npcdensbw  R Documentation 
npcdensbw
computes a conbandwidth
object for
estimating the conditional density of a p+qvariate kernel
density estimator defined over mixed continuous and discrete
(unordered, ordered) data using either the normalreference
ruleofthumb, likelihood crossvalidation, or leastsquares cross
validation using the method of Hall, Racine, and Li (2004).
npcdensbw(...) ## S3 method for class 'formula' npcdensbw(formula, data, subset, na.action, call, ...) ## S3 method for class 'NULL' npcdensbw(xdat = stop("data 'xdat' missing"), ydat = stop("data 'ydat' missing"), bws, ...) ## S3 method for class 'conbandwidth' npcdensbw(xdat = stop("data 'xdat' missing"), ydat = stop("data 'ydat' missing"), bws, bandwidth.compute = TRUE, nmulti, remin = TRUE, itmax = 10000, ftol = 1.490116e07, tol = 1.490116e04, small = 1.490116e05, memfac = 500, lbc.dir = 0.5, dfc.dir = 3, cfac.dir = 2.5*(3.0sqrt(5)), initc.dir = 1.0, lbd.dir = 0.1, hbd.dir = 1, dfac.dir = 0.25*(3.0sqrt(5)), initd.dir = 1.0, lbc.init = 0.1, hbc.init = 2.0, cfac.init = 0.5, lbd.init = 0.1, hbd.init = 0.9, dfac.init = 0.375, scale.init.categorical.sample = FALSE, ...) ## Default S3 method: npcdensbw(xdat = stop("data 'xdat' missing"), ydat = stop("data 'ydat' missing"), bws, bandwidth.compute = TRUE, nmulti, remin, itmax, ftol, tol, small, memfac, lbc.dir, dfc.dir, cfac.dir, initc.dir, lbd.dir, hbd.dir, dfac.dir, initd.dir, lbc.init, hbc.init, cfac.init, lbd.init, hbd.init, dfac.init, scale.init.categorical.sample, bwmethod, bwscaling, bwtype, cxkertype, cxkerorder, cykertype, cykerorder, uxkertype, uykertype, oxkertype, oykertype, ...)
formula 
a symbolic description of variables on which bandwidth selection is to be performed. The details of constructing a formula are described below. 
data 
an optional data frame, list or environment (or object
coercible to a data frame by 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
na.action 
a function which indicates what should happen when the data contain

call 
the original function call. This is passed internally by

xdat 
a pvariate data frame of explanatory data on which bandwidth selection will be performed. The data types may be continuous, discrete (unordered and ordered factors), or some combination thereof. 
ydat 
a qvariate data frame of dependent data on which bandwidth selection will be performed. The data types may be continuous, discrete (unordered and ordered factors), or some combination thereof. 
bws 
a bandwidth specification. This can be set as a 
... 
additional arguments supplied to specify the bandwidth type, kernel types, selection methods, and so on, detailed below. 
bwmethod 
which method to use to select
bandwidths. 
bwscaling 
a logical value that when set to 
bwtype 
character string used for the continuous variable bandwidth type,
specifying the type of bandwidth to compute and return in the

bandwidth.compute 
a logical value which specifies whether to do a numerical search for
bandwidths or not. If set to 
cxkertype 
character string used to specify the continuous kernel type for

