# np.plot: General Purpose Plotting of Nonparametric Objects In np: Nonparametric Kernel Smoothing Methods for Mixed Data Types

 npplot R Documentation

## General Purpose Plotting of Nonparametric Objects

### Description

npplot is invoked by plot and generates plots of nonparametric statistical objects such as regressions, quantile regressions, partially linear regressions, single-index models, densities and distributions, given training data and a bandwidth object.

### Usage

npplot(bws = stop("'bws' has not been set"), ..., random.seed = 42)

## S3 method for class 'bandwidth'
npplot(bws,
xdat,
data = NULL,
xq = 0.5,
xtrim = 0.0,
neval = 50,
common.scale = TRUE,
perspective = TRUE,
main = NULL,
type = NULL,
border = NULL,
cex.axis = NULL,
cex.lab = NULL,
cex.main = NULL,
cex.sub = NULL,
col = NULL,
ylab = NULL,
xlab = NULL,
zlab = NULL,
sub = NULL,
ylim = NULL,
xlim = NULL,
zlim = NULL,
lty = NULL,
lwd = NULL,
theta = 0.0,
phi = 10.0,
view = c("rotate","fixed"),
plot.behavior = c("plot","plot-data","data"),
plot.errors.method = c("none","bootstrap","asymptotic"),
plot.errors.boot.method = c("inid", "fixed", "geom"),
plot.errors.boot.blocklen = NULL,
plot.errors.boot.num = 399,
plot.errors.center = c("estimate","bias-corrected"),
plot.errors.type = c("standard","quantiles"),
plot.errors.quantiles = c(0.025,0.975),
plot.errors.style = c("band","bar"),
plot.errors.bar = c("|","I"),
plot.errors.bar.num = min(neval,25),
plot.bxp = FALSE,
plot.bxp.out = TRUE,
plot.par.mfrow = TRUE,
...,
random.seed)

## S3 method for class 'conbandwidth'
npplot(bws,
xdat,
ydat,
data = NULL,
xq = 0.5,
yq = 0.5,
xtrim = 0.0,
ytrim = 0.0,
neval = 50,
common.scale = TRUE,
perspective = TRUE,
main = NULL,
type = NULL,
border = NULL,
cex.axis = NULL,
cex.lab = NULL,
cex.main = NULL,
cex.sub = NULL,
col = NULL,
ylab = NULL,
xlab = NULL,
zlab = NULL,
sub = NULL,
ylim = NULL,
xlim = NULL,
zlim = NULL,
lty = NULL,
lwd = NULL,
theta = 0.0,
phi = 10.0,
tau = 0.5,
view = c("rotate","fixed"),
plot.behavior = c("plot","plot-data","data"),
plot.errors.method = c("none","bootstrap","asymptotic"),
plot.errors.boot.method = c("inid", "fixed", "geom"),
plot.errors.boot.blocklen = NULL,
plot.errors.boot.num = 399,
plot.errors.center = c("estimate","bias-corrected"),
plot.errors.type = c("standard","quantiles"),
plot.errors.quantiles = c(0.025,0.975),
plot.errors.style = c("band","bar"),
plot.errors.bar = c("|","I"),
plot.errors.bar.num = min(neval,25),
plot.bxp = FALSE,
plot.bxp.out = TRUE,
plot.par.mfrow = TRUE,
...,
random.seed)

## S3 method for class 'plbandwidth'
npplot(bws,
xdat,
ydat,
zdat,
data = NULL,
xq = 0.5,
zq = 0.5,
xtrim = 0.0,
ztrim = 0.0,
neval = 50,
common.scale = TRUE,
perspective = TRUE,
main = NULL,
type = NULL,
border = NULL,
cex.axis = NULL,
cex.lab = NULL,
cex.main = NULL,
cex.sub = NULL,
col = NULL,
ylab = NULL,
xlab = NULL,
zlab = NULL,
sub = NULL,
ylim = NULL,
xlim = NULL,
zlim = NULL,
lty = NULL,
lwd = NULL,
theta = 0.0,
phi = 10.0,
view = c("rotate","fixed"),
plot.behavior = c("plot","plot-data","data"),
plot.errors.method = c("none","bootstrap","asymptotic"),
plot.errors.boot.method = c("inid", "fixed", "geom"),
plot.errors.boot.blocklen = NULL,
plot.errors.boot.num = 399,
plot.errors.center = c("estimate","bias-corrected"),
plot.errors.type = c("standard","quantiles"),
plot.errors.quantiles = c(0.025,0.975),
plot.errors.style = c("band","bar"),
plot.errors.bar = c("|","I"),
plot.errors.bar.num = min(neval,25),
plot.bxp = FALSE,
plot.bxp.out = TRUE,
plot.par.mfrow = TRUE,
...,
random.seed)

