npglpreg: Generalized Local Polynomial Regression

In JeffreyRacine/R-Package-crs: Categorical Regression Splines

Generalized Local Polynomial Regression

Description

npglpreg computes a generalized local polynomial kernel regression estimate (Hall and Racine (2015)) of a one (1) dimensional dependent variable on an r-dimensional vector of continuous and categorical (factor/ordered) predictors.

Usage

npglpreg(...)

## Default S3 method:
npglpreg(tydat = NULL,
         txdat = NULL,
         eydat = NULL,
         exdat = NULL,
         bws = NULL,
         degree = NULL,
         leave.one.out = FALSE,
         ckertype = c("gaussian", "epanechnikov", "uniform", "truncated gaussian"),
         ckerorder = 2,
         ukertype = c("liracine", "aitchisonaitken"),
         okertype = c("liracine", "wangvanryzin"),
         bwtype = c("fixed", "generalized_nn", "adaptive_nn", "auto"),
         gradient.vec = NULL,
         gradient.categorical = FALSE,
         cv.shrink = TRUE,
         cv.maxPenalty = sqrt(.Machine$double.xmax),
         cv.warning = FALSE,
         Bernstein = TRUE,
         mpi = FALSE,
         ...)

## S3 method for class 'formula'
npglpreg(formula,
         data = list(),
         tydat = NULL,
         txdat = NULL,
         eydat = NULL,
         exdat = NULL,
         bws = NULL,
         degree = NULL,
         leave.one.out = FALSE,
         ckertype = c("gaussian", "epanechnikov","uniform","truncated gaussian"),
         ckerorder = 2,
         ukertype = c("liracine", "aitchisonaitken"),
         okertype = c("liracine", "wangvanryzin"),
         bwtype = c("fixed", "generalized_nn", "adaptive_nn", "auto"),
         cv = c("degree-bandwidth", "bandwidth", "none"),
         cv.func = c("cv.ls", "cv.aic"),
         nmulti = 5,
         random.seed = 42,
         degree.max = 10,
         degree.min = 0,
         bandwidth.max = .Machine$double.xmax,
         bandwidth.min = sqrt(.Machine$double.eps),
         bandwidth.min.numeric = 1.0e-02,
         bandwidth.switch = 1.0e+06,
         bandwidth.scale.categorical = 1.0e+04,
         max.bb.eval = 10000,
         min.epsilon = .Machine$double.eps,
         initial.mesh.size.real = 1,
         initial.mesh.size.integer = 1,
         min.mesh.size.real = sqrt(.Machine$double.eps),
         min.mesh.size.integer = 1, 
         min.poll.size.real = 1,
         min.poll.size.integer = 1, 
         opts=list(),
         restart.from.min = FALSE,
         gradient.vec = NULL,
         gradient.categorical = FALSE,
         cv.shrink = TRUE,
         cv.maxPenalty = sqrt(.Machine$double.xmax),
         cv.warning = FALSE,
         Bernstein = TRUE,
         mpi = FALSE,
         ...)

