%
\name{npreg}
\alias{npreg}
\alias{npreg.default}
\alias{npreg.formula}
\alias{npreg.rbandwidth}

\title{Kernel Regression with Mixed Data Types}

\description{
  \code{npreg} computes a kernel regression estimate of a one
  (1) dimensional dependent variable on \eqn{p}-variate explanatory
  data, given a set of evaluation points, training points (consisting of
  explanatory data and dependent data), and a bandwidth specification
  using the method of Racine and Li (2004) and Li and Racine (2004). A
  bandwidth specification can be a \code{rbandwidth} object, or a
  bandwidth vector, bandwidth type and kernel type.
}

\usage{
npreg(bws, \dots)

\method{npreg}{formula}(bws, 
      data = NULL, 
      newdata = NULL, 
      y.eval = FALSE, 
      \dots)

\method{npreg}{default}(bws, 
      txdat, 
      tydat, 
      nomad = FALSE, 
      \dots)

\method{npreg}{rbandwidth}(bws,
      txdat = stop("training data 'txdat' missing"),
      tydat = stop("training data 'tydat' missing"),
      exdat,
      eydat,
      gradient.order = 1L,
      gradients = FALSE,
      residuals = FALSE,
      \dots)
}

\arguments{
\subsection{Data, Bandwidth Inputs And Formula Interface}{
These arguments identify the bandwidth specification, formula/data interface, and training data.
}

  \item{bws}{
    a bandwidth specification. This can be set as a \code{rbandwidth}
    object returned from an invocation of \code{\link{npregbw}}, or
    as a vector of bandwidths, with each element \eqn{i} corresponding
    to the bandwidth for column \eqn{i} in \code{txdat}. If specified as
    a vector, then additional arguments will need to be supplied as
    necessary to specify the bandwidth type, kernel types, and so on.
  }


  \item{data}{
    an optional data frame, list or environment (or object
    coercible to a data frame by \code{\link{as.data.frame}}) containing the variables
    in the model. If not found in data, the variables are taken from
    \code{environment(bws)}, typically the environment from which
    \code{\link{npregbw}} was called.
  }


  \item{txdat}{
    a \eqn{p}-variate data frame of explanatory data (training data) used to
    calculate the regression estimators. Defaults to the training data used to
    compute the bandwidth object.
  }


  \item{tydat}{
    a one (1) dimensional numeric or integer vector of dependent data, each
    element \eqn{i} corresponding to each observation (row) \eqn{i} of
    \code{txdat}. Defaults to the training data used to
    compute the bandwidth object.
  }


\subsection{Local-Polynomial Degree And Bandwidth Search}{
This argument controls the recommended automatic local-polynomial NOMAD route, which jointly selects continuous polynomial degree and bandwidths when these are computed inside \code{npreg}.
}

  \item{nomad}{
    logical shortcut passed through to \code{\link{npregbw}} when
    bandwidths are computed inside \code{npreg}. When \code{TRUE}, the
    regression bandwidth route fills any missing values among
    \code{regtype}, \code{search.engine}, \code{degree.select},
    \code{bernstein.basis}, \code{degree.min}, \code{degree.max},
    \code{degree.verify}, and \code{bwtype} with the recommended
    automatic local-polynomial degree-and-bandwidth NOMAD preset documented in \code{\link{npregbw}}.
    Additional NOMAD tuning arguments such as \code{nomad.nmulti} may
    also be supplied through \code{\dots}; \code{nmulti} remains the
    outer restart count while \code{nomad.nmulti} controls inner
    native \pkg{crs} NOMAD API multistarts within each outer restart.
    After fitting, inspect \code{fit$bws$nomad.shortcut} on the
    returned object \code{fit} to see the normalized shortcut metadata.
  }


\subsection{Evaluation Data And Returned Quantities}{
These arguments control where the regression is evaluated and which fitted quantities are returned.
}

  \item{exdat}{
    a \eqn{p}-variate data frame of points on which the regression will be
    estimated (evaluation data). By default,
    evaluation takes place on the data provided by \code{txdat}.
  }


  \item{eydat}{
    a one (1) dimensional numeric or integer vector of the true values
    of the dependent variable. Optional, and used only to calculate the
    true errors.
  }