## S3 method for class 'rbandwidth'
npplot(bws,
xdat,
ydat,
data = NULL,
xq = 0.5,
xtrim = 0.0,
neval = 50,
common.scale = TRUE,
perspective = TRUE,
main = NULL,
type = NULL,
border = NULL,
cex.axis = NULL,
cex.lab = NULL,
cex.main = NULL,
cex.sub = NULL,
col = NULL,
ylab = NULL,
xlab = NULL,
zlab = NULL,
sub = NULL,
ylim = NULL,
xlim = NULL,
zlim = NULL,
lty = NULL,
lwd = NULL,
theta = 0.0,
phi = 10.0,
view = c("rotate","fixed"),
plot.behavior = c("plot","plot-data","data"),
plot.errors.method = c("none","bootstrap","asymptotic"),
plot.errors.boot.num = 399,
plot.errors.boot.method = c("inid", "fixed", "geom"),
plot.errors.boot.blocklen = NULL,
plot.errors.center = c("estimate","bias-corrected"),
plot.errors.type = c("standard","quantiles"),
plot.errors.quantiles = c(0.025,0.975),
plot.errors.style = c("band","bar"),
plot.errors.bar = c("|","I"),
plot.errors.bar.num = min(neval,25),
plot.bxp = FALSE,
plot.bxp.out = TRUE,
plot.par.mfrow = TRUE,
...,
random.seed)

## S3 method for class 'scbandwidth'
npplot(bws,
xdat,
ydat,
zdat = NULL,
data = NULL,
xq = 0.5,
zq = 0.5,
xtrim = 0.0,
ztrim = 0.0,
neval = 50,
common.scale = TRUE,
perspective = TRUE,
main = NULL,
type = NULL,
border = NULL,
cex.axis = NULL,
cex.lab = NULL,
cex.main = NULL,
cex.sub = NULL,
col = NULL,
ylab = NULL,
xlab = NULL,
zlab = NULL,
sub = NULL,
ylim = NULL,
xlim = NULL,
zlim = NULL,
lty = NULL,
lwd = NULL,
theta = 0.0,
phi = 10.0,
view = c("rotate","fixed"),
plot.behavior = c("plot","plot-data","data"),
plot.errors.method = c("none","bootstrap","asymptotic"),
plot.errors.boot.num = 399,
plot.errors.boot.method = c("inid", "fixed", "geom"),
plot.errors.boot.blocklen = NULL,
plot.errors.center = c("estimate","bias-corrected"),
plot.errors.type = c("standard","quantiles"),
plot.errors.quantiles = c(0.025,0.975),
plot.errors.style = c("band","bar"),
plot.errors.bar = c("|","I"),
plot.errors.bar.num = min(neval,25),
plot.bxp = FALSE,
plot.bxp.out = TRUE,
plot.par.mfrow = TRUE,
...,
random.seed)

## S3 method for class 'sibandwidth'
npplot(bws,
xdat,
ydat,
data = NULL,
common.scale = TRUE,
main = NULL,
type = NULL,
cex.axis = NULL,
cex.lab = NULL,
cex.main = NULL,
cex.sub = NULL,
col = NULL,
ylab = NULL,
xlab = NULL,
sub = NULL,
ylim = NULL,
xlim = NULL,
lty = NULL,
lwd = NULL,
plot.behavior = c("plot","plot-data","data"),
plot.errors.method = c("none","bootstrap","asymptotic"),
plot.errors.boot.num = 399,
plot.errors.boot.method = c("inid", "fixed", "geom"),
plot.errors.boot.blocklen = NULL,
plot.errors.center = c("estimate","bias-corrected"),
plot.errors.type = c("standard","quantiles"),
plot.errors.quantiles = c(0.025,0.975),
plot.errors.style = c("band","bar"),
plot.errors.bar = c("|","I"),
plot.errors.bar.num = NULL,
plot.par.mfrow = TRUE,
...,
random.seed)



