pairs.stan_nma function

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.

## 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)?

Returns

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

Examples

## Not run:

## 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.
## End(Not run)