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 nma_nodesplit_df
summary(
object,
consistency = NULL,
...,
probs = c(0.025, 0.25, 0.5, 0.75, 0.975)
)
# S3 method for nma_nodesplit
summary(
object,
consistency = NULL,
...,
probs = c(0.025, 0.25, 0.5, 0.75, 0.975)
)
# S3 method for nma_nodesplit
plot(x, consistency = NULL, ...)
# S3 method for 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 1.10 0.43 0.28 0.81 1.09 1.38 2.01 1792 2159 1
#> d_dir 1.04 0.72 -0.37 0.56 1.03 1.49 2.55 3366 2654 1
#> d_ind 1.15 0.53 0.10 0.80 1.15 1.50 2.21 1888 2127 1
#> omega -0.10 0.88 -1.77 -0.69 -0.13 0.47 1.70 2427 2263 1
#> tau 0.85 0.19 0.54 0.72 0.83 0.95 1.31 1095 1824 1
#> tau_consistency 0.83 0.18 0.55 0.71 0.81 0.94 1.26 1549 1758 1
#>
#> Residual deviance: 54.6 (on 50 data points)
#> pD: 44.4
#> DIC: 99
#>
#> Bayesian p-value: 0.88
#>
#> ------------------------- 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 1.00 1.34 1277 1723 1.00
#> d_dir 0.89 0.26 0.40 0.72 0.88 1.04 1.40 1915 2427 1.00
#> d_ind 0.58 0.67 -0.71 0.14 0.56 1.00 1.97 1532 1599 1.01
#> omega 0.31 0.68 -1.04 -0.12 0.32 0.76 1.61 1555 1671 1.00
#> tau 0.86 0.20 0.55 0.71 0.83 0.97 1.34 1221 1853 1.00
#> tau_consistency 0.83 0.18 0.55 0.71 0.81 0.94 1.26 1549 1758 1.00
#>
#> Residual deviance: 54 (on 50 data points)
#> pD: 44.1
#> DIC: 98.1
#>
#> Bayesian p-value: 0.63
#>
#> -------------------------------------- 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 0.39 -0.25 0.24 0.49 0.75 1.30 2036 2569 1
#> d_dir 0.33 0.53 -0.70 -0.02 0.33 0.68 1.36 2751 2877 1
#> d_ind 0.71 0.63 -0.52 0.29 0.68 1.11 2.04 2006 2216 1
#> omega -0.38 0.83 -2.10 -0.90 -0.36 0.15 1.25 2098 2263 1
#> tau 0.87 0.20 0.55 0.73 0.84 0.98 1.34 1242 2029 1
#> tau_consistency 0.83 0.18 0.55 0.71 0.81 0.94 1.26 1549 1758 1
#>
#> Residual deviance: 53.8 (on 50 data points)
#> pD: 44.2
#> DIC: 98
#>
#> 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 -0.24 0.41 -1.09 -0.51 -0.23 0.02 0.56 2722 2753 1
#> d_dir -0.12 0.48 -1.06 -0.44 -0.11 0.19 0.81 3173 2531 1
#> d_ind -0.56 0.61 -1.80 -0.95 -0.54 -0.15 0.57 1681 2376 1
#> omega 0.44 0.66 -0.82 0.00 0.43 0.86 1.77 1662 2490 1
#> tau 0.86 0.20 0.55 0.72 0.83 0.97 1.32 1128 1775 1
#> tau_consistency 0.83 0.18 0.55 0.71 0.81 0.94 1.26 1549 1758 1
#>
#> Residual deviance: 54 (on 50 data points)
#> pD: 44.2
#> DIC: 98.2
#>
#> 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 0.49 -1.59 -0.91 -0.59 -0.28 0.36 2352 2587 1
#> d_dir -0.60 0.66 -1.90 -1.03 -0.60 -0.17 0.67 3904 2823 1
#> d_ind -0.64 0.68 -1.98 -1.11 -0.63 -0.17 0.66 2065 2802 1
#> omega 0.04 0.89 -1.65 -0.54 0.04 0.61 1.82 2222 2632 1
#> tau 0.88 0.20 0.57 0.74 0.85 0.99 1.34 1012 2140 1
#> tau_consistency 0.83 0.18 0.55 0.71 0.81 0.94 1.26 1549 1758 1
#>
#> Residual deviance: 53.6 (on 50 data points)
#> pD: 44
#> DIC: 97.6
#>
#> 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 -0.36 0.41 -1.14 -0.62 -0.36 -0.09 0.46 2328 2818 1
#> d_dir 0.08 0.64 -1.16 -0.34 0.07 0.49 1.39 2949 2875 1
#> d_ind -0.62 0.53 -1.70 -0.95 -0.62 -0.28 0.43 1734 2371 1
#> omega 0.69 0.82 -0.97 0.17 0.69 1.23 2.32 1978 2413 1
#> tau 0.86 0.19 0.56 0.72 0.83 0.97 1.30 1070 2089 1
#> tau_consistency 0.83 0.18 0.55 0.71 0.81 0.94 1.26 1549 1758 1
#>
#> Residual deviance: 53.6 (on 50 data points)
#> pD: 44
#> DIC: 97.7
#>
#> Bayesian p-value: 0.38
# Plot the node-splitting results
plot(smk_fit_RE_nodesplit)
# }