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),
f(x|xcond)
, ofX_{t+1}
given the past valuesxcond
. The values inxcond
are in natural time order, e.g. the last value inxcond
isx_{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 eacht\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")
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.