Given all that it may be that I will redo this same exercise with a different estimation, but that is yet to be decided.
Adaptive-Mixture Metropolis
No specs
Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = Initial.Values,
Algorithm = "AMM")
Acceptance Rate: 0.284
Algorithm: Adaptive-Mixture Metropolis
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
2.73756468 0.00197592
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 45.095 44.425
pD 234.487 2.231
DIC 279.582 46.656
Initial Values:
[1] -10 0
Iterations: 10000
Log(Marginal Likelihood): NA
Minutes of run-time: 0.05
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 500
Recommended Burn-In of Un-thinned Samples: 5000
Recommended Thinning: 150
Specs: (NOT SHOWN HERE)
Status is displayed every 100 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 1000
Thinning: 10
Summary of All Samples
Mean SD MCSE ESS LB Median
beta[1] -10.8694827 1.66603784 0.154095363 57.79995 -15.5489329 -10.2511972
beta[2] 0.2682103 0.04423248 0.004039057 58.80563 0.2003859 0.2543845
Deviance 45.0951406 21.65580992 1.178504393 534.75650 42.5189305 43.4676853
LP -31.3536988 10.82813264 0.589266937 534.75770 -33.8231778 -30.5333506
UB
beta[1] -8.3168119
beta[2] 0.3923192
Deviance 50.0480146
LP -30.0738495
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -11.574823 2.1300652 0.205608788 285.1749 -16.3533092 -11.3357688
beta[2] 0.286758 0.0553065 0.005306451 287.9282 0.1924482 0.2804307
Deviance 44.425140 2.1124891 0.191862970 191.2321 42.4735763 43.8636196
LP -31.027498 1.0677294 0.589266937 190.2838 -34.0072489 -30.7386292
UB
beta[1] -7.9334688
beta[2] 0.4128574
Deviance 50.4694327
LP -30.0463562
Affine-Invariant Ensemble Sampler
It seems to go somewhere, then gets stuck without an exit.
Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = Initial.Values,
Iterations = 20000, Status = 2000, Thinning = 35, Algorithm = "AIES",
Specs = list(Nc = 16, Z = NULL, beta = 1.1, CPUs = 1, Packages = NULL,
Dyn.libs = NULL))
Acceptance Rate: 0.9773
Algorithm: Affine-Invariant Ensemble Sampler
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
0.5252284175 0.0004811633
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 43.004 43.005
pD 0.053 0.000
DIC 43.057 43.005
Initial Values:
[1] -10 0
Iterations: 20000
Log(Marginal Likelihood): NA
Minutes of run-time: 0.8
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 399
Recommended Burn-In of Un-thinned Samples: 13965
Recommended Thinning: 27
Specs: (NOT SHOWN HERE)
Status is displayed every 2000 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 571
Thinning: 35
Summary of All Samples
Mean SD MCSE ESS LB Median
beta[1] -10.2521485 0.72528513 0.153054828 9.623682 -12.7424753 -9.9662793
beta[2] 0.2513389 0.01927108 0.004080774 11.791793 0.2404582 0.2438793
Deviance 43.0041950 0.32410474 0.046924334 74.412690 42.5190044 43.0023753
LP -30.3005775 0.16647750 0.024331033 74.198162 -30.8672783 -30.2965116
UB
beta[1] -9.8180273
beta[2] 0.3153106
Deviance 44.0738314
LP -30.0671736
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -9.9558233 0.0082421078 2.797169e-03 12.56157 -9.9743833 -9.9550952
beta[2] 0.2436365 0.0001725992 5.836733e-05 12.63574 0.2433173 0.2436223
Deviance 43.0047636 0.0021518709 7.092913e-04 12.84743 43.0002894 43.0048552
LP -30.2976030 0.0009940593 2.433103e-02 12.86979 -30.2995034 -30.2976427
UB
beta[1] -9.9405874
beta[2] 0.2440232
Deviance 43.0088727
LP -30.2955405
Componentwise Hit-And-Run Metropolis
This never was able to get to the target.
Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = Initial.Values,
Iterations = 40000, Status = 2000, Thinning = 30, Algorithm = "CHARM")
Acceptance Rate: 0.31229
Algorithm: Componentwise Hit-And-Run Metropolis
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
3.580895236 0.002467357
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 44.445 45.021
pD 2.023 2.256
DIC 46.468 47.278
Initial Values:
[1] -10 0
Iterations: 40000
Log(Marginal Likelihood): NA
Minutes of run-time: 0.18
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 1064
Recommended Burn-In of Un-thinned Samples: 31920
Recommended Thinning: 31
Specs: (NOT SHOWN HERE)
Status is displayed every 2000 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 1333
Thinning: 30
Summary of All Samples
Mean SD MCSE ESS LB Median
beta[1] -10.9964257 1.89283881 0.49785194 13.06079 -14.8229746 -10.9766992
beta[2] 0.2717979 0.04913034 0.01300998 11.03343 0.1856506 0.2705021
Deviance 44.4449406 2.01148697 0.18601589 82.06199 42.4984709 43.8291949
LP -31.0303916 1.00254773 0.09222924 83.71010 -33.6460481 -30.7196890
UB
beta[1] -7.6255135
beta[2] 0.3698866
Deviance 49.6364683
LP -30.0586484
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -9.5579858 1.34107513 0.62509204 4.739982 -12.03957 -9.313436
beta[2] 0.2340237 0.03463118 0.01639968 4.804878 0.18134 0.227444
Deviance 45.0214688 2.12434347 0.28430825 16.656844 42.51149 44.655282
LP -31.3029682 1.05482519 0.09222924 17.255007 -33.92636 -31.139132
UB
beta[1] -7.398433
beta[2] 0.297938
Deviance 50.284536
LP -30.061842
Delayed Rejection Adaptive Metropolis
This is an interesting algorithm. One can see during sampling the algorithm shifts from a faster to a slower sampling approach. The same shift in gears is seen in the plot. Notice that it recommends thinning 90. In fact I had it to the point of proposing a thinning of 1000. Since the manual also states on using DRAM as final algorithm: 'DRAM may be used if diminishing adaptation occurs and adaptation ceases effectively'. Given these texts and effects, I tried a different problem, starting with wrong initial values. Indeed, it was able to get close to the true values in all such runs.
Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = Initial.Values,
Thinning = 30, Algorithm = "DRAM")
Acceptance Rate: 0.5221
Algorithm: Delayed Rejection Adaptive Metropolis
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
11.556472479 0.007722216
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 470.735 48.093
pD 1803475.978 35.962
DIC 1803946.712 84.055
Initial Values:
[1] -10 0
Iterations: 10000
Log(Marginal Likelihood): NA
Minutes of run-time: 0.2
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 165
Recommended Burn-In of Un-thinned Samples: 4950
Recommended Thinning: 270
Specs: (NOT SHOWN HERE)
Status is displayed every 100 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 333
Thinning: 30
Summary of All Samples
Mean SD MCSE ESS LB
beta[1] -11.7526275 2.3119401 0.34580302 43.51566 -17.027894
beta[2] 0.2000943 0.4891327 0.02860336 130.61590 -1.487342
Deviance 470.7346820 1899.1977136 105.17220909 100.44119 42.511693
LP -244.1848392 949.6008219 52.58640512 100.44148 -3481.777935
Median UB
beta[1] -11.6929022 -7.8194808
beta[2] 0.2842366 0.4423707
Deviance 44.3755257 6945.9261923
LP -31.0009124 -30.0634427
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -11.7250338 2.48894626 0.2213773 168 -17.044268 -11.6851217
beta[2] 0.2921779 0.06547124 0.0053019 168 0.166793 0.2909701
Deviance 48.0932958 8.48081577 0.7474430 168 42.527075 45.2507580
LP -32.8641423 4.24023747 52.5864051 168 -47.454476 -31.4673576
UB
beta[1] -7.5769778
beta[2] 0.4250974
Deviance 77.3040995
LP -30.0696373
Delayed Rejection Metropolis
This algorithm has the instruction to use the covariance matrix from for instance DRAM. So I pulled those and the summary of stationary samples as input.
Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = c(-11.72,
0.29), Covar = covar, Algorithm = "DRM")
Acceptance Rate: 0.5659
Algorithm: Delayed Rejection Metropolis
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
11.556472479 0.007722216
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 48.417 48.417
pD 59.001 59.001
DIC 107.419 107.419
Initial Values:
[1] -11.72 0.29
Iterations: 10000
Log(Marginal Likelihood): -38.65114
Minutes of run-time: 0.09
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 0
Recommended Burn-In of Un-thinned Samples: 0
Recommended Thinning: 10
Specs: (NOT SHOWN HERE)
Status is displayed every 100 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 1000
Thinning: 10
Summary of All Samples
Mean SD MCSE ESS LB Median
beta[1] -11.6326715 2.89304045 0.111808577 891.8417 -18.12638 -11.5067562
beta[2] 0.2883743 0.07495814 0.002893592 894.3397 0.13874 0.2834856
Deviance 48.4174842 10.86289496 0.377770754 897.9490 42.52784 44.7049759
LP -33.0262590 5.43029899 0.188915825 897.4927 -47.25058 -31.1763877
UB
beta[1] -6.014343
beta[2] 0.452027
Deviance 76.915884
LP -30.075104
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -11.6326715 2.89304045 0.111808577 891.8417 -18.12638 -11.5067562
beta[2] 0.2883743 0.07495814 0.002893592 894.3397 0.13874 0.2834856
Deviance 48.4174842 10.86289496 0.377770754 897.9490 42.52784 44.7049759
LP -33.0262590 5.43029899 0.188915825 897.4927 -47.25058 -31.1763877
UB
beta[1] -6.014343
beta[2] 0.452027
Deviance 76.915884
LP -30.075104
Differential Evolution Markov Chain
Following LP, one can see this algorithm shift its step to step towards the target distribution. The same is visible in the samples.
Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = Initial.Values,
Iterations = 70000, Status = 2000, Thinning = 36, Algorithm = "DEMC",
Specs = list(Nc = 3, Z = NULL, gamma = 0, w = 0.1))
Acceptance Rate: 0.94571
Algorithm: Differential Evolution Markov Chain
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
90.26633832 0.04206898
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 89.209 43.944
pD 5238.430 1.706
DIC 5327.639 45.650
Initial Values:
[1] -10 0
Iterations: 70000
Log(Marginal Likelihood): NA
Minutes of run-time: 0.73
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 1164
Recommended Burn-In of Un-thinned Samples: 41904
Recommended Thinning: 32
Specs: (NOT SHOWN HERE)
Status is displayed every 2000 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 1944
Thinning: 36
Summary of All Samples
Mean SD MCSE ESS LB Median
beta[1] -17.482864 9.5017889 2.35451645 2.787539 -36.9570864 -13.2979369
beta[2] 0.410902 0.2049482 0.04994949 4.145223 0.1652045 0.3204944
Deviance 89.209031 102.3565307 23.04234800 7.261840 42.4986747 44.6939882
LP -53.548197 51.3373427 11.56914508 7.198904 -167.9946428 -31.1456515
UB
beta[1] -7.1204957
beta[2] 0.7804724
Deviance 317.1315784
LP -30.0563707
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -11.9431454 1.7007792 0.215271093 125.13410 -15.7999987 -11.9807022
beta[2] 0.2969431 0.0441033 0.005730276 118.23767 0.2259635 0.2946702
Deviance 43.9443086 1.8471515 0.371329880 63.98536 42.4849394 43.3846382
LP -30.7905955 0.9340381 11.569145079 63.51772 -33.2807813 -30.5163527
UB
beta[1] -9.0646733
beta[2] 0.4059097
Deviance 48.8635918
LP -30.0522792
Elliptical Slice Sampler
Manual states. 'This algorithm is applicable only to models in which the prior mean of all parameters is zero.' That is true for my prior, yet I am not impressed at all. Maybe I should be centering or such, but the current formulation was not a successCall:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = Initial.Values,
