| Title: | Shape-Constrained Kernel Density Estimation |
|---|---|
| Description: | Implements methods for obtaining kernel density estimates subject to a variety of shape constraints (unimodality, bimodality, symmetry, tail monotonicity, bounds, and constraints on the number of inflection points). Enforcing constraints can eliminate unwanted waves or kinks in the estimate, which improves its subjective appearance and can also improve statistical performance. The main function scdensity() is very similar to the density() function in 'stats', allowing shape-restricted estimates to be obtained with little effort. The methods implemented in this package are described in Wolters and Braun (2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012) <doi:10.18637/jss.v047.i06>, and Hall and Huang (2002) <https://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>. See the scdensity() help for for full citations. |
| Authors: | Mark A. Wolters [aut, cre]
|
| Maintainer: | Mark A. Wolters <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.3 |
| Built: | 2024-11-26 06:41:48 UTC |
| Source: | CRAN |
improve(startValue, x, confun) uses a greedy algorithm to move the elements of a
user-supplied vector startValue closer to their target values x, while
continually satisfying the constraint-checking function confun.
improve( startValue, x, confun, verbose = FALSE, maxpasses = 500, tol = diff(range(c(startValue, x))/1e+05) )improve( startValue, x, confun, verbose = FALSE, maxpasses = 500, tol = diff(range(c(startValue, x))/1e+05) )
startValue |
The vector of starting values for the search. Must satisfy
|
x |
The target values. |
confun |
The constraint-checking function. |
verbose |
A logical value indicating whether or not information about iteration progress should be printed to the console. |
maxpasses |
The maximum allowable number of sweeps through the data points. At each pass, every point that is not pinned at the constraint boundary is moved toward its target point in a stepping-out procedure. |
tol |
Numerical tolerance for constraint checking. A point is considered to be at the
constraint boundary if adding |
The algorithm implemented here is the one in Wolters (2012), "A Greedy Algorithm for Unimodal Kernel Density Estimation by Data Sharpening," Journal of Statistical Software, 47(6). It could conceivably be useful as a part of other gradient-free optimization schemes where we have an infeasible point and a feasible one, and we seek a point that is on the constraint boundary near the infeasible one.
A vector of the same length as startValue, with elements closer to x.
#Constrain points to be inside the hypercube with vertices at -1 and +1. #The target point is a vector of independent random standard normal variates. #Start at rep(0,n) and "improve" the solution toward the target. n <- 20 incube <- function(x) all(x <= 1 & x >= -1) x0 <- rep(0,n) target <- sort(rnorm(n)) xstar <- improve(x0, target, incube, verbose=TRUE) dist <- abs(target - xstar) zapsmall(cbind(target, xstar, dist), 4)#Constrain points to be inside the hypercube with vertices at -1 and +1. #The target point is a vector of independent random standard normal variates. #Start at rep(0,n) and "improve" the solution toward the target. n <- 20 incube <- function(x) all(x <= 1 & x >= -1) x0 <- rep(0,n) target <- sort(rnorm(n)) xstar <- improve(x0, target, incube, verbose=TRUE) dist <- abs(target - xstar) zapsmall(cbind(target, xstar, dist), 4)
scdensity.Creates a plot of a shape-constrained kernel density estimate. The amount of information
in the plot is controlled by detail.
