np.regression.bw: Kernel Regression Bandwidth Selection with Mixed Data Types

In np: Nonparametric Kernel Smoothing Methods for Mixed Data Types

Kernel Regression Bandwidth Selection with Mixed Data Types

Description

npregbw computes a bandwidth object for a p-variate kernel regression estimator defined over mixed continuous and discrete (unordered, ordered) data using expected Kullback-Leibler cross-validation, or least-squares cross validation using the method of Racine and Li (2004) and Li and Racine (2004).

Usage

npregbw(...)

## S3 method for class 'formula'
npregbw(formula, 
        data, 
        subset, 
        na.action, 
        call, 
        ...)

## Default S3 method:
npregbw(xdat = stop("invoked without data 'xdat'"),
        ydat = stop("invoked without data 'ydat'"),
        bws,
        bandwidth.compute = TRUE,
        basis,
        bernstein.basis,
        bwmethod,
        bwscaling,
        bwtype,
        cfac.dir,
        scale.factor.init,
        ckerbound,
        ckerlb,
        ckerorder,
        ckertype,
        ckerub,
        degree,
        degree.select = c("manual", "coordinate", "exhaustive"),
        search.engine = c("nomad+powell", "cell", "nomad"),
        nomad = FALSE,
        nomad.nmulti = 0L,
        degree.min = NULL,
        degree.max = NULL,
        degree.start = NULL,
        degree.restarts = 0L,
        degree.max.cycles = 20L,
        degree.verify = FALSE,
        dfac.dir,
        dfac.init,
        dfc.dir,
        ftol,
        scale.factor.init.upper,
        hbd.dir,
        hbd.init,
        initc.dir,
        initd.dir,
        invalid.penalty = c("baseline","dbmax"),
        itmax,
        lbc.dir,
        scale.factor.init.lower,
        lbd.dir,
        lbd.init,
        nmulti,
        okertype,
        penalty.multiplier = 10,
        regtype,
        remin,
        scale.init.categorical.sample,
        scale.factor.search.lower = NULL,
        small,
        tol,
        transform.bounds = FALSE,
        ukertype,
        ...)

## S3 method for class 'rbandwidth'
npregbw(xdat = stop("invoked without data 'xdat'"),
        ydat = stop("invoked without data 'ydat'"),
        bws,
        bandwidth.compute = TRUE,
        cfac.dir = 2.5*(3.0-sqrt(5)),
        scale.factor.init = 0.5,
        dfac.dir = 0.25*(3.0-sqrt(5)),
        dfac.init = 0.375,
        dfc.dir = 3,
        ftol = 1.490116e-07,
        scale.factor.init.upper = 2.0,
        hbd.dir = 1,
        hbd.init = 0.9,
        initc.dir = 1.0,
        initd.dir = 1.0,
        invalid.penalty = c("baseline","dbmax"),
        itmax = 10000,
        lbc.dir = 0.5,
        scale.factor.init.lower = 0.1,
        lbd.dir = 0.1,
        lbd.init = 0.1,
        nmulti,
        penalty.multiplier = 10,
        remin = TRUE,
        scale.init.categorical.sample = FALSE,
        scale.factor.search.lower = NULL,
        small = 1.490116e-05,
        tol = 1.490116e-04,
        transform.bounds = FALSE,
        ...)

Arguments

Data, Bandwidth Inputs And Formula Interface

These arguments identify the data, formula interface, and whether bandwidths are supplied or computed.

