R: Smooth Coefficient Kernel Regression Bandwidth Selection

npscoefbw {np}

R Documentation

Smooth Coefficient Kernel Regression Bandwidth Selection

Description

npscoefbw computes a bandwidth object for a smooth coefficient kernel regression estimate of a one (1) dimensional dependent variable on p+q-variate explanatory data, using the model Y_i = t(W_i) * gamma(Z_i) + u_i where t(W_i) = (1,t(X_i)) given training points (consisting of explanatory data and dependent data), and a bandwidth specification, which can be a rbandwidth object, or a bandwidth vector, bandwidth type and kernel type.

Usage

npscoefbw(...)

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

## S3 method for class 'NULL':
npscoefbw(xdat = stop("invoked without data 'xdat'"),
          ydat = stop("invoked without data 'ydat'"),
          zdat = NULL,
          bws,
          ...)

## Default S3 method:
npscoefbw(xdat = stop("invoked without data 'xdat'"),
          ydat = stop("invoked without data 'ydat'"),
          zdat = NULL,
          bws,
          nmulti,
          cv.iterate,
          cv.num.iterations,
          backfit.iterate,
          backfit.maxiter,
          backfit.tol,
          bandwidth.compute = TRUE,
          bwmethod,
          bwscaling,
          bwtype,
          ckertype,
          ckerorder,
          optim.method,
          optim.maxattempts,
          optim.reltol,
          optim.abstol,
          optim.maxit,
          ...)

## S3 method for class 'scbandwidth':
npscoefbw(xdat = stop("invoked without data 'xdat'"),
          ydat = stop("invoked without data 'ydat'"),
          zdat = NULL,
          bws,
          nmulti,
          cv.iterate = FALSE,
          cv.num.iterations = 1,
          backfit.iterate = FALSE,
          backfit.maxiter = 100,
          backfit.tol = .Machine$double.eps,
          bandwidth.compute = TRUE,
          optim.method = c("Nelder-Mead", "BFGS", "CG"),
          optim.maxattempts = 10,
          optim.reltol = sqrt(.Machine$double.eps),
          optim.abstol = .Machine$double.eps,
          optim.maxit = 500,
          ...)

Arguments

`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 `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.
`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 `NA`s. 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`.
`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.
`xdat`	a p-variate data frame of explanatory data (training data), which, by default, populates the columns 2 through p+1 of W in the model equation, and in the absence of `zdat`, will also correspond to Z from the model equation.
`ydat`	a one (1) dimensional numeric or integer vector of dependent data, each element i corresponding to each observation (row) i of `xdat`.
`zdat`	an optionally specified q-variate data frame of explanatory data (training data), which corresponds to Z in the model equation. Defaults to be the same as `xdat`.
`bws`	a bandwidth specification. This can be set as a `scbandwidth` 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.
`...`	additional arguments supplied to specify the regression type, bandwidth type, kernel types, selection methods, and so on, detailed below.
`bandwidth.compute`	a logical value which specifies whether to do a numerical search for bandwidths or not. If set to `FALSE`, a `scbandwidth` object will be returned with bandwidths set to those specified in `bws`. Defaults to `TRUE`.
`bwmethod`	which method was used to select bandwidths. `cv.ls` specifies least-squares cross-validation, which is all that is currently supported. 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, 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[j]n^(-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 provided. Defaults to `fixed`. Option summary: `fixed`: fixed bandwidths or scale factors `generalized_nn`: generalized nearest neighbors `adaptive_nn`: adaptive nearest neighbors
`ckertype`	character string used to specify the continuous kernel type. Can be set as `gaussian`, `epanechnikov`, or `uniform`. Defaults to `gaussian`.
`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`.
`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(5,ncol(xdat))`.
`optim.method`	method used by `optim` for minimization of the objective function. See `?optim` for references. Defaults to `"Nelder-Mead"`. the default method is an implementation of that of Nelder and Mead (1965), that uses only function values and is robust but relatively slow. It will work reasonably well for non-differentiable functions. method `"BFGS"` is a quasi-Newton method (also known as a variable metric algorithm), specifically that published simultaneously in 1970 by Broyden, Fletcher, Goldfarb and Shanno. This uses function values and gradients to build up a picture of the surface to be optimized. method `"CG"` is a conjugate gradients method based on that by Fletcher and Reeves (1964) (but with the option of Polak-Ribiere or Beale-Sorenson updates). Conjugate gradient methods will generally be more fragile than the BFGS method, but as they do not store a matrix they may be successful in much larger optimization problems.
`optim.maxattempts`	maximum number of attempts taken trying to achieve successful convergence in `optim`. Defaults to `100`.
`optim.abstol`	the absolute convergence tolerance used by `optim`. Only useful for non-negative functions, as a tolerance for reaching zero. Defaults to `.Machine$double.eps`.
`optim.reltol`	relative convergence tolerance used by `optim`. The algorithm stops if it is unable to reduce the value by a factor of 'reltol * (abs(val) + reltol)' at a step. Defaults to `sqrt(.Machine$double.eps)`, typically about `1e-8`.
`optim.maxit`	maximum number of iterations used by `optim`. Defaults to `500`.
`cv.iterate`	boolean value specifying whether or not to perform iterative, cross-validated backfitting on the data. See details for limitations of the backfitting procedure. Defaults to `FALSE`.
`cv.num.iterations`	integer specifying the number of times to iterate the backfitting process over all covariates. Defaults to `1`.
`backfit.iterate`	boolean value specifying whether or not to iterate evaluations of the smooth coefficient estimator, for extra accuracy, during the cross-validated backfitting procedure. Defaults to `FALSE`.
`backfit.maxiter`	integer specifying the maximum number of times to iterate the evaluation of the smooth coefficient estimator in the attempt to obtain the desired accuracy. Defaults to `100`.
`backfit.tol`	tolerance to determine convergence of iterated evaluations of the smooth coefficient estimator. Defaults to `.Machine$double.eps`.

