State | R Documentation |
Method returning the filtered, predictive, and smoothed probabilities of the states, and the most probable path computed with the Viterbi algorithm.
State(object, ...) ## S3 method for class 'MSGARCH_SPEC' State(object, par, data, ...) ## S3 method for class 'MSGARCH_ML_FIT' State(object, newdata = NULL, ...) ## S3 method for class 'MSGARCH_MCMC_FIT' State(object, newdata = NULL, ...)
object |
Model specification of class |
... |
Not used. Other arguments to |
par |
Vector (of size d) or matrix (of size |
data |
Vector (of size T) of observations. |
newdata |
Vector (of size T*) of new observations. (Default |
If a matrix of parameter estimates is given, each parameter estimate (each row) is evaluated individually.
A list of class MSGARCH_PSTATE
with the following elements:
FiltProb
: Filtered probabilities (array of size (T + T*) x (nmcmc or 1
) x K).
PredProb
: Predictive probabilities (array of size (T + T* + 1) x (nmcmc or 1
) x K).
SmoothProb
: Smoothed probabilities (array of size (T + T* + 1) x (nmcmc or 1
) x K).
Viterbi
: Most likely path (matrix of size (T + T*) x (nmcmc
or 1)).
The class MSGARCH_PSTATE
contains the plot
method. The plot method contains
as input type.prob
which is one of "filtered", "predictive", "smoothed", "viterbi"
.
(Default: type.prob = "smoothed"
)
# create specification spec <- CreateSpec() # load data data("SMI", package = "MSGARCH") # state from specification par <- c(0.1, 0.1, 0.8, 0.2, 0.1, 0.8, 0.99, 0.01) state <- State(object = spec, par = par, data = SMI) plot(state, type.prob = "filtered") # state from ML fit fit <- FitML(spec = spec, data = SMI) state <- State(object = fit) plot(state, type.prob = "smoothed") ## Not run: # state from MCMC fit set.seed(1234) fit <- FitMCMC(spec = spec, data = SMI) state <- State(object = fit) plot(state, type.prob = "smoothed") ## End(Not run)
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