## S3 method for class 'scdensity' plot( x, detail = 4, main = c("Density Estimate", "Q-Q Plot"), xlab = c(x$data.name, "Constrained KDE Quantiles"), ylab = c("Density", "Sample Quantiles"), type = c("l", "l", "p"), lty = c(1, 2, 0), pch = c(-1, -1, 1), col = c("black", gray(0.4), "black"), lwd = c(2, 1, 0), zero.line = TRUE, ... )## S3 method for class 'scdensity' plot( x, detail = 4, main = c("Density Estimate", "Q-Q Plot"), xlab = c(x$data.name, "Constrained KDE Quantiles"), ylab = c("Density", "Sample Quantiles"), type = c("l", "l", "p"), lty = c(1, 2, 0), pch = c(-1, -1, 1), col = c("black", gray(0.4), "black"), lwd = c(2, 1, 0), zero.line = TRUE, ... )
x |
An object of S3 class |
detail |
An integer from 1 to 4, indicating the level of information to include in the plot. 1: plot only the constrained estimate. 2: draw both the constrained and unconstrained estimates on the same plot. 3: add a rug showing the data points. 4: additionally plot a Q-Q plot of the observed data versus the constrained estimate in a second panel (for qualitative assessment of goodness-of-fit). |
main |
A string passed on to the |
xlab |
A string passed on to the |
ylab |
A string passed on to the |
type |
A vector of up to 3 strings specifying the |
lty |
A vector of up to length 3, specifying the |
pch |
A vector of up to 3 integers specifying the |
col |
A vector of up to 3 strings specifying the |
lwd |
A vector of up to length 3 specifying the |
zero.line |
A logical value indicating whether or not a horizontal line should be drawn through zero to aid visualization. |
... |
Extra parameters passed to the initial |
# Basic usage: x <- rlnorm(30) scKDE <- scdensity(x) plot(scKDE) # Show only the constrained estimate plot(scKDE, detail=1) # Show the constrained and unconstrained estimates. Change line color and width. plot(scKDE, detail=2, col=c("red","blue"), lwd=c(3,2)) # Show the Q-Q plot, but change that plot's symbol and its size. plot(scKDE, detail=4, pch=c(-1, -1, 3), cex=0.5)# Basic usage: x <- rlnorm(30) scKDE <- scdensity(x) plot(scKDE) # Show only the constrained estimate plot(scKDE, detail=1) # Show the constrained and unconstrained estimates. Change line color and width. plot(scKDE, detail=2, col=c("red","blue"), lwd=c(3,2)) # Show the Q-Q plot, but change that plot's symbol and its size. plot(scKDE, detail=4, pch=c(-1, -1, 3), cex=0.5)
scdensity.Displays the names of the elements of the scdensity list object and their sizes and types. Includes minimal comments about the most important elements.
## S3 method for class 'scdensity' print(x, ...)## S3 method for class 'scdensity' print(x, ...)
x |
An object of S3 class |
... |
Included for consistency with generic functions. |
summary.scdensity object to the console.Prints the information in a summary.scdensity object to the console.
## S3 method for class 'summary.scdensity' print(x, ...)## S3 method for class 'summary.scdensity' print(x, ...)
x |
An object of S3 class |
... |
Included for consistency with generic functions. |
scdensity computes kernel density estimates that satisfy specified shape
restrictions. It is used in the same way as stats::density(), and takes
most of that function's arguments. Its default behavior is to compute a unimodal estimate.
Use argument constraint to choose different shape constraints, method to
choose a different estimation method, and opts to specify method- and
constraint-specific options. The result is a list of S3 class scdensity, which
may be inspected via print, summary, and plot methods.
scdensity( x, bw = "nrd0", constraint = c("unimodal", "monotoneRightTail", "monotoneLeftTail", "twoInflections", "twoInflections+", "boundedLeft", "boundedRight", "symmetric", "bimodal"), method = c("adjustedKDE", "weightedKDE", "greedySharpenedKDE"), opts = NULL, adjust = 1, n = 512, na.rm = FALSE )scdensity( x, bw = "nrd0", constraint = c("unimodal", "monotoneRightTail", "monotoneLeftTail", "twoInflections", "twoInflections+", "boundedLeft", "boundedRight", "symmetric", "bimodal"), method = c("adjustedKDE", "weightedKDE", "greedySharpenedKDE"), opts = NULL, adjust = 1, n = 512, na.rm = FALSE )
x |
A vector of data from which the estimate is to be computed. |
bw |
The bandwidth. It is specified as either a numerical value or as one of the
character strings |
constraint |
A vector of strings giving the operative shape constraints. Elements
must partially match different alternatives among |
method |
A string giving the method of enforcing shape constraints. It must
paritally match one of |
opts |
A list giving options specific to the chosen constraints and/or method. E.g.
use |
adjust |
A scaling factor for the bandwidth, just as in |
n |
The number of points returned in the density estimate. Same as in
|
na.rm |
Logical indicating whether or not to remove missing values from |
All density estimates in this package use the Gaussian kernel. It is the only common
kernel function with three continuous derivatives everywhere. The adjustedKDE and
weightedKDE methods require continuous derivatives to ensure numerical stability.
