Package 'nprobust' reference manual

Title:	Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation
Description:	Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>): lprobust() for local polynomial point estimation and robust bias-corrected inference, lpbwselect() for local polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect() for kernel density bandwidth selection, and nprobust.plot() for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).
Authors:	Sebastian Calonico <[email protected]>, Matias D. Cattaneo <[email protected]>, Max H. Farrell <[email protected]>
Maintainer:	Sebastian Calonico <[email protected]>
License:	GPL-2
Version:	0.4.0
Built:	2024-12-23 06:36:05 UTC
Source:	CRAN

Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation

Description

This package provides tools for data-driven statistical analysis using local polynomial regression (LPR) and kernel density estimation (KDE) methods as described in Calonico, Cattaneo and Farrell (2018): lprobust for local polynomial point estimation and robust bias-corrected inference, lpbwselect for local polynomial bandwidth selection, kdrobust for kernel density point estimation and robust bias-corrected inference, kdbwselect for kernel density bandwidth selection, and nprobust.plot for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019).

Details

Package:	nprobust
Type:	Package
Version:	0.4.0
Date:	2020-08-24
License:	GPL-2

Function for LPR estimation and inference: lprobust
Function for LPR bandwidth selection: lpbwselect
Function for KDE estimation and inference: kdrobust
Function for KDE bandwidth selection: kdbwselect
Function for graphical analysis: nprobust.plot

Author(s)

Sebastian Calonico, Columbia University, New York, NY. [email protected].

Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].

Max H. Farrell, University of Chicago, Chicago, IL. [email protected].

References

Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2018. On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference. Journal of the American Statistical Association, 113(522): 767-779. doi:10.1080/01621459.2017.1285776.

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. doi:10.18637/jss.v091.i08.

Bandwidth Selection Procedures for Kernel Density Estimation and Inference

Description

kdbwselect implements bandwidth selectors for kernel density point estimators and inference procedures developed in Calonico, Cattaneo and Farrell (2018). See also Calonico, Cattaneo and Farrell (2020) for related optimality results. It also implements other bandwidth selectors available in the literature. See Wand and Jones (1995) for background references.

Companion commands are: kdrobust for kernel density point estimation and inference procedures.

A detailed introduction to this command is given in Calonico, Cattaneo and Farrell (2019). For more details, and related Stata and R packages useful for empirical analysis, visit https://nppackages.github.io/.

Usage

kdbwselect(x, eval = NULL, neval = NULL, kernel = "epa", 
bwselect = "mse-dpi", bwcheck=21, imsegrid=30, subset = NULL)
kdbwselect(x, eval = NULL, neval = NULL, kernel = "epa", 
bwselect = "mse-dpi", bwcheck=21, imsegrid=30, subset = NULL)

Arguments

`x`	independent variable.
`eval`	vector of evaluation point(s). By default it uses 30 equally spaced points over to support of `x`.
`neval`	number of quantile-spaced evaluation points on support of `x`. Default is `neval=30`.
`kernel`	kernel function used to construct the kernel estimators. Options are `epa` for the epanechnikov kernel, and `uni` for the uniform kernel. Default is `kernel = epa`.
`bwselect`	bandwidth selection procedure to be used. Options are: `mse-dpi` second-generation DPI implementation of MSE-optimal bandwidth. Default option. `imse-dpi` second-generation DPI implementation of IMSE-optimal bandwidth (computed using grid of evaluation points selected). `imse-rot` ROT implementation of IMSE-optimal bandwidth (computed using grid of evaluation points selected). `ce-dpi` second generation DPI implementation of CE-optimal bandwidth. `ce-rot` ROT implementation of CE-optimal bandwidth. `all` reports all available bandwidth selection procedures. Note: MSE = Mean Square Error; IMSE = Integrated Mean Squared Error; CE = Coverage Error; DPI = Direct Plug-in; ROT = Rule-of-Thumb. For details on implementation see Calonico, Cattaneo and Farrell (2019).
`bwcheck`	if a positive integer is provided, then the selected bandwidth is enlarged so that at least `bwcheck` effective observations are available at each evaluation point. Default is `bwcheck = 15`.
`imsegrid`	number of evaluations points used to compute the IMSE bandwidth selector. Default is `imsegrid = 30`.
`subset`	optional rule specifying a subset of observations to be used.

