mbnma.update.RdUseful for obtaining deviance contributions or fitted values. Same function used in MBNMAdose and MBNMAtime packages.
mbnma.update(
mbnma,
param = "theta",
armdat = TRUE,
n.iter = mbnma$BUGSoutput$n.iter,
n.thin = mbnma$BUGSoutput$n.thin
)An S3 object of class "mbnma" generated by running
a dose-response MBNMA model
Used to indicate which node to monitor in the model. Can be any parameter in the model code that varies by all arms within all studies. These are some typical parameters that it might be of interest to monitor, provided they are in the original model code:
"theta" for fitted values
"psi" for fitted values on natural scale (e.g. probabilities)
"dev" for deviance contributions
"resdev" for residual deviance contributions
"delta" for within-study relative effects versus the study reference treatment
Include raw arm-level data for each data point (agent, dose, study grouping)
number of total iterations per chain (including burn in; default: 2000)
thinning rate. Must be a positive integer. Set
n.thin > 1 to save memory and computation time if
n.iter is large. Default is max(1, floor(n.chains *
(n.iter-n.burnin) / 1000)) which will only thin if there are at
least 2000 simulations.
A data frame containing the posterior mean of the updates by arm and study, with arm and study identifiers.
For MBNMAdose:
facet indicates the agent identifier in the given arm of a study
fupdose indicates the dose in the given arm of a study
For MBNMAtime:
facet indicates the treatment identifier in the given arm of the study
fupdose indicates the follow-up time at the given observation in the given
arm of the study
# \donttest{
# Using the triptans data
network <- mbnma.network(triptans)
#> Values for `agent` with dose = 0 have been recoded to `Placebo`
#> agent is being recoded to enforce sequential numbering
# Fit a dose-response MBNMA, monitoring "psi" and "resdev"
result <- mbnma.run(network, fun=dloglin(), method="random",
parameters.to.save=c("psi", "resdev"))
#> `likelihood` not given by user - set to `binomial` based on data provided
#> `link` not given by user - set to `logit` based on assigned value for `likelihood`
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 182
#> Unobserved stochastic nodes: 190
#> Total graph size: 4074
#>
#> Initializing model
#>
mbnma.update(result, param="theta") # monitor theta
#> study arm mean facet fupdose groupvar
#> 1 1 1 -1.29550967 1 0.00 1
#> 2 2 1 -2.45255172 1 0.00 2
#> 3 3 1 -2.97579248 1 0.00 3
