Description Usage Arguments Value See Also Examples
Highest density region (HDR) for an arbitrary distributions
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | HDR.monotone(
cover.prob,
Q,
decreasing = TRUE,
distribution = UNSPECIFIED_LABEL,
...
)
HDR.unimodal(
cover.prob,
Q,
f = NULL,
u = NULL,
distribution = UNSPECIFIED_LABEL,
...,
gradtol = 1e-10,
steptol = 1e-10,
iterlim = 100
)
HDR.bimodal(
cover.prob,
Q,
f = NULL,
u = NULL,
distribution = UNSPECIFIED_LABEL,
...,
gradtol = 1e-10,
steptol = 1e-10,
iterlim = 100
)
HDR.discrete.unimodal(
cover.prob,
Q,
F,
f = NULL,
u = NULL,
distribution = UNSPECIFIED_LABEL,
...,
gradtol = 1e-10,
steptol = 1e-10,
iterlim = 100
)
|
cover.prob |
The probability coverage for the HDR (scalar between zero and one). The significance level for the HDR i is |
Q |
an inverse CDF of a distribution |
decreasing |
Direction of monotone distribution |
distribution |
a label |
... |
Arguments for Q, f and u |
f |
a PDF of a distribution |
u |
a log-derivative of f |
gradtol |
Parameter for the nlm optimisation - a positive scalar giving the tolerance at which the scaled gradient is considered close enough to zero to terminate the algorithm (see [ |
steptol |
Parameter for the nlm optimisation - a positive scalar providing the minimum allowable relative step length (see [ |
iterlim |
Parameter for the nlm optimisation - a positive integer specifying the maximum number of iterations to be performed before the program is terminated (see [ |
F |
a CDF of a distribution |
An interval object with classes hdr
and interval
containing the highest density region and related information.
HDR.discrete
1 2 3 4 5 6 7 | HDR.monotone(.95, Q=qexp)
HDR.unimodal(.95, Q=qnorm)
HDR.bimodal(.95, Q=qbeta, shape1=1/2, shape2=1/2)
HDR.discrete.unimodal(.95, Q=qpois, F=ppois, lambda=1)
|
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