The default estimation method, adjustedKDE, can handle all of the available constraints. The
weightedKDE method can handle every constraint except symmetric, while the
greedySharpenedKDE method can handle only unimodal, monotoneRightTail,
monotoneLeftTail, boundedLeft, and boundedRight. The opts list can
also be used to supply method-specific control parameters. See the "Method details" section
for more.
Each constraint has a corresponding control parameter that can be supplied as an element of
opts. The control parameters are described in the following table. See the "Constraint
details" section for definitions of each constraint.
More than one shape constraint can be specified simultaneously. Certain combinations of constraints
(e.g., unimodal and monotoneRightTail) are redundant, and will cause a warning. Other
combinations (e.g., unimodal and bimodal) are incompatible and will cause an error.
The figure below summarizes the valid constraint combinations.
A list with the following elements:
constraint The constraint(s) used for estimation. Might differ from
the constraints supplied to the function if they included redundant constraints.
method The estimation method used.
f0 A function. Use f0(v) to evaluate the unconstrained KDE at the points in
v.
fhat A function. Use fhat(v) to evaluate the constrained KDE at the points in
v.
data The data used to generate the estimate.
bw The bandwidth used.
extra A list holding additional outputs that are specific to the chosen method.
See the "method details" section.
x A vector of abscissa values for plotting the estimate. Same as in
stats::density().
y A vector of ordinate values for plotting the estimate. Same as in
stats::density().
n The sample size, not including missing values. Note, this n has
no relation to the n provided in the arguments.
data.name Deparsed name of the x argument, used in plotting.
call The call to the function.
has.na Always FALSE. Included for consistency with
stats::density().
All of the constraints other than symmetric are restrictions on the sign of the estimate, or
its derviatives, over certain intervals. The boundaries of the intervals may be called
important points. If method="greedySharpenedKDE", the important points are determined
implicitly during estimation. For the other methods, the locations of the important points may be
supplied in opts; in most cases they are optional. If they are not provided, estimation
will be run iteratively inside a search routine (SequentialLineMin) to find good values,
and these values will be returned in the extra list.
Here is a list of the constraints with their definitions and any relevant comments about their usage.
unimodal: The estimate is nondecreasing to the left of opts$modeLocation, and
nonincreasing to the right. If modeLocation is not supplied, it is found by search.
monotoneRightTail: The estimate is nonincreasing to the right of the opts$rightTail
percentile of the unconstrained estimate. rightTail is a numeric value between 0 and 100.
If it is not supplied, it is set to its default value, 90.
monotoneLeftTail: The estimate is nondecreasing to the left of the opts$leftTail
percentile of the unconstrained estimate. leftTail is a numeric value between 0 and 100. If
it is not supplied, it is set to its default value, 10.
twoInflections: The estimate has two inflection points, found at
opts$inflectionPoints[1] and opts$inflectionPoints[2]. This constraint implies unimodality,
but provides greater smoothness than unimodal. If inflectionPoints is not supplied, it is
found by search.
twoInflections+: The derivative of the estimate has three inflection
points, located at opts$inflectionPoints[1], opts$inflectionPoints[2], and
opts$inflectionPoints[3]. This constraint implies twoInflections but is even smoother.
