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 0.30 0.82 1.10 1.38 2.00 2000 2306 1
#> d_dir 1.08 0.74 -0.31 0.58 1.05 1.55 2.58 3392 2712 1
#> d_ind 1.12 0.55 0.07 0.77 1.12 1.48 2.17 1637 2163 1
#> omega -0.05 0.90 -1.79 -0.65 -0.07 0.53 1.83 2205 2310 1
#> tau 0.87 0.20 0.55 0.73 0.85 0.99 1.34 1171 1658 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.29 1314 1713 1
#>
#> Residual deviance: 53.6 (on 50 data points)
#> pD: 43.8
#> DIC: 97.4
#>
#> Bayesian p-value: 0.94
#>
#> ------------------------- 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 0.38 0.69 0.84 1.00 1.32 1237 1718 1
#> d_dir 0.88 0.26 0.39 0.70 0.87 1.04 1.40 1371 1797 1
#> d_ind 0.56 0.69 -0.78 0.09 0.55 0.99 1.95 1345 1649 1
#> omega 0.31 0.71 -1.09 -0.14 0.33 0.78 1.74 1411 1773 1
#> tau 0.87 0.20 0.55 0.72 0.84 0.98 1.33 1143 2092 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.29 1314 1713 1
#>
#> Residual deviance: 54.1 (on 50 data points)
#> pD: 44.3
#> DIC: 98.4
#>
#> 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 0.51 0.40 -0.26 0.25 0.50 0.75 1.30 2087 2276 1.00
#> d_dir 0.33 0.54 -0.76 -0.01 0.32 0.66 1.43 3295 2569 1.00
#> d_ind 0.71 0.62 -0.52 0.30 0.72 1.12 1.94 2329 2835 1.00
#> omega -0.38 0.82 -1.94 -0.92 -0.39 0.15 1.27 2479 2626 1.00
#> tau 0.88 0.20 0.57 0.74 0.85 0.99 1.33 1114 1538 1.01
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.29 1314 1713 1.00
#>
#> Residual deviance: 53.7 (on 50 data points)
#> pD: 44.2
#> DIC: 98
#>
#> Bayesian p-value: 0.62
#>
#> ----------------------- Node-split Individual counselling vs. Group counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.26 0.41 -1.09 -0.53 -0.26 0.01 0.54 2760 2738 1
#> d_dir -0.10 0.50 -1.09 -0.44 -0.10 0.23 0.87 3180 3177 1
#> d_ind -0.55 0.61 -1.78 -0.95 -0.55 -0.15 0.64 1707 2182 1
#> omega 0.45 0.68 -0.89 0.00 0.45 0.89 1.80 1631 1984 1
#> tau 0.87 0.20 0.57 0.73 0.84 0.98 1.33 1275 1693 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.29 1314 1713 1
#>
#> Residual deviance: 53.4 (on 50 data points)
#> pD: 43.9
#> DIC: 97.3
#>
#> 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 -0.60 0.48 -1.57 -0.90 -0.60 -0.28 0.30 2989 2245 1
#> d_dir -0.60 0.65 -1.90 -1.02 -0.60 -0.18 0.72 3690 3112 1
#> d_ind -0.63 0.66 -1.92 -1.07 -0.64 -0.20 0.67 1966 2537 1
#> omega 0.02 0.87 -1.66 -0.54 0.02 0.58 1.72 1983 1936 1
#> tau 0.87 0.20 0.56 0.73 0.84 0.98 1.32 1077 1882 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.29 1314 1713 1
#>
#> Residual deviance: 54.1 (on 50 data points)
#> pD: 44.3
#> DIC: 98.5
#>
#> Bayesian p-value: 0.97
#>
#> ------------------------------- Node-split Self-help vs. Individual counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.34 0.40 -1.11 -0.60 -0.33 -0.08 0.48 2355 2418 1
#> d_dir 0.05 0.64 -1.24 -0.37 0.05 0.46 1.33 3370 2887 1
#> d_ind -0.64 0.53 -1.70 -0.98 -0.63 -0.29 0.37 1939 2329 1
#> omega 0.70 0.81 -0.83 0.15 0.68 1.21 2.33 2371 2334 1
#> tau 0.86 0.20 0.56 0.72 0.83 0.96 1.33 1182 1691 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.95 1.29 1314 1713 1
#>
#> Residual deviance: 53.6 (on 50 data points)
#> pD: 44
#> DIC: 97.6
#>
#> Bayesian p-value: 0.38
# Plot the node-splitting results
plot(smk_fit_RE_nodesplit)
# }