Iterations = 60000, Status = 2000, Thinning = 1000, Algorithm = "ESS")
Acceptance Rate: 1
Algorithm: Elliptical Slice Sampler
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
1.514016386 0.001094917
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 53.903 53.806
pD 11.574 13.346
DIC 65.477 67.152
Initial Values:
[1] -10 0
Iterations: 60000
Log(Marginal Likelihood): NA
Minutes of run-time: 0.77
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 18
Recommended Burn-In of Un-thinned Samples: 18000
Recommended Thinning: 1000
Specs: (NOT SHOWN HERE)
Status is displayed every 2000 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 60
Thinning: 1000
Summary of All Samples
Mean SD MCSE ESS LB Median
beta[1] -5.9519978 1.12538724 0.199063822 34.62487 -8.11739884 -5.904983
beta[2] 0.1419102 0.02788798 0.004825592 38.79318 0.09329411 0.141184
Deviance 53.9025233 4.81129661 0.854043909 46.96804 46.49740770 53.653605
LP -35.7152403 2.39947768 0.425934607 46.93833 -41.22733361 -35.594423
UB
beta[1] -3.9669525
beta[2] 0.1932619
Deviance 64.9487765
LP -32.0237607
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -5.9962946 1.24391453 0.253903583 22.52123 -8.27636391 -5.9679392
beta[2] 0.1430514 0.03108438 0.006168722 27.21658 0.09456836 0.1467105
Deviance 53.8060933 5.16634523 1.088725764 34.31438 46.18528394 53.6113477
LP -35.6674227 2.57618453 0.425934607 34.28162 -40.73404524 -35.5728340
UB
beta[1] -4.0287456
beta[2] 0.1938871
Deviance 63.9614942
LP -31.8687620
Gibbs Sampler
This needs derivatives, hence skipped.Griddy Gibbs
This takes a grid from which a density is estimated and on which sampling is based. It may be a bit difficult for this grid, since the two parameters have different scales and the same grid is used. With only two parameters it was possible to take a rather high value for the number of grid points. Even so, I am not so happy with the final outcome.Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = Initial.Values,
Iterations = 30000, Status = 2000, Thinning = 100, Algorithm = "GG",
Specs = list(Grid = seq(from = -0.25, to = 0.25, len = 13),
dparm = NULL, CPUs = 1, Packages = NULL, Dyn.libs = NULL))
Acceptance Rate: 1
Algorithm: Griddy-Gibbs
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
11.378198005 0.008486228
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 66.161 66.161
pD 1339.075 1339.075
DIC 1405.236 1405.236
Initial Values:
[1] -10 0
Iterations: 30000
Log(Marginal Likelihood): NA
Minutes of run-time: 2.09
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 0
Recommended Burn-In of Un-thinned Samples: 0
Recommended Thinning: 900
Specs: (NOT SHOWN HERE)
Status is displayed every 2000 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 300
Thinning: 100
Summary of All Samples
Mean SD MCSE ESS LB Median
beta[1] -11.00845 3.3782928 0.77873105 23.0348 -18.26815566 -10.7755255
beta[2] 0.27170 0.0909315 0.01994284 30.0812 0.09175425 0.2612613
Deviance 66.16122 51.7508405 2.84979150 300.0000 42.82591844 50.8991200
LP -41.89256 25.8754878 1.42498409 300.0000 -139.85980754 -34.2665415
UB
beta[1] -4.870858
beta[2] 0.450951
Deviance 262.096671
LP -30.229348
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -11.00845 3.3782928 0.77873105 23.0348 -18.26815566 -10.7755255
beta[2] 0.27170 0.0909315 0.01994284 30.0812 0.09175425 0.2612613
Deviance 66.16122 51.7508405 2.84979150 300.0000 42.82591844 50.8991200
LP -41.89256 25.8754878 1.42498409 300.0000 -139.85980754 -34.2665415
UB
beta[1] -4.870858
beta[2] 0.450951
Deviance 262.096671
LP -30.229348
Hamiltonian Monte Carlo
A set was of specs was found. Acceptance rate is a bit high compared to the manual.
Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = Initial.Values,
Thinning = 100, Algorithm = "HMC", Specs = list(epsilon = 0.9 *
c(0.1, 0.01), L = 11))
Acceptance Rate: 0.8385
Algorithm: Hamiltonian Monte Carlo
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
3.515108412 0.003083421
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 44.429 44.562
pD 1.941 1.869
DIC 46.369 46.431
Initial Values:
[1] -10 0
Iterations: 10000
Log(Marginal Likelihood): NA
Minutes of run-time: 0.59
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 80
Recommended Burn-In of Un-thinned Samples: 8000
Recommended Thinning: 100
Specs: (NOT SHOWN HERE)
Status is displayed every 100 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 100
Thinning: 100
Summary of All Samples
Mean SD MCSE ESS LB Median
beta[1] -11.421073 1.87894067 0.242559120 100 -15.4741232 -11.3104311
beta[2] 0.283175 0.04808956 0.006413119 100 0.1975276 0.2818055
Deviance 44.428764 1.97004886 0.159856183 100 42.5400966 43.7163906
LP -31.027024 0.98807551 0.080511201 100 -33.5265769 -30.6632451
UB
beta[1] -7.9608191
beta[2] 0.3807289
Deviance 49.3945952
LP -30.0829425
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -11.0974590 1.92886822 0.226325971 20 -15.4741232 -10.9792610
beta[2] 0.2750898 0.04775153 0.005911688 20 0.2034193 0.2713854
Deviance 44.5623988 1.93322645 0.408444645 20 42.5740058 44.0794655
LP -31.0902147 0.97037005 0.080511201 20 -33.0194587 -30.8456818
UB
beta[1] -8.2355095
beta[2] 0.3807289
Deviance 48.3972203
LP -30.0962034
Another set of specs
Call:
LaplacesDemon(Model = Model, Data = MyData, Initial.Values = Initial.Values,
Thinning = 100, Algorithm = "HMC", Specs = list(epsilon = 3 *
c(0.1, 0.001), L = 18))
Acceptance Rate: 0.8855
Algorithm: Hamiltonian Monte Carlo
Covariance Matrix: (NOT SHOWN HERE; diagonal shown instead)
beta[1] beta[2]
3.640714435 0.003207219
Covariance (Diagonal) History: (NOT SHOWN HERE)
Deviance Information Criterion (DIC):
All Stationary
Dbar 44.404 44.404
pD 2.051 2.051
DIC 46.455 46.455
Initial Values:
[1] -10 0
Iterations: 10000
Log(Marginal Likelihood): NA
Minutes of run-time: 0.96
Model: (NOT SHOWN HERE)
Monitor: (NOT SHOWN HERE)
Parameters (Number of): 2
Posterior1: (NOT SHOWN HERE)
Posterior2: (NOT SHOWN HERE)
Recommended Burn-In of Thinned Samples: 0
Recommended Burn-In of Un-thinned Samples: 0
Recommended Thinning: 100
Specs: (NOT SHOWN HERE)
Status is displayed every 100 iterations
Summary1: (SHOWN BELOW)
Summary2: (SHOWN BELOW)
Thinned Samples: 100
Thinning: 100
Summary of All Samples
Mean SD MCSE ESS LB Median
beta[1] -11.5949171 1.91103354 0.200246790 100 -15.6570246 -11.5727273
beta[2] 0.2867121 0.04916803 0.005146306 100 0.2097083 0.2865395
Deviance 44.4043210 2.02528350 0.193624072 100 42.4813611 43.7159364
LP -31.0168639 1.01912132 0.097084186 100 -33.7046786 -30.6665144
UB
beta[1] -8.4758710
beta[2] 0.3936658
Deviance 49.8533556
LP -30.0520014
Summary of Stationary Samples
Mean SD MCSE ESS LB Median
beta[1] -11.5949171 1.91103354 0.200246790 100 -15.6570246 -11.5727273
beta[2] 0.2867121 0.04916803 0.005146306 100 0.2097083 0.2865395
Deviance 44.4043210 2.02528350 0.193624072 100 42.4813611 43.7159364
LP -31.0168639 1.01912132 0.097084186 100 -33.7046786 -30.6665144
UB
beta[1] -8.4758710
beta[2] 0.3936658
Deviance 49.8533556
LP -30.0520014
No comments:
Post a Comment