Value

`Estimate`	A matrix containing `eval` (grid points), `h` and `b` (bandwidths).
`opt`	A list containing options passed to the function.

Author(s)

Sebastian Calonico, Columbia University, New York, NY. [email protected].

Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].

Max H. Farrell, University of Chicago, Chicago, IL. [email protected].

References

Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2020. Coverage Error Optimal Confidence Intervals for Local Polynomial Regression. Working Paper.

Fan, J., and Gijbels, I. 1996. Local polynomial modelling and its applications, London: Chapman and Hall.

Wand, M., and Jones, M. 1995. Kernel Smoothing, Florida: Chapman & Hall/CRC.

Examples

x   <- rnorm(500)
est <- kdbwselect(x)
summary(est)
x   <- rnorm(500)
est <- kdbwselect(x)
summary(est)

Kernel Density Methods with Robust Bias-Corrected Inference

Description

kdrobust implements kernel density point estimators, with robust bias-corrected confidence intervals and inference procedures developed in Calonico, Cattaneo and Farrell (2018). See also Calonico, Cattaneo and Farrell (2020) for related optimality results. It also implements other estimation and inference procedures available in the literature. See Wand and Jones (1995) for background references.

Companion commands: kdbwselect for kernel density data-driven bandwidth selection, and nprobust.plot for plotting results.

Usage

kdrobust(x, eval = NULL, neval = NULL, h = NULL, b = NULL, rho = 1, 
kernel = "epa", bwselect = NULL, bwcheck = 21, imsegrid=30, level = 95, subset = NULL) 
kdrobust(x, eval = NULL, neval = NULL, h = NULL, b = NULL, rho = 1, 
kernel = "epa", bwselect = NULL, bwcheck = 21, imsegrid=30, level = 95, subset = NULL)

Arguments

`x`	independent variable.
`eval`	vector of evaluation point(s). By default it uses 30 equally spaced points over to support of `x`.
`neval`	number of quantile-spaced evaluation points on support of `x`. Default is `neval=30`.
`h`	main bandwidth used to construct the kernel density point estimator. Can be either scalar (same bandwidth for all evaluation points), or vector of same dimension as `eval`. If not specified, bandwidth `h` is computed by the companion command `kdbwselect`.
`b`	bias bandwidth used to construct the bias-correction estimator. Can be either scalar (same bandwidth for all evaluation points), or vector of same dimension as `eval`. By default it is set equal to `h`. If `rho` is set to zero, `b` is computed by the companion command `kdbwselect`.
`rho`	Sets `b=h/rho`. Default is `rho = 1`.
`kernel`	kernel function used to construct local polynomial estimators. Options are `epa` for the epanechnikov kernel, `tri` for the triangular kernel and `uni` for the uniform kernel. Default is `kernel = epa`.
`bwselect`	bandwidth selection procedure to be used via `lpbwselect`. By default it computes `h` and sets `b=h/rho` (with `rho=1` by default). It computes both `h` and `b` if `rho` is set equal to zero. Options are: `mse-dpi` second-generation DPI implementation of MSE-optimal bandwidth. Default option if only one evaluation point is chosen. `imse-dpi` second-generation DPI implementation of IMSE-optimal bandwidth (computed using a grid of evaluation points). Default option if more than one evaluation point is chosen. `imse-rot` ROT implementation of IMSE-optimal bandwidth (computed using a grid of evaluation points). `ce-dpi` second generation DPI implementation of CE-optimal bandwidth. `ce-rot` ROT implementation of CE-optimal bandwidth. `all` reports all available bandwidth selection procedures. Note: MSE = Mean Square Error; IMSE = Integrated Mean Squared Error; CE = Coverage Error; DPI = Direct Plug-in; ROT = Rule-of-Thumb. For details on implementation see Calonico, Cattaneo and Farrell (2019).
`bwcheck`	if a positive integer is provided, then the selected bandwidth is enlarged so that at least `bwcheck` effective observations are available at each evaluation point. Default is `bwcheck = 21`.
`imsegrid`	number of evaluations points used to compute the IMSE bandwidth selector. Default is `imsegrid = 30`.
`level`	confidence level used for confidence intervals; default is `level = 95`.
`subset`	optional rule specifying a subset of observations to be used.

