Compute and plot oneway analysis of covariance
ancova(formula, data.in = sys.parent(), ..., x, groups, transpose = FALSE, display.plot.command = FALSE, superpose.level.name = "superpose", ignore.groups = FALSE, ignore.groups.name = "ignore.groups", blocks, blocks.pch = letters[seq(levels(blocks))], layout, between, main) panel.ancova(x, y, subscripts, groups, transpose = FALSE, ..., coef, contrasts, classes, ignore.groups, blocks, blocks.pch, blocks.cex) ## The following are ancova methods for generic functions. ## S3 method for class 'ancova': anova(object, ...) ## S3 method for class 'ancova': predict(object, ...) ## S3 method for class 'ancova': print(x, ...) ## prints the anova(x) and the trellis attribute ## S3 method for class 'ancova': model.frame(formula, ...) ## S3 method for class 'ancova': summary(object, ...) ## S3 method for class 'ancova': plot(x, y, ...) ## standard lm plot. y is always ignored. ## S3 method for class 'ancova': coef(object, ...) ## S3 method for class 'ancova': coefficients(object, ...)
- A formula specifying the model.
- A data frame in which the variables specified in the formula will be found. If missing, the variables are searched for in the standard way.
- Arguments to be passed to
aov, such as
- Covariate in
ancova, needed for plotting when the formula does not include
print.ancova, to match the argument of the
- Factor. Needed for plotting when the formula does not
groupsafter the conditioning bar
- S-Plus: The axes in each panel of the plot are transposed. The analysis is identical, just the axes displaying it have been interchanged. R: no effect.
- The default setting is usually what the user
wants. The alternate value
TRUEprints on the console the command that draws the graph. This is strictly for debugging the
- Name used in strip label for superposed panel.
TRUE, an additional panel showing all groups together with a common regression line is displayed.
- Name used in strip label for
- Additional factor used to label points in the panels.
- Alternate set of labels used when a
blocksfactor is specified.
- Alternate set of
cexused when a
blocksfactor is specified.
- The layout of multiple panels. The default is a single row. See details.
- Space between the panels for the individual group levels and the superpose panel including all groups.
- Character with a main header title to be done on the top of each page.
panel.xyplotin R and both
- coef, contrasts, classes
- Internal variables used to communicate between
panel.ancova. They keep track of the constant or different slopes and intercepts in each panel of the plot.
ancova function does two things. It passes its
arguments directly to the
aov function and returns the entire
aov object. It also rearranges the data and formula in its
argument and passes that to the
xyplot function. The
trellis attribute is a
trellis object consisting of
a series of plots of
y ~ x. The left set of panels is
conditioned on the levels of the factor
groups. The right
panel is a superpose of all the groups.
- The result object is an
ancovaobject which consists of an ordinary
aovobject with an additional
trellisattribute. The default print method is to print both the
anovaof the object and the
Heiberger, Richard~M. and Holland, Burt (2004b). Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer. ISBN 0-387-40270-5.
- analysis of covariance
hotdog <- read.table(hh("datasets/hotdog.dat"), header=TRUE) ## y ~ x ## constant line across all groups ancova(Sodium ~ Calories, data=hotdog, groups=Type) ## y ~ a ## different horizontal line in each group ancova(Sodium ~ Type, data=hotdog, x=Calories) ## This is the usual usage ## y ~ x + a or y ~ a + x ## constant slope, different intercepts ancova(Sodium ~ Calories + Type, data=hotdog) ancova(Sodium ~ Type + Calories, data=hotdog) ## y ~ x * a or y ~ a * x ## different slopes, and different intercepts ancova(Sodium ~ Calories * Type, data=hotdog) ancova(Sodium ~ Type * Calories, data=hotdog) ## y ~ a * x ## save the object and print the trellis graph hotdog.ancova <- ancova(Sodium ~ Type * Calories, data=hotdog) attr(hotdog.ancova, "trellis") ## label points in the panels by the value of the block factor apple <- read.table(hh("datasets/apple.dat"), header=TRUE) apple$treat <- factor(apple$treat) contrasts(apple$treat) <- contr.treatment(6) apple$block <- factor(apple$block) ancova(yield ~ treat + pre, data=apple, blocks=block)