plot.lpdensity: Plot Method for Local Polynomial Density Estimation and Inference

Description

The plot method for local polynomial density objects.

Usage

# S3 method for lpdensity
plot(
  ...,
  alpha = NULL,
  type = NULL,
  lty = NULL,
  lwd = NULL,
  lcol = NULL,
  pty = NULL,
  pwd = NULL,
  pcol = NULL,
  grid = NULL,
  CItype = NULL,
  CIuniform = FALSE,
  CIsimul = 2000,
  CIshade = NULL,
  CIcol = NULL,
  hist = FALSE,
  histData = NULL,
  histBreaks = NULL,
  histFillCol = 3,
  histFillShade = 0.2,
  histLineCol = "white",
  title = NULL,
  xlabel = NULL,
  ylabel = NULL,
  legendTitle = NULL,
  legendGroups = NULL
)

Value

A stadnard ggplot object is returned, hence can be used for further customization.

Arguments

...: Class "lpdensity" object, obtained from calling lpdensity.
alpha: Numeric scalar between 0 and 1, specifies the significance level for plotting confidence intervals/bands. If more than one is provided, they will be applied to each data series accordingly.
type: String, one of "line" (default), "points" and "both", specifies how the point estimates are plotted. If more than one is provided, they will be applied to each data series accordingly.
lty: Line type for point estimates, only effective if type is "line" or "both". 1 for solid line, 2 for dashed line, 3 for dotted line. For other options, see the instructions for ggplot2 or par. If more than one is provided, they will be applied to each data series accordingly.
lwd: Line width for point estimates, only effective if type is "line" or "both". Should be strictly positive. For other options, see the instructions for ggplot2 or par. If more than one is provided, they will be applied to each data series accordingly.
lcol: Line color for point estimates, only effective if type is "line" or "both". 1 for black, 2 for red, 3 for green, 4 for blue. For other options, see the instructions for ggplot2 or par. If more than one is provided, they will be applied to each data series accordingly.
pty: Scatter plot type for point estimates, only effective if type is "points" or "both". For options, see the instructions for ggplot2 or par. If more than one is provided, they will be applied to each data series accordingly.
pwd: Scatter plot size for point estimates, only effective if type is "points" or "both". Should be strictly positive. If more than one is provided, they will be applied to each data series accordingly.
pcol: Scatter plot color for point estimates, only effective if type is "points" or "both". 1 for black, 2 for red, 3 for green, 4 for blue. For other options, see the instructions for ggplot2 or par. If more than one is provided, they will be applied to each data series accordingly.
grid: Numeric vector, specifies a subset of grid points to plot point estimates. This option is effective only if type is "points" or "both"; or if CItype is "ebar" or "all".
CItype: String, one of "region" (shaded region, default), "line" (dashed lines), "ebar" (error bars), "all" (all of the previous) or "none" (no confidence region), how the confidence region should be plotted. If more than one is provided, they will be applied to each data series accordingly.
CIuniform: TRUE or FALSE (default), plotting either pointwise confidence intervals (FALSE) or uniform confidence bands (TRUE).
CIsimul: Positive integer, specifies the number of simulations used to construct critical values (default is 2000). This option is ignored if CIuniform=FALSE.
CIshade: Numeric, specifies the opaqueness of the confidence region, should be between 0 (transparent) and 1. Default is 0.2. If more than one is provided, they will be applied to each data series accordingly.
CIcol: Color of the confidence region. 1 for black, 2 for red, 3 for green, 4 for blue. For other options, see the instructions for ggplot2 or par. If more than one is provided, they will be applied to each data series accordingly.
hist: TRUE or FALSE (default), specifies whether a histogram should be added to the background.
histData: Numeric vector, specifies the data used to construct the histogram plot.
histBreaks: Numeric vector, specifies the breakpoints between histogram cells.
histFillCol: Color of the histogram cells.
histFillShade: Opaqueness of the histogram cells, should be between 0 (transparent) and 1. Default is 0.2.
histLineCol: Color of the histogram lines.
title, xlabel, ylabel: Strings, specifies the title of the plot and labels for the x- and y-axis.
legendTitle: String, specifies the legend title.
legendGroups: String vector, specifies the group names used in legend.

Author

Matias D. Cattaneo, Princeton University. cattaneo@princeton.edu.

Michael Jansson, University of California Berkeley. mjansson@econ.berkeley.edu.

Xinwei Ma (maintainer), University of California San Diego. x1ma@ucsd.edu.

Examples

Run this code

# Generate a random sample
set.seed(42); X <- rnorm(2000)

# Generate a density discontinuity at 0
X <- X - 0.5; X[X>0] <- X[X>0] * 2

# Density estimation, left of 0 (scaled by the relative sample size)
est1 <- lpdensity(data = X[X<=0], grid = seq(-2.5, 0, 0.05), bwselect = "imse-dpi",
  scale = sum(X<=0)/length(X))
# Density estimation, right of 0 (scaled by the relative sample size)
est2 <- lpdensity(data = X[X>0],  grid = seq(0, 2, 0.05), bwselect = "imse-dpi",
  scale = sum(X>0)/length(X))

# Plot
plot(est1, est2, legendTitle="My Plot", legendGroups=c("Left", "Right"))

# Plot uniform confidence bands
set.seed(42) # fix the seed for simulating critical values
plot(est1, est2, legendTitle="My Plot", legendGroups=c("Left", "Right"), CIuniform=TRUE)

# Adding a histogram to the background
plot(est1, est2, legendTitle="My Plot", legendGroups=c("Left", "Right"),
  hist=TRUE, histBreaks=seq(-2.4, 2, 0.2), histData=X)

# Plot point estimates for a subset of evaluation points
plot(est1, est2, legendTitle="My Plot", legendGroups=c("Left", "Right"),
  type="both", CItype="all", grid=seq(-2, 2, 0.5))