  \item{gradient.order}{
    for \code{regtype="lp"} with \code{gradients=TRUE}, a positive
    integer (or integer vector with one entry per continuous predictor)
    specifying derivative order(s). Defaults to \code{1L}. Orders
    exceeding \code{degree} for a variable are returned as
    \code{NA}. Higher-order derivatives are available for continuous
    predictors when the requested order does not exceed the
    corresponding local-polynomial degree.
  }


  \item{gradients}{
    a logical value indicating that you want gradients computed and
    returned in the resulting \code{npregression} object. Defaults to
    \code{FALSE}. For \code{regtype="lp"}, derivative components that
    are not defined (requested order exceeds the variable-specific
    polynomial degree) are returned as \code{NA}. For ordered and
    unordered predictors the gradient columns are first-order
    effects/contrasts where supported; \code{gradient.order} controls
    derivative order for continuous predictors.
  }


  \item{newdata}{
    An optional data frame in which to look for evaluation data. If
    omitted, the training data are used.
  }


  \item{residuals}{
    a logical value indicating that you want residuals computed and
    returned in the resulting \code{npregression} object. Defaults to
    \code{FALSE}.
  }


  \item{y.eval}{
    If \code{newdata} contains dependent data and \code{y.eval = TRUE},
    \code{\link{np}} will compute goodness of fit statistics on these
    data and return them. Defaults to \code{FALSE}.
  }


\subsection{Additional Arguments}{
Further arguments are passed to \code{\link{npregbw}} when bandwidths are computed internally, or used to interpret a numeric \code{bws} vector.
}

  \item{\dots}{
    additional arguments supplied to \code{\link{npregbw}} when
    \code{npreg} computes bandwidths internally, or arguments needed to
    interpret a numeric \code{bws} vector. This is where bandwidth
    selection controls such as \code{bwmethod}, \code{bwtype},
    \code{bwscaling}, kernel/support controls such as \code{ckertype},
    \code{ckerorder}, and \code{ckerbound}, categorical kernel controls
    such as \code{ukertype} and \code{okertype}, search controls such
    as \code{nmulti} and \code{scale.factor.search.lower}, and
    local-polynomial/NOMAD controls such as \code{regtype},
    \code{degree}, \code{basis}, \code{bernstein.basis},
    \code{degree.select}, and \code{nomad.nmulti} are supplied. See
    \code{\link{npregbw}} for the complete bandwidth-selection argument
    surface.
  }
}
\details{
  Documentation guide: see \code{\link{npregbw}} for bandwidth
  selection and search controls, \code{\link{np.kernels}} for kernels,
  \code{\link{np.options}} for global options, and \code{\link{plot}},
  \code{\link{plot.np}} for plotting options.

  When \code{bws} is omitted, the formula and default methods call
  \code{\link{npregbw}} first and pass bandwidth-selection arguments
  from \code{\dots} to that call. When \code{bws} is already an
  \code{rbandwidth} object, \code{npreg} estimates with the stored
  bandwidth metadata in that object.

  Argument groups for bandwidth selection are documented on
  \code{\link{npregbw}}. The most common workflow is to choose data and
  bandwidth inputs first, then bandwidth criterion and representation,
  then kernel/support controls, and finally local-polynomial/NOMAD
  controls when using polynomial-adaptive fits.

  For S3 plotting help, see \code{\link{plot.np}}. You can list
  available plot methods with \code{methods("plot")}.


  Typical usages are (see below for a complete list of options and also
  the examples at the end of this help file)
  \preformatted{
    Usage 1: first compute the bandwidth object via npregbw and then
    compute the conditional mean:

    bw <- npregbw(y~x)
    ghat <- npreg(bw)

    Usage 2: alternatively, compute the bandwidth object indirectly:

    ghat <- npreg(y~x)

    Usage 3: modify the default kernel and order:

    ghat <- npreg(y~x, ckertype="epanechnikov", ckerorder=4)

    Usage 4: use the data frame interface rather than the formula
    interface:

    ghat <- npreg(tydat=y, txdat=x, ckertype="epanechnikov", ckerorder=4)
  }



  \code{npreg} implements a variety of methods for regression on
  multivariate (\eqn{p}-variate) data, the types of which are possibly
  continuous and/or discrete (unordered, ordered). The approach is
  based on Li and Racine (2003) who employ \sQuote{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, \eqn{x_i}{x[i]}, when estimating the
  density at the point \eqn{x}. Generalized nearest-neighbor bandwidths change
  with the point at which the density is estimated, \eqn{x}. Fixed bandwidths
  are constant over the support of \eqn{x}.

