Plots of model fit diagnostics
## S3 method for class 'nma_dic'
plot(
x,
y,
...,
show_uncertainty = TRUE,
stat = "pointinterval",
orientation = c("vertical", "horizontal", "x", "y")
)x |
A |
y |
(Optional) A second |
... |
Additional arguments passed on to other methods |
show_uncertainty |
Logical, show uncertainty with a |
stat |
Character string specifying the |
orientation |
Whether the |
When a single nma_dic object is given, a plot of the residual
deviance contribution for each data point is produced. For a good fitting
model, each data point is expected to have a residual deviance of 1; larger
values indicate data points that are fit poorly by the model.
When two nma_dic objects are given, a "dev-dev" plot comparing the
residual deviance contributions under each model is produced. Data points
with residual deviance contributions lying on the line of equality are fit
equally well under either model. Data points lying below the line of
equality indicate better fit under the second model (y); conversely, data
points lying above the line of equality indicate better fit under the first
model (x). A common use case is to compare a standard consistency model
(fitted using nma() with consistency = "consistency") with an unrelated
mean effects (UME) inconsistency model (fitted using nma() with
consistency = "ume"), to check for potential inconsistency.
See Dias et al. (2011) for further details.
A ggplot object.
Dias S, Welton NJ, Sutton AJ, Ades AE (2011). “NICE DSU Technical Support Document 2: A generalised linear modelling framework for pair-wise and network meta-analysis of randomised controlled trials.” National Institute for Health and Care Excellence. http://nicedsu.org.uk/.
## Smoking cessation
# Run smoking FE NMA example if not already available
if (!exists("smk_fit_FE")) example("example_smk_fe", run.donttest = TRUE)
# Run smoking RE NMA example if not already available
if (!exists("smk_fit_RE")) example("example_smk_re", run.donttest = TRUE)
# Compare DIC of FE and RE models
(smk_dic_FE <- dic(smk_fit_FE))
(smk_dic_RE <- dic(smk_fit_RE)) # substantially better fit
# Plot residual deviance contributions under RE model
plot(smk_dic_RE)
# Further customisation is possible using ggplot commands
# For example, highlighting data points with residual deviance above a certain threshold
plot(smk_dic_RE) +
ggplot2::aes(colour = ifelse(..y.. > 1.5, "darkorange", "black")) +
ggplot2::scale_colour_identity()
# Or by posterior probability, for example here a central probability of 0.6
# corresponds to a lower tail probability of (1 - 0.6)/2 = 0.2
plot(smk_dic_RE, .width = c(0.6, 0.95)) +
ggplot2::aes(colour = ifelse(..ymin.. > 1, "darkorange", "black")) +
ggplot2::scale_colour_identity()
# Check for inconsistency using UME model
# Run smoking UME NMA example if not already available
if (!exists("smk_fit_RE_UME")) example("example_smk_ume", run.donttest = TRUE)
# Compare DIC
smk_dic_RE
(smk_dic_RE_UME <- dic(smk_fit_RE_UME)) # no difference in fit
# Compare residual deviance contributions with a "dev-dev" plot
plot(smk_dic_RE, smk_dic_RE_UME)
# By default the dev-dev plot can be a little cluttered
# Hiding the credible intervals
plot(smk_dic_RE, smk_dic_RE_UME, show_uncertainty = FALSE)
# Changing transparency
plot(smk_dic_RE, smk_dic_RE_UME, point_alpha = 0.5, interval_alpha = 0.1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.