Matrix of plots for a stan_nma object

A pairs() method for stan_nma objects, which calls bayesplot::mcmc_pairs() on the underlying stanfit object.

Usage

# S3 method for class 'stan_nma'
pairs(x, ..., pars, include = TRUE)

Arguments

x

An object of class stan_nma

...

Other arguments passed to bayesplot::mcmc_pairs()

pars

Optional character vector of parameter names to include in output. If not specified, all parameters are used.

include

Logical, are parameters in pars to be included (TRUE, default) or excluded (FALSE)?

Value

A grid of ggplot objects produced by bayesplot::mcmc_pairs().

Examples

if (FALSE) { # \dontrun{
## Parkinson's mean off time reduction
park_net <- set_agd_arm(parkinsons,
                        study = studyn,
                        trt = trtn,
                        y = y,
                        se = se,
                        sample_size = n)

# Fitting a RE model
park_fit_RE <- nma(park_net,
                   trt_effects = "random",
                   prior_intercept = normal(scale = 100),
                   prior_trt = normal(scale = 100),
                   prior_het = half_normal(scale = 5))

# We see a small number of divergent transition errors
# These do not go away entirely when adapt_delta is increased

# Try to diagnose with a pairs plot
pairs(park_fit_RE, pars = c("mu[4]", "d[3]", "delta[4: 3]", "tau"))

# Transforming tau onto log scale
pairs(park_fit_RE, pars = c("mu[4]", "d[3]", "delta[4: 3]", "tau"),
      transformations = list(tau = "log"))

# The divergent transitions occur in the upper tail of the heterogeneity
# standard deviation. In this case, with only a small number of studies, there
# is not very much information to estimate the heterogeneity standard deviation
# and the prior distribution may be too heavy-tailed. We could consider a more
# informative prior distribution for the heterogeneity variance to aid
# estimation.
} # }