Simulate Intercept Vectors in a Continuous-Time Vector Autoregressive Model from the Multivariate Normal Distribution
Source:R/RcppExports.R
SimIotaN.Rd
This function simulates random intercept vectors in a continuous-time vector autoregressive model from the multivariate normal distribution.
See also
Other Simulation of State Space Models Data Functions:
LinSDE2SSM()
,
LinSDECovEta()
,
LinSDECovY()
,
LinSDEMeanEta()
,
LinSDEMeanY()
,
ProjectToHurwitz()
,
ProjectToStability()
,
SSMCovEta()
,
SSMCovY()
,
SSMMeanEta()
,
SSMMeanY()
,
SimAlphaN()
,
SimBetaN()
,
SimBetaN2()
,
SimCovDiagN()
,
SimCovN()
,
SimPhiN()
,
SimPhiN2()
,
SimSSMFixed()
,
SimSSMIVary()
,
SimSSMLinGrowth()
,
SimSSMLinGrowthIVary()
,
SimSSMLinSDEFixed()
,
SimSSMLinSDEIVary()
,
SimSSMOUFixed()
,
SimSSMOUIVary()
,
SimSSMVARFixed()
,
SimSSMVARIVary()
,
SpectralRadius()
,
TestPhi()
,
TestPhiHurwitz()
,
TestStability()
,
TestStationarity()
Examples
n <- 10
iota <- c(0, 0, 0)
vcov_iota_l <- t(chol(0.001 * diag(3)))
SimIotaN(n = n, iota = iota, vcov_iota_l = vcov_iota_l)
#> [[1]]
#> [1] -0.01971238 0.01106751 -0.01125932
#>
#> [[2]]
#> [1] -0.02521992 -0.02068220 0.06565717
#>
#> [[3]]
#> [1] -0.009554499 -0.020656927 -0.040582313
#>
#> [[4]]
#> [1] 0.0060266889 0.0358791638 -0.0009399133
#>
#> [[5]]
#> [1] -0.06423957 -0.04651410 0.01509581
#>
#> [[6]]
#> [1] -0.02321449 0.06976612 0.01854302
#>
#> [[7]]
#> [1] 0.108917731 -0.014241541 -0.001112522
#>
#> [[8]]
#> [1] 0.0212965004 -0.0666725421 0.0001880568
#>
#> [[9]]
#> [1] 0.007311496 0.002311165 0.010663016
#>
#> [[10]]
#> [1] 0.04836385 0.01192810 0.03895627
#>