Most parametric densities with two tails satisfy this constraint. If inflectionPoints is not
supplied, it is found by search.
boundedLeft: The estimate is zero to the left of opts$lowerBound. The value of
lowerBound must be specified in opts. This constraint is implemented only up to a
numerical tolerance. Consequently it is still possible to use it with the Gaussian kernel.
boundedRight: The estimate is zero to the right of opts$upperBound. The value of
upperBound must be specified in opts. This constraint is
implemented only up to a numerical tolerance. Consequently it is still possible to use
it with the Gaussian kernel.
symmetric: The estimate is symmetric around opts$pointOfSymmetry. If
pointOfSymmetry is not provided, it is found by search.
bimodal: The estimate has modes at opts$modeLocation[1] and opts$modeLocation[3],
with an antimode (local minimum) at opts$modeLocation[2]. If modeLocation is not
specified, it is found by search.
The adjustedKDE and weightedKDE methods are implemented using a common framework
where the standard KDE is first approximated by a binning step, after which the constrained estimate
is obtained. The greedySharpenedKDE method uses a different approach.
The adjustedKDE method is based on the method of Wolters and Braun (2017). The method
uses the usual unconstrained kernel density estimate as a pilot estimate, and adjusts the shape of
this estimate by adding a function to it. The function is selected to minimally change the
shape of the pilot estimate while ensuring the constraints are satisfied. Any of the constraints
can be used with this method.
The weightedKDE method is based on the method of Hall and Huang (2002).
The method uses a weighted kernel density estimator, with the weights minimally
perturbed such that the constraint is satisfied. Any of the constraints except symmetric
may be used with this method.
For either of these methods, the following optional arguments can be provided as elements of opts:
ncheck: The number of abscissa points used for constraint checking. By default,
this is set to max(100, ceiling((diff(range(x)) + 6*h) / h)), where h is
the bandwidth. With this default it should be rare to encounter constraint violations large enough
to be visible in a plot. In the event that constraint violations are observed, re-run the estimation
with a larger value of ncheck.
verbose: If TRUE, progress information will be displayed in the console.
The main use of this is to track the progress of the search for important points. Default is FALSE.
When either of these methods are used, the output list extra contains elements giving the locations of the
important points used in the final estimate (e.g., modeLocation if the estimate is unimodal or
bimodal). Additionally, it containts the following elements:
conCheckGrid: A vector giving the abscissa values at which the constraints were enforced.
binnedCenters: A vector giving the locations of the kernel centers determined in the
binning step.
binnedWeights: The weights corresponding to the binned centers.
finalCenters: The kernel centers used for the final estimate.
finalWeights: The weights used for the final estimate.
The greedySharpenedKDE method is described in Wolters (2012a, 2012b). It uses a data sharpening
(shifting the data points) approach. Starting from an initial solution that satisfies the constraints,
a greedy algorithm (implemented in the function improve) is used to move the points as close as
possible to the observed data while maintaining feasibility.
The following optional arguments can be provided as elements of opts:
startValue — A vector of the same length as x, giving the feasible
initial solution from which the algorithm is started. If not specified, a vector with
all data points at the location of the unconstrained estimate's highest mode will be used.
Note, it is not guaranteed that the default will satisfy every constraint for every data
set.
verbose: If TRUE, information about iteration progress will be printed
to the console. Default is FALSE.
maxpasses: Each "pass" through the data points moves each point one-by-one in a greedy fasion.
This option limits the maximum number of passes. Default is 500.
tol: A numerical tolerance for constraint checking. See improve.
ILS: An integer greater than zero. If supplied, the greedy algorithm is run inside an
iterated local search metaheuristic, as described in Wolters (2012b, sec. 3.4). This can improve solution
quality, but requires the greedy search to be run 2*ILS extra times.
When this method is used, the output list extra contains the following elements:
xstar: The final vector of "sharpened" data points used to generate the
estimate.
Hall and Huang (2002), Unimodal Density Estimation Using Kernel Methods, Statistica Sinica, 12, 965-990.
Wolters and Braun (2017), Enforcing Shape Constraints on a Probability Density Estimate Using an Additive Adjustment curve, Communications in Statistics - Simulation and Computation, 47(3), 672-691.
Wolters (2012a), A Greedy Algorithm for Unimodal Kernel Density Estimation by Data Sharpening, Journal of Statistical Software, 46(6), 1–26.
