ancovaplot: Analysis of Covariance Plots

In HH: Statistical Analysis and Data Display: Heiberger and Holland

Analysis of Covariance Plots

Description

Analysis of Covariance Plots. Any of the ancova models
y ~ x * t
y ~ t * x
y ~ x + t
y ~ t + x
y ~ x , groups=t
y ~ t, x=x
y ~ x * t, groups=b
y ~ t * x, groups=b
y ~ x + t, groups=b
y ~ t + x, groups=b

Usage

ancovaplot(object, ...)
## S3 method for class 'formula'
ancovaplot(object, data, groups=NULL, x=NULL, ...,
           formula=object,
           col=rep(tpg$col,
             length=length(levels(as.factor(groups)))),
           pch=rep(c(15,19,17,18,16,20, 0:14),
             length=length(levels(as.factor(groups)))),
           slope, intercept,
           layout=c(length(levels(cc)), 1),
           col.line=col, lty=1,
           superpose.panel=TRUE,
           between=if (superpose.panel)
                      list(x=c(rep(0, length(levels(cc))-1), 1))
                   else
                      list(x=0),
           col.by.groups=FALSE ## ignored unless groups= is specified
           )

panel.ancova.superpose(x, y, subscripts, groups,
                       slope, intercept,
                       col, pch, ...,
                       col.line, lty,
                       superpose.panel,
                       col.by.groups,
                       condition.factor,
                       groups.cc.incompatible,
                       plot.resids=FALSE,
                       print.resids=FALSE,
                       mean.x.line=FALSE,
                       col.mean.x.line="gray80")

Arguments

formula, object	formula specifying the aov model. The function modifies it for the xyplot specification.
data	data.frame
groups	If the treatment factor is included in the formula, then groups is not needed. By default groups will be set to the treatment factor, but the user may specify another factor for groups, usually a blocking factor. The pch will follow the value of groups. If the treatment is not included in the formula, then groups is required.
x	Covariate. Required by ancovaplot.formula if the covariate is not included in the formula. For panel.ancova.superpose, see panel.superpose.
...	Other arguments to be passed to xyplot.
col, pch	Standard lattice arguments. pch follows the value of groups. When col.by.groups is TRUE, then col follow the value of groups. When col.by.groups is FALSE, then col follows the value of the treatment factor, and is constant in each panel.
slope, intercept	Vector, the length of the number of treatment levels, containing slope and intercept of the abline in each panel. This is by default calculated based on the formula. The user may override each independently.
layout, between	Standard lattice arguments.
col.line, lty	Standard lattice arguments. By default, they follow the value of the treatment factor in the formula. col.line is recycled to the number of panels in the plot.
y, subscripts	See panel.xyplot.
superpose.panel	logical. if TRUE (the default), there is an additional panel on the right containing the superposition of the points and lines for all treatment levels.
col.by.groups	logical. See the discussion in argument col.
condition.factor, groups.cc.incompatible	These are both internal variables. condition.factor contains a copy of the treatment factor. groups.cc.incompatible is a logical which is set to TRUE when the groups argument is explicitly set by the user.
plot.resids, print.resids, mean.x.line, col.mean.x.line	logical, logical, logical or numeric, color name. When plot.resids==TRUE then vertical line segments connecting the data points and the fitted line are drawn. The other two arguments are interpreted only when plot.resids==TRUE. When print.resids==TRUE then the values of the residuals are printed on the console. When is.numeric(mean.x.line) then a vertical reference line is drawn at the specified value, which will normally be specified by the user as the mean of the full set of x values. The reference line will have color specified by col.mean.x.line.

Details

ancova=aov specification	xyplot specification	abline
y ~ x * t	y ~ x | t, groups=t	lm(y[t] ~ x[t])	## separate lines
y ~ t * x	y ~ x | t, groups=t	lm(y[t] ~ x[t])	## separate lines
y ~ x + t	y ~ x | t, groups=t	lm(y ~ x + t)	## parallel lines
y ~ t + x	y ~ x | t, groups=t	lm(y ~ x + t)	## parallel lines
y ~ x , groups=t	y ~ x | t, groups=t	lm(y ~ x)	## single regression line
y ~ t, x=x	y ~ x | t, groups=t	mean(t)	## separate horizontal lines
y ~ x * t, groups=b	y ~ x | t, groups=b	lm(y[t] ~ x[t])	## sep lines, pch&col follow b
y ~ t * x, groups=b	y ~ x | t, groups=b	lm(y[t] ~ x[t])	## sep lines, pch&col follow b
y ~ x + t, groups=b	y ~ x | t, groups=b	lm(y ~ x + t)	## par lines, pch&col follow b
y ~ t + x, groups=b	y ~ x | t, groups=b	lm(y ~ x + t)	## par lines, pch&col follow b

Value

ancovaplot returns a c("ancova","trellis") object. panel.ancova.superpose is an ordinary lattice panel function.

Author(s)

Richard M. Heiberger <[e-mail]>

References

Heiberger, Richard M. and Holland, Burt (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R. Second Edition. Springer-Verlag, New York. https://link.springer.com/book/10.1007/978-1-4939-2122-5

See Also

See the older ancova.

Examples

  data(hotdog, package="HH")
  ancovaplot(Sodium ~ Calories + Type, data=hotdog)
  ancovaplot(Sodium ~ Calories * Type, data=hotdog)
  ancovaplot(Sodium ~ Calories, groups=Type, data=hotdog)
  ancovaplot(Sodium ~ Type, x=Calories, data=hotdog)

  ## Please see demo("ancova", package="HH") to coordinate placement
  ## of all four of these plots on the same page.

  ancovaplot(Sodium ~ Calories + Type, data=hotdog, plot.resids=TRUE)

HH documentation built on May 29, 2024, 6:24 a.m.