Update MBNMA to obtain deviance contributions or fitted values
mb.update.RdUpdate MBNMA to obtain deviance contributions or fitted values
Usage
mb.update(
mbnma,
param = "theta",
n.iter = mbnma$BUGSoutput$n.iter,
n.thin = mbnma$BUGSoutput$n.thin
)Arguments
- mbnma
An S3 object of class
"mbnma"generated by running a time-course MBNMA model- param
A character object that represents the parameter within the model to monitor when updating. Can currently only be used for monitoring fitted values and deviance contributions and so can take either
"dev"(for deviance contributions),"resdev"(for residual deviance contributions) or"theta"(for fitted values).- n.iter
The number of iterations to update the model whilst monitoring additional parameters (if necessary). Must be a positive integer. Default is the value used in
mbnma.- n.thin
The thinning rate. Must be a positive integer. Default is the value used in
mbnma.
Value
A data frame containing posterior means for the specified param at each observation, arm and study.
Examples
# \donttest{
# Using the alogliptin dataset
network <- mb.network(alog_pcfb)
#> Reference treatment is `placebo`
#> Studies reporting change from baseline automatically identified from the data
# Run Emax model
emax <- mb.run(network, fun=temax())
#> 'et50' parameters must take positive values.
#> Default half-normal prior restricts posterior to positive values.
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 233
#> Unobserved stochastic nodes: 38
#> Total graph size: 4166
#>
#> Initializing model
#>
# Update model for 500 iterations to monitor fitted values
mb.update(emax, param="theta", n.iter=500)
#> study arm fup mean
#> 1 1 1 1 -0.038440984
#> 2 2 1 1 -0.035750606
#> 3 3 1 1 -0.471549428
#> 4 4 1 1 -0.006972121
#> 5 5 1 1 -0.086279714
#> 6 6 1 1 -0.033534680
#> 7 7 1 1 -0.103824620
#> 8 8 1 1 -0.008257415
#> 9 9 1 1 -0.098746079
#> 10 10 1 1 0.054666475
#> 11 11 1 1 -0.045707557
#> 12 12 1 1 0.039463207
#> 13 13 1 1 -0.763912083
#> 14 14 1 1 -0.719835399
#> 15 1 2 1 -0.222695865
#> 16 2 2 1 -0.156092952
#> 17 3 2 1 -0.671028136
#> 18 4 2 1 -0.361359932
#> 19 5 2 1 -0.415645968
#> 20 6 2 1 -0.350390209
#> 21 7 2 1 -0.505180241
#> 22 8 2 1 -0.386367412
#> 23 9 2 1 -0.258559201
#> 24 10 2 1 -0.187818341
#> 25 11 2 1 -0.250782805
#> 26 12 2 1 -0.168316834
#> 27 13 2 1 -0.824779219
#> 28 14 2 1 -0.784629563
#> 29 1 3 1 -0.265322096
#> 30 2 3 1 -0.211795533
#> 31 3 3 1 -0.756092301
#> 32 4 3 1 -0.391121838
#> 33 5 3 1 -0.443904941
#> 34 6 3 1 -0.377029926
#> 35 7 3 1 -0.540342609
#> 36 8 3 1 -0.418297585