### Arguments

 bws a bandwidth specification. This should be a bandwidth object returned from an invocation of npudensbw, npcdensbw, npregbw, npplregbw, npindexbw, or npscoefbw. ... additional arguments supplied to control various aspects of plotting, depending on the type of object to be plotted, detailed below. data an optional data frame, list or environment (or object coercible to a data frame by as.data.frame) containing the variables in the model. If not found in data, the variables are taken from environment(bws), typically the environment where the bandwidth object was generated. xdat a p-variate data frame of sample realizations (training data). ydat a q-variate data frame of sample realizations (training data). In a regression or conditional density context, this is the dependent data. zdat a p-variate data frame of sample realizations (training data). xq a numeric p-vector of quantiles. Each element i of xq corresponds to the ith column of txdat. Defaults to the median (0.5). See details. yq a numeric q-vector of quantiles. Each element i of yq corresponds to the ith column of tydat. Only to be specified in a conditional density context. Defaults to the median (0.5). See details. zq a numeric q-vector of quantiles. Each element i of zq corresponds to the ith column of tzdat. Only to be specified in a semiparametric model context. Defaults to the median (0.5). See details. xtrim a numeric p-vector of quantiles. Each element i of xtrim corresponds to the ith column of txdat. Defaults to 0.0. See details. ytrim a numeric q-vector of quantiles. Each element i of ytrim corresponds to the ith column of tydat. Defaults to 0.0. See details. ztrim a numeric q-vector of quantiles. Each element i of ztrim corresponds to the ith column of tzdat. Defaults to 0.0. See details. neval an integer specifying the number of evaluation points. Only applies to continuous variables however, as discrete variables will be evaluated once at each category. Defaults to 50. common.scale a logical value specifying whether or not all graphs are to be plotted on a common scale. Defaults to TRUE. perspective a logical value specifying whether a perspective plot should be displayed (if possible). Defaults to TRUE. gradients a logical value specifying whether gradients should be plotted (if possible). Defaults to FALSE. main optional title, see title. Defaults to NULL. sub optional subtitle, see sub. Defaults to NULL. type optional character indicating the type of plotting; actually any of the types as in plot.default. Defaults to NULL. border optional character indicating the border of plotting; actually any of the borders as in plot.default. Defaults to NULL. cex.axis The magnification to be used for axis annotation relative to the current setting of cex. cex.lab The magnification to be used for x and y labels relative to the current setting of cex. cex.main The magnification to be used for main titles relative to the current setting of cex. cex.sub The magnification to be used for sub-titles relative to the current setting of cex. col optional character indicating the color of plotting; actually any of the colours as in plot.default. Defaults to NULL. ylab optional character indicating the y axis label of plotting; actually any of the ylabs as in plot.default. Defaults to NULL. xlab optional character indicating the x axis label of plotting; actually any of the xlabs as in plot.default. Defaults to NULL. zlab optional character indicating the z axis label of plotting; actually any of the zlabs as in plot.default. Defaults to NULL. ylim optional a two-element numeric vector of the minimum and maximum y plotting limits. Defaults to NULL. xlim a two-element numeric vector of the minimum and maximum x plotting limits. Defaults to NULL. zlim a two-element numeric vector of the minimum and maximum z plotting limits. Defaults to NULL. lty a numeric value indicating the line type of plotting; actually any of the ltys as in plot.default. Defaults to 1. lwd a numeric value indicating the width of the line of plotting; actually any of the lwds as in plot.default. Defaults to 1. theta a numeric value specifying the starting azimuthal angle of the perspective plot. Defaults to 0.0. phi a numeric value specifying the starting zenith angle of the perspective plot. Defaults to 10.0. tau a numeric value specifying the tauth quantile is desired when plotting quantile regressions. Defaults to 0.5. view a character string used to specify the viewing mode of the perspective plot. Can be set as rotate or fixed. Defaults to rotate. plot.behavior a character string used to specify the net behavior of npplot. Can be set as plot, plot-data or data. Defaults to plot. See value. plot.errors.method a character string used to specify the method to calculate errors. Can be set as none, bootstrap, or asymptotic. Defaults to none. plot.errors.boot.method a character string used to specify the bootstrap method. Can be set as inid, fixed, or geom (see below for details). Defaults to inid. plot.errors.boot.blocklen an integer used to specify the block length b for the fixed or geom bootstrap (see below for details). plot.errors.boot.num an integer used to specify the number of bootstrap samples to use for the calculation of errors. Defaults to 399. plot.errors.center a character string used to specify where to center the errors on the plot(s). Can be set as estimate or bias-corrected. Defaults to estimate. plot.errors.type a character string used to specify the type of error to calculate. Can be set as standard or quantiles. Defaults to standard. plot.errors.quantiles a numeric vector specifying the quantiles of the statistic to calculate for the purpose of error plotting. Defaults to c(0.025,0.975). plot.errors.style a character string used to specify the style of error plotting. Can be set as band or bar. Defaults to band. Bands are not drawn for discrete variables. plot.errors.bar a character string used to specify the error bar shape. Can be set as | (vertical bar character) for a dashed vertical bar, or as I for an ‘I’ shaped error bar with horizontal bounding bars. Defaults to |. plot.errors.bar.num an integer specifying the number of error bars to plot. Defaults to min(neval,25). plot.bxp a logical value specifying whether boxplots should be produced when appropriate. Defaults to FALSE. plot.bxp.out a logical value specifying whether outliers should be plotted on boxplots. Defaults to TRUE. plot.par.mfrow a logical value specifying whether par(mfrow=c(,)) should be called before plotting. Defaults to TRUE. random.seed an integer used to seed R's random number generator. This ensures replicability of the bootstrapped errors. Defaults to 42.

