Single Replication from the Simulation Study
Ivan Jacob Agaloos Pesigan
Source:vignettes/sim-rep.Rmd
sim-rep.RmdData Generation
plot(data)
summary(data)
#> id time y1 y2
#> Min. : 1.00 Min. : 0.00 Min. :-3.247 Min. :-4.140
#> 1st Qu.:13.00 1st Qu.: 28.00 1st Qu.: 1.798 1st Qu.: 1.060
#> Median :26.00 Median : 61.00 Median : 2.941 Median : 2.191
#> Mean :25.57 Mean : 73.63 Mean : 2.890 Mean : 2.175
#> 3rd Qu.:38.00 3rd Qu.:110.00 3rd Qu.: 3.988 3rd Qu.: 3.250
#> Max. :50.00 Max. :199.00 Max. : 9.199 Max. : 8.406MetaVAR
dtvar <- FitDTVAR(
data = data,
seed = seed
)
summary(dtvar, means = TRUE)
#> Call:
#> FitVARMxID(data = data$data, observed = paste0("y", seq_len(model$k)),
#> id = "id", time = NULL, ct = FALSE, center = TRUE, mu_fixed = FALSE,
#> mu_free = NULL, mu_values = data$mu, mu_lbound = NULL, mu_ubound = NULL,
#> alpha_fixed = FALSE, alpha_free = NULL, alpha_values = NULL,
#> alpha_lbound = NULL, alpha_ubound = NULL, beta_fixed = FALSE,
#> beta_free = NULL, beta_values = data$beta, beta_lbound = NULL,
#> beta_ubound = NULL, psi_diag = FALSE, psi_fixed = FALSE,
#> psi_d_free = NULL, psi_d_values = model$psi_d_ldl, psi_d_lbound = NULL,
#> psi_d_ubound = NULL, psi_d_equal = FALSE, psi_l_free = NULL,
#> psi_l_values = model$psi_l_ldl, psi_l_lbound = NULL, psi_l_ubound = NULL,
#> nu_fixed = TRUE, nu_free = NULL, nu_values = NULL, nu_lbound = NULL,
#> nu_ubound = NULL, theta_diag = TRUE, theta_fixed = TRUE,
#> theta_d_free = NULL, theta_d_values = NULL, theta_d_lbound = NULL,
#> theta_d_ubound = NULL, theta_d_equal = FALSE, theta_l_free = NULL,
#> theta_l_values = NULL, theta_l_lbound = NULL, theta_l_ubound = NULL,
#> mu0_fixed = TRUE, mu0_func = TRUE, mu0_free = NULL, mu0_values = NULL,
#> mu0_lbound = NULL, mu0_ubound = NULL, sigma0_fixed = TRUE,
#> sigma0_func = TRUE, sigma0_diag = FALSE, sigma0_d_free = NULL,
#> sigma0_d_values = NULL, sigma0_d_lbound = NULL, sigma0_d_ubound = NULL,
#> sigma0_d_equal = FALSE, sigma0_l_free = NULL, sigma0_l_values = NULL,
#> sigma0_l_lbound = NULL, sigma0_l_ubound = NULL, robust = FALSE,
#> seed = seed, tries_explore = 1000, tries_local = 1000, max_attempts = 100,
#> silent = TRUE, ncores = ncores)
#>
#> Convergence:
#> 100.0%
#>
#> Means of the estimated paramaters per individual.
#> mu[1,1] mu[2,1] beta[1,1] beta[2,1] beta[1,2] beta[2,2] psi[1,1] psi[2,1]
#> 2.8930 2.3276 0.2549 -0.0486 -0.0590 0.2070 1.2448 0.5272
#> psi[2,2]
#> 1.4661
metavar <- FitMetaVAR(
fit = dtvar,
seed = seed
)
summary(metavar)
#> Call:
#> MetaVARMx(object = fit$output, x = NULL, random = TRUE, alpha_values = model$ma_fixed,
#> tau_sqr_diag = FALSE, tau_sqr_d_free = TRUE, tau_sqr_d_values = model$ma_random_d_ldl,
#> tau_sqr_l_free = matrix(data = c(FALSE, FALSE, FALSE, FALSE,
#> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE,
#> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
#> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE,
#> FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE),
#> byrow = TRUE, nrow = 6, ncol = 6), tau_sqr_l_values = model$ma_random_l_ldl,
#> effects = TRUE, set_point = TRUE, int_meas = FALSE, int_dyn = FALSE,
#> cov_meas = FALSE, cov_dyn = FALSE, robust_v = FALSE, robust = FALSE,
#> seed = seed, tries_explore = 1000, tries_local = 1000, max_attempts = 100,
#> silent = TRUE, ncores = ncores)
#>
#> Status code:
#> 0
#>
#> CI type:
#> "normal"
#>
#> est se z p 2.5% 97.5%
#> alpha[1,1] 2.8951 0.1661 17.4252 0.0000 2.5695 3.2208
#> alpha[2,1] 2.3329 0.1456 16.0172 0.0000 2.0474 2.6183
#> alpha[3,1] 0.2561 0.0225 11.3805 0.0000 0.2120 0.3002
#> alpha[4,1] -0.0658 0.0212 -3.0991 0.0019 -0.1074 -0.0242
#> alpha[5,1] -0.0532 0.0140 -3.8035 0.0001 -0.0806 -0.0258
#> alpha[6,1] 0.2211 0.0290 7.6128 0.0000 0.1642 0.2780
#> tau_sqr[1,1] 1.3523 0.2765 4.8905 0.0000 0.8103 1.8942
#> tau_sqr[2,1] 0.5742 0.1902 3.0190 0.0025 0.2014 0.9470
#> tau_sqr[2,2] 1.0252 0.2126 4.8216 0.0000 0.6085 1.4420
#> tau_sqr[3,3] 0.0146 0.0049 2.9567 0.0031 0.0049 0.0243
#> tau_sqr[4,3] 0.0067 0.0036 1.8437 0.0652 -0.0004 0.0138
#> tau_sqr[5,3] -0.0009 0.0024 -0.3728 0.7093 -0.0056 0.0038
#> tau_sqr[6,3] 0.0104 0.0048 2.1576 0.0310 0.0010 0.0199
#> tau_sqr[4,4] 0.0101 0.0043 2.3442 0.0191 0.0017 0.0186
#> tau_sqr[5,4] 0.0012 0.0020 0.5972 0.5504 -0.0027 0.0050
#> tau_sqr[6,4] 0.0030 0.0043 0.7114 0.4768 -0.0053 0.0114