#> 4 4 1 -1.88862195 1 0.00 4
#> 5 5 1 -2.30960074 1 0.00 5
#> 6 6 1 -2.25495020 1 0.00 6
#> 7 7 1 -2.18086053 1 0.00 7
#> 8 8 1 -2.72694568 1 0.00 8
#> 9 9 1 -3.54924990 1 0.00 9
#> 10 10 1 -2.42621489 1 0.00 10
#> 11 11 1 -1.23081804 1 0.00 11
#> 12 12 1 -1.31776628 1 0.00 12
#> 13 13 1 -1.48350969 1 0.00 13
#> 14 14 1 -1.89135116 1 0.00 14
#> 15 15 1 -1.09387993 1 0.00 15
#> 16 16 1 -1.70301954 1 0.00 16
#> 17 17 1 -0.92180345 1 0.00 17
#> 18 18 1 -0.96357956 1 0.00 18
#> 19 19 1 -0.87720651 1 0.00 19
#> 20 20 1 -2.41076920 1 0.00 20
#> 21 21 1 -2.71829338 1 0.00 21
#> 22 22 1 -3.41968508 1 0.00 22
#> 23 23 1 -2.98596386 1 0.00 23
#> 24 24 1 -2.70954293 1 0.00 24
#> 25 25 1 -1.83696339 1 0.00 25
#> 26 26 1 -1.63435302 1 0.00 26
#> 27 27 1 -2.04724540 1 0.00 27
#> 28 28 1 -2.60229023 1 0.00 28
#> 29 29 1 -2.13946052 1 0.00 29
#> 30 30 1 -2.05653532 1 0.00 30
#> 31 31 1 -2.65105093 1 0.00 31
#> 32 32 1 -2.91331720 1 0.00 32
#> 33 33 1 -1.03433500 1 0.00 33
#> 34 34 1 -0.17240785 5 1.00 34
#> 35 35 1 -2.19116380 1 0.00 35
#> 36 36 1 -0.88240423 1 0.00 36
#> 37 37 1 -0.86989615 1 0.00 37
#> 38 38 1 -1.12360306 1 0.00 38
#> 39 39 1 -2.65688595 1 0.00 39
#> 40 40 1 -1.77048854 1 0.00 40
#> 41 41 1 -1.33889040 1 0.00 41
#> 42 42 1 -1.38029472 1 0.00 42
#> 43 43 1 -1.55001839 1 0.00 43
#> 44 44 1 -1.85533047 1 0.00 44
#> 45 45 1 -0.71001759 3 1.00 45
#> 46 46 1 -0.98023742 3 0.50 46
#> 47 47 1 -3.31603155 1 0.00 47
#> 48 48 1 -3.36453513 1 0.00 48
#> 49 49 1 -3.59953973 1 0.00 49
#> 50 50 1 -2.41418185 1 0.00 50
#> 51 51 1 -1.15156611 3 1.00 51
#> 52 52 1 -1.84319827 1 0.00 52
#> 53 53 1 -3.48273492 1 0.00 53
#> 54 54 1 -2.18232385 1 0.00 54
#> 55 55 1 -1.96147624 1 0.00 55
#> 56 56 1 -2.26315394 1 0.00 56
#> 57 57 1 -2.25937912 1 0.00 57
#> 58 58 1 -2.50062947 1 0.00 58
#> 59 59 1 -2.68368835 1 0.00 59
#> 60 60 1 -0.93963958 1 0.00 60
#> 61 61 1 -2.29257754 1 0.00 61
#> 62 62 1 -2.21448591 1 0.00 62
#> 63 63 1 -1.33326346 1 0.00 63
#> 64 64 1 -1.95474342 1 0.00 64
#> 65 65 1 -2.64799590 1 0.00 65
#> 66 66 1 -2.23033963 1 0.00 66
#> 67 67 1 -2.32775493 1 0.00 67
#> 68 68 1 -2.29687713 1 0.00 68
#> 69 69 1 -1.42079240 1 0.00 69
#> 70 70 1 -1.22537497 1 0.00 70
#> 71 1 2 -0.89033958 3 0.50 1
#> 72 2 2 -0.77760016 2 1.00 2
#> 73 3 2 -1.87197831 2 0.50 3
#> 74 4 2 -0.64938997 3 2.00 4
#> 75 5 2 -1.44125690 2 0.50 5
#> 76 6 2 -1.81470707 6 0.40 6
#> 77 7 2 -0.70870230 3 2.00 7
#> 78 8 2 -2.13230331 8 0.25 8
#> 79 9 2 -3.02136762 6 0.40 9
#> 80 10 2 -1.65683148 3 0.50 10