bandwidth.compute	a logical value which specifies whether to do a numerical search for bandwidths or not. If set to FALSE, a rbandwidth object will be returned with bandwidths set to those specified in bws. Defaults to TRUE.
bws	a bandwidth specification. This can be set as a rbandwidth object returned from a previous invocation, or as a vector of bandwidths, with each element i corresponding to the bandwidth for column i in xdat. In either case, the bandwidth supplied will serve as a starting point in the numerical search for optimal bandwidths. If specified as a vector, then additional arguments will need to be supplied as necessary to specify the bandwidth type, kernel types, selection methods, and so on. This can be left unset.
call	the original function call. This is passed internally by np when a bandwidth search has been implied by a call to another function. It is not recommended that the user set this.
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(formula), typically the environment from which the function is called.
formula	a symbolic description of variables on which bandwidth selection is to be performed. The details of constructing a formula are described below.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The (recommended) default is na.omit.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
xdat	a p-variate data frame of regressors on which bandwidth selection will be performed. The data types may be continuous, discrete (unordered and ordered factors), or some combination thereof.
ydat	a one (1) dimensional numeric or integer vector of dependent data, each element i corresponding to each observation (row) i of xdat.

Automatic Degree Search Controls

These arguments control automatic local-polynomial degree search when regtype="lp".

degree.max	optional scalar or integer vector giving upper bounds for automatic degree search when degree.select != "manual". If scalar, the value is recycled over continuous predictors.
degree.max.cycles	positive integer giving the maximum number of coordinate-search sweeps over the continuous-predictor degree vector. Ignored for degree.select="manual" and "exhaustive".
degree.min	optional scalar or integer vector giving lower bounds for automatic degree search when degree.select != "manual". If scalar, the value is recycled over continuous predictors.
degree.restarts	non-negative integer giving the number of additional deterministic restarts used by coordinate search. Ignored for degree.select="manual" and "exhaustive".
degree.select	character string controlling local-polynomial degree handling when regtype="lp". "manual" (default) treats degree as fixed. "coordinate" performs cached coordinate-wise search over admissible degree vectors. "exhaustive" evaluates the full admissible degree grid when search.engine="cell". For NOMAD-based search engines, any non-"manual" value requests direct joint search over degree and bandwidth coordinates.
degree.start	optional starting degree vector for automatic degree search when degree.select="coordinate". If omitted, cell-based search starts from the degree-zero local-constant baseline on the continuous predictors, while NOMAD-based search starts from a clipped degree-one vector on the searchable continuous predictors. For NOMAD multistarts, later restart starts are generated reproducibly from a conservative proposal box and screened using dim_basis() so that the initial basis dimension remains well below the training-sample limit. This avoids wasting starts on flat penalty or heavily ridged designs while leaving the full user requested degree search region unchanged.
degree.verify	logical value indicating whether a coordinate-search solution should be exhaustively verified over the admissible degree grid after the heuristic phase completes. Available only for search.engine="cell".

Bandwidth Criterion And Representation

These arguments choose the selection criterion and the way continuous bandwidths are represented.

bwmethod	which method to use to select bandwidths. cv.aic specifies expected Kullback-Leibler cross-validation (Hurvich, Simonoff, and Tsai (1998)), and cv.ls specifies least-squares cross-validation. Defaults to cv.ls.
bwscaling	a logical value that when set to TRUE the supplied bandwidths are interpreted as ‘scale factors’ (c_j), otherwise when the value is FALSE they are interpreted as ‘raw bandwidths’ (h_j for continuous data types, \lambda_j for discrete data types). For continuous data types, c_j and h_j are related by the formula h_j = c_j \sigma_j n^{-1/(2P+l)}, where \sigma_j is an adaptive measure of spread of continuous variable j defined as min(standard deviation, mean absolute deviation/1.4826, interquartile range/1.349), n the number of observations, P the order of the kernel, and l the number of continuous variables. For discrete data types, c_j and h_j are related by the formula h_j = c_jn^{-2/(2P+l)}, where here j denotes discrete variable j. Defaults to FALSE.
bwtype	character string used for the continuous variable bandwidth type, specifying the type of bandwidth to compute and return in the bandwidth object. Defaults to fixed. Option summary: fixed: compute fixed bandwidths generalized_nn: compute generalized nearest neighbors adaptive_nn: compute adaptive nearest neighbors

Categorical Search Initialization

These controls set categorical search starts and categorical direction-set initialization.

