Plot deviance contributions from an MBNMA model

Usage

devplot(
  mbnma,
  dev.type = "dev",
  plot.type = "box",
  xaxis = "time",
  facet = TRUE,
  n.iter = round(mbnma$BUGSoutput$n.iter/4),
  n.thin = mbnma$BUGSoutput$n.thin,
  ...
)

Arguments

mbnma: An S3 object of class "mbnma" generated by running a time-course MBNMA model
dev.type: Deviances to plot - can be either residual deviances ("resdev") or deviances ("dev", the default)
plot.type: Deviances can be plotted either as scatter points ("scatter" - using geom_point()) or as boxplots ("box", the default)
xaxis: A character object that indicates whether deviance contributions should be plotted by time ("time") or by follow-up count ("fup")
facet: A boolean object that indicates whether or not to facet by treatment
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.
...: Arguments to be sent to ggplot2::ggplot()

Value

Generates a plot of deviance contributions and returns a list containing the plot (as an object of class c("gg", "ggplot")), and a data.frame of posterior mean deviance/residual deviance contributions for each observation.

Details

Deviances should only be plotted for models that have converged successfully. If deviance contributions have not been monitored in mbnma$parameters.to.save then additional iterations will have to be run to get results for these.

Deviance contributions cannot be calculated for models with a multivariate likelihood (i.e. those that account for correlation between observations) because the covariance matrix in these models is treated as unknown (if rho="estimate") and deviance contributions will be correlated.

Examples

# \donttest{
# Make network
alognet <- mb.network(alog_pcfb)
#> Reference treatment is `placebo`
#> Studies reporting change from baseline automatically identified from the data

# Run MBNMA
mbnma <- mb.run(alognet, fun=tpoly(degree=2), intercept=FALSE)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 233
#>    Unobserved stochastic nodes: 38
#>    Total graph size: 4178
#> 
#> Initializing model
#> 

# Plot residual deviance contributions in a scatterplot
devplot(mbnma)
#> `dev` not monitored in mbnma$parameters.to.save.
#> additional iterations will be run in order to obtain results for `dev`


# Plot deviance contributions in boxplots at each follow-up measurement
# Monitor for 500 additional iterations
devplot(mbnma, dev.type="dev", plot.type="box", xaxis="fup", n.iter=500)
#> `dev` not monitored in mbnma$parameters.to.save.
#> additional iterations will be run in order to obtain results for `dev`

# }