#> 81 11 2 -0.11525500 3 1.00 11
#> 82 12 2 -0.60537422 2 0.50 12
#> 83 13 2 -0.41658590 3 1.00 13
#> 84 14 2 -0.47309509 3 1.00 14
#> 85 15 2 0.10391857 3 1.00 15
#> 86 16 2 -0.59879017 2 1.00 16
#> 87 17 2 0.01666502 3 1.00 17
#> 88 18 2 -0.09656003 3 1.00 18
#> 89 19 2 0.09446645 3 1.00 19
#> 90 20 2 -0.71809754 2 1.00 20
#> 91 21 2 -0.61231155 2 1.00 21
#> 92 22 2 -2.03540855 4 1.00 22
#> 93 23 2 -0.82824047 2 1.00 23
#> 94 24 2 -1.01255899 2 1.00 24
#> 95 25 2 -0.56644401 5 1.00 25
#> 96 26 2 -0.92559904 5 0.50 26
#> 97 27 2 -0.64073017 6 1.00 27
#> 98 28 2 -1.56124583 2 0.50 28
#> 99 29 2 -1.37956750 7 1.00 29
#> 100 30 2 -0.70240119 8 0.50 30
#> 101 31 2 -0.77971836 6 2.00 31
#> 102 32 2 -1.31653794 3 2.00 32
#> 103 33 2 -0.13421438 3 1.00 33
#> 104 34 2 -0.06288069 6 1.00 34
#> 105 35 2 -1.01074807 6 1.00 35
#> 106 36 2 0.31614698 8 1.00 36
#> 107 37 2 0.39127749 8 1.00 37
#> 108 38 2 -0.30135566 7 2.00 38
#> 109 39 2 -1.46236471 3 1.00 39
#> 110 40 2 -0.73672505 5 1.00 40
#> 111 41 2 -0.32311458 6 1.00 41
#> 112 42 2 -0.30682766 6 1.00 42
#> 113 43 2 -0.83581304 4 1.00 43
#> 114 44 2 -1.09427118 3 1.00 44
#> 115 45 2 -0.46376687 8 1.00 45
#> 116 46 2 -0.68544302 8 0.50 46
#> 117 47 2 -1.88857555 4 1.00 47
#> 118 48 2 -1.93305580 4 1.00 48
#> 119 49 2 -2.33527865 4 1.00 49
#> 120 50 2 -1.05181544 6 1.00 50
#> 121 51 2 -1.48824835 5 1.00 51
#> 122 52 2 -0.74296191 3 1.00 52
#> 123 53 2 -2.89739495 7 1.00 53
#> 124 54 2 -0.25918523 8 1.00 54
#> 125 55 2 -0.22442718 3 2.00 55
#> 126 56 2 -1.27834375 6 1.00 56
#> 127 57 2 -1.08377234 3 2.00 57
#> 128 58 2 -1.28336846 3 1.00 58
#> 129 59 2 -1.11054143 3 2.00 59
#> 130 60 2 0.67756478 8 1.00 60
#> 131 61 2 -0.79322023 6 2.00 61
#> 132 62 2 -0.87456770 8 1.00 62
#> 133 63 2 -0.24882424 5 1.00 63
#> 134 64 2 -0.55755189 8 1.00 64
#> 135 65 2 -0.72305460 8 1.00 65
#> 136 66 2 -0.70743226 2 1.00 66
#> 137 67 2 -1.09897064 3 1.70 67
#> 138 68 2 -1.20472602 3 1.70 68
#> 139 69 2 -0.65586170 3 1.00 69
#> 140 70 2 -0.49057968 5 1.00 70
#> 141 1 3 -0.88249037 3 1.00 1
#> 142 2 3 -0.23118261 2 2.00 2
#> 143 3 3 -1.08935486 2 1.00 3
#> 144 4 3 -0.98703736 5 1.00 4
#> 145 5 3 -1.17374095 2 1.00 5
#> 146 6 3 -1.42405241 6 1.00 6
#> 147 7 3 -1.12508436 8 0.50 7
#> 148 8 3 -1.41446241 8 0.50 8
#> 149 9 3 -1.96398023 6 2.00 9
#> 150 10 3 -1.55600246 3 1.00 10
#> 151 11 3 0.27972429 3 2.00 11
#> 152 12 3 -0.08828417 2 1.00 12
#> 153 13 3 -0.09349754 3 2.00 13
#> 154 14 3 -0.12508329 3 2.00 14
#> 155 15 3 0.89250192 3 2.00 15
#> 156 16 3 -1.48244095 7 1.00 16
