Package {kde1d}

One-Dimensional Kernel Density Estimation

Description

Provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded, discrete, and zero-inflated data. The implementation utilizes spline interpolation to reduce memory usage and computational demand for large data sets.

Author(s)

Maintainer: Thomas Nagler mail@tnagler.com

Authors:

• Thomas Nagler mail@tnagler.com

• Thibault Vatter thibault.vatter@gmail.com

References

Geenens, G. (2014). Probit transformation for kernel density estimation on the unit interval. Journal of the American Statistical Association, 109:505, 346-358, arXiv:1303.4121

Geenens, G., Wang, C. (2018). Local-likelihood transformation kernel density estimation for positive random variables. Journal of Computational and Graphical Statistics, 27(4), 822-835. arXiv:1602.04862

Nagler, T. (2018a). A generic approach to nonparametric function estimation with mixed data. Statistics & Probability Letters, 137:326–330, arXiv:1704.07457

Nagler, T. (2018b). Asymptotic analysis of the jittering kernel density estimator. Mathematical Methods of Statistics, 27, 32-46. arXiv:1705.05431

See Also

Useful links:

• https://tnagler.github.io/kde1d/

• Report bugs at https://github.com/tnagler/kde1d/issues/

Working with a kde1d object

Description

Density, distribution function, quantile function and random generation for a 'kde1d' kernel density estimate.

Usage

dkde1d(x, obj)

pkde1d(q, obj)

qkde1d(p, obj)

rkde1d(n, obj, quasi = FALSE)

Arguments

Details

dkde1d() gives the density, pkde1d() gives the distribution function, qkde1d() gives the quantile function, and rkde1d() generates random deviates.

The length of the result is determined by n for rkde1d(), and is the length of the numerical argument for the other functions.

Value

The density, distribution function or quantile functions estimates evaluated respectively at x, q, or p, or a sample of n random deviates from the estimated kernel density.

See Also

kde1d()

Examples

set.seed(0) # for reproducibility
x <- rnorm(100) # simulate some data
fit <- kde1d(x) # estimate density
dkde1d(0, fit) # evaluate density estimate (close to dnorm(0))
pkde1d(0, fit) # evaluate corresponding cdf (close to pnorm(0))
qkde1d(0.5, fit) # quantile function (close to qnorm(0))
hist(rkde1d(100, fit)) # simulate

Conditionally equidistant jittering

Description

Converts ordered variables to numeric and Adds deterministic uniform noise. See Details.

Usage

equi_jitter(x)

Arguments

Details

Jittering makes discrete variables continuous by adding noise. This simple trick allows to consistently estimate densities with tools designed for the continuous case (see, Nagler, 2018a/b). The drawback is that estimates are random and the noise may deteriorate the estimate by chance.

Here, we add a form of deterministic noise that makes estimators well behaved. Tied occurrences of a factor level are spread out uniformly (i.e., equidistantly) on the interval [-0.5, 0.5]. This is similar to adding random noise that is uniformly distributed, conditional on the observed outcome. Integrating over the outcome, one can check that the unconditional noise distribution is also uniform on [-0.5, 0.5].

Asymptotically, the deterministic jittering variant is equivalent to the random one.

References

Nagler, T. (2018a). A generic approach to nonparametric function estimation with mixed data. Statistics & Probability Letters, 137:326–330, arXiv:1704.07457

Nagler, T. (2018b). Asymptotic analysis of the jittering kernel density estimator. Mathematical Methods of Statistics, in press, arXiv:1705.05431

Examples

x <- as.factor(rbinom(10, 1, 0.5))
equi_jitter(x)

Univariate local-polynomial likelihood kernel density estimation

Description

The estimators can handle data with bounded, unbounded, and discrete support, see Details.

Usage

kde1d(
  x,
  xmin = NaN,
  xmax = NaN,
  type = "continuous",
  mult = 1,
  bw = NA,
  deg = 2,
  weights = numeric(0),
  boundary_repair = TRUE
)

Arguments

Details

A Gaussian kernel is used in all cases. With one finite endpoint, the continuous component is fitted after an endpoint-anchored, scaled Box-Cox transformation with power parameter 1/4. With two finite endpoints, it uses a regularized probit transformation (Geenens, 2014).

If boundary_repair = TRUE, each finite endpoint is classified from the observed tail. Endpoints consistent with a finite nonzero limiting density receive a nonnegative local-linear boundary estimate, which is fused with the transformed estimate using shrinking biweight weights. Other endpoints retain the transformed estimate. The result is normalized on the original scale.

