mix_moment | R Documentation |
Conditional moments of MixAR models.
mix_location(model, x, index, xcond)
mix_variance(model, x, index, xcond)
mix_central_moment(model, x, index, xcond, k)
mix_moment(model, x, index, xcond, k)
mix_kurtosis(...)
mix_ekurtosis(...)
model |
a MixAR object. |
x |
a time series. |
index |
a vector of indices in |
xcond |
a time series, the point prediction is computed for the
first value after the end of the time series. Only the last
|
k |
a positive integer specifying the moment to compute. |
... |
passed on to |
These functions compute conditional moments and related quantities.
kurtosis
and ekurtosis
compute conditional kurtosis and
excess kurtosis, respectively. Effectively, they have the same
parameters as mix_central_moment
, since they pass "..."
to it along with k = 4
. It is an error to supply argument
k
to the kurtosis functions.
when called with one argument (model
), a function with argument xcond
;
otherwise if xcond
is not missing, a single numeric value;
otherwise a vector of length length(index)
.
I wrote the above description recently from reading six years old code, it may need further verification.
Georgi N. Boshnakov
boshnakov2009marmixAR
mix_pdf
, mix_cdf
, mix_qf
for the predictive distributions (pdf, cdf, quantiles);
## data(ibmclose, package = "fma") # `ibmclose'
ibmclose <- as.numeric(fma::ibmclose)
length(ibmclose) # 369
max(exampleModels$WL_ibm@order) # 2
## compute point predictions for t = 3,...,369
pred <- mix_location(exampleModels$WL_ibm, ibmclose)
plot(pred)
## compute one-step point predictions for t = 360,...369
mix_location(exampleModels$WL_ibm, ibmclose, index = 369 - 9:0 )
f <- mix_location(exampleModels$WL_ibm) # a function
## predict the value after the last
f(ibmclose)
## a different way to compute one-step point predictions for t = 360,...369
sapply(369 - 10:1, function(k) f(ibmclose[1:k]))
## the results are the same, but notice that xcond gives past values
## while index above specifies the times for which to compute the predictions.
identical(sapply(369 - 10:1, function(k) f(ibmclose[1:k])),
mix_location(exampleModels$WL_ibm, ibmclose, index = 369 - 9:0 ))
## conditional variance
f <- mix_variance(exampleModels$WL_ibm) # a function
## predict the value after the last
f(ibmclose)
## a different way to compute one-step point predictions for t = 360,...369
sapply(369 - 10:1, function(k) f(ibmclose[1:k]))
## the results are the same, but notice that xcond gives past values
## while index above specifies the times for which to compute the predictions.
identical(sapply(369 - 10:1, function(k) f(ibmclose[1:k])),
mix_variance(exampleModels$WL_ibm, ibmclose, index = 369 - 9:0 ))
# interesting example
# bimodal distribution, low kurtosis, 4th moment not much larger than 2nd
moWL <- exampleModels$WL_ibm
mix_location(moWL,xcond = c(500,450))
mix_kurtosis(moWL,xcond = c(500,450))
f1pdf <- mix_pdf(moWL,xcond = c(500,450))
f1cdf <- mix_cdf(moWL,xcond = c(500,450))
gbutils::plotpdf(f1pdf,cdf=f1cdf)
gbutils::plotpdf(f1cdf,cdf=f1cdf)
f1cdf(c(400,480))
mix_variance(moWL,xcond = c(500,450))
mix_central_moment(moWL,xcond = c(500,450), k=2)
sqrt(mix_variance(moWL,xcond = c(500,450)))
sqrt(mix_central_moment(moWL,xcond = c(500,450), k=2))
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