#> tau_sqr[5,5] 0.0016 0.0022 0.7533 0.4513 -0.0026 0.0059
#> tau_sqr[6,5] 0.0003 0.0029 0.0994 0.9208 -0.0054 0.0060
#> tau_sqr[6,6] 0.0311 0.0083 3.7352 0.0002 0.0148 0.0475
#> i_sqr[1,1] 0.9876 0.0025 393.4447 0.0000 0.9827 0.9925
#> i_sqr[2,1] 0.9804 0.0040 245.9510 0.0000 0.9726 0.9883
#> i_sqr[3,1] 0.6171 0.0799 7.7224 0.0000 0.4605 0.7737
#> i_sqr[4,1] 0.4403 0.1131 3.8912 0.0001 0.2185 0.6620
#> i_sqr[5,1] 0.2825 0.1511 1.8697 0.0615 -0.0136 0.5786
#> i_sqr[6,1] 0.8221 0.0372 22.1173 0.0000 0.7493 0.8950Mplus
mplus <- FitMplus(
data = data,
seed = seed
)
summary(mplus)
#> est se R 2.5% 97.5%
#> psi[1,1] 1.2997 0.0245 80000 1.2528 1.3489
#> psi[2,1] 0.5576 0.0205 80000 0.5185 0.5982
#> psi[2,2] 1.5381 0.0291 80000 1.4823 1.5965
#> mean(beta[1,1]) 0.2658 0.0264 80000 0.2140 0.3179
#> mean(beta[2,1]) -0.0659 0.0243 80000 -0.1130 -0.0169
#> mean(beta[1,2]) -0.0537 0.0169 80000 -0.0872 -0.0205
#> mean(beta[2,2]) 0.2350 0.0336 80000 0.1682 0.3007
#> mean(mu[1,1]) 2.8979 0.1794 80000 2.5420 3.2506
#> mean(mu[2,1]) 2.3268 0.1569 80000 2.0183 2.6342
#> cov(beta[1,1],beta[1,1]) 0.0217 0.0093 80000 0.0112 0.0420
#> cov(beta[2,1], beta[1,1]) 0.0088 0.0056 80000 0.0004 0.0225
#> cov(beta[2,1],beta[2,1]) 0.0151 0.0084 80000 0.0061 0.0325
#> cov(beta[1,2],beta[1,1]) -0.0021 0.0037 80000 -0.0107 0.0040
#> cov(beta[1,2],beta[2,1]) 0.0013 0.0032 80000 -0.0052 0.0076
#> cov(beta[1,2],beta[1,2]) 0.0048 0.0059 80000 0.0013 0.0133
#> cov(beta[2,2],beta[1,1]) 0.0130 0.0074 80000 0.0005 0.0301
#> cov(beta[2,2],beta[2,1]) 0.0029 0.0065 80000 -0.0102 0.0157
#> cov(beta[2,2],beta[1,2]) 0.0014 0.0046 80000 -0.0069 0.0115
#> cov(beta[2,2],beta[2,2]) 0.0422 0.0138 80000 0.0243 0.0747
#> cov(mu[1,1],mu[1,1]) 1.5259 0.3539 80000 1.0216 2.3955
#> cov(mu[2,1],mu[1,1]) 0.6482 0.2439 80000 0.2750 1.2270
#> cov(mu[2,1],mu[2,1]) 1.1664 0.2749 80000 0.7768 1.8436
coef(mplus)
#> psi[1,1] psi[2,1] psi[2,2]
#> 1.299690 0.557640 1.538150
#> mean(beta[1,1]) mean(beta[2,1]) mean(beta[1,2])
#> 0.265790 -0.065930 -0.053740
#> mean(beta[2,2]) mean(mu[1,1]) mean(mu[2,1])
#> 0.235020 2.897875 2.326820
#> cov(beta[1,1],beta[1,1]) cov(beta[2,1], beta[1,1]) cov(beta[2,1],beta[2,1])
#> 0.021730 0.008830 0.015100
#> cov(beta[1,2],beta[1,1]) cov(beta[1,2],beta[2,1]) cov(beta[1,2],beta[1,2])
#> -0.002060 0.001320 0.004810
#> cov(beta[2,2],beta[1,1]) cov(beta[2,2],beta[2,1]) cov(beta[2,2],beta[1,2])
#> 0.013040 0.002850 0.001450
#> cov(beta[2,2],beta[2,2]) cov(mu[1,1],mu[1,1]) cov(mu[2,1],mu[1,1])
#> 0.042150 1.525950 0.648185
#> cov(mu[2,1],mu[2,1])
#> 1.166365
vcov(mplus)
#> psi[1,1] psi[2,1] psi[2,2]
#> psi[1,1] 6.011430e-04 2.578751e-04 1.111061e-04
#> psi[2,1] 2.578751e-04 4.212409e-04 3.099608e-04
#> psi[2,2] 1.111061e-04 3.099608e-04 8.489078e-04
#> mean(beta[1,1]) 4.665108e-07 -1.979524e-06 -3.587198e-06
#> mean(beta[2,1]) 1.818003e-06 1.466471e-06 -6.950930e-06
#> mean(beta[1,2]) 2.665926e-06 5.884659e-06 4.859973e-06
#> mean(beta[2,2]) -4.483612e-06 3.476255e-06 9.994828e-06
#> mean(mu[1,1]) 2.492690e-05 7.635059e-06 -2.548229e-05
#> mean(mu[2,1]) 1.625315e-05 9.038175e-06 -2.545665e-05
#> cov(beta[1,1],beta[1,1]) -2.567731e-06 -1.482490e-05 -5.419533e-06
#> cov(beta[2,1], beta[1,1]) -2.184635e-06 -2.461513e-06 -2.087431e-06
#> cov(beta[2,1],beta[2,1]) 3.540883e-07 -1.575631e-05 -1.023452e-05
#> cov(beta[1,2],beta[1,1]) 2.090166e-06 9.166240e-07 4.506451e-07
#> cov(beta[1,2],beta[2,1]) 7.448303e-07 1.115372e-06 1.512597e-06
#> cov(beta[1,2],beta[1,2]) -2.001453e-06 -1.526452e-05 -5.304346e-06
#> cov(beta[2,2],beta[1,1]) 1.531802e-06 1.434757e-06 -4.354801e-07
#> cov(beta[2,2],beta[2,1]) 1.110595e-06 1.561562e-06 1.935587e-06
#> cov(beta[2,2],beta[1,2]) -6.187418e-07 -1.854614e-06 -7.467501e-07
#> cov(beta[2,2],beta[2,2]) 1.207397e-06 -1.473430e-05 -1.086736e-05
#> cov(mu[1,1],mu[1,1]) -9.970645e-07 -3.171432e-05 -7.038720e-06
#> cov(mu[2,1],mu[1,1]) -2.465360e-06 -1.478780e-05 -8.582027e-06
#> cov(mu[2,1],mu[2,1]) 6.880111e-06 1.991190e-05 -1.851064e-05
#> mean(beta[1,1]) mean(beta[2,1]) mean(beta[1,2])
#> psi[1,1] 4.665108e-07 1.818003e-06 2.665926e-06
#> psi[2,1] -1.979524e-06 1.466471e-06 5.884659e-06
#> psi[2,2] -3.587198e-06 -6.950930e-06 4.859973e-06
#> mean(beta[1,1]) 6.967775e-04 2.934238e-04 -1.194349e-04
#> mean(beta[2,1]) 2.934238e-04 5.927277e-04 7.321855e-07
