombiana de Estadistica, 37, 261-283, http://ref.scielo.org/6gq33g.
Anova, linearHypothesis for details on
testing MLMs.
heplot1d, heplot3d, pairs.mlm, mark.H0 for other
HE plot functions. coefplot.mlm for plotting confidence ellipses for parameters
in MLMs.
trans.colors for calculation of transparent colors.
label.ellipse for labeling positions in plotting H and E ellipses.
candisc, heplot.candisc for reduced-rank views of mlms
in canonical space.
## iris data
contrasts(iris$Species)<-matrix(c(0,-1,1, 2, -1, -1), 3,2)
contrasts(iris$Species)
iris.mod <- lm(cbind(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) ~
Species, data=iris)
hyp <- list("V:V"="Species1","S:VV"="Species2")
heplot(iris.mod, hypotheses=hyp)
# compare with effect-size scaling
heplot(iris.mod, hypotheses=hyp, size="effect", add=TRUE)
# try filled ellipses
heplot(iris.mod, hypotheses=hyp, fill=TRUE, fill.alpha=0.2, col=c("red", "blue"))
heplot(iris.mod, hypotheses=hyp, fill=TRUE, col=c("red", "blue"), lty=c(0,0,1,1))
# vary label position and fill.alpha
heplot(iris.mod, hypotheses=hyp, fill=TRUE, fill.alpha=c(0.3,0.1), col=c("red", "blue"),
lty=c(0,0,1,1), label.pos=0:3)
hep <-heplot(iris.mod, variables=c(1,3), hypotheses=hyp)
str(hep)
# all pairs
pairs(iris.mod, hypotheses=hyp, hyp.labels=FALSE)
## Pottery data, from car package
data(Pottery)
pottery.mod <- lm(cbind(Al, Fe, Mg, Ca, Na) ~ Site, data=Pottery)
heplot(pottery.mod)
heplot(pottery.mod, terms=FALSE, add=TRUE, col="blue",
hypotheses=list(c("SiteCaldicot = 0", "SiteIsleThorns=0")),
hyp.labels="Sites Caldicot and Isle Thorns")
## Rohwer data, multivariate multiple regression/ANCOVA
#-- ANCOVA, assuming equal slopes
rohwer.mod <- lm(cbind(SAT, PPVT, Raven) ~ SES + n + s + ns + na + ss, data=Rohwer)
Anova(rohwer.mod)
col <- c("red", "black", "blue", "cyan", "magenta", "brown", "gray")
heplot(rohwer.mod, col=col)
# Add ellipse to test all 5 regressors
heplot(rohwer.mod, hypotheses=list("Regr" = c("n", "s", "ns", "na", "ss")), col=col, fill=TRUE)
# View all pairs
pairs(rohwer.mod, hypotheses=list("Regr" = c("n", "s", "ns", "na", "ss")))
# or 3D plot
col <- c("pink", "black", "blue", "cyan", "magenta", "brown", "gray")
heplot3d(rohwer.mod, hypotheses=list("Regr" = c("n", "s", "ns", "na", "ss")), col=col)
Run the code above in your browser using DataLab