prob <- c(2,3,1,6,1)values <-1:5par(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 onedempiricalC(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 dimensionmcstoc(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 pi, the distribution value for xi
with i the rank i=0,1,2,…,N+1, x0=min and x(N+1)=max the density is:
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.