Plots of model fit diagnostics

The plot() method for nma_dic objects produced by dic() produces several useful diagnostic plots for checking model fit and model comparison. Further detail on these plots and their interpretation is given by Dias et al. (2011) .

Usage

# S3 method for class 'nma_dic'
plot(
  x,
  y,
  ...,
  type = c("resdev", "leverage"),
  show_uncertainty = TRUE,
  stat = "pointinterval",
  orientation = c("vertical", "horizontal", "x", "y"),
  dic_contours = 1:4
)

Arguments

x

A nma_dic object

y

(Optional) A second nma_dic object, to produce "dev-dev" plots for model comparison.

...

Additional arguments passed on to other methods

type

With a single nma_dic object, whether to show the residual deviance contribution for each data point ("resdev", the default), or a leverage plot ("leverage").

show_uncertainty

Logical, show uncertainty with a ggdist plot stat? Default TRUE. Ignored for type = "leverage".

stat

Character string specifying the ggdist plot stat to use if show_uncertainty = TRUE, default "pointinterval". If y is provided, currently only "pointinterval" is supported.

orientation

Whether the ggdist geom is drawn horizontally ("horizontal") or vertically ("vertical"). Only used for residual deviance plots, default "vertical".

dic_contours

Numeric vector of DIC contribution contours to show on leverage plots, default 1:4.

Value

A ggplot object.

Details

When a single nma_dic object is given, the default plot (type = "resdev") shows the residual deviance contribution for each data point. For a good fitting model, each data point is expected to have a residual deviance equal to its degrees of freedom (typically 1, except for multi-arm trials in contrast format or multinomial outcomes); larger values indicate data points that are fit poorly by the model. A leverage plot can be produced with type = "leverage", plotting the leverages (contributions to \(p_D\)) against the signed square root residual deviance, both standardised by the degrees of freedom. Contours for different DIC contribution cutoffs are also shown; points which lie outside the DIC=3 contour are generally identified as contributing to a model's poor fit.

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.

References

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. https://www.sheffield.ac.uk/nice-dsu.

Examples

## Smoking cessation
# \donttest{
# Run smoking FE NMA example if not already available
if (!exists("smk_fit_FE")) example("example_smk_fe", run.donttest = TRUE)
# }
# \donttest{
# Run smoking RE NMA example if not already available
if (!exists("smk_fit_RE")) example("example_smk_re", run.donttest = TRUE)
# }
# \donttest{
# Compare DIC of FE and RE models
(smk_dic_FE <- dic(smk_fit_FE))
#> Residual deviance: 267.2 (on 50 data points)
#>                pD: 27.1
#>               DIC: 294.2
(smk_dic_RE <- dic(smk_fit_RE))   # substantially better fit
#> Residual deviance: 53.8 (on 50 data points)
#>                pD: 43.7
#>               DIC: 97.5

# 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 = ggplot2::after_stat(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 = ggplot2::after_stat(ifelse(ymin > 1, "darkorange", "black"))) +
  ggplot2::scale_colour_identity()


# Leverage plot for RE model
plot(smk_dic_RE, type = "leverage")


# Check for inconsistency using UME model
# }
# \donttest{
# Run smoking UME NMA example if not already available
if (!exists("smk_fit_RE_UME")) example("example_smk_ume", run.donttest = TRUE)
# }
# \donttest{
# Compare DIC
smk_dic_RE
#> Residual deviance: 53.8 (on 50 data points)
#>                pD: 43.7
#>               DIC: 97.5
(smk_dic_RE_UME <- dic(smk_fit_RE_UME))  # no difference in fit
#> Residual deviance: 53.6 (on 50 data points)
#>                pD: 44.8
#>               DIC: 98.4

# 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)

# }