Last chance! 50% off unlimited learning
Sale ends in
Density, distribution function, quantile function and random generation for the Zipf distribution.
dzipf(x, N, shape, log = FALSE)
pzipf(q, N, shape, log.p = FALSE)
qzipf(p, N, shape)
rzipf(n, N, shape)
Same as Poisson
.
the number of elements, and the exponent characterizing the
distribution.
See zipf
for more details.
Same meaning as in Normal
.
dzipf
gives the density,
pzipf
gives the cumulative distribution function,
qzipf
gives the quantile function, and
rzipf
generates random deviates.
This is a finite version of the zeta distribution.
See zetaff
for more details.
In general, these functions runs slower and slower as N
increases.
# NOT RUN {
N <- 10; shape <- 0.5; y <- 1:N
proby <- dzipf(y, N = N, shape = shape)
# }
# NOT RUN {
plot(proby ~ y, type = "h", col = "blue", ylab = "Probability",
ylim = c(0, 0.2), main = paste("Zipf(N = ",N,", shape = ",shape,")", sep = ""),
lwd = 2, las = 1)
# }
# NOT RUN {
sum(proby) # Should be 1
max(abs(cumsum(proby) - pzipf(y, N = N, shape = shape))) # Should be 0
# }
Run the code above in your browser using DataLab