kdensity: Parametrically guided kernel density estimation

In kdensity: Kernel Density Estimation with Parametric Starts and Asymmetric Kernels

Description

kdensity computes a parametrically guided kernel density estimate for univariate data. It supports asymmetric kernels and parametric starts through the kernel and start arguments.

Usage

kdensity(
  x,
  bw = NULL,
  adjust = 1,
  kernel = NULL,
  start = NULL,
  support = NULL,
  na.rm = FALSE,
  normalized = TRUE,
  tolerance = 0.01
)

Arguments

x	Numeric vector containing the data.
bw	A bandwidth function. Can be either a string, a custom-made function, or a double. The supported bandwidth functions are documented in bandwidths().
adjust	An adjustment constant, so that h = adjust*bw*sd, where sd varies with the chosen kernel.
kernel	The kernel function. Can be chosen from the list of built-in kernels or be custom-made. See kernels() for details.
start	Parametric start. Can be chosen from the list of built-in parametric starts or be custom-made. See parametric_starts() for details.
support	The support of the data. Must be compatible with the supplied x and the supplied start and kernel. Is used to find the normalization constant, see normalized.
na.rm	Logical; if TRUE, NAs will be removed from x.
normalized	Logical; if TRUE, the density is normalized.
tolerance	Numeric; the relative error to tolerate in normalization.

Details

The default values for bw, kernel, start, and support are interdependent, and are chosen to make sense. E.g., the default value for support when start = beta is c(0, 1).

The start argument defaults to uniform, which corresponds to ordinary kernel density estimation. The typical default value for kernel is gaussian.

If normalized is FALSE and start != "uniform", the resulting density will not integrate to 1 in general.

Value

kdensity returns an S3 function object of base::class() "kdensity". This is a callable function with the following elements, accessible by '$':

x

The data supplied in x.

bw_str, bw, adjust, h

The bandwidth function, the resulting bandwidth, the adjust argument, and the adjusted bandwidth.

kernel_str, kernel, start, start_str, support

Name of the kernel, the kernel object, name of the parametric start, the start object, and the support of the density.

data.name, n, range, has.na, na.rm, normalized

Name of the data, number of observations, the range of the data, whether the data x contained NA values, whether na.rm is TRUE or not, and whether the density is normalized.

call

The call to kdensity.

estimates

Named numeric vector containing the parameter estimates from the parametric start.

logLik

The log-likelihood of the parametric starts. Is NA for the uniform start.

References

Hjort, Nils Lid, and Ingrid K. Glad. "Nonparametric density estimation with a parametric start." The Annals of Statistics (1995): 882-904.

Jones, M. C., and D. A. Henderson. "Miscellanea kernel-type density estimation on the unit interval." Biometrika 94.4 (2007): 977-984.

Chen, Song Xi. "Probability density function estimation using gamma kernels." Annals of the Institute of Statistical Mathematics 52.3 (2000): 471-480.

Silverman, Bernard W. Density estimation for statistics and data analysis. Vol. 26. CRC press, 1986.

See Also

The stats package function stats::density().

Examples

## Use gamma kernels to model positive data, the concentration of
## theophylline

concentration = Theoph$conc + 0.001
plot(kdensity(concentration, start = "gamma", kernel = "gamma", adjust = 1/3),
     ylim = c(0, 0.15), lwd = 2, main = "Concentration of theophylline")
lines(kdensity(concentration, start = "gamma", kernel = "gaussian"),
      lty = 2, col = "grey", lwd = 2)
lines(kdensity(concentration, start = "gaussian", kernel = "gaussian"),
      lty = 3, col = "blue", lwd = 2)
lines(kdensity(concentration, start = "gaussian", kernel = "gamma", adjust = 1/3),
      lty = 4, col = "red", lwd = 2)
rug(concentration)

## Using a density and and estimator from another package.

skew_hyperbolic = list(
  density   = SkewHyperbolic::dskewhyp,
  estimator = function(x) SkewHyperbolic::skewhypFit(x, printOut = FALSE)$param,
  support   = c(-Inf, Inf)
)

kde = kdensity(diff(LakeHuron), start = skew_hyperbolic)
plot(kde, lwd = 2, col = "blue",
     main = "Annual differences in water level (ft) of Lake Huron, 1875 - 1972")
lines(kde, plot_start = TRUE, lty = 2, lwd = 2) # Plots the skew hyperbolic density.
rug(diff(LakeHuron))

kde$estimates # Also: coef(kde)
# Displays the parameter estimates:
#        mu     delta      beta        nu
# -1.140713  3.301112  2.551657 26.462469

kdensity documentation built on Oct. 23, 2020, 8:32 p.m.