#> 157 17 3 0.46978029 3 2.00 17
#> 158 18 3 0.20963502 3 2.00 18
#> 159 19 3 0.51325542 3 2.00 19
#> 160 20 3 -0.26478082 2 2.00 20
#> 161 21 3 -0.98959814 3 2.00 21
#> 162 22 3 -1.69064163 4 2.00 22
#> 163 23 3 -0.63657813 2 2.00 23
#> 164 24 3 -0.47535952 2 2.00 24
#> 165 25 3 -0.18635595 5 2.00 25
#> 166 26 3 -0.48061917 5 1.00 26
#> 167 27 3 -0.27348624 8 1.00 27
#> 168 28 3 -1.17511092 3 2.00 28
#> 169 29 3 -0.24762671 8 1.00 29
#> 170 30 3 -0.30994137 8 1.00 30
#> 171 31 3 -0.52195830 6 4.00 31
#> 172 32 3 -1.13102532 8 1.00 32
#> 211 1 4 -0.57533755 3 2.00 1
#> 212 2 4 -1.07530324 6 1.00 2
#> 213 3 4 -0.92563325 2 2.00 3
#> 214 4 4 -0.60747838 5 2.00 4
#> 215 5 4 -0.61777737 2 2.00 5
#> 216 6 4 -0.96250256 6 2.00 6
#> 217 7 4 -0.41170755 8 1.00 7
#> 218 8 4 -0.97349795 8 1.00 8
#> 219 9 4 -0.20306753 6 10.00 9
#> 220 10 4 -1.08316700 3 2.00 10
mbnma.update(result, param="rhat") # monitor rhat
#> study arm mean facet fupdose groupvar
#> 1 1 1 29.8179509 1 0.00 1
#> 2 2 1 10.8955872 1 0.00 2
#> 3 3 1 14.5078711 1 0.00 3
#> 4 4 1 13.3534329 1 0.00 4
#> 5 5 1 7.4511833 1 0.00 5
#> 6 6 1 4.8202867 1 0.00 6
#> 7 7 1 16.3474180 1 0.00 7
#> 8 8 1 4.2830515 1 0.00 8
#> 9 9 1 0.6320621 1 0.00 9
#> 10 10 1 5.3865474 1 0.00 10
#> 11 11 1 14.4140862 1 0.00 11
#> 12 12 1 41.5662127 1 0.00 12
#> 13 13 1 84.4542253 1 0.00 13
#> 14 14 1 57.1533322 1 0.00 14
#> 15 15 1 25.2624237 1 0.00 15
#> 16 16 1 14.0880857 1 0.00 16
#> 17 17 1 33.8932100 1 0.00 17
#> 18 18 1 32.5412217 1 0.00 18
#> 19 19 1 35.1659034 1 0.00 19
#> 20 20 1 6.4215074 1 0.00 20
#> 21 21 1 24.8163693 1 0.00 21
#> 22 22 1 6.3580867 1 0.00 22
#> 23 23 1 11.6418546 1 0.00 23
#> 24 24 1 6.6450868 1 0.00 24
#> 25 25 1 11.4073133 1 0.00 25
#> 26 26 1 28.5965921 1 0.00 26
#> 27 27 1 16.8065942 1 0.00 27
#> 28 28 1 8.9837701 1 0.00 28
#> 29 29 1 11.6586480 1 0.00 29
#> 30 30 1 20.9422471 1 0.00 30
#> 31 31 1 8.3169760 1 0.00 31
#> 32 32 1 4.5785077 1 0.00 32
#> 33 33 1 5.9162936 1 0.00 33
#> 34 34 1 213.3586392 5 1.00 34
#> 35 35 1 16.3686970 1 0.00 35
#> 36 36 1 52.0201461 1 0.00 36
#> 37 37 1 49.5363122 1 0.00 37
#> 38 38 1 28.0830270 1 0.00 38
#> 39 39 1 16.0958936 1 0.00 39
#> 40 40 1 14.7370194 1 0.00 40
#> 41 41 1 57.0787895 1 0.00 41
#> 42 42 1 25.5570791 1 0.00 42
#> 43 43 1 24.4842411 1 0.00 43
#> 44 44 1 20.8798048 1 0.00 44
#> 45 45 1 181.5517866 3 1.00 45
#> 46 46 1 151.4519582 3 0.50 46
#> 47 47 1 3.9014479 1 0.00 47
#> 48 48 1 13.0476922 1 0.00 48
#> 49 49 1 6.7665176 1 0.00 49