Arguments

formula	a symbolic description of the model to be fit
data	an optional data frame containing the variables in the model
tydat	a one (1) dimensional numeric or integer vector of dependent data, each element i corresponding to each observation (row) i of txdat. Defaults to the training data used to compute the bandwidth object
txdat	a 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
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
exdat	a 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 txdat
bws	a vector of bandwidths, with each element i corresponding to the bandwidth for column i in txdat
degree	integer/vector specifying the polynomial degree of the for each dimension of the continuous x in txdat
leave.one.out	a logical value to specify whether or not to compute the leave one out sums. Will not work if exdat is specified. Defaults to FALSE
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.
ukertype	character string used to specify the unordered categorical kernel type. Can be set as aitchisonaitken or liracine. Defaults to liracine
okertype	character string used to specify the ordered categorical kernel type. Can be set as wangvanryzin or liracine. Defaults to liracine
bwtype	character string used for the continuous variable bandwidth type, specifying the type of bandwidth to compute and return in the bandwidth object. If bwtype="auto", the bandwidth type type will be automatically determined by cross-validation. Defaults to fixed. Option summary: fixed: compute fixed bandwidths generalized_nn: compute generalized nearest neighbor bandwidths adaptive_nn: compute adaptive nearest neighbor bandwidths
cv	a character string (default cv="nomad") indicating whether to use nonsmooth mesh adaptive direct search, or no search (i.e. use supplied values for degree and bws)
cv.func	a character string (default cv.func="cv.ls") indicating which method to use to select smoothing parameters. cv.aic specifies expected Kullback-Leibler cross-validation (Hurvich, Simonoff, and Tsai (1998)), and cv.ls specifies least-squares cross-validation
max.bb.eval	argument passed to the NOMAD solver (see snomadr for further details)
min.epsilon	argument passed to the NOMAD solver (see snomadr for further details)
initial.mesh.size.real	argument passed to the NOMAD solver (see snomadr for further details)
initial.mesh.size.integer	argument passed to the NOMAD solver (see snomadr for further details)
min.mesh.size.real	argument passed to the NOMAD solver (see snomadr for further details)
min.mesh.size.integer	arguments passed to the NOMAD solver (see snomadr for further details)
min.poll.size.real	arguments passed to the NOMAD solver (see snomadr for further details)
min.poll.size.integer	arguments passed to the NOMAD solver (see snomadr for further details)
opts	list of optional arguments passed to the NOMAD solver (see snomadr for further details)
nmulti	integer number of times to restart the process of finding extrema of the cross-validation function from different (random) initial points (default nmulti=5)
random.seed	when it is not missing and not equal to 0, the initial points will be generated using this seed when using snomadr
degree.max	the maximum degree of the polynomial for each of the continuous predictors (default degree.max=10)
degree.min	the minimum degree of the polynomial for each of the continuous predictors (default degree.min=0)
bandwidth.max	the maximum bandwidth scale (i.e. number of scaled standard deviations) for each of the continuous predictors (default bandwidth.max=.Machine$double.xmax)
bandwidth.min	the minimum bandwidth scale for each of the categorical predictors (default sqrt(.Machine$double.eps))
bandwidth.min.numeric	the minimum bandwidth scale (i.e. number of scaled standard deviations) for each of the continuous predictors (default bandwidth.min=1.0e-02)
bandwidth.switch	the minimum bandwidth scale (i.e. number of scaled standard deviations) for each of the continuous predictors (default bandwidth.switch=1.0e+06) before the local polynomial is treated as global during cross-validation at which point a global categorical kernel weighted least squares fit is used for computational efficiency
bandwidth.scale.categorical	the upper end for the rescaled bandwidths for the categorical predictors (default bandwidth.scale.categorical=1.0e+04) - the aim is to ‘even up’ the scale of the search parameters as much as possible, so when very large scale factors are selected for the continuous predictors, a larger value may improve search
restart.from.min	a logical value indicating to recommence snomadr with the optimal values found from its first invocation (typically quick but sometimes recommended in the field of optimization)
gradient.vec	a vector corresponding to the order of the partial (or cross-partial) and which variable the partial (or cross-partial) derivative(s) are required
gradient.categorical	a logical value indicating whether discrete gradients (i.e. differences in the response from the base value for each categorical predictor) are to be computed
cv.shrink	a logical value indicating whether to use ridging (Seifert and Gasser (2000)) for ill-conditioned inversion during cross-validation (default cv.shrink=TRUE) or to instead test for ill-conditioned matrices and penalize heavily when this is the case (much stronger condition imposed on cross-validation)
cv.maxPenalty	a penalty applied during cross-validation when a delete-one estimate is not finite or the polynomial basis is not of full column rank
cv.warning	a logical value indicating whether to issue an immediate warning message when ill conditioned bases are encountered during cross-validation (default cv.warning=FALSE)
Bernstein	a logical value indicating whether to use raw polynomials or Bernstein polynomials (default) (note that a Bernstein polynomial is also know as a Bezier curve which is also a B-spline with no interior knots)
mpi	a logical value (default mpi=FALSE) that, when mpi=TRUE, can call the npRmpi rather than the np package (note - code needs to mirror examples in the demo directory of the npRmpi package, you need to broadcast loading of the crs package, and need .Rprofile in your current directory)
...	additional arguments supplied to specify the regression type, bandwidth type, kernel types, training data, and so on, detailed below

Details

Typical usages are (see below for a list of options and also the examples at the end of this help file)

    ## Conduct generalized local polynomial estimation
    
    model <- npglpreg(y~x1+x2)
    
    ## Conduct degree 0 local polynomial estimation
    ## (i.e. Nadaraya-Watson)
    
    model <- npglpreg(y~x1+x2,cv="bandwidth",degree=c(0,0))    
    
    ## Conduct degree 1 local polynomial estimation (i.e. local linear)
    
    model <- npglpreg(y~x1+x2,cv="bandwidth",degree=c(1,1))    
    
    ## Conduct degree 2 local polynomial estimation (i.e. local
    ## quadratic)
    
    model <- npglpreg(y~x1+x2,cv="bandwidth",degree=c(2,2))

    ## Plot the mean and bootstrap confidence intervals

    plot(model,ci=TRUE)

    ## Plot the first partial derivatives and bootstrap confidence
    ## intervals

    plot(model,deriv=1,ci=TRUE)

    ## Plot the first second partial derivatives and bootstrap
    ## confidence intervals

    plot(model,deriv=2,ci=TRUE)        
    
  

This function is in beta status until further notice (eventually it will be rolled into the np/npRmpi packages after the final status of snomadr/NOMAD gets sorted out).