#> mean(beta[1,2]) -1.194349e-04 7.321855e-07 2.844964e-04
#> mean(beta[2,2]) 2.449070e-04 -2.987555e-05 1.067004e-04
#> mean(mu[1,1]) 6.700807e-06 -1.521443e-05 -7.724714e-06
#> mean(mu[2,1]) 1.384592e-05 -1.559666e-05 -8.003261e-07
#> cov(beta[1,1],beta[1,1]) 1.510375e-06 5.583283e-06 -5.328497e-07
#> cov(beta[2,1], beta[1,1]) 8.430484e-06 7.473980e-06 -2.696682e-06
#> cov(beta[2,1],beta[2,1]) -1.324822e-06 1.576193e-05 -7.536372e-07
#> cov(beta[1,2],beta[1,1]) -4.916498e-07 -1.375728e-07 2.101726e-06
#> cov(beta[1,2],beta[2,1]) -2.739272e-06 -2.423685e-06 4.476248e-06
#> cov(beta[1,2],beta[1,2]) -7.734548e-06 5.917324e-07 8.049645e-07
#> cov(beta[2,2],beta[1,1]) 5.677251e-07 2.699762e-07 2.497493e-06
#> cov(beta[2,2],beta[2,1]) -5.051502e-07 -2.384973e-06 2.643263e-06
#> cov(beta[2,2],beta[1,2]) 2.183298e-06 1.140451e-06 -2.533509e-06
#> cov(beta[2,2],beta[2,2]) -5.125971e-06 9.575966e-06 -8.805831e-07
#> cov(mu[1,1],mu[1,1]) -1.007048e-04 -9.700686e-05 9.503753e-06
#> cov(mu[2,1],mu[1,1]) -4.676840e-06 -4.314738e-05 -4.980468e-06
#> cov(mu[2,1],mu[2,1]) 2.840646e-06 -5.791621e-05 2.180104e-07
#> mean(beta[2,2]) mean(mu[1,1]) mean(mu[2,1])
#> psi[1,1] -4.483612e-06 2.492690e-05 1.625315e-05
#> psi[2,1] 3.476255e-06 7.635059e-06 9.038175e-06
#> psi[2,2] 9.994828e-06 -2.548229e-05 -2.545665e-05
#> mean(beta[1,1]) 2.449070e-04 6.700807e-06 1.384592e-05
#> mean(beta[2,1]) -2.987555e-05 -1.521443e-05 -1.559666e-05
#> mean(beta[1,2]) 1.067004e-04 -7.724714e-06 -8.003261e-07
#> mean(beta[2,2]) 1.130087e-03 -4.030758e-07 -1.461202e-05
#> mean(mu[1,1]) -4.030758e-07 3.220145e-02 1.355340e-02
#> mean(mu[2,1]) -1.461202e-05 1.355340e-02 2.461164e-02
#> cov(beta[1,1],beta[1,1]) -7.969669e-06 -6.930629e-06 -1.464087e-05
#> cov(beta[2,1], beta[1,1]) -7.270201e-07 -3.780589e-06 -8.566037e-06
#> cov(beta[2,1],beta[2,1]) -1.167253e-05 3.757119e-07 -6.574450e-06
#> cov(beta[1,2],beta[1,1]) 9.333046e-07 -2.983439e-06 1.198750e-06
#> cov(beta[1,2],beta[2,1]) 1.644386e-06 -6.895311e-07 2.938641e-06
#> cov(beta[1,2],beta[1,2]) -5.869864e-06 -1.156715e-06 -2.833091e-06
#> cov(beta[2,2],beta[1,1]) 5.951051e-07 -6.982999e-06 -3.649644e-06
#> cov(beta[2,2],beta[2,1]) 8.364588e-06 2.518915e-06 -1.730509e-06
#> cov(beta[2,2],beta[1,2]) -1.522733e-06 2.800091e-06 1.753456e-06
#> cov(beta[2,2],beta[2,2]) -1.435605e-05 1.087898e-05 1.237967e-05
#> cov(mu[1,1],mu[1,1]) -3.088815e-05 -2.322307e-04 -3.466462e-06
#> cov(mu[2,1],mu[1,1]) 1.021193e-07 5.108757e-05 1.301801e-04
#> cov(mu[2,1],mu[2,1]) 6.648867e-05 -1.077403e-04 -5.712663e-05
#> cov(beta[1,1],beta[1,1]) cov(beta[2,1], beta[1,1])
#> psi[1,1] -2.567731e-06 -2.184635e-06
#> psi[2,1] -1.482490e-05 -2.461513e-06
#> psi[2,2] -5.419533e-06 -2.087431e-06
#> mean(beta[1,1]) 1.510375e-06 8.430484e-06
#> mean(beta[2,1]) 5.583283e-06 7.473980e-06
#> mean(beta[1,2]) -5.328497e-07 -2.696682e-06
#> mean(beta[2,2]) -7.969669e-06 -7.270201e-07
#> mean(mu[1,1]) -6.930629e-06 -3.780589e-06
#> mean(mu[2,1]) -1.464087e-05 -8.566037e-06
#> cov(beta[1,1],beta[1,1]) 8.698817e-05 2.672648e-05
#> cov(beta[2,1], beta[1,1]) 2.672648e-05 3.158531e-05
#> cov(beta[2,1],beta[2,1]) 3.716566e-05 2.309145e-05
#> cov(beta[1,2],beta[1,1]) -1.181744e-05 -2.738627e-06
#> cov(beta[1,2],beta[2,1]) -5.082144e-06 -4.602614e-06
#> cov(beta[1,2],beta[1,2]) 2.722284e-05 5.570573e-08
#> cov(beta[2,2],beta[1,1]) 2.398067e-05 4.748166e-06
#> cov(beta[2,2],beta[2,1]) 9.064898e-06 9.138752e-06
#> cov(beta[2,2],beta[1,2]) -4.215033e-06 -6.683286e-08
#> cov(beta[2,2],beta[2,2]) 3.417712e-05 -2.100004e-07
#> cov(mu[1,1],mu[1,1]) -2.898000e-05 -1.785610e-05
#> cov(mu[2,1],mu[1,1]) -2.235159e-05 -9.114509e-06
#> cov(mu[2,1],mu[2,1]) 3.256720e-06 -1.829019e-06
#> cov(beta[2,1],beta[2,1]) cov(beta[1,2],beta[1,1])
#> psi[1,1] 3.540883e-07 2.090166e-06
#> psi[2,1] -1.575631e-05 9.166240e-07
#> psi[2,2] -1.023452e-05 4.506451e-07
#> mean(beta[1,1]) -1.324822e-06 -4.916498e-07
#> mean(beta[2,1]) 1.576193e-05 -1.375728e-07
#> mean(beta[1,2]) -7.536372e-07 2.101726e-06
#> mean(beta[2,2]) -1.167253e-05 9.333046e-07
#> mean(mu[1,1]) 3.757119e-07 -2.983439e-06
#> mean(mu[2,1]) -6.574450e-06 1.198750e-06
#> cov(beta[1,1],beta[1,1]) 3.716566e-05 -1.181744e-05
#> cov(beta[2,1], beta[1,1]) 2.309145e-05 -2.738627e-06
#> cov(beta[2,1],beta[2,1]) 7.040985e-05 -4.195750e-07