dfac.dir	stretch factor for direction set search for Powell's algorithm for categorical variables. See Details
dfac.init	non-random initial values for scale factors for categorical variables for Powell's algorithm. See Details
hbd.dir	upper bound for direction set search for Powell's algorithm for categorical variables. See Details
hbd.init	upper bound for scale factors for categorical variables for Powell's algorithm. See Details
initd.dir	initial non-random values for direction set search for Powell's algorithm for categorical variables. See Details
lbd.dir	lower bound for direction set search for Powell's algorithm for categorical variables. See Details
lbd.init	lower bound for scale factors for categorical variables for Powell's algorithm. See Details
scale.init.categorical.sample	a logical value that when set to TRUE scales lbd.dir, hbd.dir, dfac.dir, and initd.dir by n^{-2/(2P+l)}, n the number of observations, P the order of the kernel, and l the number of numeric variables. See Details

Continuous Direction-Set Search Controls

These controls set Powell direction-set initialization for continuous variables.

cfac.dir	stretch factor for direction set search for Powell's algorithm for numeric variables. See Details
dfc.dir	chi-square degrees of freedom for direction set search for Powell's algorithm for numeric variables. See Details
initc.dir	initial non-random values for direction set search for Powell's algorithm for numeric variables. See Details
lbc.dir	lower bound for direction set search for Powell's algorithm for numeric variables. See Details

Continuous Kernel Support Controls

These controls choose and parameterize bounded support for continuous kernels.

ckerbound	character string controlling continuous-kernel support handling. Can be set as none (default kernel on full support), range (use sample min/max), or fixed (use ckerlb/ckerub). The bounded-kernel route reuses the selected continuous kernel and renormalizes it on the chosen support; see np.kernels.
ckerlb	numeric scalar/vector of lower bounds for continuous variables used when ckerbound="fixed". Must satisfy lower-bound validity for each continuous variable (e.g., <= min(variable)). Use -Inf for unbounded below. See np.kernels for bounded-kernel normalization details.
ckerub	numeric scalar/vector of upper bounds for continuous variables used when ckerbound="fixed". Must satisfy upper-bound validity for each continuous variable (e.g., >= max(variable)). Use Inf for unbounded above. See np.kernels for bounded-kernel normalization details.

Continuous Scale-Factor Search Initialization

These controls define deterministic and random continuous scale-factor starts and the lower admissibility floor for fixed-bandwidth search.

scale.factor.init	deterministic initial scale factor for continuous fixed-bandwidth search. Defaults to 0.5. The value supplied by the user is not rewritten, but the effective first start passed to the optimizer is max(scale.factor.init, scale.factor.search.lower). See Details.
scale.factor.init.lower	lower endpoint for random continuous scale-factor starts. Defaults to 0.1. The value supplied by the user is not rewritten, but the effective random-start lower endpoint is max(scale.factor.init.lower, scale.factor.search.lower). See Details.
scale.factor.init.upper	upper endpoint for random continuous scale-factor starts. Defaults to 2.0. It must be greater than or equal to the effective lower endpoint, max(scale.factor.init.lower, scale.factor.search.lower); otherwise bandwidth search errors rather than silently expanding the interval. See Details.
scale.factor.search.lower	optional nonnegative scalar giving the hard lower admissibility bound for continuous fixed-bandwidth search candidates. Defaults to NULL. If NULL, an existing bandwidth object's stored value is inherited when available; otherwise the package default 0.1 is used. This floor applies to computed/search bandwidth candidates and to effective search starts only. It does not rewrite explicit bandwidths supplied for storage with bandwidth.compute = FALSE. Final fixed-bandwidth search candidates must also have a finite valid raw objective value.

Kernel Type Controls

These controls choose continuous, unordered, and ordered kernels.

ckerorder	numeric value specifying kernel order (one of (2,4,6,8)). Kernel order specified along with a uniform continuous kernel type will be ignored. Defaults to 2.
ckertype	character string used to specify the continuous kernel type. Can be set as gaussian, epanechnikov, or uniform. Defaults to gaussian.
okertype	character string used to specify the ordered categorical kernel type. Can be set as wangvanryzin, liracine, or racineliyan. Defaults to liracine.
ukertype	character string used to specify the unordered categorical kernel type. Can be set as aitchisonaitken or liracine. Defaults to aitchisonaitken.