Value

`Estimate`	A matrix containing `eval` (grid points), `h`, `b` (bandwidths), `N` (effective sample sizes), `tau.us` (point estimates with p-th order kernel function), `tau.bc` (bias corrected point estimates, `se.us` (standard error corresponding to `tau.us`), and `se.rb` (robust standard error).
`opt`	A list containing options passed to the function.

Author(s)

Sebastian Calonico, Columbia University, New York, NY. [email protected].

Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].

Max H. Farrell, University of Chicago, Chicago, IL. [email protected].

References

Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2020. Coverage Error Optimal Confidence Intervals for Local Polynomial Regression. Working Paper.

Fan, J., and Gijbels, I. 1996. Local polynomial modelling and its applications, London: Chapman and Hall.

Wand, M., and Jones, M. 1995. Kernel Smoothing, Florida: Chapman & Hall/CRC.

Examples

x   <- rnorm(500)
est <- kdrobust(x)
summary(est)
x   <- rnorm(500)
est <- kdrobust(x)
summary(est)

Bandwidth Selection Procedures for Local Polynomial Regression Estimation and Inference

Description

lpbwselect implements bandwidth selectors for local polynomial regression point estimators and inference procedures developed in Calonico, Cattaneo and Farrell (2018). See also Calonico, Cattaneo and Farrell (2020) for related optimality results. It also implements other bandwidth selectors available in the literature. See Wand and Jones (1995) and Fan and Gijbels (1996) for background references.

Companion commands: lprobust for local polynomial point estimation and inference procedures.

Usage

lpbwselect(y, x, eval = NULL, neval = NULL, p = NULL, deriv = NULL,
kernel = "epa", bwselect = "mse-dpi", bwcheck = 21, bwregul = 1, 
imsegrid = 30, vce = "nn", cluster = NULL,
nnmatch = 3, interior = FALSE, subset = NULL)
lpbwselect(y, x, eval = NULL, neval = NULL, p = NULL, deriv = NULL,
kernel = "epa", bwselect = "mse-dpi", bwcheck = 21, bwregul = 1, 
imsegrid = 30, vce = "nn", cluster = NULL,
nnmatch = 3, interior = FALSE, subset = NULL)