#> cov(beta[1,2],beta[1,1]) -4.195750e-07 1.374802e-05
#> cov(beta[1,2],beta[2,1]) -9.055626e-09 5.356939e-06
#> cov(beta[1,2],beta[1,2]) 2.485377e-05 -4.713211e-06
#> cov(beta[2,2],beta[1,1]) -4.598642e-07 1.854582e-06
#> cov(beta[2,2],beta[2,1]) -8.956643e-07 2.039725e-06
#> cov(beta[2,2],beta[1,2]) -3.017564e-07 4.110188e-06
#> cov(beta[2,2],beta[2,2]) 2.460196e-05 3.089588e-06
#> cov(mu[1,1],mu[1,1]) -1.115896e-05 -4.172284e-06
#> cov(mu[2,1],mu[1,1]) -2.004309e-05 -6.963011e-06
#> cov(mu[2,1],mu[2,1]) 7.771333e-06 -9.996596e-06
#> cov(beta[1,2],beta[2,1]) cov(beta[1,2],beta[1,2])
#> psi[1,1] 7.448303e-07 -2.001453e-06
#> psi[2,1] 1.115372e-06 -1.526452e-05
#> psi[2,2] 1.512597e-06 -5.304346e-06
#> mean(beta[1,1]) -2.739272e-06 -7.734548e-06
#> mean(beta[2,1]) -2.423685e-06 5.917324e-07
#> mean(beta[1,2]) 4.476248e-06 8.049645e-07
#> mean(beta[2,2]) 1.644386e-06 -5.869864e-06
#> mean(mu[1,1]) -6.895311e-07 -1.156715e-06
#> mean(mu[2,1]) 2.938641e-06 -2.833091e-06
#> cov(beta[1,1],beta[1,1]) -5.082144e-06 2.722284e-05
#> cov(beta[2,1], beta[1,1]) -4.602614e-06 5.570573e-08
#> cov(beta[2,1],beta[2,1]) -9.055626e-09 2.485377e-05
#> cov(beta[1,2],beta[1,1]) 5.356939e-06 -4.713211e-06
#> cov(beta[1,2],beta[2,1]) 1.019156e-05 5.745675e-08
#> cov(beta[1,2],beta[1,2]) 5.745675e-08 3.471913e-05
#> cov(beta[2,2],beta[1,1]) 1.407870e-06 -1.764178e-06
#> cov(beta[2,2],beta[2,1]) 3.245522e-06 -2.845893e-08
#> cov(beta[2,2],beta[1,2]) 1.498100e-08 3.007679e-06
#> cov(beta[2,2],beta[2,2]) -2.797617e-07 2.583393e-05
#> cov(mu[1,1],mu[1,1]) 4.289445e-06 8.432778e-06
#> cov(mu[2,1],mu[1,1]) -2.129549e-06 -9.628633e-06
#> cov(mu[2,1],mu[2,1]) -5.961644e-06 1.253078e-05
#> cov(beta[2,2],beta[1,1]) cov(beta[2,2],beta[2,1])
#> psi[1,1] 1.531802e-06 1.110595e-06
#> psi[2,1] 1.434757e-06 1.561562e-06
#> psi[2,2] -4.354801e-07 1.935587e-06
#> mean(beta[1,1]) 5.677251e-07 -5.051502e-07
#> mean(beta[2,1]) 2.699762e-07 -2.384973e-06
#> mean(beta[1,2]) 2.497493e-06 2.643263e-06
#> mean(beta[2,2]) 5.951051e-07 8.364588e-06
#> mean(mu[1,1]) -6.982999e-06 2.518915e-06
#> mean(mu[2,1]) -3.649644e-06 -1.730509e-06
#> cov(beta[1,1],beta[1,1]) 2.398067e-05 9.064898e-06
#> cov(beta[2,1], beta[1,1]) 4.748166e-06 9.138752e-06
#> cov(beta[2,1],beta[2,1]) -4.598642e-07 -8.956643e-07
#> cov(beta[1,2],beta[1,1]) 1.854582e-06 2.039725e-06
#> cov(beta[1,2],beta[2,1]) 1.407870e-06 3.245522e-06
#> cov(beta[1,2],beta[1,2]) -1.764178e-06 -2.845893e-08
#> cov(beta[2,2],beta[1,1]) 5.540296e-05 2.056741e-05
#> cov(beta[2,2],beta[2,1]) 2.056741e-05 4.224385e-05
#> cov(beta[2,2],beta[1,2]) -7.108179e-06 1.980759e-07
#> cov(beta[2,2],beta[2,2]) 3.752049e-05 -3.195931e-06
#> cov(mu[1,1],mu[1,1]) -1.513020e-05 -1.277881e-05
#> cov(mu[2,1],mu[1,1]) -9.023581e-06 -1.810876e-06
#> cov(mu[2,1],mu[2,1]) -8.301910e-06 -2.745993e-06
#> cov(beta[2,2],beta[1,2]) cov(beta[2,2],beta[2,2])
#> psi[1,1] -6.187418e-07 1.207397e-06
#> psi[2,1] -1.854614e-06 -1.473430e-05
#> psi[2,2] -7.467501e-07 -1.086736e-05
#> mean(beta[1,1]) 2.183298e-06 -5.125971e-06
#> mean(beta[2,1]) 1.140451e-06 9.575966e-06
#> mean(beta[1,2]) -2.533509e-06 -8.805831e-07
#> mean(beta[2,2]) -1.522733e-06 -1.435605e-05
#> mean(mu[1,1]) 2.800091e-06 1.087898e-05
#> mean(mu[2,1]) 1.753456e-06 1.237967e-05
#> cov(beta[1,1],beta[1,1]) -4.215033e-06 3.417712e-05
#> cov(beta[2,1], beta[1,1]) -6.683286e-08 -2.100004e-07
#> cov(beta[2,1],beta[2,1]) -3.017564e-07 2.460196e-05
#> cov(beta[1,2],beta[1,1]) 4.110188e-06 3.089588e-06
#> cov(beta[1,2],beta[2,1]) 1.498100e-08 -2.797617e-07
#> cov(beta[1,2],beta[1,2]) 3.007679e-06 2.583393e-05
#> cov(beta[2,2],beta[1,1]) -7.108179e-06 3.752049e-05
#> cov(beta[2,2],beta[2,1]) 1.980759e-07 -3.195931e-06
#> cov(beta[2,2],beta[1,2]) 2.137072e-05 1.577545e-05
#> cov(beta[2,2],beta[2,2]) 1.577545e-05 1.915309e-04
#> cov(mu[1,1],mu[1,1]) 1.829040e-06 -1.992182e-05
#> cov(mu[2,1],mu[1,1]) -3.356357e-06 -3.945250e-05
#> cov(mu[2,1],mu[2,1]) -8.154988e-06 -3.106763e-05
#> cov(mu[1,1],mu[1,1]) cov(mu[2,1],mu[1,1])
#> psi[1,1] -9.970645e-07 -2.465360e-06
#> psi[2,1] -3.171432e-05 -1.478780e-05
#> psi[2,2] -7.038720e-06 -8.582027e-06
#> mean(beta[1,1]) -1.007048e-04 -4.676840e-06
#> mean(beta[2,1]) -9.700686e-05 -4.314738e-05
#> mean(beta[1,2]) 9.503753e-06 -4.980468e-06
#> mean(beta[2,2]) -3.088815e-05 1.021193e-07
