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Create distributional object based on a kernel density estimate

Source: R/dist_kde.R
dist_kde.Rd

Creates a distributional object using a kernel density estimate with a Gaussian kernel obtained from the kde() function. The bandwidth can be specified; otherwise the kde_bandwidth() function is used. The cdf, quantiles and moments are consistent with the kde. Generating random values from the kde is equivalent to a smoothed bootstrap.

Usage

dist_kde(
  y,
  h = NULL,
  H = NULL,
  method = c("normal", "robust", "plugin", "lookout"),
  ...
)

Arguments

y

Numerical vector or matrix of data, or a list of such objects. If a list is provided, then all objects should be of the same dimension. e.g., all vectors, or all matrices with the same number of columns.

h

Bandwidth for univariate distribution. Ignored if y has 2 or more columns. If NULL, the kde_bandwidth function is used.

H

Bandwidth matrix for multivariate distribution. If NULL, the kde_bandwidth function is used.

method

The method of bandwidth estimation to use. See kde_bandwidth() for details. Ignored if h or H are specified.

...

Other arguments are passed to kde.

Examples

dist_kde(c(rnorm(200), rnorm(100, 5)))
#> <distribution[1]>
#> [1] kde[1d, h=0.85]
dist_kde(cbind(rnorm(200), rnorm(200, 5)))
#> <distribution[1]>
#> [1] kde[2d, H={(0.18, 0.019)', (0.019, 0.16)'}]