HH (version 3.1-47)

ancovaplot: 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 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.

Value

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

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

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/us/book/9781493921218

See Also

See the older ancova.

Examples

# NOT RUN {
  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)

# }