#> mean(mu[1,1]) -2.322307e-04 5.108757e-05
#> mean(mu[2,1]) -3.466462e-06 1.301801e-04
#> cov(beta[1,1],beta[1,1]) -2.898000e-05 -2.235159e-05
#> cov(beta[2,1], beta[1,1]) -1.785610e-05 -9.114509e-06
#> cov(beta[2,1],beta[2,1]) -1.115896e-05 -2.004309e-05
#> cov(beta[1,2],beta[1,1]) -4.172284e-06 -6.963011e-06
#> cov(beta[1,2],beta[2,1]) 4.289445e-06 -2.129549e-06
#> cov(beta[1,2],beta[1,2]) 8.432778e-06 -9.628633e-06
#> cov(beta[2,2],beta[1,1]) -1.513020e-05 -9.023581e-06
#> cov(beta[2,2],beta[2,1]) -1.277881e-05 -1.810876e-06
#> cov(beta[2,2],beta[1,2]) 1.829040e-06 -3.356357e-06
#> cov(beta[2,2],beta[2,2]) -1.992182e-05 -3.945250e-05
#> cov(mu[1,1],mu[1,1]) 1.252395e-01 5.329994e-02
#> cov(mu[2,1],mu[1,1]) 5.329994e-02 5.948788e-02
#> cov(mu[2,1],mu[2,1]) 2.427844e-02 4.196161e-02
#> cov(mu[2,1],mu[2,1])
#> psi[1,1] 6.880111e-06
#> psi[2,1] 1.991190e-05
#> psi[2,2] -1.851064e-05
#> mean(beta[1,1]) 2.840646e-06
#> mean(beta[2,1]) -5.791621e-05
#> mean(beta[1,2]) 2.180104e-07
#> mean(beta[2,2]) 6.648867e-05
#> mean(mu[1,1]) -1.077403e-04
#> mean(mu[2,1]) -5.712663e-05
#> cov(beta[1,1],beta[1,1]) 3.256720e-06
#> cov(beta[2,1], beta[1,1]) -1.829019e-06
#> cov(beta[2,1],beta[2,1]) 7.771333e-06
#> cov(beta[1,2],beta[1,1]) -9.996596e-06
#> cov(beta[1,2],beta[2,1]) -5.961644e-06
#> cov(beta[1,2],beta[1,2]) 1.253078e-05
#> cov(beta[2,2],beta[1,1]) -8.301910e-06
#> cov(beta[2,2],beta[2,1]) -2.745993e-06
#> cov(beta[2,2],beta[1,2]) -8.154988e-06
#> cov(beta[2,2],beta[2,2]) -3.106763e-05
#> cov(mu[1,1],mu[1,1]) 2.427844e-02
#> cov(mu[2,1],mu[1,1]) 4.196161e-02
#> cov(mu[2,1],mu[2,1]) 7.558693e-02
Mplus Output
Mplus VERSION 9 (Linux)
MUTHEN & MUTHEN
02/27/2026 4:15 AM
INPUT INSTRUCTIONS
TITLE:
Multilevel Vector Autoregressive Model
DATA:
FILE = mplus_cOyAJKESetHBNc5GPxDQ_data.dat;
VARIABLE:
NAMES = ID TIME Y1 Y2;
USEVARIABLES = Y1 Y2;
CLUSTER = ID;
LAGGED = Y1(1) Y2(1);
ANALYSIS:
TYPE = TWOLEVEL RANDOM;
ESTIMATOR = BAYES;
CHAINS = 2;
FBITER = (40000);
PROCESSORS = 1;
BSEED = 42;
MODEL:
%WITHIN%
! transition matrix (beta)
BETA11 | Y1 ON Y1&1;
BETA21 | Y2 ON Y1&1;
BETA12 | Y1 ON Y2&1;
BETA22 | Y2 ON Y2&1;
! process noise covariance matrix (psi)
Y1;
Y2 WITH Y1;
Y2;
%BETWEEN%
! person-specific means (mu)
[Y1];
[Y2];
Y1;
Y2 WITH Y1;
Y2;
! person-specific lagged effects (beta)
[BETA11];
[BETA21];
[BETA12];
[BETA22];
BETA11;
BETA21 WITH BETA11;
BETA12 WITH BETA11;
BETA22 WITH BETA11;
BETA21;
BETA12 WITH BETA21;
BETA22 WITH BETA21;
BETA12;
BETA22 WITH BETA12;
BETA22;
OUTPUT:
TECH1 TECH8;
SAVEDATA:
BPARAMETERS = mplus_cOyAJKESetHBNc5GPxDQ_posterior.dat;
INPUT READING TERMINATED NORMALLY
Multilevel Vector Autoregressive Model
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 5750
Number of dependent variables 2
Number of independent variables 2
Number of continuous latent variables 4
Observed dependent variables
Continuous
Y1 Y2
Observed independent variables
Y1&1 Y2&1
Continuous latent variables
BETA11 BETA21 BETA12 BETA22
Variables with special functions
Cluster variable ID
Within variables
Y1&1 Y2&1
Estimator BAYES
Specifications for Bayesian Estimation
Point estimate MEDIAN
Number of Markov chain Monte Carlo (MCMC) chains 2
Random seed for the first chain 42
Starting value information UNPERTURBED
Algorithm used for Markov chain Monte Carlo GIBBS(PX1)
Fixed number of iterations 40000
K-th iteration used for thinning 1
Input data file(s)
mplus_cOyAJKESetHBNc5GPxDQ_data.dat
Input data format FREE
SUMMARY OF DATA
Number of clusters 50
Size (s) Cluster ID with Size s
50 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49
100 26 14 29 8 32 17 35 5 38 20 41 11 44 23 47 2 50
200 24 36 12 18 39 27 9 42 3 30 45 21 15 48 33 6
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
Number of missing data patterns 2
PROPORTION OF DATA PRESENT
Covariance Coverage
Y1 Y2
________ ________
Y1 1.000
Y2 1.000 1.000
UNIVARIATE SAMPLE STATISTICS
UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
Y1 2.890 -0.090 -3.247 0.02% 1.475 2.544 2.941
5750.000 2.692 -0.005 9.199 0.02% 3.328 4.254
Y2 2.175 0.012 -4.140 0.02% 0.790 1.741 2.191
5750.000 2.671 -0.011 8.406 0.02% 2.597 3.541
THE MODEL ESTIMATION TERMINATED NORMALLY
USE THE FBITERATIONS OPTION TO INCREASE THE NUMBER OF ITERATIONS BY A FACTOR
OF AT LEAST TWO TO CHECK CONVERGENCE AND THAT THE PSR VALUE DOES NOT INCREASE.
