ash1_wgt: Compute the average shifted histogram (ASH) for...

In spsurvey: Spatial Sampling Design and Analysis

Compute the average shifted histogram (ASH) for one-dimensional weighted data

Description

Calculate the average shifted histogram estimate of a density based on one-dimensional data from a survey design with weights.

Usage

ash1_wgt(
  x,
  wgt = rep(1, length(x)),
  m = 5,
  nbin = 50,
  ab = NULL,
  support = "Continuous"
)

Arguments

x	Vector used to estimate the density. NA values are allowed.
wgt	Vector of weights for each observation from a probability sample. The default assigns equal weights (equal probability).
m	Number of empty bins to add to the ends when the range is not completely specified. The default is 5.
nbin	Number of bins for density estimation. The default is 50.
ab	Optional range for support associated with the density. Both values may be equal to NA. If equal to NA, then corresponding limit will be based on nicerange(). The default is NULL.
support	Type of support. If equal to "Continuous", then data are from a continuous distribution. If equal to "Ordinal", then data are from a discrete distribution defined for integers only. The default is "Continuous".

Value

List containing the ASH density estimate. List consists of

tcen

x-coordinate for center of bin

f

y-coordinate for density estimate height

Author(s)

Tony Olsen [e-mail]

References

Scott, D. W. (1985). "Averaged shifted histograms: effective nonparametric density estimators in several dimensions." The Annals of Statistics 13(3): 1024-1040.

Examples

x <- rnorm(100, 10, sqrt(10))
wgt <- runif(100, 10, 100)
rslt <- ash1_wgt(x, wgt)
plot(rslt)

spsurvey documentation built on May 31, 2023, 6:25 p.m.