3D MCMC arrays (Iterations, Chains, Parameters) are produced by as.array()
methods applied to stan_nma
or nma_summary
objects.
Value
The summary()
method returns a nma_summary object, the print()
method returns x
invisibly. The names()
method returns a character
vector of parameter names, and names()<-
returns the object with updated
parameter names. The plot()
method is a shortcut for
plot(summary(x), ...)
, passing all arguments on to plot.nma_summary()
.
Examples
## Smoking cessation
# \donttest{
# Run smoking RE NMA example if not already available
if (!exists("smk_fit_RE")) example("example_smk_re", run.donttest = TRUE)
# }
# \donttest{
# Working with arrays of posterior draws (as mcmc_array objects) is
# convenient when transforming parameters
# Transforming log odds ratios to odds ratios
LOR_array <- as.array(relative_effects(smk_fit_RE))
OR_array <- exp(LOR_array)
# mcmc_array objects can be summarised to produce a nma_summary object
smk_OR_RE <- summary(OR_array)
# This can then be printed or plotted
smk_OR_RE
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS
#> d[Group counselling] 3.33 1.60 1.32 2.25 3.00 4.00 7.33 2276 2783
#> d[Individual counselling] 2.40 0.60 1.46 1.99 2.33 2.73 3.80 1454 2018
#> d[Self-help] 1.75 0.73 0.75 1.25 1.61 2.10 3.46 2017 2344
#> Rhat
#> d[Group counselling] 1
#> d[Individual counselling] 1
#> d[Self-help] 1
plot(smk_OR_RE, ref_line = 1)
# Transforming heterogeneity SD to variance
tau_array <- as.array(smk_fit_RE, pars = "tau")
tausq_array <- tau_array^2
# Correct parameter names
names(tausq_array) <- "tausq"
# Summarise
summary(tausq_array)
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> tausq 0.74 0.33 0.31 0.51 0.67 0.89 1.6 1368 2250 1
# }