Dev-dev plot for comparing deviance contributions from two models

Plots the deviances of two model types for comparison. Often used to assess consistency by comparing consistency (NMA or MBNMA) and unrelated mean effects (UME) models (see Pedder et al. (2021) ). Models must be run on the same set of data or the deviance comparisons will not be valid.

devdev(mod1, mod2, dev.type = "resdev", n.iter = 2000, n.thin = 1, ...)

Arguments

mod1

First model for which to plot deviance contributions

mod2

Second model for which to plot deviance contributions

dev.type

STILL IN DEVELOPMENT FOR MBNMAdose! Deviances to plot - can be either residual deviances ("resdev", the default) or deviances ("dev")

n.iter

number of total iterations per chain (including burn in; default: 2000)

n.thin

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.

...

Arguments to be sent to ggplot2::geom_point() or ggplot2::geom_boxplot

Examples

# \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

# Run an poorly fitting linear dose-response
lin <- mbnma.run(network, fun=dpoly(degree=1))
#> `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: 77
#>    Total graph size: 3630
#> 
#> Initializing model
#> 

# Run a better fitting Emax dose-response
emax <- mbnma.run(network, fun=demax())
#> `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: 84
#>    Total graph size: 3684
#> 
#> Initializing model
#> 

# Run a standard NMA with unrelated mean effects (UME)
ume <- nma.run(network, UME=TRUE)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 182
#>    Unobserved stochastic nodes: 323
#>    Total graph size: 3641
#> 
#> Initializing model
#> 
#> Warning: error/missing in parameter d in parameters.to.save, 
#>    Be aware of the output results.

# Compare residual deviance contributions from linear and Emax
devdev(lin, emax) # Suggests model fit is very different
#> `resdev` not monitored in mod1$parameters.to.save.
#> additional iterations will be run in order to obtain results for `resdev`
#> `resdev` not monitored in mod2$parameters.to.save.
#> additional iterations will be run in order to obtain results for `resdev`


# Compare deviance contributions from Emax and UME
devdev(emax, ume) # Suggests model fit is similar
#> `resdev` not monitored in mod1$parameters.to.save.
#> additional iterations will be run in order to obtain results for `resdev`
#> `resdev` not monitored in mod2$parameters.to.save.
#> additional iterations will be run in order to obtain results for `resdev`


# }