MODEL FIT INFORMATION
Number of Free Parameters 22
Information Criteria
Deviance (DIC) 35866.748
Estimated Number of Parameters (pD) 227.012
MODEL RESULTS
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
Within Level
Y2 WITH
Y1 0.558 0.020 0.000 0.518 0.598 *
Residual Variances
Y1 1.299 0.024 0.000 1.253 1.348 *
Y2 1.538 0.029 0.000 1.482 1.597 *
Between Level
BETA21 WITH
BETA11 0.009 0.006 0.019 0.000 0.022 *
BETA12 0.001 0.003 0.322 -0.005 0.008
BETA22 0.003 0.007 0.313 -0.010 0.016
BETA12 WITH
BETA11 -0.002 0.004 0.254 -0.011 0.004
BETA22 0.001 0.005 0.361 -0.007 0.012
BETA22 WITH
BETA11 0.013 0.007 0.020 0.001 0.030 *
Y2 WITH
Y1 0.647 0.244 0.000 0.273 1.225 *
Means
Y1 2.898 0.179 0.000 2.543 3.251 *
Y2 2.326 0.156 0.000 2.018 2.631 *
BETA11 0.266 0.026 0.000 0.214 0.318 *
BETA21 -0.066 0.024 0.005 -0.113 -0.018 *
BETA12 -0.054 0.017 0.001 -0.087 -0.021 *
BETA22 0.235 0.033 0.000 0.169 0.301 *
Variances
Y1 1.526 0.355 0.000 1.022 2.400 *
Y2 1.166 0.275 0.000 0.775 1.847 *
BETA11 0.022 0.008 0.000 0.011 0.042 *
BETA21 0.015 0.007 0.000 0.006 0.033 *
BETA12 0.005 0.003 0.000 0.001 0.013 *
BETA22 0.042 0.013 0.000 0.024 0.074 *
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR WITHIN
NU
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
0 0 0 0
LAMBDA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0 0 0 0
Y2 0 0 0 0
Y1&1 0 0 0 0
Y2&1 0 0 0 0
THETA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0
Y2 0 0
Y1&1 0 0 0
Y2&1 0 0 0 0
ALPHA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
0 0 0 0
BETA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0 0 0 0
Y2 0 0 0 0
Y1&1 0 0 0 0
Y2&1 0 0 0 0
PSI
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 1
Y2 2 3
Y1&1 0 0 0
Y2&1 0 0 0 0
PARAMETER SPECIFICATION FOR BETWEEN
NU
Y1 Y2
________ ________
0 0
LAMBDA
BETA11 BETA21 BETA12 BETA22 Y1
________ ________ ________ ________ ________
Y1 0 0 0 0 0
Y2 0 0 0 0 0
LAMBDA
Y2
________
Y1 0
Y2 0
THETA
Y1 Y2
________ ________
Y1 0
Y2 0 0
ALPHA
BETA11 BETA21 BETA12 BETA22 Y1
________ ________ ________ ________ ________
4 5 6 7 8
ALPHA
Y2
________
9
BETA
BETA11 BETA21 BETA12 BETA22 Y1
________ ________ ________ ________ ________
BETA11 0 0 0 0 0
BETA21 0 0 0 0 0
BETA12 0 0 0 0 0
BETA22 0 0 0 0 0
Y1 0 0 0 0 0
Y2 0 0 0 0 0
BETA
Y2
________
BETA11 0
BETA21 0
BETA12 0
BETA22 0
Y1 0
Y2 0
PSI
BETA11 BETA21 BETA12 BETA22 Y1
________ ________ ________ ________ ________
BETA11 10
BETA21 11 12
BETA12 13 14 15
BETA22 16 17 18 19
Y1 0 0 0 0 20
Y2 0 0 0 0 21
PSI
Y2
________
Y2 22
STARTING VALUES FOR WITHIN
NU
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
0.000 0.000 0.000 0.000
LAMBDA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 1.000 0.000 0.000 0.000
Y2 0.000 1.000 0.000 0.000
Y1&1 0.000 0.000 1.000 0.000
Y2&1 0.000 0.000 0.000 1.000
THETA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0.000
Y2 0.000 0.000
Y1&1 0.000 0.000 0.000
Y2&1 0.000 0.000 0.000 0.000
ALPHA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
0.000 0.000 0.000 0.000
BETA
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 0.000 0.000 0.000 0.000
Y2 0.000 0.000 0.000 0.000
Y1&1 0.000 0.000 0.000 0.000
Y2&1 0.000 0.000 0.000 0.000
PSI
Y1 Y2 Y1&1 Y2&1
________ ________ ________ ________
Y1 1.346
Y2 0.000 1.335
Y1&1 0.000 0.000 1.350
Y2&1 0.000 0.000 0.000 1.332
STARTING VALUES FOR BETWEEN
NU
Y1 Y2
________ ________
0.000 0.000
LAMBDA
BETA11 BETA21 BETA12 BETA22 Y1
________ ________ ________ ________ ________
Y1 0.000 0.000 0.000 0.000 1.000
Y2 0.000 0.000 0.000 0.000 0.000
LAMBDA
Y2
________
Y1 0.000
Y2 1.000
THETA
Y1 Y2
________ ________
Y1 0.000
Y2 0.000 0.000
ALPHA
BETA11 BETA21 BETA12 BETA22 Y1
________ ________ ________ ________ ________
0.000 0.000 0.000 0.000 2.890
ALPHA
Y2
________
2.175
BETA
BETA11 BETA21 BETA12 BETA22 Y1
________ ________ ________ ________ ________
BETA11 0.000 0.000 0.000 0.000 0.000
BETA21 0.000 0.000 0.000 0.000 0.000
BETA12 0.000 0.000 0.000 0.000 0.000
BETA22 0.000 0.000 0.000 0.000 0.000
Y1 0.000 0.000 0.000 0.000 0.000
Y2 0.000 0.000 0.000 0.000 0.000
BETA
Y2
________
BETA11 0.000
BETA21 0.000
BETA12 0.000
BETA22 0.000
Y1 0.000
Y2 0.000
PSI
BETA11 BETA21 BETA12 BETA22 Y1
________ ________ ________ ________ ________
BETA11 1.000
BETA21 0.000 1.000
BETA12 0.000 0.000 1.000
BETA22 0.000 0.000 0.000 1.000
Y1 0.000 0.000 0.000 0.000 1.346
Y2 0.000 0.000 0.000 0.000 0.000
PSI
Y2
________
Y2 1.335
PRIORS FOR ALL PARAMETERS PRIOR MEAN PRIOR VARIANCE PRIOR STD. DEV.
