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Produce deviance plots using the output of gemtc::mtc.deviance() for Bayesian and covariate models. Because these plots are interactive, it is not currently possible to download them, although they can be included in html reports.

Usage

bayes_deviance(model, n_adapt = 5000, n_iter = 20000, async = FALSE)

covariate_deviance(...)

Arguments

model

Bayesian model produced by bayes_model() or covariate_model()

n_adapt

numeric. Number of adaptation iterations. Defaults to 5000 and can normally be left unchanged

n_iter

numeric. Number of simulation iterations. Defaults to 20000 and can normally be left unchanged

async

Whether or not the function is being used asynchronously. Default FALSE

...

Parameters passed to bayes_deviance()

Value

A list containing different elements depending on the input model:

When model was created by bayes_model() containing:

deviance_mtc

results from gemtc::mtc.deviance() for model$mtcResults

deviance_ume

results from gemtc::mtc.deviance() for UME model

scat_plot

plotly object

stem_plot

plotly object

lev_plot

plotly object

When model was created by covariate_model() containing:

deviance_mtc

results from gemtc::mtc.deviance() for model$mtcResults

stem_plot

plotly object

lev_plot

plotly object

Examples

configured_data_path <- system.file("extdata", "configured_data.Rds", package = "metainsight")
configured_data <- readRDS(configured_data_path)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

fitted_bayes_model <- bayes_model(configured_data = configured_data,
                                  n_adapt = 100,
                                  n_iter = 100)

bayes_deviance(model = fitted_bayes_model,
               n_adapt = 100,
               n_iter = 100)
#> $deviance_mtc
#> $Dbar
#> [1] 10.26853
#> 
#> $pD
#> [1] 9.915554
#> 
#> $DIC
#> [1] 20.18408
#> 
#> $`data points`
#> [1] 10
#> 
#> $dev.ab
#>                  [,1]      [,2]
#> Constantine 1.0289917 1.0731834
#> Justinian   0.8952413 1.0040952
#> Leo         1.0361644 0.9935584
#> Minerva     1.1031708 1.0751766
#> Nero        1.0667055 0.9922414
#> 
#> $dev.re
#> NULL
#> 
#> $fit.ab
#>                    [,1]        [,2]
#> Constantine 0.012273095 0.031399703
#> Justinian   0.007264789 0.005550462
#> Leo         0.174964730 0.083820254
#> Minerva     0.023230402 0.006405278
#> Nero        0.001458993 0.006606685
#> 
#> $fit.re
#> NULL
#> 
#> $lev.ab
#>                  [,1]      [,2]
#> Constantine 1.0167186 1.0417837
#> Justinian   0.8879765 0.9985447
#> Leo         0.8611997 0.9097382
#> Minerva     1.0799404 1.0687713
#> Nero        1.0652465 0.9856347
#> 
#> $lev.re
#> NULL
#> 
#> $nd.ab
#> Constantine   Justinian         Leo     Minerva        Nero 
#>           2           2           2           2           2 
#> 
#> $nd.re
#> NULL
#> 
#> $fitted
#> $fitted$theta
#>           [,1]      [,2]
#> [1,]  1.079774 12.231826
#> [2,] 15.485988  3.312417
#> [3,]  2.272815 14.350348
#> [4,]  5.524099 17.687501
#> [5,] 16.593884  4.413186
#> 
#> 
#> attr(,"class")
#> [1] "mtc.deviance"
#> 
#> $deviance_ume
#> $Dbar
#> [1] 10.24259
#> 
#> $pD
#> [1] 9.912225
#> 
#> $DIC
#> [1] 20.15481
#> 
#> $`data points`
#> [1] 10
#> 
#> $dev.ab
#>                  [,1]      [,2]
#> Constantine 0.8892939 0.9756528
#> Justinian   1.0003473 0.9540050
#> Leo         1.0378612 1.1126090
#> Minerva     1.2057239 1.0778474
#> Nero        1.0044067 0.9848403
#> 
#> $dev.re
#> NULL
#> 
#> $fit.ab
#>                    [,1]        [,2]
#> Constantine 0.017871613 0.042603096
#> Justinian   0.012047075 0.002301228
#> Leo         0.120319223 0.088122815
#> Minerva     0.041308614 0.003978194
#> Nero        0.000616155 0.001194353
#> 
#> $fit.re
#> NULL
#> 
#> $lev.ab
#>                  [,1]      [,2]
#> Constantine 0.8714223 0.9330497
#> Justinian   0.9883003 0.9517038
#> Leo         0.9175419 1.0244862
#> Minerva     1.1644153 1.0738692
#> Nero        1.0037906 0.9836460
#> 
#> $lev.re
#> NULL
#> 
#> $nd.ab
#> Constantine   Justinian         Leo     Minerva        Nero 
#>           2           2           2           2           2 
#> 
#> $nd.re
#> NULL
#> 
#> $fitted
#> $fitted$theta
#>           [,1]      [,2]
#> [1,]  1.075593 12.237071
#> [2,] 15.481956  3.307995
#> [3,]  2.260382 14.349090
#> [4,]  5.532136 17.690150
#> [5,] 16.596025  4.405606
#> 
#> 
#> attr(,"class")
#> [1] "mtc.deviance"
#> 
#> $scat_plot
#> 
#> $stem_plot
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> A marker object has been specified, but markers is not in the mode
#> Adding markers to the mode...
#> 
#> $lev_plot
#> No trace type specified:
#>   Based on info supplied, a 'scatter' trace seems appropriate.
#>   Read more about this trace type -> https://plotly.com/r/reference/#scatter
#> No trace type specified:
#>   Based on info supplied, a 'scatter' trace seems appropriate.
#>   Read more about this trace type -> https://plotly.com/r/reference/#scatter
#> No trace type specified:
#>   Based on info supplied, a 'scatter' trace seems appropriate.
#>   Read more about this trace type -> https://plotly.com/r/reference/#scatter
#> No trace type specified:
#>   Based on info supplied, a 'scatter' trace seems appropriate.
#>   Read more about this trace type -> https://plotly.com/r/reference/#scatter
#> No trace type specified:
#>   Based on info supplied, a 'scatter' trace seems appropriate.
#>   Read more about this trace type -> https://plotly.com/r/reference/#scatter
#> No scatter mode specifed:
#>   Setting the mode to markers
#>   Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode
#>