#> 37 9 3 1 -0.272749079
#> 38 10 3 1 -0.207885561
#> 39 11 3 1 -0.268653328
#> 40 12 3 1 -0.185438871
#> 43 1 4 1 -0.283680525
#> 44 2 4 1 -0.226880592
#> 45 3 4 1 -0.850929377
#> 57 1 5 1 -0.313454813
#> 58 2 5 1 -0.224586471
#> 71 1 6 1 -0.281831852
#> 85 1 1 2 -0.070068674
#> 86 2 1 2 -0.058551272
#> 87 3 1 2 -0.474966338
#> 88 4 1 2 -0.010006453
#> 89 5 1 2 -0.121592838
#> 90 6 1 2 -0.048136905
#> 91 7 1 2 -0.136741978
#> 92 8 1 2 -0.011429578
#> 93 9 1 2 -0.163603911
#> 94 10 1 2 0.083303195
#> 95 11 1 2 -0.072358824
#> 96 12 1 2 0.062350199
#> 97 13 1 2 -0.799925095
#> 98 14 1 2 -0.738377133
#> 99 1 2 2 -0.360296659
#> 100 2 2 2 -0.267625642
#> 101 3 2 2 -0.632119439
#> 102 4 2 2 -0.507027546
#> 103 5 2 2 -0.593917702
#> 104 6 2 2 -0.507238548
#> 105 7 2 2 -0.678686780
#> 106 8 2 2 -0.531629279
#> 107 9 2 2 -0.431941651
#> 108 10 2 2 -0.292187435
#> 109 11 2 2 -0.401588678
#> 110 12 2 2 -0.270188195
#> 111 13 2 2 -0.862142068
#> 112 14 2 2 -0.803322571
#> 113 1 3 2 -0.416598321
#> 114 2 3 2 -0.349177287
#> 115 3 3 2 -0.711104386
#> 116 4 3 2 -0.547846458
#> 117 5 3 2 -0.633348448
#> 118 6 3 2 -0.545031400
#> 119 7 3 2 -0.724355314
#> 120 8 3 2 -0.574468891
#> 121 9 3 2 -0.455252971
#> 122 10 3 2 -0.322860174
#> 123 11 3 2 -0.429656046
#> 124 12 3 2 -0.297307606
#> 127 1 4 2 -0.444389618
#> 128 2 4 2 -0.373657532
#> 129 3 4 2 -0.845215157
#> 141 1 5 2 -0.500657164
#> 142 2 5 2 -0.382227314
#> 155 1 6 2 -0.534671562
#> 170 2 1 3 -0.086030505
#> 171 3 1 3 -0.477062442
#> 172 4 1 3 -0.011697256
#> 173 5 1 3 -0.140857018
#> 174 6 1 3 -0.056337626
#> 175 7 1 3 -0.153008654
#> 176 8 1 3 -0.013109601
#> 177 9 1 3 -0.243713969
#> 178 10 1 3 0.112972759
#> 179 11 1 3 -0.102198274
#> 180 12 1 3 0.087871802
#> 181 13 1 3 -0.819455728
#> 182 14 1 3 -0.748121296
#> 184 2 2 3 -0.416602939
#> 185 3 2 3 -0.611177759
#> 186 4 2 3 -0.586076455
#> 187 5 2 3 -0.693207177
#> 188 6 2 3 -0.596438647
#> 189 7 2 3 -0.766808599
#> 190 8 2 3 -0.608062176
#> 191 9 2 3 -0.650009420
#> 192 10 2 3 -0.404861950
#> 193 11 2 3 -0.574498705
#> 194 12 2 3 -0.387634471
#> 195 13 2 3 -0.882381876
#> 196 14 2 3 -0.813138449
#> 198 2 3 3 -0.517074639
#> 199 3 3 3 -0.686938046
#> 200 4 3 3 -0.632685975
#> 201 5 3 3 -0.738622931
#> 202 6 3 3 -0.640374382
#> 203 7 3 3 -0.817535093
#> 204 8 3 3 -0.656418585
#> 205 9 3 3 -0.684358937
#> 206 10 3 3 -0.446565112
#> 207 11 3 3 -0.613760462
#> 208 12 3 3 -0.425939630
#> 212 2 4 3 -0.552645509
#> 213 3 4 3 -0.842200738
#> 226 2 5 3 -0.589229867
#> 254 2 1 4 -0.102023801
#> 255 3 1 4 -0.478479752
#> 256 4 1 4 -0.012774561