cxkerorder 
numeric value specifying kernel order for

cykertype 
character string used to specify the continuous kernel type for

cykerorder 
numeric value specifying kernel order for

uxkertype 
character string used to specify the unordered categorical
kernel type. Can be set as 
uykertype 
character string used to specify the unordered categorical
kernel type. Can be set as 
oxkertype 
character string used to specify the ordered categorical
kernel type. Can be set as 
oykertype 
character string used to specify the ordered categorical
kernel type. Can be set as 
nmulti 
integer number of times to restart the process of finding extrema of the crossvalidation function from different (random) initial points 
remin 
a logical value which when set as 
itmax 
integer number of iterations before failure in the numerical
optimization routine. Defaults to 
ftol 
fractional tolerance on the value of the crossvalidation function
evaluated at located minima (of order the machine precision or
perhaps slightly larger so as not to be diddled by
roundoff). Defaults to 
tol 
tolerance on the position of located minima of the crossvalidation
function (tol should generally be no smaller than the square root of
your machine's floating point precision). Defaults to 
small 
a small number used to bracket a minimum (it is hopeless to ask for
a bracketing interval of width less than sqrt(epsilon) times its
central value, a fractional width of only about 1004 (single
precision) or 3x108 (double precision)). Defaults to 
lbc.dir,dfc.dir,cfac.dir,initc.dir 
lower bound, chisquare
degrees of freedom, stretch factor, and initial nonrandom values
for direction set search for Powell's algorithm for 
lbd.dir,hbd.dir,dfac.dir,initd.dir 
lower bound, upper bound, stretch factor, and initial nonrandom values for direction set search for Powell's algorithm for categorical variables. See Details 
lbc.init, hbc.init, cfac.init 
lower bound, upper bound, and
nonrandom initial values for scale factors for 
lbd.init, hbd.init, dfac.init 
lower bound, upper bound, and nonrandom initial values for scale factors for categorical variables for Powell's algorithm. See Details 
scale.init.categorical.sample 
a logical value that when set
to 
memfac 
The algorithm to compute the leastsquares objective function uses a blockbased algorithm to eliminate or minimize redundant kernel evaluations. Due to memory, hardware and software constraints, a maximum block size must be imposed by the algorithm. This block size is roughly equal to memfac*10^5 elements. Empirical tests on modern hardware find that a memfac of 500 performs well. If you experience out of memory errors, or strange behaviour for large data sets (>100k elements) setting memfac to a lower value may fix the problem. 
npcdensbw
implements a variety of methods for choosing
bandwidths for multivariate distributions (p+qvariate) defined
over a set of possibly continuous and/or discrete (unordered, ordered)
data. The approach is based on Li and Racine (2004) who employ
‘generalized product kernels’ that admit a mix of continuous
and discrete data types.
The crossvalidation methods employ multivariate numerical search algorithms (direction set (Powell's) methods in multidimensions).
Bandwidths can (and will) differ for each variable which is, of course, desirable.
Three classes of kernel estimators for the continuous data types are available: fixed, adaptive nearestneighbor, and generalized nearestneighbor. Adaptive nearestneighbor bandwidths change with each sample realization in the set, x[i], when estimating the density at the point x. Generalized nearestneighbor bandwidths change with the point at which the density is estimated, x. Fixed bandwidths are constant over the support of x.
npcdensbw
may be invoked either with a formulalike
symbolic
description of variables on which bandwidth selection is to be
performed or through a simpler interface whereby data is passed
directly to the function via the xdat
and ydat
parameters. Use of these two interfaces is mutually exclusive.
Data contained in the data frames xdat
and ydat
may be a
mix of continuous (default), unordered discrete (to be specified in
the data frames using factor
), and ordered discrete (to be
specified in the data frames using ordered
). Data can be
entered in an arbitrary order and data types will be detected
automatically by the routine (see np
for details).
Data for which bandwidths are to be estimated may be specified
symbolically. A typical description has the form dependent data
~ explanatory data
,
where dependent data
and explanatory data
are both
series of variables specified by name, separated by
the separation character '+'. For example, y1 + y2 ~ x1 + x2
specifies that the bandwidths for the joint distribution of variables
y1
and y2
conditioned on x1
and x2
are to
be estimated. See below for further examples.
A variety of kernels may be specified by the user. Kernels implemented for continuous data types include the second, fourth, sixth, and eighth order Gaussian and Epanechnikov kernels, and the uniform kernel. Unordered discrete data types use a variation on Aitchison and Aitken's (1976) kernel, while ordered data types use a variation of the Wang and van Ryzin (1981) kernel.