Arguments

`y`	dependent variable.
`x`	independent variable.
`eval`	vector of evaluation point(s). By default it uses 30 equally spaced points over to support of `x`.
`neval`	number of quantile-spaced evaluation points on support of `x`. Default is `neval=30`.
`p`	polynomial order used to construct point estimator; default is `p = 1` (local linear regression).
`deriv`	derivative order of the regression function to be estimated. Default is `deriv=0` (regression function).
`kernel`	kernel function used to construct local polynomial estimators. Options are `epa` for the epanechnikov kernel, `tri` for the triangular kernel, `uni` for the uniform kernel and `gau` for the gaussian kernel. Default is `kernel = epa`.
`bwselect`	bandwidth selection procedure to be used. Options are: `mse-dpi` second-generation DPI implementation of MSE-optimal bandwidth. Default option. `mse-rot` ROT implementation of MSE-optimal bandwidth. `imse-dpi` second-generation DPI implementation of IMSE-optimal bandwidth (computed using grid of evaluation points selected). `imse-rot` ROT implementation of IMSE-optimal bandwidth (computed using grid of evaluation points selected). `ce-dpi` second generation DPI implementation of CE-optimal bandwidth. `ce-rot` ROT implementation of CE-optimal bandwidth. `all` reports all available bandwidth selection procedures. Note: MSE = Mean Square Error; IMSE = Integrated Mean Squared Error; CE = Coverage Error; DPI = Direct Plug-in; ROT = Rule-of-Thumb. For details on implementation see Calonico, Cattaneo and Farrell (2019).
`bwcheck`	if a positive integer is provided, then the selected bandwidth is enlarged so that at least `bwcheck` effective observations are available at each evaluation point. Default is `bwcheck = 21`.
`bwregul`	specifies scaling factor for the regularization term added to the denominator of bandwidth selectors. Setting `bwregul = 0` removes the regularization term from the bandwidth selectors. Default is `bwregul = 1`.
`imsegrid`	number of evaluations points used to compute the IMSE bandwidth selector. Default is `imsegrid = 30`.
`vce`	procedure used to compute the variance-covariance matrix estimator. Options are: `nn` heteroskedasticity-robust nearest neighbor variance estimator with `nnmatch` the (minimum) number of neighbors to be used. Default choice. `hc0` heteroskedasticity-robust plug-in residuals variance estimator without weights. `hc1` heteroskedasticity-robust plug-in residuals variance estimator with `hc1` weights. `hc2` heteroskedasticity-robust plug-in residuals variance estimator with `hc2` weights. `hc3` heteroskedasticity-robust plug-in residuals variance estimator with `hc3` weights.
`cluster`	indicates the cluster ID variable used for cluster-robust variance estimation with degrees-of-freedom weights. By default it is combined with `vce=nn` for cluster-robust nearest neighbor variance estimation. Another option is plug-in residuals combined with `vce=hc1`.
`nnmatch`	to be combined with for `vce=nn` for heteroskedasticity-robust nearest neighbor variance estimator with `nnmatch` indicating the minimum number of neighbors to be used. Default is `nnmatch=3`

`interior`	if TRUE, all evaluation points are assumed to be interior points. This option affects only data-driven bandwidth selection via `lpbwselect`. Default is `interior = FALSE`.
`subset`	optional rule specifying a subset of observations to be used.

Value

`Estimate`	A matrix containing `grid` (grid points), `h` and `b` (bandwidths), `N` (sample size)
`opt`	A list containing options passed to the function.

Author(s)

Sebastian Calonico, Columbia University, New York, NY. [email protected].

Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].

Max H. Farrell, University of Chicago, Chicago, IL. [email protected].

References

Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2020. Coverage Error Optimal Confidence Intervals for Local Polynomial Regression. Working Paper.

Fan, J., and Gijbels, I. 1996. Local polynomial modelling and its applications, London: Chapman and Hall.

Wand, M., and Jones, M. 1995. Kernel Smoothing, Florida: Chapman & Hall/CRC.

Examples

x   <- runif(500)
y   <- sin(4*x) + rnorm(500)
est <- lpbwselect(y,x)
summary(est)
x   <- runif(500)
y   <- sin(4*x) + rnorm(500)
est <- lpbwselect(y,x)
summary(est)

Local Polynomial Methods with Robust Bias-Corrected Inference

Description

lprobust implements local polynomial regression point estimators, with robust bias-corrected confidence intervals and inference procedures developed in Calonico, Cattaneo and Farrell (2018). See also Calonico, Cattaneo and Farrell (2020) for related optimality results. It also implements other estimation and inference procedures available in the literature. See Wand and Jones (1995) and Fan and Gijbels (1996) for background references.

