mix_pdf-methods | R Documentation |
Gives conditional probability densities and distribution functions of mixture autoregressive models.
mix_pdf
gives a probability density, mix_cdf
a
distribution function. If argument x
is supplied, the functions
are evaluated for the specified values of x
, otherwise function
objects are returned and can be used for further computations, eg for
graphs.
mix_pdf
and mix_cdf
have methods with the following
signatures.
signature(model = "MixARGaussian", x = "missing", index = "missing", xcond = "numeric")
Return (as a function of one argument) the conditional density
(respectively cdf), f(x|xcond)
, of X_{t+1}
given
the past values xcond
. The values in xcond
are in
natural time order, e.g. the last value in xcond
is
x_{t}
. xcond
must contain enough values for the
computation of the conditional density (cdf) but if more are given,
only the necessary ones are used.
signature(model = "MixARGaussian", x = "numeric", index = "missing", xcond = "numeric")
Compute the conditional density (respectively cdf) at the values given
by x
.
signature(model = "MixARGaussian", x = "numeric", index = "numeric", xcond = "missing")
Compute conditional densities (respectively cdf) for times
specified in index
. For each t\in{}
index
the
past values needed for the computation of the pdf (cdf) are
...,x[t-2],x[t-1]
.
signature(model = "MixARgen", x = "missing", index = "missing",
xcond = "numeric")
signature(model = "MixARgen", x = "numeric", index = "missing",
xcond = "numeric")
signature(model = "MixARgen", x = "numeric", index = "numeric",
xcond = "missing")
Georgi N. Boshnakov
mix_moment
for examples and computation of summary statistics of the
predictive distributions
mix_qf
for computation of quantiles.
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