Partial Variables Plot — pvPlot • heplots

A partial variable plot is a visualization of a partial correlation of two variables in the context of other variables in a dataset. For two variables $x_i$ and $x_j$, it is simply an enhanced scatterplot of the partial residuals, $e_i = (x_i - \hat{x}_i)$ from a regression of $x_i$ on all other variables $Z$ against those $e_j = (x_j - \hat{x}_j)$ for another variable $x_j$. Consequently, it shows directly the net, conditional relation between $x_i, x_j \vert \text{others}$ when all of the others in $Z$ have been controlled/adjusted-for.

As implemented here, the basic scatterplot of these residuals can be enhanced by also showing the data ellipse of these residuals, the linear regression line, which reflects the partial correlation, and point labels to identify unusual data.

Usage

pvPlot(
  X,
  vars = 1:2,
  others = NULL,
  labels,
  id = FALSE,
  ellipse = TRUE,
  ellipse.args = list(levels = 0.68, fill = TRUE, fill.alpha = 0.05, robust = FALSE, col
    = "black"),
  draw = TRUE,
  col = "black",
  pch = 16,
  cex = par("cex"),
  axes = TRUE,
  regline = TRUE,
  show.partial = list(loc = c(0.025, 0.95), cex = 1.2),
  ...
)

Arguments

X: a data.frame of numeric variables
vars: either the character names of two variables in X or their indices
others: character names or indices of the variables to partial out. If NULL (the default), all variables in X other than vars are used.
labels: id labels for the points. If not supplied, rownames of the dataset are used.
id: controls point identification; if FALSE (the default), no points are identified; can be a list of named arguments to the showLabels function
ellipse: logical; whether to draw the data ellipse
ellipse.args: a list of arguments controlling the ellipse: levels, fill, fill.alpha, robust, and col (ellipse outline/fill color, independent of the point col). See dataEllipse for what these mean.
draw: logical; if TRUE produce graphical output; if FALSE, only invisibly return coordinates of ellipse(s).
col: color used for points
pch: the plotting character for points
cex: Character expansion for points and labels
axes: logical; if TRUE (the default), grey axes lines are drawn at 0 on both coordinates
regline: controls the regression line. FALSE suppresses it; TRUE (default) draws it with default style; a list with named elements col and/or lwd draws it with those attributes, e.g. regline = list(col="red", lwd=3).
show.partial: controls whether the partial correlation value is displayed in the plot. If FALSE the value is not shown. Otherwise, can be a list containing the location (loc) and character size (cex) of the label.
...: other arguments passed to dataEllipse

Value

 This functions is mainly used for their side effect of producing plots. For greater
         flexibility (e.g., adding plot annotations), it returns invisibly the coordinates of the residuals
         plotted.

Details

Partial variable plots are intimately related to an added-variable plot, such as produced by avPlots. However, that implementation is designed for a linear model, rather than a data.frame.

The present version assumes that all variables passed are numeric.

This function uses dataEllipse for drawing, so further documentation of arguments passed there should be consulted.

Author

Michael Friendly

Examples

data(crime, package = "ggbiplot")
crime.num <- crime |>
  tibble::column_to_rownames("st") |>
  dplyr::select(where(is.numeric))
  
pvPlot(crime.num, vars = c("burglary", "larceny"))

pvPlot(crime.num, vars = c("auto", "robbery"))


# ellipse color independent of point color
pvPlot(crime.num, vars = c("auto", "robbery"),
       ellipse.args = list(col="red"))


# styled regression line
pvPlot(crime.num, vars = c("burglary", "larceny"),
       regline = list(col = "red", lwd = 3))


# partial out only a subset of the other variables
pvPlot(crime.num, vars = c("burglary", "larceny"),
       others = c("murder", "rape"))


# suppress the ellipse
pvPlot(crime.num, vars = c("burglary", "larceny"), ellipse=FALSE)


# label some observations
pvPlot(crime.num, vars = c("burglary", "larceny"), 
       id = list(n=5),
       cex.lab = 1.5)