Companion commands: lpbwselect for local polynomial data-driven bandwidth selection, and nprobust.plot for plotting results.

Usage

lprobust(y, x, eval = NULL, neval = NULL, p = NULL, deriv = NULL, 
h = NULL, b = NULL, rho = 1, kernel = "epa", bwselect = NULL, 
bwcheck = 21, bwregul = 1, imsegrid = 30, vce = "nn", covgrid = FALSE, 
cluster = NULL, nnmatch = 3, level = 95, interior = FALSE, subset = NULL) 
lprobust(y, x, eval = NULL, neval = NULL, p = NULL, deriv = NULL, 
h = NULL, b = NULL, rho = 1, kernel = "epa", bwselect = NULL, 
bwcheck = 21, bwregul = 1, imsegrid = 30, vce = "nn", covgrid = FALSE, 
cluster = NULL, nnmatch = 3, level = 95, interior = FALSE, subset = NULL)

Arguments

`y`	dependent variable.
`x`	independent variable.
`eval`	vector of evaluation point(s). By default it uses 30 equally spaced points over to support of `x`.
`neval`	number of quantile-spaced evaluation points on support of `x`. Default is `neval=30`.
`p`	polynomial order used to construct point estimator; default is `p = 1` (local linear regression).
`deriv`	derivative order of the regression function to be estimated. Default is `deriv=0` (regression function).
`h`	main bandwidth used to construct local polynomial point estimator. Can be either scalar (same bandwidth for all evaluation points), or vector of same dimension as `eval`. If not specified, bandwidth `h` is computed by the companion command `lpbwselect`.
`b`	bias bandwidth used to construct the bias-correction estimator. Can be either scalar (same bandwidth for all evaluation points), or vector of same dimension as `eval`. By default it is set equal to `h`. If `rho` is set to zero, `b` is computed by the companion command `lpbwselect`.
`rho`	Sets `b=h/rho`. Default is `rho = 1`.
`kernel`	kernel function used to construct local polynomial estimators. Options are `epa` for the epanechnikov kernel, `tri` for the triangular kernel, `uni` for the uniform kernel and `gau` for the gaussian kernel. Default is `kernel = epa`.
`bwselect`	bandwidth selection procedure to be used via `lpbwselect`. By default it computes `h` and sets `b=h/rho` (with `rho=1` by default). It computes both `h` and `b` if `rho` is set equal to zero. Options are: `mse-dpi` second-generation DPI implementation of MSE-optimal bandwidth. Default option if only one evaluation point is chosen. `mse-rot` ROT implementation of MSE-optimal bandwidth. `imse-dpi` second-generation DPI implementation of IMSE-optimal bandwidth (computed using a grid of evaluation points). Default option if more than one evaluation point is chosen. `imse-rot` ROT implementation of IMSE-optimal bandwidth (computed using a grid of evaluation points). `ce-dpi` second generation DPI implementation of CE-optimal bandwidth. `ce-rot` ROT implementation of CE-optimal bandwidth. `all` reports all available bandwidth selection procedures. Note: MSE = Mean Square Error; IMSE = Integrated Mean Squared Error; CE = Coverage Error; DPI = Direct Plug-in; ROT = Rule-of-Thumb. For details on implementation see Calonico, Cattaneo and Farrell (2019).
`bwcheck`	if a positive integer is provided, then the selected bandwidth is enlarged so that at least `bwcheck` effective observations are available at each evaluation point. Default is `bwcheck = 21`.
`bwregul`	specifies scaling factor for the regularization term added to the denominator of bandwidth selectors. Setting `bwregul = 0` removes the regularization term from the bandwidth selectors. Default is `bwregul = 1`.
`imsegrid`	number of evaluations points used to compute the IMSE bandwidth selector. Default is `imsegrid = 30`.
`vce`	procedure used to compute the variance-covariance matrix estimator. Options are: `nn` heteroskedasticity-robust nearest neighbor variance estimator with `nnmatch` the (minimum) number of neighbors to be used. Default choice. `hc0` heteroskedasticity-robust plug-in residuals variance estimator without weights. `hc1` heteroskedasticity-robust plug-in residuals variance estimator with `hc1` weights. `hc2` heteroskedasticity-robust plug-in residuals variance estimator with `hc2` weights. `hc3` heteroskedasticity-robust plug-in residuals variance estimator with `hc3` weights.
`covgrid`	if TRUE, it computes two covariance matrices (cov.us and cov.rb) for classical and robust covariances across point estimators over the grid of evaluation points.
`cluster`	indicates the cluster ID variable used for cluster-robust variance estimation with degrees-of-freedom weights. By default it is combined with `vce=nn` for cluster-robust nearest neighbor variance estimation. Another option is plug-in residuals combined with `vce=hc1`.
`nnmatch`	to be combined with for `vce=nn` for heteroskedasticity-robust nearest neighbor variance estimator with `nnmatch` indicating the minimum number of neighbors to be used. Default is `nnmatch=3`

