lsprobust.plot: Graphic Presentation of Results for 'lspartition' Package

In lspartition: Nonparametric Estimation and Inference Procedures using Partitioning-Based Least Squares Regression

Description

lsprobust.plot plots estimated regression functions and confidence regions using the lspartition package. See Cattaneo and Farrell (2013) and Cattaneo, Farrell and Feng (2019a) for complete details.

Companion command: lsprobust for partitioning-based least squares regression estimation and inference; lsprobust.plot for plotting results; lsplincom for multiple sample estimation and inference.

A detailed introduction to this command is given in Cattaneo, Farrell and Feng (2019b).

For more details, and related Stata and R packages useful for empirical analysis, visit https://sites.google.com/site/nppackages/.

Usage

lsprobust.plot(..., alpha = NULL, type = NULL, CS = "ci",
  CStype = NULL, title = "", xlabel = "", ylabel = "",
  lty = NULL, lwd = NULL, lcol = NULL, pty = NULL, pwd = NULL,
  pcol = NULL, CSshade = NULL, CScol = NULL, legendTitle = NULL,
  legendGroups = NULL)

Arguments

...	Objects returned by lsprobust.
alpha	Numeric scalar between 0 and 1, the significance level for plotting confidence regions. If more than one is provided, they will be applied to data series accordingly.
type	String, one of "line" (default), "points", "binscatter", "none" or "both", how the point estimates are plotted. If more than one is provided, they will be applied to data series accordingly.
CS	String, type of confidence sets. Options are "ci" for pointwise confidence intervals, "cb" for uniform confidence bands, and "all" for both.
CStype	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 data series accordingly. If CS = "all", pointwise confidence intervals are forced to be represented by error bars, and uniform bands are represented by both lines and regions.
title	String, title of the plot.
xlabel	Strings, labels for x-axis.
ylabel	Strings, labels for y-axis.
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 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 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 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 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 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 data series accordingly.
CSshade	Numeric, 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 data series accordingly.
CScol	Color for 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 data series accordingly.
legendTitle	String, title of legend.
legendGroups	String vector, group names used in legend.

Details

Companion command: lsprobust for partition-based least-squares regression estimation.

Value

A standard ggplot2 object is returned, hence can be used for further customization.

Author(s)

Matias D. Cattaneo, Princeton University, Princeton, NJ. [e-mail].

Max H. Farrell, University of Chicago, Chicago, IL. [e-mail].

Yingjie Feng (maintainer), Princeton University, Princeton, NJ. [e-mail].

References

Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019a): Large Sample Properties of Partitioning-Based Series Estimators. Annals of Statistics, forthcoming. arXiv:1804.04916.

Cattaneo, M. D., M. H. Farrell, and Y. Feng (2019b): lspartition: Partitioning-Based Least Squares Regression. R Journal, forthcoming. arXiv:1906.00202.

See Also

lsprobust, lspkselect, lsplincom, ggplot2.

Examples

x   <- runif(500)
y   <- sin(4*x)+rnorm(500)
est <- lsprobust(y, x)
lsprobust.plot(est)

lspartition documentation built on Aug. 9, 2019, 1:03 a.m.