### Details

npplot is a general purpose plotting routine for visually exploring objects generated by the np library, such as regressions, quantile regressions, partially linear regressions, single-index models, densities and distributions. There is no need to call npplot directly as it is automatically invoked when plot is used with an object generated by the np package.

Visualizing one and two dimensional datasets is a straightforward process. The default behavior of npplot is to generate a standard 2D plot to visualize univariate data, and a perspective plot for bivariate data. When visualizing higher dimensional data, npplot resorts to plotting a series of 1D slices of the data. For a slice along dimension i, all other variables at indices j != i are held constant at the quantiles specified in the jth element of xq. The default is the median.

The slice itself is evaluated on a uniformly spaced sequence of neval points. The interval of evaluation is determined by the training data. The default behavior is to evaluate from min(txdat[,i]) to max(txdat[,i]). The xtrim variable allows for control over this behavior. When xtrim is set, data is evaluated from the xtrim[i]th quantile of txdat[,i] to the 1.0-xtrim[i]th quantile of txdat[,i].

Furthermore, xtrim can be set to a negative value in which case it will expand the limits of the evaluation interval beyond the support of the training data, by measuring the distance between min(txdat[,i]) and the xtrim[i]th quantile of txdat[,i], and extending the support by that distance on the lower limit of the interval. npplot uses an analogous procedure to extend the upper limit of the interval.

Bootstrap resampling is conducted pairwise on (y,X,Z) (i.e., by resampling from rows of the (y,X) data or (y,X,Z) data where appropriate). inid admits general heteroskedasticity of unknown form, though it does not allow for dependence. fixed conducts Kunsch's (1988) block bootstrap for dependent data, while geom conducts Politis and Romano's (1994) stationary bootstrap.

For consistency of the block and stationary bootstrap, the (mean) block length b should grow with the sample size n at an appropriate rate. If b is not given, then a default growth rate of const n^{1/3} is used. This rate is “optimal” under certain conditions (see Politis and Romano (1994) for more details). However, in general the growth rate depends on the specific properties of the DGP. A default value for const (3.15) has been determined by a Monte Carlo simulation using a Gaussian AR(1) process (AR(1)-parameter of 0.5, 500 observations). const has been chosen such that the mean square error for the bootstrap estimate of the variance of the empirical mean is minimized.

