mix_pdf-methods {mixAR} | R Documentation |
Conditional pdf's and cdf's of MixAR models
Description
Gives conditional probability densities and distribution functions of mixture autoregressive models.
Methods
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),
, of
given the past values
xcond
. The values inxcond
are in natural time order, e.g. the last value inxcond
is.
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 eachindex
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")
Author(s)
Georgi N. Boshnakov
See Also
mix_moment
for examples and computation of summary statistics of the
predictive distributions
mix_qf
for computation of quantiles.