This function is called by several graphical functions in the car
package to mark extreme points in a 2D plot. Although the user is unlikely
to call this function directly, the documentation below applies to all
these other functions.
showLabels(x, y, labels=NULL, id.method="identify",
id.n = length(x), id.cex=1, id.col=palette()[1], id.location="lr", ...)Plotted horizontal coordinates.
Plotted vertical coordinates.
Plotting labels. If NULL, case numbers will be used. If labels are long, the
substr or abbreviate function can be used to shorten them.
How points are to be identified. See Details below.
Number of points to be identified. If set to zero, no points are identified.
Controls the size of the plotted labels. The default is 1.
Controls the color of the plotted labels.
Where should the label be drawn? The default is "lr" to draw the label to the left of the point for points in the right-half of the graph and to the right for points in the left-half. The other option is "ab" for above the point for points below the middle of the graph and above the point below the middle.
additional arguments passed to identify or to text.
A utility function primarily used for its side-effect of drawing labels on a plot. Returns invisibly the labels of the selected points, or NULL if no points are selected. Although intended for use with other functions in the car package, this function can be used directly.
The argument id.method determine how the points
to be identified are selected. For the default value of id.method="identify",
the identify function is used to identify points
interactively using the mouse. Up to id.n points can be identified,
so if id.n=0, which is the default in many functions in the car
package, then no point identification is done.
Automatic point identification can be done depending on the value of the
argument id.method.
id.method = "x" select points according to their value of abs(x - mean(x))
id.method = "y" select points according to their value of abs(y - mean(y))
id.method = "r" select points according to their value of abs(y), as may be
appropriate in residual plots, or others with a meaningful origin at 0
id.method = "mahal" Treat (x, y) as if it were a bivariate sample, and
select cases according to their Mahalanobis distance from (mean(x), mean(y))
id.method can be a vector of the same length as x consisting of
values to determine the points to be labeled. For example, for a linear model
m, setting id.method=cooks.distance(m), id.n=4 will label the
points corresponding to the four largest values of Cook's distance, or
id.method = which(abs(residuals(m, type="pearson")) > 2 would label
all observations with Pearson residuals greater than 2 in absolute value.
Warning: If missing data are present, points may be incorrectly labelled.
id.method can be a vector of case numbers or case-labels, in which case
those cases will be labeled. Warning: If missing data are present, a list of
case numbers may identify the wrong points. A list of case labels, however,
will work correctly with missing values.
With showLabels, the id.method argument can be a list, so, for
example id.method=list("x", "y") would label according to the horizontal
and vertical axes variables.
Finally, if the axes in the graph are logged, the function uses logged-variables where appropriate.
Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.
Weisberg, S. (2014) Applied Linear Regression, Fourth Edition, Wiley.
# NOT RUN {
plot(income ~ education, Prestige)
with(Prestige, showLabels(education, income,
labels = rownames(Prestige), id.method=list("x", "y"), id.n=3))
m <- lm(income ~ education, Prestige)
plot(income ~ education, Prestige)
abline(m)
with(Prestige, showLabels(education, income,
labels=rownames(Prestige), id.method=abs(residuals(m)), id.n=4))
# }
Run the code above in your browser using DataLab