#> 257 5 1 4 -0.152996370
#> 258 6 1 4 -0.061594648
#> 259 7 1 4 -0.162720935
#> 260 8 1 4 -0.014150243
#> 261 9 1 4 -0.291319435
#> 262 10 1 4 0.128239710
#> 263 11 1 4 -0.118517106
#> 264 12 1 4 0.101784697
#> 265 13 1 4 -0.831718125
#> 266 14 1 4 -0.754130138
#> 268 2 2 4 -0.511675180
#> 269 3 2 4 -0.598080604
#> 270 4 2 4 -0.635759432
#> 271 5 2 4 -0.756522849
#> 272 6 2 4 -0.654038557
#> 273 7 2 4 -0.820177962
#> 274 8 2 4 -0.655254730
#> 275 9 2 4 -0.781655221
#> 276 10 2 4 -0.464703025
#> 277 11 2 4 -0.670903413
#> 278 12 2 4 -0.453427839
#> 279 13 2 4 -0.895081374
#> 280 14 2 4 -0.819188526
#> 282 2 3 4 -0.615947676
#> 283 3 3 4 -0.671840601
#> 284 4 3 4 -0.685934963
#> 285 5 3 4 -0.805668194
#> 286 6 3 4 -0.701866132
#> 287 7 3 4 -0.873880740
#> 288 8 3 4 -0.706943215
#> 289 9 3 4 -0.822440559
#> 290 10 3 4 -0.512092740
#> 291 11 3 4 -0.716178854
#> 292 12 3 4 -0.497840216
#> 296 2 4 4 -0.657856461
#> 297 3 4 4 -0.840356134
#> 310 2 5 4 -0.719259525
#> 339 3 1 5 -0.479502117
#> 340 4 1 5 -0.013520912
#> 341 5 1 5 -0.161349116
#> 342 6 1 5 -0.065253192
#> 343 7 1 5 -0.169178978
#> 344 8 1 5 -0.015578123
#> 349 13 1 5 -0.840134610
#> 350 14 1 5 -0.758206422
#> 353 3 2 5 -0.589112493
#> 354 4 2 5 -0.669892201
#> 355 5 2 5 -0.800427354
#> 356 6 2 5 -0.694317088
#> 357 7 2 5 -0.855978691
#> 358 8 2 5 -0.719835516
#> 363 13 2 5 -0.903794244
#> 364 14 2 5 -0.823291408
#> 367 3 3 5 -0.661510029
#> 368 4 3 5 -0.722485266
#> 369 5 3 5 -0.852119548
#> 370 6 3 5 -0.744831817
#> 371 7 3 5 -0.911642225
#> 372 8 3 5 -0.775994392
#> 381 3 4 5 -0.839117404
#> 423 3 1 6 -0.480274460
#> 424 4 1 6 -0.014291002
#> 425 5 1 6 -0.169920702
#> 426 6 1 6 -0.069043432
#> 427 7 1 6 -0.175625272
#> 433 13 1 6 -0.846269769
#> 434 14 1 6 -0.761153440
#> 437 3 2 6 -0.582585661
#> 438 4 2 6 -0.704878057
#> 439 5 2 6 -0.845771147
#> 440 6 2 6 -0.736212331
#> 441 7 2 6 -0.891964737
#> 447 13 2 6 -0.910143598
#> 448 14 2 6 -0.826256937
#> 451 3 3 6 -0.653995283
#> 452 4 3 6 -0.759922019
#> 453 5 3 6 -0.900060140
#> 454 6 3 6 -0.789492431
#> 455 7 3 6 -0.949570825
#> 465 3 4 6 -0.838230810
#> 507 3 1 7 -0.480878517
#> 517 13 1 7 -0.850940662
#> 518 14 1 7 -0.763383416
#> 521 3 2 7 -0.577622272
#> 531 13 2 7 -0.914976519
#> 532 14 2 7 -0.828500511
#> 535 3 3 7 -0.648282680
#> 549 3 4 7 -0.837566129
#> 591 3 1 8 -0.481363893
#> 601 13 1 8 -0.854615713
#> 602 14 1 8 -0.765129684
#> 605 3 2 8 -0.573720457
#> 615 13 2 8 -0.918778419
#> 616 14 2 8 -0.830257177
#> 619 3 3 8 -0.643793135
#> 633 3 4 8 -0.837049947
#> 685 13 1 9 -0.857582767
#> 699 13 2 9 -0.921847476
