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Approximate density function, cdf, or quantile function on the interior of provided quantiles by representing the distribution as a sum of a discrete part at any duplicated qs and a continuous part for which the cdf is estimated using a monotonic Hermite spline. See details for more.

spline_cdf.Rd

Approximate density function, cdf, or quantile function on the interior of provided quantiles by representing the distribution as a sum of a discrete part at any duplicated qs and a continuous part for which the cdf is estimated using a monotonic Hermite spline. See details for more.

Internal function that constructs a monotonic Hermite spline interpolating ps and qs.

Usage

spline_cdf(ps, qs, tail_dist, fn_type = c("d", "p", "q"), n_grid = 20)

spline_cdf_grid_interp(
  ps,
  qs,
  tail_dist,
  fn_type = c("d", "p", "q"),
  n_grid = 20
)

spline_cdf_direct(ps, qs, tail_dist, fn_type = c("d", "p", "q"))

Arguments

ps

vector of probability levels

qs

vector of quantile values correponding to ps

tail_dist

name of parametric distribution for the tails

fn_type

the type of function that is requested: "d" for a pdf, "p" for a cdf, or "q" for a quantile function.

n_grid

grid size to use when augmenting the input qs to obtain a finer grid of points along which we form a piecewise linear approximation to the spline. n_grid evenly-spaced points are inserted between each pair of consecutive values in qs. The default value is 20. This can be set to NULL, in which case the piecewise linear approximation is not used. This is not recommended if the fn_type is "q".

Value

a function to evaluate the pdf, cdf, or quantile function.

Details

The cdf of the continuous part of the distribution is estimated using a monotonic degree 3 Hermite spline that interpolates the quantiles after subtracting the discrete distribution and renormalizing. In theory, an estimate of the quantile function could be obtained by directly inverting this spline. However, in practice, we have observed that this can suffer from numerical problems. Therefore, the default behavior of this function is to evaluate the "stage 1" cdf estimate corresponding to discrete point masses plus monotonic spline at a fine grid of points, and use the "stage 2" cdf estimate that linearly interpolates these points with steps at any duplicated q values. The quantile function estimate is obtained by inverting this "stage 2" cdf estimate. When the distribution is continuous, we can obtain an estimate of the pdf by differentiating the cdf estimate, resulting in a discontinuous "histogram density". The size of the grid can be specified with the n_grid argument. In settings where it is desirable to obtain a continuous density function, the "stage 1" cdf estimate can be used by setting n_grid = NULL.