  Data contained in the data frame \code{txdat} may be a mix of
  continuous (default), unordered discrete (to be specified in the data
  frame \code{txdat} using \code{\link{factor}}), and ordered discrete
  (to be specified in the data frame \code{txdat} using
  \code{\link{ordered}}). Data can be entered in an arbitrary order and
  data types will be detected automatically by the routine (see
  \code{\link{np}} for details).

  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 bandwidths are obtained with \code{regtype="lp"}, C-level
  \code{npreg} supports heterogeneous continuous polynomial degrees via
  \code{degree}. The \code{basis} selector currently supports
  \code{basis="glp"}, \code{"additive"}, and \code{"tensor"}.
  For continuous predictors with degree vector
  \eqn{d}{d}, additive basis size is
  \eqn{1+\sum_j d_j}{1+sum(d[j])}, tensor basis size is
  \eqn{\prod_j (d_j+1)}{prod(d[j]+1)}, and GLP uses admissible
  multi-indices \eqn{\alpha}{alpha} with
  \eqn{\alpha_j \le d_j}{alpha[j] <= d[j]} and
  \eqn{0<\sum_j \alpha_j \le \max_j d_j}{0<sum(alpha)<=max(d)} plus
  an intercept. The optional flag \code{bernstein.basis}
  controls basis construction: \code{FALSE} (default) uses raw
  local-polynomial powers, while \code{TRUE} uses a Bernstein/B-spline basis. The
  homogeneous degree-0 and degree-1 cases remain
  equivalent to \code{lc} and \code{ll}, respectively. For continuous
  predictors, \code{gradients=TRUE} returns the derivative order(s)
  requested by \code{gradient.order}; the associated \code{gerr}
  entries are standard errors for those same derivative orders. Both
  raw and Bernstein/B-spline bases report fitted values and derivatives
  on the original response/predictor scale. In mixed-data GLP settings,
  unordered and ordered predictors contribute first-order
  effects/contrasts rather than higher-order derivatives.
  When \code{npregbw(..., regtype="lp")} is used with
  \code{degree.select="manual"}, the degree vector remains fixed user
  input. When \code{degree.select != "manual"}, \code{npregbw} can
  jointly select polynomial degree and bandwidth using either the
  cached cell-search backend or the direct
  \code{search.engine="nomad"}/\code{"nomad+powell"} route described in
  \code{\link{npregbw}}; the latter follows Hall and Racine (2015). For
  practitioners who want that recommended route without spelling out all
  LP tuning arguments, \code{npreg(..., nomad=TRUE)} and
  \code{npregbw(..., nomad=TRUE)} expand missing settings to the same
  documented automatic-LP NOMAD preset. Explicit incompatible settings
  fail fast rather than being silently rewritten. The direct NOMAD
  backend is provided by the suggested package \code{crs}, so install
  \code{crs} before using \code{search.engine="nomad"},
  \code{"nomad+powell"}, or \code{nomad=TRUE}. For
  \code{bernstein.basis=TRUE}, evaluation points for continuous predictors
  must lie within training support; use \code{bernstein.basis=FALSE} for
  extrapolation. For \code{regtype="ll"} and \code{regtype="lp"}, the
  training continuous design is checked for rank deficiency and extreme
  condition number before estimation proceeds.

  The use of compactly supported kernels or the occurrence of small
  bandwidths 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
  \sQuote{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).