# Update model for 500 iterations to monitor residual deviance contributions
mb.update(emax, param="resdev", n.iter=500)
#> study arm fup mean
#> 1 1 1 1 0.67567970
#> 2 2 1 1 3.03057487
#> 3 3 1 1 1.24503777
#> 4 4 1 1 4.19207492
#> 5 5 1 1 1.70607179
#> 6 6 1 1 2.32627489
#> 7 7 1 1 12.13613538
#> 8 8 1 1 16.46603634
#> 9 9 1 1 11.91449138
#> 10 10 1 1 0.93433257
#> 11 11 1 1 3.02900049
#> 12 12 1 1 1.48323545
#> 13 13 1 1 3.04397612
#> 14 14 1 1 2.48295064
#> 15 1 2 1 1.29045052
#> 16 2 2 1 0.22705650
#> 17 3 2 1 0.52123778
#> 18 4 2 1 0.34907850
#> 19 5 2 1 0.61491293
#> 20 6 2 1 0.40030609
#> 21 7 2 1 0.99297139
#> 22 8 2 1 0.64468126
#> 23 9 2 1 7.39988304
#> 24 10 2 1 8.30239206
#> 25 11 2 1 13.18876250
#> 26 12 2 1 3.61343553
#> 27 13 2 1 1.19067436
#> 28 14 2 1 3.41030720
#> 29 1 3 1 0.85180517
#> 30 2 3 1 4.06340139
#> 31 3 3 1 0.47286441
#> 32 4 3 1 2.95600741
#> 33 5 3 1 0.38724864
#> 34 6 3 1 0.97371832
#> 35 7 3 1 1.20100481
#> 36 8 3 1 2.77629585
#> 37 9 3 1 12.81161392
#> 38 10 3 1 15.09497707
#> 39 11 3 1 9.12586351
#> 40 12 3 1 13.09555139
#> 43 1 4 1 0.77997172
#> 44 2 4 1 10.12414885
#> 45 3 4 1 0.47544479
#> 57 1 5 1 0.22106682
#> 58 2 5 1 13.67432146
#> 71 1 6 1 0.40662804
#> 85 1 1 2 0.65635772
#> 86 2 1 2 1.79970930
#> 87 3 1 2 1.44337681
#> 88 4 1 2 3.01619706
#> 89 5 1 2 1.00039353
#> 90 6 1 2 7.82173224
#> 91 7 1 2 4.91373951
#> 92 8 1 2 8.60358456
#> 93 9 1 2 5.49064592
#> 94 10 1 2 0.33086466
#> 95 11 1 2 2.34681970
#> 96 12 1 2 1.22043263
#> 97 13 1 2 2.50300924
#> 98 14 1 2 3.93379385
#> 99 1 2 2 2.08584869
#> 100 2 2 2 7.05828874
#> 101 3 2 2 3.67948017
#> 102 4 2 2 0.32693155
#> 103 5 2 2 0.13307653
#> 104 6 2 2 4.29785786
#> 105 7 2 2 1.93649482
#> 106 8 2 2 1.08876429
#> 107 9 2 2 3.09729208
#> 108 10 2 2 0.71247476
#> 109 11 2 2 2.14477896
#> 110 12 2 2 0.59856433
#> 111 13 2 2 3.99653987
#> 112 14 2 2 3.55752017
#> 113 1 3 2 1.13572408
#> 114 2 3 2 0.93114258
#> 115 3 3 2 0.87933816
#> 116 4 3 2 3.48834692
#> 117 5 3 2 5.41878239
#> 118 6 3 2 1.36310841
#> 119 7 3 2 3.61826441
#> 120 8 3 2 3.85555314
#> 121 9 3 2 0.21835831
#> 122 10 3 2 1.36379165
#> 123 11 3 2 0.15012895
#> 124 12 3 2 0.39633699
#> 127 1 4 2 1.09123562
#> 128 2 4 2 0.67570741
#> 129 3 4 2 0.99316501
#> 141 1 5 2 0.45859931
#> 142 2 5 2 0.52739968
#> 155 1 6 2 0.71343144
#> 170 2 1 3 2.76326680
#> 171 3 1 3 1.30453451
#> 172 4 1 3 2.45902322
#> 173 5 1 3 1.78112247
#> 174 6 1 3 2.46135121
#> 175 7 1 3 2.70145397
#> 176 8 1 3 5.23622208
#> 177 9 1 3 3.70892519
#> 178 10 1 3 1.90275069
#> 179 11 1 3 4.06897539
#> 180 12 1 3 1.12266725