Local-Polynomial Model Specification

These arguments control the local-polynomial estimator, basis, and fixed degree specification.

basis	basis selector relevant only when regtype="lp". Supported values are "glp", "additive", and "tensor". Let d_j denote the degree for continuous predictor j and q the number of continuous predictors. With one segment per predictor (no internal knots), basis dimensions are: 1+\sum_{j=1}^q d_j for additive, \prod_{j=1}^q (d_j+1) for tensor, and 1 + |\{\alpha:\alpha_j \le d_j,\ 0<\sum_j \alpha_j \le \max_j d_j\}| for generalized local-polynomial (GLP) basis construction.
bernstein.basis	logical flag relevant only when regtype="lp". If FALSE (default), the GLP basis uses raw local-polynomial powers (stable for extrapolation). If TRUE, a Bernstein (B-spline) basis is used for continuous predictors. For bernstein.basis=TRUE, prediction/evaluation points must lie within the training support of each continuous predictor. For automatic degree search, if bernstein.basis is not explicitly supplied, the search route defaults to TRUE for numerical stability. Explicit bernstein.basis=FALSE is honored, but raw-polynomial search can be poorly conditioned at higher degrees. For regtype="ll" and regtype="lp", a pre-optimization design-conditioning check is performed on the training continuous design: rank deficiency triggers an error, and large condition number (\kappa(B)) triggers warning/error thresholds to avoid unstable optimization dominated by ridging.
degree	a user-supplied vector of fixed polynomial degrees for the continuous predictors (exactly one degree per continuous predictor), relevant only when regtype="lp". When degree.select="manual", this must be supplied explicitly. Entries must be non-negative integers in [0,12]. Bandwidth optimization treats this vector as fixed input and optimizes only bandwidths.
regtype	a character string specifying which type of kernel regression estimator to use. lc specifies a local-constant estimator (Nadaraya-Watson) and ll specifies a local-linear estimator. lp specifies a local polynomial estimator with polynomial degree(s) given by degree for continuous predictors, or selected automatically when degree.select != "manual". Defaults to lc.

NOMAD Search Controls

These arguments control the optional NOMAD direct-search route for local-polynomial degree and bandwidth search.

nomad	logical shortcut for the recommended automatic local-polynomial NOMAD route. When TRUE, any missing values among regtype, search.engine, degree.select, bernstein.basis, degree.min, degree.max, degree.verify, and bwtype are filled with regtype="lp", search.engine="nomad+powell", degree.select="coordinate", bernstein.basis=TRUE, degree.min=0L, degree.max=10L, degree.verify=FALSE, and bwtype="fixed". Explicit incompatible settings error immediately; in particular, nomad=TRUE currently requires regtype="lp", bwtype="fixed", automatic degree search, bernstein.basis=TRUE, no explicit degree, and search.engine %in% c("nomad", "nomad+powell"). This shortcut does not change the meaning of nmulti or nomad.nmulti: nmulti remains the outer restart count, while nomad.nmulti controls inner crs::snomadr() multistarts within each outer restart. Returned bandwidth objects retain this normalized preset metadata in bw$nomad.shortcut for a returned object bw; when available, nomad.time and powell.time record the direct-search and Powell-polish timing components.
nomad.nmulti	non-negative integer controlling the inner crs::snomadr() multistart count used within each outer NOMAD restart when regtype="lp" and automatic degree search uses search.engine="nomad" or "nomad+powell". Defaults to 0L, which preserves the current one-start-per- restart behavior. This does not replace nmulti: nmulti controls outer restarts, while nomad.nmulti controls inner NOMAD multistarts within each outer restart.
search.engine	character string controlling the automatic local-polynomial search backend when regtype="lp" and degree.select != "manual". "nomad+powell" (default) performs direct joint mixed discrete/continuous search over fixed bandwidths and the degree vector using crs::snomadr(), followed by one Powell hot start from the NOMAD solution. "nomad" performs the direct joint NOMAD search without the Powell refinement. "cell" uses the legacy profiled degree-grid search built from repeated fixed-degree bandwidth solves. NOMAD-based search currently requires bwtype="fixed", degree.verify=FALSE, and the suggested package crs to be installed.

