Description Usage Arguments Details Value References
View source: R/geneticAlgorithm.R
random_regime
generates random regime parameters.
1 2 3 4 5 6 7 8 9 | random_regime(
p,
mu_scale,
sigma_scale,
restricted = FALSE,
constraints = NULL,
m,
forcestat = FALSE
)
|
p |
a positive integer specifying the autoregressive order of the model. |
mu_scale |
a real valued vector of length two specifying the mean (the first element) and standard deviation (the second element)
of the normal distribution from which the μ_{m} mean-parameters are generated in random mutations in the genetic algorithm.
Default is |
sigma_scale |
a positive real number specifying the standard deviation of the (zero mean, positive only by taking absolute value)
normal distribution from which the component variance parameters are generated in the random mutations in the genetic algorithm.
Default is |
restricted |
a logical argument stating whether the AR coefficients φ_{m,1},...,φ_{m,p} are restricted to be the same for all regimes. |
constraints |
specifies linear constraints imposed to each regime's autoregressive parameters separately.
The symbol φ denotes an AR coefficient. Note that regardless of any constraints, the autoregressive order
is always |
m |
which regime? This is required for models with constraints for which a list of possibly differing constraint matrices is provided. |
forcestat |
use the algorithm by Monahan (1984) to force stationarity on the AR parameters (slower)? Not supported for constrained models. |
If forcestat==TRUE
, then the AR coefficients are relatively large, otherwise they are usually relatively small.
υ_{m}=(φ_{m,0},φ_{m},σ_{m}^2) where φ_{m}=(φ_{m,1},...,φ_{m,p}).
Not supported!
Replace the vectors φ_{m} with vectors ψ_{m}.
Monahan J.F. 1984. A Note on Enforcing Stationarity in Autoregressive-Moving Average Models. Biometrica 71, 403-404.
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