Simulate Transition Matrices from the Multivariate Normal Distribution
Source:R/RcppExports.R
SimBetaN.Rd
This function simulates random transition matrices
from the multivariate normal distribution.
The function ensures that the generated transition matrices are stationary
using TestStationarity()
with a rejection sampling approach.
See also
Other Simulation of State Space Models Data Functions:
LinSDE2SSM()
,
LinSDECovEta()
,
LinSDECovY()
,
LinSDEMeanEta()
,
LinSDEMeanY()
,
ProjectToHurwitz()
,
ProjectToStability()
,
SSMCovEta()
,
SSMCovY()
,
SSMMeanEta()
,
SSMMeanY()
,
SimAlphaN()
,
SimBetaN2()
,
SimCovDiagN()
,
SimCovN()
,
SimIotaN()
,
SimPhiN()
,
SimPhiN2()
,
SimSSMFixed()
,
SimSSMIVary()
,
SimSSMLinGrowth()
,
SimSSMLinGrowthIVary()
,
SimSSMLinSDEFixed()
,
SimSSMLinSDEIVary()
,
SimSSMOUFixed()
,
SimSSMOUIVary()
,
SimSSMVARFixed()
,
SimSSMVARIVary()
,
SpectralRadius()
,
TestPhi()
,
TestPhiHurwitz()
,
TestStability()
,
TestStationarity()
Examples
n <- 10
beta <- matrix(
data = c(
0.7, 0.5, -0.1,
0.0, 0.6, 0.4,
0, 0, 0.5
),
nrow = 3
)
vcov_beta_vec_l <- t(chol(0.001 * diag(9)))
SimBetaN(n = n, beta = beta, vcov_beta_vec_l = vcov_beta_vec_l)
#> [[1]]
#> [,1] [,2] [,3]
#> [1,] 0.6851834 -0.007643585 0.03225375
#> [2,] 0.4989322 0.535273121 -0.01040462
#> [3,] -0.1017514 0.431520771 0.52115628
#>
#> [[2]]
#> [,1] [,2] [,3]
#> [1,] 0.6680390 0.02629276 0.005338959
#> [2,] 0.4718801 0.58538447 -0.013719421
#> [3,] -0.1012473 0.35777458 0.502049507
#>
#> [[3]]
#> [,1] [,2] [,3]
#> [1,] 0.72547649 0.03917744 0.01601526
#> [2,] 0.46980611 0.58514000 0.01645385
#> [3,] -0.09449514 0.39903621 0.48528219
#>
#> [[4]]
#> [,1] [,2] [,3]
#> [1,] 0.67274293 0.06733423 0.01210433
#> [2,] 0.55638168 0.60140159 0.02169502
#> [3,] -0.09678768 0.43988592 0.50541587
#>
#> [[5]]
#> [,1] [,2] [,3]
#> [1,] 0.67997152 0.05736071 -0.005371841
#> [2,] 0.53904114 0.63490134 0.009108167
#> [3,] -0.06383272 0.40650940 0.517786160
#>
#> [[6]]
#> [,1] [,2] [,3]
#> [1,] 0.6840106 -0.006206552 0.04608043
#> [2,] 0.5443914 0.569218457 0.06780036
#> [3,] -0.1077355 0.414977012 0.52529448
#>
#> [[7]]
#> [,1] [,2] [,3]
#> [1,] 0.7124042 -0.04549078 0.02242287
#> [2,] 0.4636874 0.62246809 -0.02782470
#> [3,] -0.0838195 0.36851786 0.48349206
#>
#> [[8]]
#> [,1] [,2] [,3]
#> [1,] 0.7137180 0.02645314 0.02205182
#> [2,] 0.4502689 0.62743727 -0.02979570
#> [3,] -0.1022793 0.40554066 0.46948154
#>
#> [[9]]
#> [,1] [,2] [,3]
#> [1,] 0.7166506 0.04992734 -0.02545914
#> [2,] 0.4881213 0.65434223 0.04074997
#> [3,] -0.1123248 0.37019981 0.50786389
#>
#> [[10]]
#> [,1] [,2] [,3]
#> [1,] 0.69686953 -0.02832438 -0.009376019
#> [2,] 0.44792962 0.60969799 0.026254137
#> [3,] -0.08429006 0.39618472 0.507779452
#>