Numerical Search And Tolerance Controls

These controls set optimizer tolerances, restart behavior, invalid-candidate penalties, and bounded search transformations.

ftol	fractional tolerance on the value of the cross-validation function evaluated at located minima (of order the machine precision or perhaps slightly larger so as not to be diddled by roundoff). Defaults to 1.490116e-07 (1.0e+01*sqrt(.Machine$double.eps)).
invalid.penalty	a character string specifying the penalty used when the optimizer encounters invalid bandwidths. "baseline" returns a finite penalty based on a baseline objective; "dbmax" returns DBL\_MAX. Defaults to "baseline".
itmax	integer number of iterations before failure in the numerical optimization routine. Defaults to 10000.
nmulti	integer number of times to restart the process of finding extrema of the cross-validation function from different (random) initial points. Defaults to min(2,ncol(xdat)).
penalty.multiplier	a numeric multiplier applied to the baseline penalty when invalid.penalty="baseline". Defaults to 10.
remin	a logical value which when set as TRUE the search routine restarts from located minima for a minor gain in accuracy. Defaults to TRUE.
small	a small number used to bracket a minimum (it is hopeless to ask for a bracketing interval of width less than sqrt(epsilon) times its central value, a fractional width of only about 10-04 (single precision) or 3x10-8 (double precision)). Defaults to small = 1.490116e-05 (1.0e+03*sqrt(.Machine$double.eps)).
tol	tolerance on the position of located minima of the cross-validation function (tol should generally be no smaller than the square root of your machine's floating point precision). Defaults to 1.490116e-04 (1.0e+04*sqrt(.Machine$double.eps)).
transform.bounds	a logical value that when set to TRUE applies an internal transformation that maps the unconstrained search to the feasible bandwidth domain. Defaults to FALSE.

Additional Arguments

These arguments collect remaining controls passed through S3 methods.

...	additional arguments supplied to specify the bandwidth type, kernel types, selection methods, and so on, detailed below.

Details

The scale.factor.* controls are dimensionless search controls. The package converts scale factors to bandwidths using the estimator-specific scaling encoded in the bandwidth object, including kernel order and the number of continuous variables relevant for the estimator. Users should not pre-multiply these controls by sample-size or standard-deviation factors.

scale.factor.init controls the deterministic first search start. scale.factor.init.lower and scale.factor.init.upper define the random multistart interval. scale.factor.search.lower is the lower admissibility bound for continuous fixed-bandwidth search candidates. The effective first start is max(scale.factor.init, scale.factor.search.lower), and the effective random-start lower endpoint is max(scale.factor.init.lower, scale.factor.search.lower). scale.factor.init.upper must be at least that effective lower endpoint; the package errors rather than silently expanding the user's interval.

When scale.factor.search.lower is NULL, an existing bandwidth object's stored floor is inherited when available; otherwise the package default 0.1 is used. Explicit bandwidths supplied for storage with bandwidth.compute = FALSE are not rewritten by the search floor.

Categorical search-start controls such as dfac.init, lbd.init, and hbd.init have separate semantics and are not affected by scale.factor.search.lower.

Documentation guide: see np.kernels for kernels, np.options for global options, and plot for plotting options.

The bandwidth-selection argument surface is easiest to read by decision group: data and existing bandwidth inputs; local-polynomial/NOMAD controls when polynomial-adaptive regression is requested; bandwidth criterion and representation; continuous kernel and support controls beginning with cker*; categorical kernel controls ukertype and okertype; and numerical search initialization, tolerances, and feasibility controls. Users who call npreg without a bandwidth object can pass these same bandwidth-selection controls through that function's ....

