Produce summary plots of node-splitting models
Arguments
- x
A
nodesplit_summaryobject.- ...
Additional arguments passed on to the underlying
ggdistplot stat, see Details.- pars
Character vector specifying the parameters to include in the plot, choices include
"d"for the direct, indirect, and network estimates of relative effects,"omega"for the inconsistency factor, and"tau"for heterogeneity standard deviation in random effects models. Default is"d".- stat
Character string specifying the
ggdistplot stat to use. The default"dens_overlay"is a special case, producing an overlaid density plot.- orientation
Whether the
ggdistgeom is drawn horizontally ("horizontal") or vertically ("vertical"), default"horizontal".- ref_line
Numeric vector of positions for reference lines, by default no reference lines are drawn.
Details
Plotting is handled by ggplot2 and
the stats and geoms provided in the ggdist
package. As a result, the output is very flexible. Any plotting stats
provided by ggdist may be used, via the argument stat. The default
"dens_overlay" is a special exception, which uses
ggplot2::geom_density(), to plot
overlaid densities. Additional arguments in ... are passed to the
ggdist stat, to customise the output.
Alternative stats can be specified to produce different summaries. For
example, specify stat = "[half]eye" to produce (half) eye plots, or stat = "pointinterval" to produce point estimates and credible intervals.
A full list of options and examples is found in the ggdist vignette
vignette("slabinterval", package = "ggdist").
A ggplot object is returned which can be further modified through the
usual ggplot2 functions to add further
aesthetics, geoms, themes, etc.
Examples
# \donttest{
# Run smoking node-splitting example if not already available
if (!exists("smk_fit_RE_nodesplit")) example("example_smk_nodesplit", run.donttest = TRUE)
# }
# \donttest{
# Summarise the node-splitting results
(smk_nodesplit_summary <- summary(smk_fit_RE_nodesplit))
#> Node-splitting models fitted for 6 comparisons.
#>
#> ------------------------------ Node-split Group counselling vs. No intervention ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net 1.11 0.44 0.26 0.81 1.10 1.40 2.00 1743 2166 1
#> d_dir 1.06 0.76 -0.33 0.56 1.03 1.54 2.68 3785 2905 1
#> d_ind 1.15 0.55 0.11 0.79 1.14 1.50 2.26 1746 1811 1
#> omega -0.09 0.93 -1.86 -0.71 -0.12 0.51 1.79 2674 2542 1
#> tau 0.87 0.20 0.56 0.73 0.84 0.98 1.33 1007 1599 1
#> tau_consistency 0.84 0.18 0.56 0.71 0.81 0.95 1.27 1095 1647 1
#>
#> Residual deviance: 54 (on 50 data points)
#> pD: 44.2
#> DIC: 98.2
#>
#> Bayesian p-value: 0.9
#>
#> ------------------------- Node-split Individual counselling vs. No intervention ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net 0.84 0.24 0.41 0.68 0.83 0.99 1.33 1083 1746 1.01
#> d_dir 0.88 0.25 0.40 0.72 0.87 1.04 1.40 1870 1924 1.00
#> d_ind 0.58 0.68 -0.72 0.14 0.55 1.00 2.00 1446 1973 1.00
#> omega 0.31 0.70 -1.11 -0.14 0.31 0.76 1.69 1436 2038 1.00
#> tau 0.86 0.20 0.55 0.72 0.83 0.97 1.30 1340 2197 1.00
#> tau_consistency 0.84 0.18 0.56 0.71 0.81 0.95 1.27 1095 1647 1.00
#>
#> Residual deviance: 54.3 (on 50 data points)
#> pD: 44.2
#> DIC: 98.5
#>
#> Bayesian p-value: 0.64
#>
#> -------------------------------------- Node-split Self-help vs. No intervention ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net 0.49 0.39 -0.28 0.23 0.49 0.75 1.25 1805 2504 1
#> d_dir 0.34 0.54 -0.69 0.00 0.34 0.69 1.39 2465 2788 1
#> d_ind 0.71 0.62 -0.46 0.32 0.70 1.11 1.96 1694 2301 1
#> omega -0.37 0.82 -2.01 -0.89 -0.35 0.18 1.18 1784 2226 1
#> tau 0.87 0.20 0.56 0.73 0.84 0.98 1.32 1051 1317 1
#> tau_consistency 0.84 0.18 0.56 0.71 0.81 0.95 1.27 1095 1647 1
#>
#> Residual deviance: 54.2 (on 50 data points)
#> pD: 44.5
#> DIC: 98.7
#>
#> Bayesian p-value: 0.66
#>
#> ----------------------- Node-split Individual counselling vs. Group counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.27 0.42 -1.08 -0.55 -0.27 0.00 0.55 2322 2465 1
#> d_dir -0.11 0.48 -1.07 -0.42 -0.11 0.21 0.79 3184 2831 1
#> d_ind -0.55 0.63 -1.83 -0.96 -0.54 -0.15 0.70 1304 1967 1
#> omega 0.44 0.70 -0.94 -0.01 0.42 0.89 1.81 1365 1712 1
#> tau 0.86 0.20 0.56 0.73 0.84 0.98 1.33 1239 1769 1
#> tau_consistency 0.84 0.18 0.56 0.71 0.81 0.95 1.27 1095 1647 1
#>
#> Residual deviance: 53.7 (on 50 data points)
#> pD: 44
#> DIC: 97.7
#>
#> Bayesian p-value: 0.51
#>
#> ------------------------------------ Node-split Self-help vs. Group counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.62 0.49 -1.59 -0.94 -0.62 -0.30 0.34 2557 2690 1.00
#> d_dir -0.61 0.66 -1.91 -1.04 -0.60 -0.17 0.69 4162 3023 1.00
#> d_ind -0.61 0.67 -1.98 -1.05 -0.59 -0.17 0.71 2183 2432 1.00
#> omega 0.00 0.90 -1.71 -0.61 0.00 0.57 1.87 2477 2004 1.00
#> tau 0.87 0.20 0.56 0.73 0.85 0.98 1.32 1171 1933 1.01
#> tau_consistency 0.84 0.18 0.56 0.71 0.81 0.95 1.27 1095 1647 1.00
#>
#> Residual deviance: 54 (on 50 data points)
#> pD: 44.2
#> DIC: 98.2
#>
#> Bayesian p-value: 1
#>
#> ------------------------------- Node-split Self-help vs. Individual counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.35 0.41 -1.18 -0.62 -0.35 -0.09 0.46 1993 2691 1
#> d_dir 0.07 0.66 -1.23 -0.36 0.07 0.48 1.38 3595 2923 1
#> d_ind -0.63 0.51 -1.69 -0.97 -0.63 -0.28 0.34 2028 2480 1
#> omega 0.70 0.82 -0.91 0.15 0.71 1.23 2.30 2429 2427 1
#> tau 0.86 0.19 0.56 0.72 0.83 0.97 1.29 1121 2011 1
#> tau_consistency 0.84 0.18 0.56 0.71 0.81 0.95 1.27 1095 1647 1
#>
#> Residual deviance: 54.1 (on 50 data points)
#> pD: 44.5
#> DIC: 98.6
#>
#> Bayesian p-value: 0.39
# Plot the node-splitting results
plot(smk_nodesplit_summary)
# Plot the inconsistency factors instead, change the plot stat to half-eye,
# and add a reference line at 0
plot(smk_nodesplit_summary, pars = "omega", stat = "halfeye", ref_line = 0)
# Plot a comparison of the heterogeneity under the node-split models vs.
# the consistency model
plot(smk_nodesplit_summary, pars = "tau")
# }