nprobust
Package.nprobust.plot
plots estimated density and regression function using the nprobust
package. A detailed introduction to this command is given in Calonico, Cattaneo and Farrell (2019).
Companion commands: lprobust
for local polynomial point estimation and inference procedures, and kdrobust
for kernel density point estimation and inference procedures.
For more details, and related Stata and R packages useful for empirical analysis, visit https://nppackages.github.io/.
nprobust.plot(..., alpha = NULL, type = NULL, CItype = NULL,
title = "", xlabel = "", ylabel = "", lty = NULL, lwd = NULL,
lcol = NULL, pty = NULL, pwd = NULL, pcol = NULL, CIshade = NULL,
CIcol = NULL, legendTitle = NULL, legendGroups = NULL)
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.
String, one of "line"
(default), "points"
or "both"
, how
the point estimates are plotted. If more than one is provided, they will be applied to data series
accordingly.
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.
Strings, title of the plot and labels for x- and y-axis.
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.
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.
String, title of legend.
String Vector, group names used in legend.
A standard ggplot2
object is returned, hence can be used for further customization.
Companion command: lprobust
for local polynomial-based regression functions and derivatives estimation.
Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2019. nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference. Journal of Statistical Software, 91(8): 1-33. http://dx.doi.org/10.18637/jss.v091.i08.
# NOT RUN {
x <- runif(500)
y <- sin(4*x) + rnorm(500)
est <- lprobust(y,x)
nprobust.plot(est)
# }
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