For S3 plotting help, use methods("plot") and query class-specific help topics such as ?plot.npregression and ?plot.rbandwidth. You can inspect implementations with getS3method("plot","npregression").

npregbw implements a variety of methods for choosing bandwidths for multivariate (p-variate) regression data defined over a set of possibly continuous and/or discrete (unordered, ordered) data. The approach is based on Li and Racine (2003) who employ ‘generalized product kernels’ that admit a mix of continuous and discrete data types.

The cross-validation methods employ multivariate numerical search algorithms. For fixed-degree local-constant/local-linear regression, and for local-polynomial regression with degree.select="manual", the bandwidth search uses multidimensional Powell direction-set optimization.

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 nearest-neighbor, and generalized nearest-neighbor. Adaptive nearest-neighbor bandwidths change with each sample realization in the set, x_i, when estimating the density at the point x. Generalized nearest-neighbor bandwidths change with the point at which the density is estimated, x. Fixed bandwidths are constant over the support of x.

npregbw may be invoked either with a formula-like 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 frame xdat may be a mix of continuous (default), unordered discrete (to be specified in the data frame xdat using factor), and ordered discrete (to be specified in the data frame xdat 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 is a univariate response, and explanatory data is a series of variables specified by name, separated by the separation character '+'. For example, y1 ~ x1 + x2 specifies that the bandwidths for the regression of response y1 and nonparametric regressors 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.

When regtype="lp" and degree.select != "manual", npregbw can jointly determine the continuous-predictor degree vector and bandwidth coordinates. With search.engine="cell", the objective is profiled over the degree grid using cached coordinate-wise or exhaustive search together with the existing fixed-degree bandwidth optimizer. With search.engine="nomad" or "nomad+powell", the package instead evaluates the cross-validation criterion directly over the joint space of fixed bandwidths and polynomial degrees using crs::snomadr(). "nomad+powell" then performs one Powell hot start from the NOMAD solution and retains the better of the direct NOMAD and polished solutions. This direct joint-search route follows the polynomial-adaptive cross-validation rationale of Hall and Racine (2015). When bernstein.basis is not explicitly supplied, the automatic search route defaults to bernstein.basis=TRUE for numerical stability; explicit bernstein.basis=FALSE is honored but can be poorly conditioned at higher degrees. NOMAD multistarts are initialized more conservatively than the full degree search box: start 1 is the user-supplied degree/bandwidth vector when provided and otherwise a clipped degree-one vector, while later starts are reproducible random draws from a reduced degree proposal box whose candidates are screened using dim_basis(). This heuristic is used only to obtain feasible, numerically safer, and quicker initial evaluations; it does not restrict the admissible degree region searched by NOMAD. The direct NOMAD backend is provided by the suggested package crs, so install crs before using search.engine="nomad", "nomad+powell", or nomad=TRUE.

Setting nomad=TRUE is a convenience preset for this automatic LP route, not a generic optimizer alias. For regression it expands any missing values to the equivalent long-form call

    npregbw(...,
            regtype = "lp",
            search.engine = "nomad+powell",
            degree.select = "coordinate",
            bernstein.basis = TRUE,
            degree.min = 0L,
            degree.max = 10L,
            degree.verify = FALSE,
            bwtype = "fixed")
  

Compatible explicit tuning arguments are respected. Incompatible explicit settings fail fast so the shortcut never silently changes user-selected semantics. When the direct NOMAD route is active, nmulti controls the package-level outer restart count while nomad.nmulti controls the inner crs::snomadr() multistart count used within each outer restart. The default nomad.nmulti=0L preserves the current single-start inner NOMAD behavior.

The use of compactly supported kernels or the occurrence of small bandwidths during cross-validation can lead to numerical problems for the local linear estimator when computing the locally weighted least squares solution. To overcome this problem we rely on a form or ‘ridging’ proposed by Cheng, Hall, and Titterington (1997), modified so that we solve the problem pointwise rather than globally (i.e. only when it is needed).

The optimizer invoked for search is Powell's conjugate direction method which requires the setting of (non-random) 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.