}

\value{

  \code{npreg} returns a \code{npregression} object.
  The generic
  functions \code{\link{fitted}}, \code{\link{residuals}},
  \code{\link{se}}, \code{\link{predict}}, and
  \code{\link{gradients}}, extract (or generate) estimated values,
  residuals, asymptotic standard
  errors on estimates, predictions, and gradients, respectively, from
  the returned object. Furthermore, the functions \code{\link{summary}}
  and \code{\link{plot}} support objects of this type. The returned object
  has the following components:


  \item{eval}{ evaluation points }
  \item{mean}{ estimates of the regression function (conditional mean) at the
  evaluation points }
  \item{merr}{ standard errors of the regression function estimates }
  \item{grad}{ estimates of the gradients at each evaluation point }
  \item{gerr}{ standard errors of the gradient estimates }
  \item{resid}{ if \code{residuals = TRUE}, in-sample or out-of-sample
    residuals where appropriate (or possible) }
  \item{R2}{ coefficient of determination (Doksum and Samarov (1995))}
  \item{MSE}{ mean squared error }
  \item{MAE}{ mean absolute error }
  \item{MAPE}{ mean absolute percentage error }
  \item{CORR}{ absolute value of Pearson's correlation coefficient }
  \item{SIGN}{ fraction of observations where fitted and observed values
  agree in sign }

}

\section{Book And Method Pointers}{
  The regression target is the conditional mean
  \eqn{m(x)=E[Y\mid X=x]}. Local-constant estimation can be written
  schematically as \eqn{\hat m(x)=\sum_i W_i(x)Y_i}, where the weights
  are normalized generalized product-kernel weights over the continuous,
  unordered, and ordered components of \eqn{X}. Local-polynomial
  regression replaces this scalar weighted average by a weighted least
  squares fit in powers of the continuous coordinates around \eqn{x};
  the fitted intercept is the estimate of \eqn{m(x)} and the fitted
  slope terms give the reported gradients where available.

  For book-length derivations, see Li and Racine (2007), Chapter 2
  \emph{Regression}, especially Sections 2.1, 2.2, 2.4, and 2.5, and
  Chapter 4 \emph{Kernel Estimation with Mixed Data}, especially Sections
  4.2 and 4.4. The later workflow treatment is Racine (2019), Chapter 6
  \emph{Conditional Mean Function Estimation}, including the local
  polynomial and mixed-data marginal-effect discussion.
}

\references{

  Aitchison, J. and C.G.G. Aitken (1976), \dQuote{Multivariate binary
  discrimination by the kernel method,} Biometrika, 63, 413-420.

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

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

  Doksum, K. and A. Samarov (1995), \dQuote{Nonparametric estimation of
  global functionals and a measure of the explanatory power of
  covariates in regression,} The Annals of Statistics, 23 1443-1473.

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

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

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

  Li, Q. and J.S. Racine (2004), \dQuote{Cross-validated local linear
  nonparametric regression,} Statistica Sinica, 14, 485-512.

  Pagan, A. and A. Ullah (1999), \emph{Nonparametric Econometrics,}
  Cambridge University Press.

  Racine, J.S. and Q. Li (2004), \dQuote{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), \dQuote{A class of smooth
    estimators for discrete distributions,} Biometrika, 68, 301-309.

}

\author{
  Tristen Hayfield \email{tristen.hayfield@gmail.com}, Jeffrey S. Racine \email{racinej@mcmaster.ca}
}

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

\seealso{
  \code{\link{np.kernels}}, \code{\link{np.options}}, \code{\link{plot}}, \code{\link{plot.np}}
  \code{\link{loess}}
}
\examples{
\dontrun{
# 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")
with(Italy, {

# First, compute the least-squares cross-validated bandwidths for the
# local constant estimator (default).

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

# Now take these bandwidths and fit the model and gradients

model <- npreg(bws = bw, gradients = TRUE)

summary(model)

# Use plot() to visualize the regression function, add bootstrap
# error bars, and overlay the data on the same plot.

# Note - this may take a minute or two depending on the speed of your
# computer due to bootstrapping being conducted (<ctrl>-C will
# interrupt). Note - nothing will appear in the graphics window until
# all computations are completed (if you use
# errors="asymptotic" the figure will instantly appear).

if (interactive()) plot(bw, errors="bootstrap")
points(ordered(year), gdp, cex=.2, col="red")

})

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

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

# 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")
with(Italy, {

# First, compute the least-squares cross-validated bandwidths for the
# local constant estimator (default).

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

# Now take these bandwidths and fit the model and gradients

model <- npreg(bws = bw, gradients = TRUE)

summary(model)