Wolters (2012b), Methods for Shape-Constrained Kernel Density Estimation. Ph.D. Thesis, University of Western Ontario.
plot.scdensity plot method, print.scdensity print
method, and summary.scdensity summary method.
# Default method gives a unimodal estimate using adjustment curve method. x <- rlnorm(30) scKDE <- scdensity(x) scKDE summary(scKDE) plot(scKDE, detail=2) plot(scKDE, detail=4) # Constrain the first and fourth quartiles to be monotone, using greedy sharpening method. x <- rt(50, df=3) scKDE <- scdensity(x, bw="SJ", adjust=0.5, constraint=c("monotoneL", "monotoneR"), opts=list(verbose=TRUE, leftTail=25, rightTail=75), method="greedy") plot(scKDE) # Compare unimodal, twoInflections, and twoInflections+ constraints x <- rnorm(100) h <- 0.5 * bw.SJ(x) fhat1 <- scdensity(x, bw=h, constraint="unimodal") fhat2 <- scdensity(x, bw=h, constraint="twoInflections") fhat3 <- scdensity(x, bw=h, constraint="twoInflections+") plot(density(x, bw=h)) lines(fhat1$x, fhat1$y, col="red") lines(fhat2$x, fhat2$y, col="blue") lines(fhat3$x, fhat3$y, col="green", lwd=2)# Default method gives a unimodal estimate using adjustment curve method. x <- rlnorm(30) scKDE <- scdensity(x) scKDE summary(scKDE) plot(scKDE, detail=2) plot(scKDE, detail=4) # Constrain the first and fourth quartiles to be monotone, using greedy sharpening method. x <- rt(50, df=3) scKDE <- scdensity(x, bw="SJ", adjust=0.5, constraint=c("monotoneL", "monotoneR"), opts=list(verbose=TRUE, leftTail=25, rightTail=75), method="greedy") plot(scKDE) # Compare unimodal, twoInflections, and twoInflections+ constraints x <- rnorm(100) h <- 0.5 * bw.SJ(x) fhat1 <- scdensity(x, bw=h, constraint="unimodal") fhat2 <- scdensity(x, bw=h, constraint="twoInflections") fhat3 <- scdensity(x, bw=h, constraint="twoInflections+") plot(density(x, bw=h)) lines(fhat1$x, fhat1$y, col="red") lines(fhat2$x, fhat2$y, col="blue") lines(fhat3$x, fhat3$y, col="green", lwd=2)
The function seeks to minimize fcn, a scalar function of variables. v0
is a starting solution and bounds is a 2-vector giving upper and lower limits for
elements of the solution.
SequentialLineMin(fcn, bounds, v0, tol = .Machine$double.eps^0.25)SequentialLineMin(fcn, bounds, v0, tol = .Machine$double.eps^0.25)
fcn |
A function with taking an r-vector as its first argument: call as |
bounds |
A 2-vector giving the upper and lower limits for elements of a solution. |
v0 |
A starting solution, with increasing elements. An r-vector. Not used if r == 1. |
tol |
Tolerance passed to |
This algorithm is designed to search for solutions of the form ,
where bounds(1) bounds(2). It loops through the solution vector
one variable at a time, and does a 1-D line search using optimize() for an improving
value of that variable. So when optimizing , it searches the interval to maintain the increasing nature of . The overall search terminates once a
pass through all elements of fails to produce any changes to .
a list with elements:
minimizer |
An r-vector containing the solution. |
minimum |
The objective function value at the solution. |
fcn <- function(v) (v[1]+1)^2 + (v[2]-1)^2 SequentialLineMin(fcn, c(-5,5), c(-3,3))fcn <- function(v) (v[1]+1)^2 + (v[2]-1)^2 SequentialLineMin(fcn, c(-5,5), c(-3,3))
scdensity.Collects high-level information about the scdensity object and some descriptive
statistics.
## S3 method for class 'scdensity' summary(object, ...)## S3 method for class 'scdensity' summary(object, ...)
object |
An object of S3 class |
... |
Included for consistency with generic functions. |