Value

npregbw returns a rbandwidth object, with the following components:

bw	bandwidth(s), scale factor(s) or nearest neighbours for the data, xdat
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. Bandwidths are stored under the component name bw, with each element i corresponding to column i of input data xdat.

The functions predict, summary, and plot support objects of this class.

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.

Caution: multivariate data-driven 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 leave-one-out 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(2, ncol(xdat)) 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.

Author(s)

Tristen Hayfield [e-mail], Jeffrey S. Racine [e-mail]

References

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

Cheng, M.-Y. and P. Hall and D.M. Titterington (1997), “On the shrinkage of local linear curve estimators,” Statistics and Computing, 7, 11-17.

Fan, J. and I. Gijbels (1996), Local Polynomial Modelling and Its Applications, Chapman and Hall.

Hall, P. and J.S. Racine (2015), “Infinite Order Cross-Validated Local Polynomial Regression,” Journal of Econometrics, 185, 510-525.

Hall, P. and Q. Li and J.S. Racine (2007), “Nonparametric estimation of regression functions in the presence of irrelevant regressors,” The Review of Economics and Statistics, 89, 784-789.

Hurvich, C.M. and J.S. Simonoff and C.L. Tsai (1998), “Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion,” Journal of the Royal Statistical Society B, 60, 271-293.

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

Li, Q. and J.S. Racine (2004), “Cross-validated local linear nonparametric regression,” Statistica Sinica, 14, 485-512.

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

Racine, J.S. and Q. Li (2004), “Nonparametric estimation of regression functions with both categorical and continuous data,” Journal of Econometrics, 119, 99-130.

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

See Also

np.kernels, np.options, plot npreg

Examples

## Not run: 
# EXAMPLE 1 (INTERFACE=FORMULA): For this example, we compute a
# Bivariate nonparametric regression estimate for Giovanni Baiocchi's
# Italian income panel (see Italy for details)

data("Italy")
attach(Italy)

# Compute the least-squares cross-validated bandwidths for the local
# constant estimator (default)

bw <- npregbw(formula=gdp~ordered(year))

summary(bw)

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

if (interactive()) Sys.sleep(5)

# Supply your own bandwidth...

bw <- npregbw(formula=gdp~ordered(year), bws=c(0.75),
              bandwidth.compute=FALSE)

summary(bw)

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

if (interactive()) Sys.sleep(5)

# Treat year as continuous and supply your own scaling factor c in
# c sigma n^{-1/(2p+q)}

bw <- npregbw(formula=gdp~year, bws=c(1.06),
              bandwidth.compute=FALSE, 
              bwscaling=TRUE)

summary(bw)

# Note - see also the example for npudensbw() for more extensive
# multiple illustrations of how to change the kernel function, kernel
# order, bandwidth type and so forth.

detach(Italy)

# EXAMPLE 1 (INTERFACE=DATA FRAME): For this example, we compute a
# Bivariate nonparametric regression estimate for Giovanni Baiocchi's
# Italian income panel (see Italy for details)

data("Italy")
attach(Italy)

# Compute the least-squares cross-validated bandwidths for the local
# constant estimator (default)

bw <- npregbw(xdat=ordered(year), ydat=gdp)

summary(bw)

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

if (interactive()) Sys.sleep(5)

# Supply your own bandwidth...

bw <- npregbw(xdat=ordered(year), ydat=gdp, bws=c(0.75),
              bandwidth.compute=FALSE)

summary(bw)

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

if (interactive()) Sys.sleep(5)

# Treat year as continuous and supply your own scaling factor c in
# c sigma n^{-1/(2p+q)}

bw <- npregbw(xdat=year, ydat=gdp, bws=c(1.06),
              bandwidth.compute=FALSE, 
              bwscaling=TRUE)

summary(bw)

# Note - see also the example for npudensbw() for more extensive
# multiple illustrations of how to change the kernel function, kernel
# order, bandwidth type and so forth.

detach(Italy)

## End(Not run) 

np documentation built on May 3, 2026, 1:07 a.m.