z.par2qua {lmomco} | R Documentation |
This function acts as a front end or dispatcher to the distribution-specific quantile functions.
F(x) = begin{cases} 0, & x <= 0 \ p + (1-p)G(x), & x > 0 end{cases}
z.par2qua(f,p,para,z=0,...)
f |
Nonexceedance probability (0 <= F <= 1). |
p |
Nonexceedance probability of z value. |
para |
The parameters from lmom2par or similar. |
z |
Threshold value. |
... |
The additional arguments are passed to the quantile function such as paracheck = FALSE for the Generalized Lambda distribution (quagld ). |
Quantile value for f.
W.H. Asquith
# 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 PP <- pp(fake.data) # compute plotting positions of sim. sample plot(PP,fake.data,ylim=c(0,5000)) # plot the sample lines(F,quagpa(F,the.gpa)) # the parent (without zeros) lines(F,z.par2qua(F,p,gpa),lwd=3) # fitted model with zero conditional # now repeat the above code over and over again and watch the results