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dhuber(x, k = 0.862, mu = 0, sigma = 1, log = FALSE)
edhuber(x, k = 0.862, mu = 0, sigma = 1, log = FALSE)
rhuber(n, k = 0.862, mu = 0, sigma = 1)
qhuber(p, k = 0.862, mu = 0, sigma = 1)
phuber(q, k = 0.862, mu = 0, sigma = 1)
length(n) > 1
then the length is taken to be the number required.k = 0.862
refers to a 20% contamination
neighborhood of the Gaussian distributisigma = 1
defines the
distribution in standard form, with standard Gaussian centre).log = TRUE
then the logarithm of the result is returned.dhuber
gives out a vector of density values. edhuber
gives out a list with components val
(density
values) and eps
(contamination proportion).
rhuber
gives out a vector of random numbers generated by
Huber's least favourable distribution.
phuber
gives the distribution function,
qhuber
gives the quantile function.
huber2
, the
mu
and sigma
.huber2
.set.seed(123456)
edhuber(1:5, k = 1.5)
rhuber(5)
mu <- 3; xx <- seq(-2, 7, len = 100) # Plot CDF and PDF
plot(xx, dhuber(xx, mu = mu), type = "l", col = "blue", las = 1, ylab = "",
main = "blue is density, red is cumulative distribution function",
sub = "Purple lines are the 10,20,...,90 percentiles",
ylim = 0:1)
abline(h = 0, col = "blue", lty = 2)
lines(xx, phuber(xx, mu = mu), type = "l", col = "red")
probs <- seq(0.1, 0.9, by = 0.1)
Q <- qhuber(probs, mu = mu)
lines(Q, dhuber(Q, mu = mu), col = "purple", lty = 3, type = "h")
lines(Q, phuber(Q, mu = mu), col = "purple", lty = 3, type = "h")
abline(h = probs, col = "purple", lty = 3)
phuber(Q, mu = mu) - probs # Should be all 0s
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