Parameter 1~IW(0.000,-3) infinity infinity infinity
Parameter 2~IW(0.000,-3) infinity infinity infinity
Parameter 3~IW(0.000,-3) infinity infinity infinity
Parameter 4~N(0.000,infinity) 0.0000 infinity infinity
Parameter 5~N(0.000,infinity) 0.0000 infinity infinity
Parameter 6~N(0.000,infinity) 0.0000 infinity infinity
Parameter 7~N(0.000,infinity) 0.0000 infinity infinity
Parameter 8~N(0.000,infinity) 0.0000 infinity infinity
Parameter 9~N(0.000,infinity) 0.0000 infinity infinity
Parameter 10~IW(0.000,-5) infinity infinity infinity
Parameter 11~IW(0.000,-5) infinity infinity infinity
Parameter 12~IW(0.000,-5) infinity infinity infinity
Parameter 13~IW(0.000,-5) infinity infinity infinity
Parameter 14~IW(0.000,-5) infinity infinity infinity
Parameter 15~IW(0.000,-5) infinity infinity infinity
Parameter 16~IW(0.000,-5) infinity infinity infinity
Parameter 17~IW(0.000,-5) infinity infinity infinity
Parameter 18~IW(0.000,-5) infinity infinity infinity
Parameter 19~IW(0.000,-5) infinity infinity infinity
Parameter 20~IW(0.000,-3) infinity infinity infinity
Parameter 21~IW(0.000,-3) infinity infinity infinity
Parameter 22~IW(0.000,-3) infinity infinity infinity
TECHNICAL 8 OUTPUT
TECHNICAL 8 OUTPUT FOR BAYES ESTIMATION
CHAIN BSEED
1 42
2 564124
POTENTIAL PARAMETER WITH
ITERATION SCALE REDUCTION HIGHEST PSR
100 1.074 5
200 1.074 6
300 1.029 12
400 1.026 6
500 1.034 6
600 1.038 6
700 1.042 6
800 1.024 6
900 1.024 19
1000 1.021 6
1100 1.021 6
1200 1.012 6
1300 1.006 6
1400 1.005 6
1500 1.005 22
1600 1.005 7
1700 1.005 7
1800 1.010 7
1900 1.007 7
2000 1.007 7
2100 1.007 7
2200 1.010 7
2300 1.009 7
2400 1.010 7
2500 1.008 7
2600 1.007 7
2700 1.006 7
2800 1.008 7
2900 1.008 7
3000 1.008 7
3100 1.006 10
3200 1.009 10
3300 1.009 10
3400 1.008 10
3500 1.007 10
3600 1.007 10
3700 1.006 10
3800 1.005 10
3900 1.005 10
4000 1.004 4
4100 1.005 4
4200 1.004 10
4300 1.005 10
4400 1.005 10
4500 1.005 4
4600 1.003 4
4700 1.003 4
4800 1.003 4
4900 1.003 4
5000 1.004 4
5100 1.005 4
5200 1.004 4
5300 1.002 4
5400 1.001 4
5500 1.002 4
5600 1.002 4
5700 1.001 4
5800 1.002 4
5900 1.002 4
6000 1.001 4
6100 1.001 4
6200 1.001 13
6300 1.001 13
6400 1.001 13
6500 1.001 13
6600 1.001 13
6700 1.001 13
6800 1.001 13
6900 1.001 7
7000 1.001 4
7100 1.001 6
7200 1.001 13
7300 1.001 15
7400 1.001 15
7500 1.001 15
7600 1.001 15
7700 1.001 6
7800 1.001 6
7900 1.002 6
8000 1.002 6
8100 1.002 6
8200 1.002 6
8300 1.002 6
8400 1.003 6
8500 1.004 6
8600 1.003 6
8700 1.002 6
8800 1.002 6
8900 1.002 6
9000 1.003 6
9100 1.003 6
9200 1.003 6
9300 1.002 6
9400 1.002 7
9500 1.002 7
9600 1.002 7
9700 1.002 7
9800 1.002 7
9900 1.002 7
10000 1.002 7
10100 1.002 7
10200 1.002 7
10300 1.002 7
10400 1.001 7
10500 1.001 7
10600 1.001 7
10700 1.001 7
10800 1.001 7
10900 1.001 7
11000 1.001 7
11100 1.001 7
11200 1.001 7
11300 1.001 7
11400 1.001 4
11500 1.000 7
11600 1.000 7
11700 1.001 7
11800 1.000 7
11900 1.001 19
12000 1.001 19
12100 1.001 19
12200 1.001 19
12300 1.001 19
12400 1.001 19
12500 1.001 19
12600 1.001 19
12700 1.001 19
12800 1.001 19
12900 1.001 19
13000 1.001 19
13100 1.001 19
13200 1.001 19
13300 1.001 19
13400 1.000 19
13500 1.000 19
13600 1.000 19
13700 1.000 19
13800 1.000 19
13900 1.000 19
14000 1.000 6
14100 1.000 22
14200 1.000 22
14300 1.000 22
14400 1.000 12
14500 1.000 6
14600 1.000 6
14700 1.000 6
14800 1.000 6
14900 1.001 6
15000 1.001 6
15100 1.001 6
15200 1.001 6
15300 1.001 6
15400 1.001 6
15500 1.002 6
15600 1.002 6
15700 1.002 6
15800 1.002 6
15900 1.002 6
16000 1.003 6
16100 1.004 6
16200 1.004 6
16300 1.003 6
16400 1.004 6
16500 1.004 6
16600 1.004 6
16700 1.004 6
16800 1.004 6
16900 1.005 6
17000 1.005 6
17100 1.004 6
17200 1.004 6
17300 1.004 6
17400 1.004 6
17500 1.004 6
17600 1.004 6
17700 1.004 6
17800 1.004 6
17900 1.004 6
18000 1.004 6
18100 1.003 6
18200 1.004 6
18300 1.004 6
18400 1.003 6
18500 1.003 6
18600 1.002 6
18700 1.002 6
18800 1.002 6
18900 1.002 6
19000 1.001 6
19100 1.001 6
19200 1.002 6
19300 1.002 6
19400 1.002 6
19500 1.001 6
19600 1.001 6
19700 1.001 6
19800 1.001 6
19900 1.001 6
20000 1.001 6
20100 1.001 6
20200 1.001 6
20300 1.001 6
20400 1.001 6
20500 1.000 6
20600 1.000 6
20700 1.000 6
20800 1.000 6
20900 1.000 6
21000 1.000 6
21100 1.000 6
21200 1.000 6
21300 1.000 6
21400 1.000 6
21500 1.000 6
21600 1.000 6
21700 1.000 6
21800 1.000 6
21900 1.000 6
22000 1.000 6
22100 1.000 6
22200 1.000 6
22300 1.000 6
22400 1.000 6
22500 1.000 6
22600 1.000 6
22700 1.000 6
22800 1.000 6
22900 1.000 6
23000 1.000 6
23100 1.000 6
23200 1.000 6
23300 1.000 6
23400 1.000 6
23500 1.000 6
23600 1.000 6
23700 1.000 6
23800 1.000 6
23900 1.000 6
24000 1.000 6
24100 1.000 6
24200 1.000 6
24300 1.000 6
24400 1.000 6