#> 181 13 1 3 1.77946121
#> 182 14 1 3 6.65109917
#> 184 2 2 3 2.96427971
#> 185 3 2 3 1.45421587
#> 186 4 2 3 0.16819673
#> 187 5 2 3 0.10004729
#> 188 6 2 3 1.95309470
#> 189 7 2 3 1.09682942
#> 190 8 2 3 0.44387240
#> 191 9 2 3 10.88164673
#> 192 10 2 3 5.77640176
#> 193 11 2 3 11.48354949
#> 194 12 2 3 4.08181429
#> 195 13 2 3 1.51429360
#> 196 14 2 3 5.63969452
#> 198 2 3 3 3.15772670
#> 199 3 3 3 0.09714324
#> 200 4 3 3 0.32548247
#> 201 5 3 3 2.93422170
#> 202 6 3 3 0.27343067
#> 203 7 3 3 0.08088156
#> 204 8 3 3 0.61002122
#> 205 9 3 3 11.43966192
#> 206 10 3 3 8.37830553
#> 207 11 3 3 9.65831254
#> 208 12 3 3 10.60900882
#> 212 2 4 3 4.92077525
#> 213 3 4 3 0.46425042
#> 226 2 5 3 5.62881369
#> 254 2 1 4 9.80681329
#> 255 3 1 4 0.11527876
#> 256 4 1 4 1.81227984
#> 257 5 1 4 2.08665351
#> 258 6 1 4 0.98979272
#> 259 7 1 4 0.69864676
#> 260 8 1 4 4.04691041
#> 261 9 1 4 4.28667871
#> 262 10 1 4 12.65129336
#> 263 11 1 4 8.81012625
#> 264 12 1 4 3.12060674
#> 265 13 1 4 0.82099741
#> 266 14 1 4 3.15113770
#> 268 2 2 4 0.07055504
#> 269 3 2 4 0.10150734
#> 270 4 2 4 0.69239043
#> 271 5 2 4 1.23404347
#> 272 6 2 4 0.09725858
#> 273 7 2 4 0.14137108
#> 274 8 2 4 6.44998039
#> 275 9 2 4 9.52033035
#> 276 10 2 4 6.06037699
#> 277 11 2 4 21.13166966
#> 278 12 2 4 2.22862485
#> 279 13 2 4 0.07204732
#> 280 14 2 4 3.17678383
#> 282 2 3 4 1.85967627
#> 283 3 3 4 0.09958413
#> 284 4 3 4 0.45914131
#> 285 5 3 4 0.48330552
#> 286 6 3 4 1.55806129
#> 287 7 3 4 2.31893051
#> 288 8 3 4 0.97835227
#> 289 9 3 4 9.17212669
#> 290 10 3 4 7.19015848
#> 291 11 3 4 13.01629618
#> 292 12 3 4 8.04008018
#> 296 2 4 4 2.74087281
#> 297 3 4 4 0.15560682
#> 310 2 5 4 3.69810185
#> 339 3 1 5 1.16068907
#> 340 4 1 5 1.61135789
#> 341 5 1 5 2.05606576
#> 342 6 1 5 0.66553126
#> 343 7 1 5 0.11666594
#> 344 8 1 5 0.21726757
#> 349 13 1 5 0.06834204
#> 350 14 1 5 0.30681884
#> 353 3 2 5 0.21021984
#> 354 4 2 5 2.14954492
#> 355 5 2 5 4.91887473
#> 356 6 2 5 1.68766375
#> 357 7 2 5 1.66405658
#> 358 8 2 5 34.83227125
#> 363 13 2 5 0.18510289
#> 364 14 2 5 0.09402899
#> 367 3 3 5 0.08008541
#> 368 4 3 5 3.37990450
#> 369 5 3 5 0.45212586
#> 370 6 3 5 5.01647094
#> 371 7 3 5 4.93148168
#> 372 8 3 5 20.00322777
#> 381 3 4 5 0.75398248
#> 423 3 1 6 2.42243273
#> 424 4 1 6 0.12344516
#> 425 5 1 6 0.17440949
#> 426 6 1 6 0.28340830
#> 427 7 1 6 0.46318729
#> 433 13 1 6 0.28414580
#> 434 14 1 6 0.12485784
#> 437 3 2 6 0.12974136
#> 438 4 2 6 4.98354113
#> 439 5 2 6 11.16950650
#> 440 6 2 6 5.82190635
#> 441 7 2 6 11.94706273
#> 447 13 2 6 0.16320809
#> 448 14 2 6 1.99134823