### Value

Setting plot.behavior will instruct npplot what data to return. Option summary:
plot: instruct npplot to just plot the data and return NULL
plot-data: instruct npplot to plot the data and return the data used to generate the plots. The data will be a list of objects of the appropriate type, with one object per plot. For example, invoking npplot on 3D density data will have it return a list of three npdensity objects. If biases were calculated, they are stored in a component named bias
data: instruct npplot to generate data only and no plots

### Usage Issues

If you are using data of mixed types, then it is advisable to use the data.frame function to construct your input data and not cbind, since cbind will typically not work as intended on mixed data types and will coerce the data to the same type.

### Author(s)

Tristen Hayfield tristen.hayfield@gmail.com, Jeffrey S. Racine racinej@mcmaster.ca

### References

Aitchison, J. and C.G.G. Aitken (1976), “Multivariate binary discrimination by the kernel method,” Biometrika, 63, 413-420.

Hall, P. and J.S. Racine and Q. Li (2004), “Cross-validation and the estimation of conditional probability densities,” Journal of the American Statistical Association, 99, 1015-1026.

Kunsch, H.R. (1989), “The jackknife and the bootstrap for general stationary observations,” The Annals of Statistics, 17, 1217-1241.

Li, Q. and J.S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press.

Pagan, A. and A. Ullah (1999), Nonparametric Econometrics, Cambridge University Press.

Politis, D.N. and J.P. Romano (1994), “The stationary bootstrap,” Journal of the American Statistical Association, 89, 1303-1313.

Scott, D.W. (1992), Multivariate Density Estimation. Theory, Practice and Visualization, New York: Wiley.

Silverman, B.W. (1986), Density Estimation, London: Chapman and Hall.

Wang, M.C. and J. van Ryzin (1981), “A class of smooth estimators for discrete distributions,” Biometrika, 68, 301-309.

### Examples

## Not run:
# EXAMPLE 1: For this example, we load Giovanni Baiocchi's Italian GDP
# panel (see Italy for details), then create a data frame in which year
# is an ordered factor, GDP is continuous, compute bandwidths using
# likelihood cross-validation, then create a grid of data on which the
# density will be evaluated for plotting purposes

data("Italy")
attach(Italy)

data <- data.frame(ordered(year), gdp)

# Compute bandwidths using likelihood cross-validation (default). Note
# that this may take a minute or two depending on the speed of your
# computer...

bw <- npudensbw(dat=data)

# You can always do things manually, as the following example demonstrates

# Create an evaluation data matrix

year.seq <- sort(unique(year))
gdp.seq <- seq(1,36,length=50)
data.eval <- expand.grid(year=year.seq,gdp=gdp.seq)

# Generate the estimated density computed for the evaluation data

fhat <- fitted(npudens(tdat = data, edat = data.eval, bws=bw))

# Coerce the data into a matrix for plotting with persp()

f <- matrix(fhat, length(unique(year)), 50)

# Next, create a 3D perspective plot of the PDF f

persp(as.integer(levels(year.seq)), gdp.seq, f, col="lightblue",
ticktype="detailed", ylab="GDP", xlab="Year", zlab="Density",
theta=300, phi=50)

# Sleep for 5 seconds so that we can examine the output...

Sys.sleep(5)

# However, npplot simply streamlines this process and aids in the
# visualization process (<ctrl>-C will interrupt on *NIX systems, <esc>
# will interrupt on MS Windows systems).

plot(bw)

# npplot also streamlines construction of variability bounds (<ctrl>-C
# will interrupt on *NIX systems, <esc> will interrupt on MS Windows
# systems)

plot(bw, plot.errors.method = "asymptotic")

# EXAMPLE 2: For this example, we simulate multivariate data, and plot the
# partial regression surfaces for a locally linear estimator and its
# derivatives.

set.seed(123)

n <- 100

x1 <- runif(n)
x2 <- runif(n)
x3 <- runif(n)
x4 <- rbinom(n, 2, .3)

y <- 1 + x1 + x2 + x3 + x4 + rnorm(n)

X <- data.frame(x1, x2, x3, ordered(x4))

bw <- npregbw(xdat=X, ydat=y, regtype="ll", bwmethod="cv.aic")

plot(bw)