Discrete variables are handled via deterministic jittering (Nagler, 2018a, 2018b), see equi_jitter(). Finite integer bounds are shifted outward by one half before applying the same support transformations and boundary repair to the jitter density.

Zero-inflated densities are estimated by a hurdle-model with discrete mass at 0 and the nonzero observations estimated as a continuous component. Finite bounds constrain this component only.

Value

An object of class kde1d.

References

Geenens, G. (2014). Probit transformation for kernel density estimation on the unit interval. Journal of the American Statistical Association, 109:505, 346-358, arXiv:1303.4121

Box, G. E. P., Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26, 211–252.

Nagler, T. (2018a). A generic approach to nonparametric function estimation with mixed data. Statistics & Probability Letters, 137:326–330, arXiv:1704.07457

Nagler, T. (2018b). Asymptotic analysis of the jittering kernel density estimator. Mathematical Methods of Statistics, in press, arXiv:1705.05431

Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B, 53, 683–690.

See Also

dkde1d(), pkde1d(), qkde1d(), rkde1d(), plot.kde1d(), lines.kde1d()

Examples

## unbounded data
x <- rnorm(500) # simulate data
fit <- kde1d(x) # estimate density
dkde1d(0, fit) # evaluate density estimate
summary(fit) # information about the estimate
plot(fit) # plot the density estimate
curve(dnorm(x),
  add = TRUE, # add true density
  col = "red"
)

## bounded data, log-linear
x <- rgamma(500, shape = 1) # simulate data
fit <- kde1d(x, xmin = 0, deg = 1) # estimate density
dkde1d(seq(0, 5, by = 1), fit) # evaluate density estimate
summary(fit) # information about the estimate
plot(fit) # plot the density estimate
curve(dgamma(x, shape = 1), # add true density
  add = TRUE, col = "red",
  from = 1e-3
)

## discrete data
x <- rbinom(500, size = 5, prob = 0.5) # simulate data
fit <- kde1d(x, xmin = 0, xmax = 5, type = "discrete") # estimate density
fit <- kde1d(ordered(x, levels = 0:5)) # alternative API
dkde1d(sort(unique(x)), fit) # evaluate density estimate
summary(fit) # information about the estimate
plot(fit) # plot the density estimate
points(ordered(0:5, 0:5), # add true density
  dbinom(0:5, 5, 0.5),
  col = "red"
)

## zero-inflated data
x <- rexp(500, 0.5)  # simulate data
x[sample(1:500, 200)] <- 0 # add zero-inflation
fit <- kde1d(x, xmin = 0, type = "zi") # estimate density
dkde1d(sort(unique(x)), fit) # evaluate density estimate
summary(fit) # information about the estimate
plot(fit) # plot the density estimate
lines(  # add true density
  seq(0, 20, l = 100),
  0.6 * dexp(seq(0, 20, l = 100), 0.5),
  col = "red"
)
points(0, 0.4, col = "red")

## weighted estimate
x <- rnorm(100) # simulate data
weights <- rexp(100) # weights as in Bayesian bootstrap
fit <- kde1d(x, weights = weights) # weighted fit
plot(fit) # compare with unweighted fit
lines(kde1d(x), col = 2)

Plotting kde1d objects

Description

Plotting kde1d objects

Usage

## S3 method for class 'kde1d'
plot(x, ...)

## S3 method for class 'kde1d'
lines(x, ...)

## S3 method for class 'kde1d'
points(x, ...)

Arguments

See Also

kde1d()

Examples

## continuous data
x <- rbeta(100, shape1 = 0.3, shape2 = 0.4) # simulate data
fit <- kde1d(x) # unbounded estimate
plot(fit, ylim = c(0, 4)) # plot estimate
curve(dbeta(x, 0.3, 0.4), # add true density
  col = "red", add = TRUE
)
fit_bounded <- kde1d(x, xmin = 0, xmax = 1) # bounded estimate
lines(fit_bounded, col = "green")

## discrete data
x <- rpois(100, 3) # simulate data
x <- ordered(x, levels = 0:20) # declare variable as ordered
fit <- kde1d(x) # estimate density
plot(fit, ylim = c(0, 0.25)) # plot density estimate
points(ordered(0:20, 0:20), # add true density values
  dpois(0:20, 3),
  col = "red"
)

## zero-inflated data
x <- rexp(500, 0.5)  # simulate data
x[sample(1:500, 200)] <- 0 # add zero-inflation
fit <- kde1d(x, xmin = 0, type = "zi") # estimate density
plot(fit) # plot the density estimate
lines(  # add true density
  seq(0, 20, l = 100),
  0.6 * dexp(seq(0, 20, l = 100), 0.5),
  col = "red"
)
points(0, 0.4, col = "red")