#> 50 50 1 19.7941415 1 0.00 50
#> 51 51 1 140.1002983 3 1.00 51
#> 52 52 1 7.4346056 1 0.00 52
#> 53 53 1 3.4510512 1 0.00 53
#> 54 54 1 8.4933151 1 0.00 54
#> 55 55 1 5.2339042 1 0.00 55
#> 56 56 1 8.8699981 1 0.00 56
#> 57 57 1 7.9678647 1 0.00 57
#> 58 58 1 10.4000926 1 0.00 58
#> 59 59 1 5.0155439 1 0.00 59
#> 60 60 1 27.0943844 1 0.00 60
#> 61 61 1 32.4004905 1 0.00 61
#> 62 62 1 4.0645737 1 0.00 62
#> 63 63 1 43.0992781 1 0.00 63
#> 64 64 1 14.5183970 1 0.00 64
#> 65 65 1 3.4780414 1 0.00 65
#> 66 66 1 7.0120143 1 0.00 66
#> 67 67 1 32.3185451 1 0.00 67
#> 68 68 1 35.4766359 1 0.00 68
#> 69 69 1 20.8831736 1 0.00 69
#> 70 70 1 35.4803484 1 0.00 70
#> 71 1 2 43.7907987 3 0.50 1
#> 72 2 2 113.2062092 2 1.00 2
#> 73 3 2 39.0123330 2 0.50 3
#> 74 4 2 66.4697106 3 2.00 4
#> 75 5 2 15.5797757 2 0.50 5
#> 76 6 2 6.7597696 6 0.40 6
#> 77 7 2 127.8817109 3 2.00 7
#> 78 8 2 8.1477053 8 0.25 8
#> 79 9 2 1.1230763 6 0.40 9
#> 80 10 2 10.7716465 3 0.50 10
#> 81 11 2 46.2474686 3 1.00 11
#> 82 12 2 61.2516215 2 0.50 12
#> 83 13 2 178.1039969 3 1.00 13
#> 84 14 2 174.3375891 3 1.00 14
#> 85 15 2 57.8591875 3 1.00 15
#> 86 16 2 65.3531338 2 1.00 16
#> 87 17 2 58.4773954 3 1.00 17
#> 88 18 2 58.0470758 3 1.00 18
#> 89 19 2 58.1191966 3 1.00 19
#> 90 20 2 50.7238616 2 1.00 20
#> 91 21 2 273.8762782 2 1.00 21
#> 92 22 2 24.2637731 4 1.00 22
#> 93 23 2 131.0391984 2 1.00 23
#> 94 24 2 55.0973305 2 1.00 24
#> 95 25 2 59.5344780 5 1.00 25
#> 96 26 2 102.4433130 5 0.50 26
#> 97 27 2 99.8041814 6 1.00 27
#> 98 28 2 22.6083398 2 0.50 28
#> 99 29 2 43.1284946 7 1.00 29
#> 100 30 2 60.4470259 8 0.50 30
#> 101 31 2 77.1882018 6 2.00 31
#> 102 32 2 15.4138787 3 2.00 32
#> 103 33 2 14.0161494 3 1.00 33
#> 104 34 2 228.5557737 6 1.00 34
#> 105 35 2 46.6455367 6 1.00 35
#> 106 36 2 202.9071994 8 1.00 36
#> 107 37 2 197.4331816 8 1.00 37
#> 108 38 2 48.8239762 7 2.00 38
#> 109 39 2 42.8007912 3 1.00 39
#> 110 40 2 32.3400094 5 1.00 40
#> 111 41 2 115.8348804 6 1.00 41
#> 112 42 2 53.4890094 6 1.00 42
#> 113 43 2 41.4907737 4 1.00 43
#> 114 44 2 34.1236302 3 1.00 44
#> 115 45 2 211.1593126 8 1.00 45
#> 116 46 2 179.8427576 8 0.50 46
#> 117 47 2 27.1723908 4 1.00 47
#> 118 48 2 92.8665532 4 1.00 48
#> 119 49 2 42.2863826 4 1.00 49
#> 120 50 2 57.1999546 6 1.00 50
#> 121 51 2 108.5508985 5 1.00 51
#> 122 52 2 47.6624534 3 1.00 52
#> 123 53 2 5.4445822 7 1.00 53
#> 124 54 2 139.4569549 8 1.00 54
#> 125 55 2 18.7231583 3 2.00 55
#> 126 56 2 39.0332802 6 1.00 56
#> 127 57 2 36.0776584 3 2.00 57