# Use plot() to visualize the regression function, add bootstrap
# error bars, and overlay the data on the same plot.

# Note - this may take a minute or two depending on the speed of your
# computer due to bootstrapping being conducted (<ctrl>-C will
# interrupt). Note - nothing will appear in the graphics window until
# all computations are completed (if you use
# errors="asymptotic" the figure will instantly appear).

if (interactive()) plot(bw, errors="bootstrap")
points(ordered(year), gdp, cex=.2, col="red")

})

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

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

# EXAMPLE 2 (INTERFACE=FORMULA): For this example, we compute a local
# linear fit using the AIC_c bandwidth selection criterion. We then plot
# the estimator and its gradient using asymptotic standard errors.

data("cps71", package = "np")

bw <- npregbw(logwage~age, regtype="ll", bwmethod="cv.aic", data = cps71)

# Next, plot the regression function...

if (interactive()) plot(bw, errors="asymptotic")
with(cps71, points(age, logwage, cex=.2, col="red"))

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

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

# Next, plot the derivative...

if (interactive()) plot(bw, errors="asymptotic", gradient=TRUE)

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

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

# EXAMPLE 2 (INTERFACE=DATA FRAME): For this example, we compute a local
# linear fit using the AIC_c bandwidth selection criterion. We then plot
# the estimator and its gradient using asymptotic standard errors.

data("cps71", package = "np")

bw <- npregbw(xdat=cps71$age, ydat=cps71$logwage, regtype="ll", bwmethod="cv.aic")

# Next, plot the regression function...

if (interactive()) plot(bw, errors="asymptotic")
with(cps71, points(age, logwage, cex=.2, col="red"))

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

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

# Next, plot the derivative...

if (interactive()) plot(bw, errors="asymptotic", gradient=TRUE)

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

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

# EXAMPLE 3 (INTERFACE=FORMULA): For this example, we replicate the
# nonparametric regression in Maasoumi, Racine, and Stengos
# (2007) (see oecdpanel for details). Note that X is multivariate
# containing a mix of unordered, ordered, and continuous data types. Note
# - this may take a few minutes depending on the speed of your computer.

data("oecdpanel")
with(oecdpanel, {

bw <- npregbw(formula=growth~
              factor(oecd)+
              factor(year)+
              initgdp+
              popgro+
              inv+
              humancap)

if (interactive()) plot(bw, errors="asymptotic")

})

# EXAMPLE 3 (INTERFACE=DATA FRAME): For this example, we replicate the
# nonparametric regression in Maasoumi, Racine, and Stengos
# (2007) (see oecdpanel for details). Note that X is multivariate
# containing a mix of unordered, ordered, and continuous data types. Note
# - this may take a few minutes depending on the speed of your computer.

data("oecdpanel")
with(oecdpanel, {

y <- growth
X <- data.frame(factor(oecd), factor(year), initgdp, popgro, inv, humancap)

bw <- npregbw(xdat=X, ydat=y)

if (interactive()) plot(bw, errors="asymptotic")

})


# EXAMPLE 4 (INTERFACE=FORMULA): Experimental data - the effect of
# vitamin C on tooth growth in guinea pigs
#
# Description:
#
#     The response is the length of odontoblasts (teeth) in each of 10
#     guinea pigs at each of three dose levels of Vitamin C (0.5, 1, and
#     2 mg) with each of two delivery methods (orange juice or ascorbic
#     acid).
#
# Usage:
#
#     ToothGrowth
#
# Format:
#
#     A data frame with 60 observations on 3 variables.
#
#       [,1]  len   numeric  Tooth length
#       [,2]  supp  factor   Supplement type (VC or OJ).
#       [,3]  dose  numeric  Dose in milligrams.

library("datasets")
with(ToothGrowth, {

# Note - in this example, there are six cells.

bw <- npregbw(formula=len~factor(supp)+ordered(dose))

# Now plot the partial regression surfaces with bootstrapped
# nonparametric confidence bounds

if (interactive()) plot(bw, errors="bootstrap", band="simultaneous")

})