# Sleep for 5 seconds so that we can examine the output...

Sys.sleep(5)

# Plot the partial regression surfaces with bias-corrected bootstrapped
# nonparametric confidence intervals... this may take a minute or two
# depending on the speed of your computer as the bootstrapping must be
# completed prior to results being displayed...

plot(bw,
plot.errors.method="bootstrap",
plot.errors.center="bias-corrected",
plot.errors.type="quantiles")

# Note - if you wished to create, say, a postscript graph for inclusion
# in, say, a latex document, use R's postscript' command to switch to
# the postscript device (turn off the device once completed). The
# following will create a disk file graph.ps' that can be pulled into,
# say, a latex document via \includegraphics[width=5in, height=5in,
# angle=270]{graph.ps}

# Note - make sure to include the graphicx package in your latex
# you might want to use larger fonts, which can be achieved by adding the
# pointsize= argument, e.g., postscript(file="graph.ps", pointsize=20)

postscript(file="graph.ps")
plot(bw)
dev.off()

# The following latex file compiled in the same directory as graph.ps
# ought to work (remove the #s and place in a file named, e.g.,
# test.tex).
# \documentclass[]{article}
# \usepackage{graphicx}
# \begin{document}
# \begin{figure}[!ht]
# \includegraphics[width=5in, height=5in, angle=270]{graph.ps}
# \caption{Local linear partial regression surfaces.}
# \end{figure}
# \end{document}

# EXAMPLE 3: This example demonstrates how to retrieve plotting data from
# npplot(). When npplot() is called with the arguments
# plot.behavior="plot-data"' (or "data"), it returns plotting objects
# named r1, r2, and so on (rg1, rg2, and so on when gradients=TRUE' is
# set).  Each plotting object's index (1,2,...) corresponds to the index
# of the explanatory data data frame xdat (and zdat if appropriate).

# Take the cps71 data by way of example. In this case, there is only one
# object returned by default, r1', since xdat is univariate.

data("cps71")
attach(cps71)

# Compute bandwidths for local linear regression using cv.aic...

bw <- npregbw(xdat=age,ydat=logwage,regtype="ll",bwmethod="cv.aic")

# Generate the plot and return plotting data, and store output in
# plot.out' (NOTE: the call to plot.behavior' is necessary).

plot.out <- plot(bw,
perspective=FALSE,
plot.errors.method="bootstrap",
plot.errors.boot.num=25,
plot.behavior="plot-data")

# Now grab the r1 object that npplot plotted on the screen, and take
# what you need. First, take the output, lower error bound and upper
# error bound...

logwage.eval <- fitted(plot.out$r1) logwage.se <- se(plot.out$r1)
logwage.lower.ci <- logwage.eval + logwage.se[,1]
logwage.upper.ci <- logwage.eval + logwage.se[,2]

# Next grab the x data evaluation data. xdat is a data.frame(), so we
# need to coerce it into a vector (take the first column' of data frame
# even though there is only one column)

age.eval <- plot.out$r1$eval[,1]

# Now we could plot this if we wished, or direct it to whatever end use
# we envisioned. We plot the results using R's plot() routines...

plot(age,logwage,cex=0.2,xlab="Age",ylab="log(Wage)")
lines(age.eval,logwage.eval)
lines(age.eval,logwage.lower.ci,lty=3)
lines(age.eval,logwage.upper.ci,lty=3)

# If you wanted npplot() data for gradients, you would use the argument
# gradients=TRUE' in the call to npplot() as the following
# demonstrates...

plot.out <- plot(bw,
perspective=FALSE,
plot.errors.method="bootstrap",
plot.errors.boot.num=25,
plot.behavior="plot-data",

# Now grab object that npplot() plotted on the screen. First, take the
# output, lower error bound and upper error bound... note that gradients
# are stored in objects rg1, rg2 etc.

grad.eval <- gradients(plot.out$rg1) grad.se <- gradients(plot.out$rg1, errors = TRUE)

# Next grab the x evaluation data. xdat is a data.frame(), so we need to
# coerce it into a vector (take first column' of data frame even though
# there is only one column)

age.eval <- plot.out$rg1$eval[,1]

# We plot the results using R's plot() routines...