#> 128 58 2 29.7340381 3 1.00 58
#> 129 59 2 30.0477542 3 2.00 59
#> 130 60 2 60.7313985 8 1.00 60
#> 131 61 2 107.6718791 6 2.00 61
#> 132 62 2 12.8002096 8 1.00 62
#> 133 63 2 86.8252261 5 1.00 63
#> 134 64 2 12.4211129 8 1.00 64
#> 135 65 2 17.4490687 8 1.00 65
#> 136 66 2 22.9062165 2 1.00 66
#> 137 67 2 90.3727373 3 1.70 67
#> 138 68 2 83.6339630 3 1.70 68
#> 139 69 2 36.9439895 3 1.00 69
#> 140 70 2 61.5241214 5 1.00 70
#> 141 1 3 41.3967422 3 1.00 1
#> 142 2 3 159.1616374 2 2.00 2
#> 143 3 3 74.8392561 2 1.00 3
#> 144 4 3 49.8957526 5 1.00 4
#> 145 5 3 18.1542058 2 1.00 5
#> 146 6 3 10.6466157 6 1.00 6
#> 147 7 3 40.3897912 8 0.50 7
#> 148 8 3 25.6724638 8 0.50 8
#> 149 9 3 2.7300833 6 2.00 9
#> 150 10 3 10.9633827 3 1.00 10
#> 151 11 3 56.3527680 3 2.00 11
#> 152 12 3 94.1048041 2 1.00 12
#> 153 13 3 220.2590580 3 2.00 13
#> 154 14 3 206.3021376 3 2.00 14
#> 155 15 3 72.9434020 3 2.00 15
#> 156 16 3 35.5976703 7 1.00 16
#> 157 17 3 70.6690338 3 2.00 17
#> 158 18 3 63.4867064 3 2.00 18
#> 159 19 3 66.7896049 3 2.00 19
#> 160 20 3 66.1718945 2 2.00 20
#> 161 21 3 216.7770043 3 2.00 21
#> 162 22 3 31.3657524 4 2.00 22
#> 163 23 3 154.4367518 2 2.00 23
#> 164 24 3 80.2875689 2 2.00 24
#> 165 25 3 73.0804551 5 2.00 25
#> 166 26 3 142.5790848 5 1.00 26
#> 167 27 3 126.1835271 8 1.00 27
#> 168 28 3 27.3490109 3 2.00 28
#> 169 29 3 88.2397247 8 1.00 29
#> 170 30 3 79.4998247 8 1.00 30
#> 171 31 3 92.4574655 6 4.00 31
#> 172 32 3 21.9161866 8 1.00 32
#> 211 1 4 54.8471556 3 2.00 1
#> 212 2 4 95.8377524 6 1.00 2
#> 213 3 4 88.7345362 2 2.00 3
#> 214 4 4 67.4540475 5 2.00 4
#> 215 5 4 26.0050613 2 2.00 5
#> 216 6 4 14.5023009 6 2.00 6
#> 217 7 4 153.5070931 8 1.00 7
#> 218 8 4 39.9603132 8 1.00 8
#> 219 9 4 9.4529526 6 10.00 9
#> 220 10 4 16.8293828 3 2.00 10
mbnma.update(result, param="delta") # monitor delta
#> study arm mean facet fupdose groupvar
#> 1 1 1 0.0000000 1 0.00 1
#> 2 2 1 0.0000000 1 0.00 2
#> 3 3 1 0.0000000 1 0.00 3
#> 4 4 1 0.0000000 1 0.00 4
#> 5 5 1 0.0000000 1 0.00 5
#> 6 6 1 0.0000000 1 0.00 6
#> 7 7 1 0.0000000 1 0.00 7
#> 8 8 1 0.0000000 1 0.00 8
#> 9 9 1 0.0000000 1 0.00 9
#> 10 10 1 0.0000000 1 0.00 10
#> 11 11 1 0.0000000 1 0.00 11
#> 12 12 1 0.0000000 1 0.00 12
#> 13 13 1 0.0000000 1 0.00 13
#> 14 14 1 0.0000000 1 0.00 14
#> 15 15 1 0.0000000 1 0.00 15
#> 16 16 1 0.0000000 1 0.00 16
#> 17 17 1 0.0000000 1 0.00 17
#> 18 18 1 0.0000000 1 0.00 18
#> 19 19 1 0.0000000 1 0.00 19
#> 20 20 1 0.0000000 1 0.00 20
#> 21 21 1 0.0000000 1 0.00 21
