z.par2cdf {lmomco} | R Documentation |
This function acts as a front end of dispatcher to the distribution-specific cumulative distribution functions.
x(F) = begin{cases} 0, & 0 <= F <= p \ x_G(frac{F-p}{1-p}), & F > p end{cases}
z.par2cdf(x,p,para,z=0,...)
x |
A real value. |
p |
Nonexceedance probability of the z value. This probability could simply be the portion of record having zero values if z=0 . |
para |
The parameters from lmom2par or similar. |
z |
Threshold value. |
... |
The additional arguments are passed to the cumulative distribution function such as paracheck=FALSE for the Generalized Lambda distribution (cdfgld ). |
Nonexceedance probability (0 <= F <= 1) for x
.
W.H. Asquith
# see the example for z.par2qua for more context ## define the real parent (or close) the.gpa <- vec2par(c(100,1000,0.1),type='gpa') fake.data <- rlmomco(30,the.gpa) # simulate some data fake.data <- sort(c(fake.data,0,0,0,0)) # add of zero observations # next compute the parameters for the positive data gpa <- pargpa(lmoms(fake.data[fake.data > 0])) n <- length(fake.data) # sample size p <- length(fake.data[fake.data == 0])/n # est. prob of zero value F <- nonexceeds() # handy values, to get nice range of x x <- z.par2qua(F,p,gpa) # x are now computed PP <- pp(fake.data) # compute plotting positions of sim. sample plot(PP,fake.data,ylim=c(0,5000)) # plot the sample lines(cdfgpa(x,the.gpa),x) # the parent (without zeros) lines(z.par2cdf(x,p,gpa),x,lwd=3) # fitted model with zero conditional # now repeat the above code over and over again and watch the results