#> 451 3 3 6 0.36911410
#> 452 4 3 6 6.71137654
#> 453 5 3 6 3.38350402
#> 454 6 3 6 13.91482437
#> 455 7 3 6 9.55789609
#> 465 3 4 6 1.06223048
#> 507 3 1 7 3.70568169
#> 517 13 1 7 0.49912123
#> 518 14 1 7 0.80555998
#> 521 3 2 7 0.42725393
#> 531 13 2 7 0.81139002
#> 532 14 2 7 4.52329994
#> 535 3 3 7 0.10807506
#> 549 3 4 7 0.83158109
#> 591 3 1 8 4.10215597
#> 601 13 1 8 0.25282726
#> 602 14 1 8 3.96164608
#> 605 3 2 8 0.94762048
#> 615 13 2 8 0.14665875
#> 616 14 2 8 8.30657178
#> 619 3 3 8 0.13914418
#> 633 3 4 8 0.82419636
#> 685 13 1 9 0.92237727
#> 699 13 2 9 0.08785731
# Update model for 500 iterations to monitor deviance contributions
mb.update(emax, param="dev", n.iter=500)
#> study arm fup mean
#> 1 1 1 1 -2.2160827394
#> 2 2 1 1 -2.9104032916
#> 3 3 1 1 -2.1107163967
#> 4 4 1 1 0.0292964311
#> 5 5 1 1 -2.7475141360
#> 6 6 1 1 -1.9398360174
#> 7 7 1 1 7.6772580471
#> 8 8 1 1 12.0618090240
#> 9 9 1 1 5.5019206235
#> 10 10 1 1 -4.2588047943
#> 11 11 1 1 -2.9004182658
#> 12 12 1 1 -4.6202126252
#> 13 13 1 1 -1.6351270508
#> 14 14 1 1 -2.9505230109
#> 15 1 2 1 -1.5393654178
#> 16 2 2 1 -6.0194538353
#> 17 3 2 1 -3.4340889627
#> 18 4 2 1 -4.5217687627
#> 19 5 2 1 -4.6106693129
#> 20 6 2 1 -4.7262075046
#> 21 7 2 1 -3.4458761164
#> 22 8 2 1 -4.6682037087
#> 23 9 2 1 0.7795240379
#> 24 10 2 1 2.6033995532
#> 25 11 2 1 6.7597775089
#> 26 12 2 1 -2.3607479766
#> 27 13 2 1 -3.4688975162
#> 28 14 2 1 -1.6789649098
#> 29 1 3 1 -2.0715427319
#> 30 2 3 1 -1.7554946888
#> 31 3 3 1 -3.5060302666
#> 32 4 3 1 -1.8939381848
#> 33 5 3 1 -4.8125172683
#> 34 6 3 1 -4.2434155890
#> 35 7 3 1 -3.1799580862
#> 36 8 3 1 -2.5822438785
#> 37 9 3 1 6.7353327520
#> 38 10 3 1 9.2951658269
#> 39 11 3 1 3.3954997100
#> 40 12 3 1 6.6956905035
#> 43 1 4 1 -2.2059999410
#> 44 2 4 1 4.5558407900
#> 45 3 4 1 -3.5228720783
#> 57 1 5 1 -2.5955445010
#> 58 2 5 1 7.5536463072
#> 71 1 6 1 -2.4854368498
#> 85 1 1 2 -1.5464774042
#> 86 2 1 2 -2.9416672254
#> 87 3 1 2 -1.7053184526
#> 88 4 1 2 -0.4798768222
#> 89 5 1 2 -2.7124631569
#> 90 6 1 2 4.2606284426
#> 91 7 1 2 1.0757038305
#> 92 8 1 2 4.7779866089
#> 93 9 1 2 0.0938750025
#> 94 10 1 2 -4.5555286192
#> 95 11 1 2 -2.5475783740
#> 96 12 1 2 -4.0257230316
#> 97 13 1 2 -1.3181768861
#> 98 14 1 2 -0.7773271496
#> 99 1 2 2 -0.3162836281
#> 100 2 2 2 1.8750855797
#> 101 3 2 2 -0.0390061909
#> 102 4 2 2 -3.8703505081
#> 103 5 2 2 -4.3816138275
#> 104 6 2 2 -0.3847130490
#> 105 7 2 2 -1.8261771616
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#> 685 13 1 9 -2.0770379853
#> 699 13 2 9 -3.2105981352
# }