empiricalC function

The Continuous Empirical Distribution

The Continuous Empirical Distribution

Density, distribution function and random generation for a continuous empirical distribution.

dempiricalC(x, min, max, values, prob=NULL, log=FALSE) pempiricalC(q, min, max, values, prob=NULL, lower.tail=TRUE, log.p=FALSE) qempiricalC(p, min, max, values, prob=NULL, lower.tail=TRUE, log.p=FALSE) rempiricalC(n, min, max, values, prob=NULL)

Examples

prob <- c(2, 3, 1, 6, 1) values <- 1:5 par(mfrow=c(1, 2)) curve(dempiricalC(x, min=0, max=6, values, prob), from=-1, to=7, n=1001) curve(pempiricalC(x, min=0, max=6, values, prob), from=-1, to=7, n=1001) ## Varying values (values <- matrix(1:10, ncol=5)) ## the first x apply to the first row ## the second x to the second one dempiricalC(c(1, 1), values, min=0, max=11) ##Use with mc2d val <- c(100, 150, 170, 200) pr <- c(6, 12, 6, 6) out <- c("min", "mean", "max") ##First Bootstrap in the uncertainty dimension ##with rempirical D (x <- mcstoc(rempiricalD, type = "U", outm = out, nvariates = 30, values = val, prob = pr)) ##Continuous Empirical distribution in the variability dimension mcstoc(rempiricalC, type = "VU", values = x, min=90, max=210)

Arguments

  • x, q: Vector of quantiles.
  • p: Vector of probabilities.
  • n: Number of random values. If length(n) > 1 , the length is taken to be the number required.
  • min: A finite minimal value.
  • max: A finite maximal value.
  • values: Vector of numerical values.
  • prob: Optional vector of count or probabilities.
  • log, log.p: logical; if TRUE , probabilities p are given as log(p) .
  • lower.tail: logical; if TRUE (default), probabilities are P[X <= x] , otherwise, P[X > x] .

Details

Given pip_i, the distribution value for xix_i

with i the rank i=0,1,2,,N+1i = 0, 1, 2, \ldots, N+1, x0=minx_0 = min and x(N+1)=maxx_(N+1) = max the density is:

f(x)=pi+(xxixi+1xi)(pi+1pi)f(x)=pi+(p(i+1)pi)/(x(i+1)xi)forxi<=x<x(i+1) f(x)=p_{i}+(\frac{x-x_{i}}{x_{i+1}-x_{i}})(p_{i+1}-p_{i})f(x)= p_i + (p_(i+1) - p_i)/(x_(i+1) - x_i) for x_i<=x<x_(i+1)

The p values being normalized to give the distribution a unit area.

min and/or max and/or values and/or prob

may vary: in that case, min and/or max should be vector(s). values and/or prob should be matrixes, the first row being used for the first element of x , q , p or the first random value, the second row for the second element of x , q , p or random value, ... Recycling is permitted if the number of elements of min or max or the number of rows of prob and values are equal or equals one.

Returns

dempiricalC gives the density, pempiricalC gives the distribution function, qempiricalC gives the quantile function and rempiricalC generates random deviates.

See Also

empiricalD

  • Maintainer: Regis Pouillot
  • License: GPL (>= 2)
  • Last published: 2024-06-05

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