24500 1.000 6
24600 1.000 6
24700 1.000 6
24800 1.000 6
24900 1.000 5
25000 1.000 5
25100 1.000 17
25200 1.000 17
25300 1.000 17
25400 1.000 17
25500 1.000 6
25600 1.000 6
25700 1.000 6
25800 1.000 6
25900 1.000 6
26000 1.000 4
26100 1.000 10
26200 1.000 10
26300 1.000 6
26400 1.000 10
26500 1.000 10
26600 1.000 10
26700 1.000 10
26800 1.000 10
26900 1.000 10
27000 1.000 10
27100 1.000 10
27200 1.000 10
27300 1.000 10
27400 1.000 10
27500 1.000 10
27600 1.000 14
27700 1.000 14
27800 1.000 14
27900 1.000 14
28000 1.000 14
28100 1.000 14
28200 1.000 14
28300 1.000 14
28400 1.000 6
28500 1.000 14
28600 1.000 14
28700 1.000 14
28800 1.000 10
28900 1.000 10
29000 1.000 10
29100 1.000 10
29200 1.000 10
29300 1.000 10
29400 1.000 22
29500 1.000 10
29600 1.000 10
29700 1.000 22
29800 1.000 22
29900 1.000 22
30000 1.000 22
30100 1.000 22
30200 1.000 10
30300 1.000 10
30400 1.000 10
30500 1.000 10
30600 1.000 10
30700 1.000 10
30800 1.000 10
30900 1.000 10
31000 1.000 10
31100 1.000 2
31200 1.000 2
31300 1.000 2
31400 1.000 2
31500 1.000 2
31600 1.000 2
31700 1.000 2
31800 1.000 2
31900 1.000 2
32000 1.000 2
32100 1.000 2
32200 1.000 2
32300 1.000 2
32400 1.000 6
32500 1.000 5
32600 1.000 6
32700 1.000 2
32800 1.000 6
32900 1.000 2
33000 1.000 6
33100 1.000 6
33200 1.000 6
33300 1.000 6
33400 1.000 6
33500 1.000 6
33600 1.000 6
33700 1.000 6
33800 1.000 6
33900 1.000 6
34000 1.000 6
34100 1.000 6
34200 1.000 6
34300 1.000 6
34400 1.000 6
34500 1.000 6
34600 1.000 6
34700 1.000 6
34800 1.000 6
34900 1.000 6
35000 1.000 6
35100 1.000 6
35200 1.000 6
35300 1.000 6
35400 1.000 6
35500 1.000 6
35600 1.000 6
35700 1.000 6
35800 1.000 6
35900 1.000 6
36000 1.000 6
36100 1.000 6
36200 1.000 15
36300 1.000 6
36400 1.000 6
36500 1.000 6
36600 1.000 15
36700 1.000 15
36800 1.000 15
36900 1.000 15
37000 1.000 17
37100 1.000 17
37200 1.000 17
37300 1.000 17
37400 1.000 17
37500 1.000 15
37600 1.000 15
37700 1.000 15
37800 1.000 15
37900 1.000 17
38000 1.000 17
38100 1.000 17
38200 1.000 17
38300 1.000 17
38400 1.000 17
38500 1.000 17
38600 1.000 17
38700 1.000 17
38800 1.000 17
38900 1.000 17
39000 1.000 17
39100 1.000 17
39200 1.000 17
39300 1.000 17
39400 1.000 17
39500 1.000 17
39600 1.000 17
39700 1.000 17
39800 1.000 17
39900 1.000 17
40000 1.000 17
MCMC EFFECTIVE SAMPLE SIZE (ESS) IN ASCENDING ORDER
LOWEST 10 PARAMETERS
PARAMETER ESS
15 2009
14 2773
13 2845
18 4263
12 5098
11 6191
6 6417
17 8226
10 8899
16 11605
SAVEDATA INFORMATION
Bayesian Parameters
Save file
mplus_cOyAJKESetHBNc5GPxDQ_posterior.dat
Save format Free
Order of parameters saved
Chain number
Iteration number
Parameter 1, %WITHIN%: Y1
Parameter 2, %WITHIN%: Y2 WITH Y1
Parameter 3, %WITHIN%: Y2
Parameter 4, %BETWEEN%: [ BETA11 ]
Parameter 5, %BETWEEN%: [ BETA21 ]
Parameter 6, %BETWEEN%: [ BETA12 ]
Parameter 7, %BETWEEN%: [ BETA22 ]
Parameter 8, %BETWEEN%: [ Y1 ]
Parameter 9, %BETWEEN%: [ Y2 ]
Parameter 10, %BETWEEN%: BETA11
Parameter 11, %BETWEEN%: BETA21 WITH BETA11
Parameter 12, %BETWEEN%: BETA21
Parameter 13, %BETWEEN%: BETA12 WITH BETA11
Parameter 14, %BETWEEN%: BETA12 WITH BETA21
Parameter 15, %BETWEEN%: BETA12
Parameter 16, %BETWEEN%: BETA22 WITH BETA11
Parameter 17, %BETWEEN%: BETA22 WITH BETA21
Parameter 18, %BETWEEN%: BETA22 WITH BETA12
Parameter 19, %BETWEEN%: BETA22
Parameter 20, %BETWEEN%: Y1
Parameter 21, %BETWEEN%: Y2 WITH Y1
Parameter 22, %BETWEEN%: Y2
Beginning Time: 04:15:14
Ending Time: 04:20:48
Elapsed Time: 00:05:34
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naive <- FitNaive(
fit = dtvar
)
summary(naive)
#> est se z p 2.5% 97.5%
#> alpha[1,1] 2.8930 0.1661 17.4205 0.0000 2.5675 3.2185
#> alpha[2,1] 2.3276 0.1459 15.9498 0.0000 2.0415 2.6136
#> alpha[3,1] 0.2549 0.0236 10.8153 0.0000 0.2087 0.3011
#> alpha[4,1] -0.0486 0.0229 -2.1223 0.0338 -0.0934 -0.0037
#> alpha[5,1] -0.0590 0.0183 -3.2270 0.0013 -0.0949 -0.0232
#> alpha[6,1] 0.2070 0.0289 7.1559 0.0000 0.1503 0.2637
#> tau_sqr[1,1] 1.3790 0.2758 5.0000 0.0000 0.8384 1.9195
#> tau_sqr[2,1] 0.5931 0.1908 3.1085 0.0019 0.2191 0.9670
#> tau_sqr[2,2] 1.0648 0.2130 5.0000 0.0000 0.6474 1.4822
#> tau_sqr[3,3] 0.0278 0.0056 5.0000 0.0000 0.0169 0.0387
#> tau_sqr[4,3] 0.0135 0.0043 3.1569 0.0016 0.0051 0.0218
#> tau_sqr[5,3] -0.0079 0.0032 -2.4412 0.0146 -0.0143 -0.0016
#> tau_sqr[6,3] 0.0073 0.0049 1.4764 0.1398 -0.0024 0.0169
#> tau_sqr[4,4] 0.0262 0.0052 5.0000 0.0000 0.0159 0.0364
#> tau_sqr[5,4] -0.0030 0.0030 -1.0144 0.3104 -0.0089 0.0028
#> tau_sqr[6,4] -0.0022 0.0047 -0.4686 0.6393 -0.0114 0.0070
#> tau_sqr[5,5] 0.0167 0.0033 5.0000 0.0000 0.0102 0.0233
#> tau_sqr[6,5] 0.0046 0.0038 1.2097 0.2264 -0.0028 0.0120
#> tau_sqr[6,6] 0.0418 0.0084 5.0000 0.0000 0.0254 0.0582