The optimizer invoked for search is Powell's conjugate direction
method which requires the setting of (nonrandom) initial values and
search directions for bandwidths, and, when restarting, random values
for successive invocations. Bandwidths for numeric
variables
are scaled by robust measures of spread, the sample size, and the
number of numeric
variables where appropriate. Two sets of
parameters for bandwidths for numeric
can be modified, those
for initial values for the parameters themselves, and those for the
directions taken (Powell's algorithm does not involve explicit
computation of the function's gradient). The default values are set by
considering search performance for a variety of difficult test cases
and simulated cases. We highly recommend restarting search a large
number of times to avoid the presence of local minima (achieved by
modifying nmulti
). Further refinement for difficult cases can
be achieved by modifying these sets of parameters. However, these
parameters are intended more for the authors of the package to enable
‘tuning’ for various methods rather than for the user themselves.
npcdensbw
returns a conbandwidth
object, with the
following components:
xbw 
bandwidth(s), scale factor(s) or nearest neighbours for the
explanatory data, 
ybw 
bandwidth(s), scale factor(s) or nearest neighbours for the
dependent data, 
fval 
objective function value at minimum 
if bwtype
is set to fixed
, an object containing
bandwidths (or scale factors if bwscaling = TRUE
) is
returned. If it is set to generalized_nn
or adaptive_nn
,
then instead the kth nearest neighbors are returned for the
continuous variables while the discrete kernel bandwidths are returned
for the discrete variables.
The functions predict
, summary
and plot
support
objects of type conbandwidth
.
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.
Caution: multivariate datadriven bandwidth selection methods are, by
their nature, computationally intensive. Virtually all methods
require dropping the ith observation from the data set, computing an
object, repeating this for all observations in the sample, then
averaging each of these leaveoneout estimates for a given
value of the bandwidth vector, and only then repeating this a large
number of times in order to conduct multivariate numerical
minimization/maximization. Furthermore, due to the potential for local
minima/maxima, restarting this procedure a large number of times may
often be necessary. This can be frustrating for users possessing
large datasets. For exploratory purposes, you may wish to override the
default search tolerances, say, setting ftol=.01 and tol=.01 and
conduct multistarting (the default is to restart min(5, ncol(xdat,ydat))
times) as is done for a number of examples. Once the procedure
terminates, you can restart search with default tolerances using those
bandwidths obtained from the less rigorous search (i.e., set
bws=bw
on subsequent calls to this routine where bw
is
the initial bandwidth object). A version of this package using the
Rmpi
wrapper is under development that allows one to deploy
this software in a clustered computing environment to facilitate
computation involving large datasets.
Tristen Hayfield tristen.hayfield@gmail.com, Jeffrey S. Racine racinej@mcmaster.ca
Aitchison, J. and C.G.G. Aitken (1976), “Multivariate binary discrimination by the kernel method,” Biometrika, 63, 413420.
Hall, P. and J.S. Racine and Q. Li (2004), “Crossvalidation and the estimation of conditional probability densities,” Journal of the American Statistical Association, 99, 10151026.
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.
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, 301309.
bw.nrd
, bw.SJ
, hist
,
npudens
, npudist
## Not run: # EXAMPLE 1 (INTERFACE=FORMULA): For this example, we compute the # likelihood crossvalidated bandwidths (default) using a secondorder # Gaussian kernel (default). Note  this may take a minute or two # depending on the speed of your computer. data("Italy") attach(Italy) bw < npcdensbw(formula=gdp~ordered(year)) # The object bw can be used for further estimation using # npcdens(), plotting using plot() etc. Entering the name of # the object provides useful summary information, and names() will also # provide useful information. summary(bw) # Note  see the example for npudensbw() for multiple illustrations # of how to change the kernel function, kernel order, and so forth. detach(Italy) # EXAMPLE 1 (INTERFACE=DATA FRAME): For this example, we compute the # likelihood crossvalidated bandwidths (default) using a secondorder # Gaussian kernel (default). Note  this may take a minute or two # depending on the speed of your computer. data("Italy") attach(Italy) bw < npcdensbw(xdat=ordered(year), ydat=gdp) # The object bw can be used for further estimation using # npcdens(), plotting using plot() etc. Entering the name of # the object provides useful summary information, and names() will also # provide useful information. summary(bw) # Note  see the example for npudensbw() for multiple illustrations # of how to change the kernel function, kernel order, and so forth. detach(Italy) ## End(Not run)
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