#> 22 22 1 0.0000000 1 0.00 22
#> 23 23 1 0.0000000 1 0.00 23
#> 24 24 1 0.0000000 1 0.00 24
#> 25 25 1 0.0000000 1 0.00 25
#> 26 26 1 0.0000000 1 0.00 26
#> 27 27 1 0.0000000 1 0.00 27
#> 28 28 1 0.0000000 1 0.00 28
#> 29 29 1 0.0000000 1 0.00 29
#> 30 30 1 0.0000000 1 0.00 30
#> 31 31 1 0.0000000 1 0.00 31
#> 32 32 1 0.0000000 1 0.00 32
#> 33 33 1 0.0000000 1 0.00 33
#> 34 34 1 0.0000000 5 1.00 34
#> 35 35 1 0.0000000 1 0.00 35
#> 36 36 1 0.0000000 1 0.00 36
#> 37 37 1 0.0000000 1 0.00 37
#> 38 38 1 0.0000000 1 0.00 38
#> 39 39 1 0.0000000 1 0.00 39
#> 40 40 1 0.0000000 1 0.00 40
#> 41 41 1 0.0000000 1 0.00 41
#> 42 42 1 0.0000000 1 0.00 42
#> 43 43 1 0.0000000 1 0.00 43
#> 44 44 1 0.0000000 1 0.00 44
#> 45 45 1 0.0000000 3 1.00 45
#> 46 46 1 0.0000000 3 0.50 46
#> 47 47 1 0.0000000 1 0.00 47
#> 48 48 1 0.0000000 1 0.00 48
#> 49 49 1 0.0000000 1 0.00 49
#> 50 50 1 0.0000000 1 0.00 50
#> 51 51 1 0.0000000 3 1.00 51
#> 52 52 1 0.0000000 1 0.00 52
#> 53 53 1 0.0000000 1 0.00 53
#> 54 54 1 0.0000000 1 0.00 54
#> 55 55 1 0.0000000 1 0.00 55
#> 56 56 1 0.0000000 1 0.00 56
#> 57 57 1 0.0000000 1 0.00 57
#> 58 58 1 0.0000000 1 0.00 58
#> 59 59 1 0.0000000 1 0.00 59
#> 60 60 1 0.0000000 1 0.00 60
#> 61 61 1 0.0000000 1 0.00 61
#> 62 62 1 0.0000000 1 0.00 62
#> 63 63 1 0.0000000 1 0.00 63
#> 64 64 1 0.0000000 1 0.00 64
#> 65 65 1 0.0000000 1 0.00 65
#> 66 66 1 0.0000000 1 0.00 66
#> 67 67 1 0.0000000 1 0.00 67
#> 68 68 1 0.0000000 1 0.00 68
#> 69 69 1 0.0000000 1 0.00 69
#> 70 70 1 0.0000000 1 0.00 70
#> 71 1 2 0.4017732 3 0.50 1
#> 72 2 2 1.6890404 2 1.00 2
#> 73 3 2 1.0906071 2 0.50 3
#> 74 4 2 1.2278494 3 2.00 4
#> 75 5 2 0.8580290 2 0.50 5
#> 76 6 2 0.4308506 6 0.40 6
#> 77 7 2 1.4608134 3 2.00 7
#> 78 8 2 0.5933681 8 0.25 8
#> 79 9 2 0.5286465 6 0.40 9
#> 80 10 2 0.7775674 3 0.50 10
#> 81 11 2 1.1046952 3 1.00 11
#> 82 12 2 0.7088803 2 0.50 12
#> 83 13 2 1.0631462 3 1.00 13
#> 84 14 2 1.4202305 3 1.00 14
#> 85 15 2 1.2031742 3 1.00 15
#> 86 16 2 1.0897548 2 1.00 16
#> 87 17 2 0.9277361 3 1.00 17
#> 88 18 2 0.8674647 3 1.00 18
#> 89 19 2 0.9738623 3 1.00 19
#> 90 20 2 1.6885179 2 1.00 20
#> 91 21 2 2.1176761 2 1.00 21
#> 92 22 2 1.3629071 4 1.00 22
#> 93 23 2 2.1424745 2 1.00 23
#> 94 24 2 1.6842561 2 1.00 24
#> 95 25 2 1.2536135 5 1.00 25
#> 96 26 2 0.7058975 5 0.50 26
#> 97 27 2 1.4134120 6 1.00 27
#> 98 28 2 1.0413791 2 0.50 28
#> 99 29 2 0.7368687 7 1.00 29
#> 100 30 2 1.3616521 8 0.50 30
#> 101 31 2 1.8583543 6 2.00 31
#> 102 32 2 1.5902823 3 2.00 32
#> 103 33 2 0.8943476 3 1.00 33
