Prints summary of mb.predict object — summary.mb.predict • MBNMAtime

Prints a summary table of the mean of MCMC iterations at each time point for each treatment

Usage

# S3 method for mb.predict
summary(object, ...)

Arguments

object: An object of class "mb.predict"
...: further arguments passed to or from other methods

Value

A matrix containing times at which responses have been predicted (time) and an additional column for each treatment for which responses have been predicted. Each row represents mean MCMC predicted responses for each treatment at a particular time.

Examples

# \donttest{
# Define network
network <- mb.network(obesityBW_CFB, reference="plac")
#> Studies reporting change from baseline automatically identified from the data

# Run an MBNMA with a quadratic time-course function
quad <- mb.run(network,
  fun=tpoly(degree=2, pool.1="rel", method.1="common",
    pool.2="rel", method.2="common"),
  intercept=TRUE)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 648
#>    Unobserved stochastic nodes: 155
#>    Total graph size: 11509
#> 
#> Initializing model
#> 

# Predict responses
pred <- predict(quad, times=c(0:50), treats=c(1:5),
  ref.resp = network$data.ab[network$data.ab$treatment==1,],
  E0=10)
#> Data frame must contain only data from reference treatment
#> Studies reporting change from baseline automatically identified from ref.resp
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 264
#>    Unobserved stochastic nodes: 2
#>    Total graph size: 4193
#> 
#> Initializing model
#> 

# Generate summary of predictions
summary(pred)
#>       time      plac   amfe_75MG dexf_10MG dexf_30MG dexf_60MG
#>  [1,]    0 10.000000 10.00000000 10.000000 10.000000 10.000000
#>  [2,]    1  9.910655  9.52503406  9.858503  9.496888  9.316941
#>  [3,]    2  9.822257  9.06149684  9.728635  9.028670  8.679602
#>  [4,]    3  9.734805  8.60938835  9.610395  8.595346  8.087982
#>  [5,]    4  9.648300  8.16870858  9.503785  8.196916  7.542082
#>  [6,]    5  9.562741  7.73945754  9.408803  7.833380  7.041901
#>  [7,]    6  9.478129  7.32163523  9.325451  7.504739  6.587439
#>  [8,]    7  9.394463  6.91524165  9.253727  7.210991  6.178696
#>  [9,]    8  9.311743  6.52027679  9.193632  6.952138  5.815673
#> [10,]    9  9.229970  6.13674066  9.145166  6.728179  5.498369
#> [11,]   10  9.149144  5.76463325  9.108329  6.539114  5.226785
#> [12,]   11  9.069264  5.40395457  9.083121  6.384943  5.000920
#> [13,]   12  8.990330  5.05470462  9.069542  6.265666  4.820774
#> [14,]   13  8.912343  4.71688339  9.067591  6.181283  4.686348
#> [15,]   14  8.835302  4.39049089  9.077270  6.131794  4.597640
#> [16,]   15  8.759207  4.07552712  9.098577  6.117199  4.554653
#> [17,]   16  8.684059  3.77199207  9.131514  6.137499  4.557384
#> [18,]   17  8.609858  3.47988575  9.176079  6.192693  4.605835
#> [19,]   18  8.536603  3.19920816  9.232273  6.282780  4.700005
#> [20,]   19  8.464294  2.92995929  9.300096  6.407762  4.839895
#> [21,]   20  8.392932  2.67213915  9.379548  6.567638  5.025503
#> [22,]   21  8.322516  2.42574773  9.470629  6.762408  5.256832
#> [23,]   22  8.253047  2.19078504  9.573339  6.992072  5.533879
#> [24,]   23  8.184524  1.96725108  9.687678  7.256630  5.856646
#> [25,]   24  8.116948  1.75514585  9.813645  7.556083  6.225132
#> [26,]   25  8.050318  1.55446934  9.951242  7.890429  6.639338
#> [27,]   26  7.984634  1.36522156 10.100467  8.259670  7.099263
#> [28,]   27  7.919897  1.18740250 10.261321  8.663804  7.604907
#> [29,]   28  7.856107  1.02101217 10.433805  9.102833  8.156270
#> [30,]   29  7.793263  0.86605057 10.617917  9.576756  8.753353
#> [31,]   30  7.731365  0.72251769 10.813658 10.085573  9.396155
#> [32,]   31  7.670414  0.59041354 11.021028 10.629284 10.084677
#> [33,]   32  7.610409  0.46973812 11.240027 11.207889 10.818918
#> [34,]   33  7.551350  0.36049142 11.470654 11.821389 11.598878
#> [35,]   34  7.493239  0.26267345 11.712911 12.469782 12.424557
#> [36,]   35  7.436073  0.17628421 11.966796 13.153069 13.295956
#> [37,]   36  7.379854  0.10132369 12.232311 13.871251 14.213075
#> [38,]   37  7.324581  0.03779190 12.509454 14.624327 15.175912
#> [39,]   38  7.270255 -0.01431117 12.798226 15.412297 16.184469
#> [40,]   39  7.216876 -0.05498550 13.098628 16.235161 17.238745
#> [41,]   40  7.164442 -0.08423112 13.410658 17.092919 18.338741
#> [42,]   41  7.112956 -0.10204800 13.734316 17.985571 19.484456
#> [43,]   42  7.062415 -0.10843616 14.069604 18.913117 20.675890
#> [44,]   43  7.012821 -0.10339559 14.416521 19.875557 21.913043
#> [45,]   44  6.964174 -0.08692630 14.775067 20.872892 23.195916
#> [46,]   45  6.916473 -0.05902828 15.145241 21.905120 24.524508
#> [47,]   46  6.869718 -0.01970153 15.527045 22.972243 25.898820
#> [48,]   47  6.823910  0.03105394 15.920477 24.074260 27.318851
#> [49,]   48  6.779048  0.09323815 16.325538 25.211171 28.784601
#> [50,]   49  6.735133  0.16685107 16.742228 26.382976 30.296071
#> [51,]   50  6.692164  0.25189273 17.170548 27.589675 31.853260
# }