Details

npscoefbw implements a variety of methods for semiparametric regression on multivariate (p+q-variate) explanatory data defined over a set of possibly continuous 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.

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.

npscoefbw 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, ydat, and zdat parameters. Use of these two interfaces is mutually exclusive.

Data contained in the data frame xdat may be continuous and in zdat may be of mixed type. 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 ~ parametric explanatory data | nonparametric explanatory data, where dependent data is a univariate response, and parametric explanatory data and nonparametric explanatory data are both series of variables specified by name, separated by the separation character '+'. For example, y1 ~ x1 + x2 | z1 specifies that the bandwidth object for the smooth coefficient model with response y1, linear parametric regressors x1 and x2, and nonparametric regressor (that is, the slope-changing variable) z1 is to be estimated. See below for further examples. In the case where the nonparametric (slope-changing) variable is not specified, it is assumed to be the same as the parametric variable.

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.

Value

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 in a vector under the component name bw. Backfitted bandwidths are stored under the component name bw.fitted.
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 optim.reltol=.1 and conduct multistarting (the default is to restart min(5,ncol(zdat)) times). 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.

Support for backfitted bandwidths is experimental and is limited in functionality. The code does not support asymptotic standard errors or out of sample estimates with backfitting.

Author(s)

Tristen Hayfield hayfield@phys.ethz.ch, 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.

Cai Z. (2007), “Trending time-varying coefficient time series models with serially correlated errors,” Journal of Econometrics, 136, 163-188.

Hastie, T. and R. Tibshirani (1993), “Varying-coefficient models,” Journal of the Royal Statistical Society, B 55, 757-796.

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

Li, Q. and J.S. Racine (2008), “Smooth varying-coefficient estimation and inference for qualitative and quantitative data,” manuscript.

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

Racine, J.S. and D. Ouyang and Q. Li (2007), “Nonparametric multilevel models: a smoothing approach,” manuscript.

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

Examples

# EXAMPLE 1 (INTERFACE=FORMULA): 

n <- 100
x <- runif(n)
z <- runif(n, min=-2, max=2)
y <- x*exp(z)*(1.0+rnorm(n,sd = 0.2))
bw <- npscoefbw(formula=y~x|z)
summary(bw)

## Not run: 
# EXAMPLE 1 (INTERFACE=DATA FRAME): 

n <- 100
x <- runif(n)
z <- runif(n, min=-2, max=2)
y <- x*exp(z)*(1.0+rnorm(n,sd = 0.2))
bw <- npscoefbw(xdat=x, ydat=y, zdat=z)
summary(bw)
## End(Not run)

[Package np version 0.30-3 Index]