`level`	confidence level used for confidence intervals; default is `level = 95`.
`interior`	if TRUE, all evaluation points are assumed to be interior points. This option affects only data-driven bandwidth selection via `lpbwselect`. Default is `interior = FALSE`.
`subset`	optional rule specifying a subset of observations to be used.

Value

`Estimate`	A matrix containing `eval` (grid points), `h`, `b` (bandwidths), `N` (effective sample sizes), `m.us` (point estimates with p-th order local polynomial), `tau.bc` (bias corrected point estimates with (p+1)-th order local polynomial, `se.us` (standard error corresponding to `tau.us`), and `se.rb` (robust standard error).
`opt`	A list containing options passed to the function.

Author(s)

Sebastian Calonico, Columbia University, New York, NY. [email protected].

Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].

Max H. Farrell, University of Chicago, Chicago, IL. [email protected].

References

Calonico, S., M. D. Cattaneo, and M. H. Farrell. 2020. Coverage Error Optimal Confidence Intervals for Local Polynomial Regression. Working Paper.

Fan, J., and Gijbels, I. 1996. Local polynomial modelling and its applications, London: Chapman and Hall.

Wand, M., and Jones, M. 1995. Kernel Smoothing, Florida: Chapman & Hall/CRC.

Examples

x   <- runif(500)
y   <- sin(4*x) + rnorm(500)
est <- lprobust(y,x)
summary(est)
x   <- runif(500)
y   <- sin(4*x) + rnorm(500)
est <- lprobust(y,x)
summary(est)

Graphical Presentation of Results from `nprobust` Package.

Description

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/.

Usage

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)
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)

Arguments

`...`	Objects returned by `kdrobust` or `lprobust`.
`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"` or `"both"`, how the point estimates are plotted. If more than one is provided, they will be applied to data series accordingly.
`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 data series accordingly.
`title`, `xlabel`, `ylabel`	Strings, title of the plot and labels for x- and 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.
`CIshade`	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.
`CIcol`	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: lprobust for local polynomial-based regression functions and derivatives estimation.

Value

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

Author(s)

Sebastian Calonico, Columbia University, New York, NY. [email protected].

Matias D. Cattaneo, Princeton University, Princeton, NJ. [email protected].

Max H. Farrell, University of Chicago, Chicago, IL. [email protected].

References

Examples

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

Package 'nprobust'

Help Index

Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation

Description

Details

Author(s)

References

Bandwidth Selection Procedures for Kernel Density Estimation and Inference

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Kernel Density Methods with Robust Bias-Corrected Inference

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Bandwidth Selection Procedures for Local Polynomial Regression Estimation and Inference

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Local Polynomial Methods with Robust Bias-Corrected Inference

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Graphical Presentation of Results from nprobust Package.

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Graphical Presentation of Results from `nprobust` Package.