Optimizing the cross-validation function jointly for bandwidths (vectors of continuous parameters) and polynomial degrees (vectors of integer parameters) constitutes a mixed-integer optimization problem. These problems are not only ‘hard’ from the numerical optimization perspective, but are also computationally intensive (contrast this to where we conduct, say, local linear regression which sets the degree of the polynomial vector to a global value degree=1 hence we only need to optimize with respect to the continuous bandwidths). Because of this we must be mindful of the presence of local optima (the objective function is non-convex and non-differentiable). Restarting search from different initial starting points is recommended (see nmulti) and by default this is done more than once. We encourage users to adopt ‘multistarting’ and to investigate the impact of changing default search parameters such as initial.mesh.size.real, initial.mesh.size.integer, min.mesh.size.real, min.mesh.size.integer,min.poll.size.real, and min.poll.size.integer. The default values were chosen based on extensive simulation experiments and were chosen so as to yield robust performance while being mindful of excessive computation - of course, no one setting can be globally optimal.

Value

npglpreg returns a npglpreg object. The generic functions fitted and residuals extract (or generate) estimated values and residuals. Furthermore, the functions summary, predict, and plot (options deriv=0, ci=FALSE [ci=TRUE produces pointwise bootstrap error bounds], persp.rgl=FALSE, plot.behavior=c("plot","plot-data","data"), plot.errors.boot.num=99, plot.errors.type=c("quantiles","standard") ["quantiles" produces percentiles determined by plot.errors.quantiles below, "standard" produces error bounds given by +/- 1.96 bootstrap standard deviations], plot.errors.quantiles=c(.025,.975), xtrim=0.0, xq=0.5) support objects of this type. The returned object has the following components:

fitted.values	estimates of the regression function (conditional mean) at the sample points or evaluation points
residuals	residuals computed at the sample points or evaluation points
degree	integer/vector specifying the degree of the polynomial for each dimension of the continuous x
gradient	the estimated gradient (vector) corresponding to the vector gradient.vec
gradient.categorical.mat	the estimated gradient (matrix) for the categorical predictors
gradient.vec	the supplied gradient.vec
bws	vector of bandwidths
bwtype	the supplied bwtype
call	a symbolic description of the model
r.squared	coefficient of determination (Doksum and Samarov (1995))

Note

Note that the use of raw polynomials (Bernstein=FALSE) for approximation is appealing as they can be computed and differentiated easily, however, they can be unstable (their inversion can be ill conditioned) which can cause problems in some instances as the order of the polynomial increases. This can hamper search when excessive reliance on ridging to overcome ill conditioned inversion becomes computationally burdensome.

npglpreg tries to detect whether this is an issue or not when Bernstein=FALSE for each numeric predictor and will adjust the search range for snomadr and the degree fed to npglpreg if appropriate.

However, if you suspect that this might be an issue for your specific problem and you are using raw polynomials (Bernstein=FALSE), you are encouraged to investigate this by limiting degree.max to value less than the default value (say 3). Alternatively, you might consider re-scaling your numeric predictors to lie in [0,1] using scale.

For a given predictor x you can readily determine if this is an issue by considering the following: Suppose x is given by

    x <- runif(100,10000,11000)
    y <- x + rnorm(100,sd=1000)
  

so that a polynomial of order, say, 5 would be ill conditioned. This would be apparent if you considered

    X <- poly(x,degree=5,raw=TRUE)
    solve(t(X)%*%X)
  

which will throw an error when the polynomial is ill conditioned, or

    X <- poly(x,degree=5,raw=TRUE)
    lm(y~X)
  

which will return NA for one or more coefficients when this is an issue.

In such cases you might consider transforming your numeric predictors along the lines of the following:

    x <- as.numeric(scale(x))
    X <- poly(x,degree=5,raw=TRUE)
    solve(t(X)%*%X)
    lm(y~X)    
  

Note that now your least squares coefficients (i.e. first derivative of y with respect to x) represent the effect of a one standard deviation change in x and not a one unit change.

Alternatively, you can use Bernstein polynomials by not setting Bernstein=FALSE.

Author(s)

Jeffrey S. Racine [e-mail] and Zhenghua Nie [e-mail]

References

Doksum, K. and A. Samarov (1995), “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 J.S. Racine (2015), “Infinite Order Cross-Validated Local Polynomial Regression,” Journal of Econometrics, 185, 510-525.

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

Seifert, B. and T. Gasser (2000), “Data Adaptive Ridging in Local Polynomial Regression,” Journal of Computational and Graphical Statistics, 9(2), 338-360.

See Also

npreg

Examples

## Not run: 
set.seed(42)
n <- 100
x1 <- runif(n,-2,2)
x2 <- runif(n,-2,2)
y <- x1^3 + rnorm(n,sd=1)

## Ideally the method should choose large bandwidths for x1 and x2 and a
## generalized polynomial that is a cubic for x1 and degree 0 for x2.

model <- npglpreg(y~x1+x2,nmulti=1)
summary(model)

## Plot the partial means and percentile confidence intervals
plot(model,ci=T)
## Extract the data from the plot object and plot it separately
myplot.dat <- plot(model,plot.behavior="data",ci=T)
matplot(myplot.dat[[1]][,1],myplot.dat[[1]][,-1],type="l")
matplot(myplot.dat[[2]][,1],myplot.dat[[2]][,-1],type="l")

## End(Not run)

JeffreyRacine/R-Package-crs documentation built on Jan. 5, 2023, 1:30 a.m.