Summarise the results of node-splitting models
Source:R/nma_nodesplit-class.R
summary.nma_nodesplit_df.RdPosterior summaries of node-splitting models (nma_nodesplit and
nma_nodesplit_df objects) can be produced using the summary() method, and
plotted using the plot() method.
Usage
# S3 method for class 'nma_nodesplit_df'
summary(
object,
consistency = NULL,
...,
probs = c(0.025, 0.25, 0.5, 0.75, 0.975)
)
# S3 method for class 'nma_nodesplit'
summary(
object,
consistency = NULL,
...,
probs = c(0.025, 0.25, 0.5, 0.75, 0.975)
)
# S3 method for class 'nma_nodesplit'
plot(x, consistency = NULL, ...)
# S3 method for class 'nma_nodesplit_df'
plot(x, consistency = NULL, ...)Arguments
- consistency
Optional, a
stan_nmaobject for the corresponding fitted consistency model, to display the network estimates alongside the direct and indirect estimates. The fitted consistency model present in thenma_nodesplit_dfobject will be used if this is present (seeget_nodesplits()).- ...
Additional arguments passed on to other methods
- probs
Numeric vector of specifying quantiles of interest, default
c(0.025, 0.25, 0.5, 0.75, 0.975)- x, object
A
nma_nodesplitornma_nodesplit_dfobject
Value
A nodesplit_summary object
Details
The plot() method is a shortcut for plot(summary(nma_nodesplit)). For
details of plotting options, see plot.nodesplit_summary().
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
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 0.28 0.82 1.10 1.39 2.00 1803 1757 1
#> d_dir 1.07 0.75 -0.32 0.57 1.04 1.56 2.65 3298 2736 1
#> d_ind 1.15 0.55 0.08 0.79 1.14 1.50 2.26 1954 2384 1
#> omega -0.07 0.89 -1.73 -0.67 -0.09 0.50 1.75 2445 1902 1
#> tau 0.87 0.20 0.56 0.72 0.84 0.98 1.33 1200 1669 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.28 1226 1642 1
#>
#> Residual deviance: 54.1 (on 50 data points)
#> pD: 44.3
#> DIC: 98.4
#>
#> Bayesian p-value: 0.92
#>
#> ------------------------- Node-split Individual counselling vs. No intervention ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net 0.86 0.24 0.41 0.70 0.85 1.01 1.36 1045 1532 1.00
#> d_dir 0.88 0.26 0.38 0.71 0.87 1.04 1.39 1658 2244 1.00
#> d_ind 0.58 0.67 -0.69 0.13 0.56 1.00 1.95 1211 1812 1.00
#> omega 0.30 0.70 -1.11 -0.14 0.32 0.76 1.61 1207 1833 1.00
#> tau 0.86 0.20 0.55 0.72 0.84 0.97 1.33 1200 1596 1.01
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.28 1226 1642 1.00
#>
#> Residual deviance: 53.9 (on 50 data points)
#> pD: 44
#> DIC: 97.9
#>
#> 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 0.50 0.41 -0.27 0.24 0.50 0.76 1.35 1776 1992 1
#> d_dir 0.32 0.56 -0.76 -0.04 0.32 0.68 1.43 3718 2850 1
#> d_ind 0.71 0.63 -0.52 0.29 0.69 1.11 2.03 2175 2399 1
#> omega -0.38 0.83 -2.01 -0.92 -0.38 0.16 1.21 2281 2242 1
#> tau 0.87 0.20 0.56 0.72 0.84 0.99 1.32 1312 2012 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.28 1226 1642 1
#>
#> Residual deviance: 53.9 (on 50 data points)
#> pD: 44.3
#> DIC: 98.2
#>
#> Bayesian p-value: 0.64
#>
#> ----------------------- Node-split Individual counselling vs. Group counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.25 0.41 -1.07 -0.52 -0.25 0.02 0.55 2537 2194 1
#> d_dir -0.10 0.48 -1.02 -0.41 -0.10 0.21 0.91 3707 3114 1
#> d_ind -0.54 0.61 -1.78 -0.93 -0.53 -0.13 0.67 1364 1647 1
#> omega 0.44 0.68 -0.88 0.00 0.43 0.87 1.77 1235 1803 1
#> tau 0.87 0.20 0.57 0.73 0.84 0.98 1.34 1391 2434 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.28 1226 1642 1
#>
#> Residual deviance: 54.1 (on 50 data points)
#> pD: 44.5
#> DIC: 98.6
#>
#> Bayesian p-value: 0.5
#>
#> ------------------------------------ Node-split Self-help vs. Group counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.61 0.49 -1.56 -0.92 -0.60 -0.29 0.36 2449 2644 1
#> d_dir -0.62 0.67 -1.96 -1.05 -0.62 -0.18 0.72 3774 3110 1
#> d_ind -0.62 0.67 -2.01 -1.04 -0.60 -0.17 0.64 1878 2204 1
#> omega 0.00 0.89 -1.67 -0.60 -0.02 0.56 1.83 2192 2247 1
#> tau 0.87 0.20 0.56 0.72 0.83 0.98 1.33 1048 2001 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.28 1226 1642 1
#>
#> Residual deviance: 54.4 (on 50 data points)
#> pD: 44.6
#> DIC: 99
#>
#> Bayesian p-value: 0.99
#>
#> ------------------------------- 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 -1.19 -0.62 -0.34 -0.09 0.42 2188 2770 1
#> d_dir 0.07 0.64 -1.22 -0.35 0.07 0.49 1.32 3826 2819 1
#> d_ind -0.59 0.53 -1.65 -0.93 -0.60 -0.24 0.48 2040 2492 1
#> omega 0.65 0.81 -0.97 0.14 0.66 1.17 2.28 2411 2108 1
#> tau 0.86 0.19 0.57 0.72 0.83 0.96 1.30 1101 1780 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.28 1226 1642 1
#>
#> Residual deviance: 53.5 (on 50 data points)
#> pD: 43.8
#> DIC: 97.3
#>
#> Bayesian p-value: 0.4
# Plot the node-splitting results
plot(smk_fit_RE_nodesplit)
# }