#> 104 34 2 0.1126266 6 1.00 34
#> 105 35 2 1.1836685 6 1.00 35
#> 106 36 2 1.2028045 8 1.00 36
#> 107 37 2 1.2614431 8 1.00 37
#> 108 38 2 0.8159434 7 2.00 38
#> 109 39 2 1.1913419 3 1.00 39
#> 110 40 2 1.0293052 5 1.00 40
#> 111 41 2 1.0141664 6 1.00 41
#> 112 42 2 1.0698367 6 1.00 42
#> 113 43 2 0.7069731 4 1.00 43
#> 114 44 2 0.7588304 3 1.00 44
#> 115 45 2 0.2471247 8 1.00 45
#> 116 46 2 0.2973796 8 0.50 46
#> 117 47 2 1.4204539 4 1.00 47
#> 118 48 2 1.4386975 4 1.00 48
#> 119 49 2 1.2601202 4 1.00 49
#> 120 50 2 1.3686080 6 1.00 50
#> 121 51 2 -0.3426470 5 1.00 51
#> 122 52 2 1.1007025 3 1.00 52
#> 123 53 2 0.5553130 7 1.00 53
#> 124 54 2 1.9247934 8 1.00 54
#> 125 55 2 1.7405726 3 2.00 55
#> 126 56 2 0.9896359 6 1.00 56
#> 127 57 2 1.1626461 3 2.00 57
#> 128 58 2 1.2161084 3 1.00 58
#> 129 59 2 1.5698968 3 2.00 59
#> 130 60 2 1.6122002 8 1.00 60
#> 131 61 2 1.4957697 6 2.00 61
#> 132 62 2 1.3388208 8 1.00 62
#> 133 63 2 1.0826110 5 1.00 63
#> 134 64 2 1.3934697 8 1.00 64
#> 135 65 2 1.9261777 8 1.00 65
#> 136 66 2 1.5166578 2 1.00 66
#> 137 67 2 1.2293762 3 1.70 67
#> 138 68 2 1.0830661 3 1.70 68
#> 139 69 2 0.7644895 3 1.00 69
#> 140 70 2 0.7317129 5 1.00 70
#> 141 1 3 0.4091234 3 1.00 1
#> 142 2 3 2.2354269 2 2.00 2
#> 143 3 3 1.8736614 2 1.00 3
#> 144 4 3 0.8897185 5 1.00 4
#> 145 5 3 1.1222413 2 1.00 5
#> 146 6 3 0.8157473 6 1.00 6
#> 147 7 3 1.0451625 8 0.50 7
#> 148 8 3 1.3129461 8 0.50 8
#> 149 9 3 1.5874177 6 2.00 9
#> 150 10 3 0.8737465 3 1.00 10
#> 151 11 3 1.4995350 3 2.00 11
#> 152 12 3 1.2241214 2 1.00 12
#> 153 13 3 1.3864531 3 2.00 13
#> 154 14 3 1.7670397 3 2.00 14
#> 155 15 3 1.9915118 3 2.00 15
#> 156 16 3 0.2025959 7 1.00 16
#> 157 17 3 1.3789904 3 2.00 17
#> 158 18 3 1.1747869 3 2.00 18
#> 159 19 3 1.3919891 3 2.00 19
#> 160 20 3 2.1412459 2 2.00 20
#> 161 21 3 1.7403388 3 2.00 21
#> 162 22 3 1.7043867 4 2.00 22
#> 163 23 3 2.3328663 2 2.00 23
#> 164 24 3 2.2219162 2 2.00 24
#> 165 25 3 1.6335191 5 2.00 25
#> 166 26 3 1.1489584 5 1.00 26
#> 167 27 3 1.7834615 8 1.00 27
#> 168 28 3 1.4265463 3 2.00 28
#> 169 29 3 1.8723237 8 1.00 29
#> 170 30 3 1.7518470 8 1.00 30
#> 171 31 3 2.1179743 6 4.00 31
#> 172 32 3 1.7757468 8 1.00 32
#> 211 1 4 0.7167542 3 2.00 1
#> 212 2 4 1.3930508 6 1.00 2
#> 213 3 4 2.0359208 2 2.00 3
#> 214 4 4 1.2688845 5 2.00 4
#> 215 5 4 1.6747718 2 2.00 5
#> 216 6 4 1.2786582 6 2.00 6
#> 217 7 4 1.7580503 8 1.00 7
#> 218 8 4 1.7553229 8 1.00 8
#> 219 9 4 3.3464692 6 10.00 9
#> 220 10 4 1.3468515 3 2.00 10
# }