# EXAMPLE 4 (INTERFACE=DATA FRAME): Experimental data - the effect of
# vitamin C on tooth growth in guinea pigs
#
# Description:
#
#     The response is the length of odontoblasts (teeth) in each of 10
#     guinea pigs at each of three dose levels of Vitamin C (0.5, 1, and
#     2 mg) with each of two delivery methods (orange juice or ascorbic
#     acid).
#
# Usage:
#
#     ToothGrowth
#
# Format:
#
#     A data frame with 60 observations on 3 variables.
#
#       [,1]  len   numeric  Tooth length
#       [,2]  supp  factor   Supplement type (VC or OJ).
#       [,3]  dose  numeric  Dose in milligrams.

library("datasets")
with(ToothGrowth, {

# Note - in this example, there are six cells.

y <- len
X <- data.frame(supp=factor(supp), dose=ordered(dose))

bw <- npregbw(X, y)

# Now plot the partial regression surfaces with bootstrapped
# nonparametric confidence bounds

if (interactive()) plot(bw, errors="bootstrap", band="simultaneous")

})

# EXAMPLE 5 (INTERFACE=FORMULA): a pretty 2-d smoothing example adapted
# from the R mgcv library which was written by Simon N. Wood.

set.seed(12345)

# This function generates a smooth nonlinear DGP

dgp.func <- function(x, z, sx=0.3, sz=0.4)
  { (pi**sx*sz)*(1.2*exp(-(x-0.2)^2/sx^2-(z-0.3)^2/sz^2)+
                 0.8*exp(-(x-0.7)^2/sx^2-(z-0.8)^2/sz^2))
  }

# Generate 500 observations, compute the true DGP (i.e., no noise),
# then a noisy sample

n <- 500

x <- runif(n)
z <- runif(n)

xs <- seq(0, 1, length=30)
zs <- seq(0, 1, length=30)

X.eval <- data.frame(x=rep(xs, 30), z=rep(zs, rep(30, 30)))

dgp <- matrix(dgp.func(X.eval$x, X.eval$z), 30, 30)

y <- dgp.func(x, z)+rnorm(n)*0.1

# Prepare the screen for output... first, plot the true DGP

split.screen(c(2, 1))

screen(1)

persp(xs, zs, dgp, xlab="x1", ylab="x2", zlab="y", main="True DGP")

# Next, compute a local linear fit and plot that

bw <- npregbw(formula=y~x+z, regtype="ll", bwmethod="cv.aic")
fit <- fitted(npreg(bws=bw, newdata=X.eval))
fit.mat <- matrix(fit, 30, 30)

screen(2)

persp(xs, zs, fit.mat, xlab="x1", ylab="x2", zlab="y",
      main="Local linear estimate")

# EXAMPLE 5 (INTERFACE=DATA FRAME): a pretty 2-d smoothing example
# adapted from the R mgcv library which was written by Simon N. Wood.

set.seed(12345)

# This function generates a smooth nonlinear DGP

dgp.func <- function(x, z, sx=0.3, sz=0.4)
  { (pi**sx*sz)*(1.2*exp(-(x-0.2)^2/sx^2-(z-0.3)^2/sz^2)+
                 0.8*exp(-(x-0.7)^2/sx^2-(z-0.8)^2/sz^2))
  }

# Generate 500 observations, compute the true DGP (i.e., no noise),
# then a noisy sample

n <- 500

x <- runif(n)
z <- runif(n)

xs <- seq(0, 1, length=30)
zs <- seq(0, 1, length=30)

X <- data.frame(x, z)
X.eval <- data.frame(x=rep(xs, 30), z=rep(zs, rep(30, 30)))

dgp <- matrix(dgp.func(X.eval$x, X.eval$z), 30, 30)

y <- dgp.func(x, z)+rnorm(n)*0.1

# Prepare the screen for output... first, plot the true DGP

split.screen(c(2, 1))

screen(1)

persp(xs, zs, dgp, xlab="x1", ylab="x2", zlab="y", main="True DGP")

# Next, compute a local linear fit and plot that

bw <- npregbw(xdat=X, ydat=y, regtype="ll", bwmethod="cv.aic")
fit <- fitted(npreg(exdat=X.eval, bws=bw))
fit.mat <- matrix(fit, 30, 30)

screen(2)

persp(xs, zs, fit.mat, xlab="x1", ylab="x2", zlab="y",
      main="Local linear estimate")
} % enddontrun
}
\keyword{nonparametric}
