multinma (version 0.1.3)

plot.nma_summary: Plots of summary results

Description

The plot method for nma_summary objects is used to produce plots of parameter estimates (when called on a stan_nma object or its summary), relative effects (when called on the output of relative_effects()), absolute predictions (when called on the output of predict.stan_nma()), posterior ranks and rank probabilities (when called on the output of posterior_ranks() or posterior_rank_probs()).

Usage

# S3 method for nma_summary
plot(
  x,
  ...,
  stat = "pointinterval",
  orientation = c("horizontal", "vertical", "y", "x"),
  ref_line = NA_real_
)
# S3 method for nma_parameter_summary
plot(
  x,
  ...,
  stat = "pointinterval",
  orientation = c("horizontal", "vertical", "y", "x"),
  ref_line = NA_real_
)
# S3 method for nma_rank_probs
plot(x, ...)

Arguments

x

A nma_summary object

...

Additional arguments passed on to the underlying ggdist plot stat, see Details

stat

Character string specifying the ggdist plot stat to use, default "pointinterval"

orientation

Whether the ggdist geom is drawn horizontally ("horizontal") or vertically ("vertical"), default "horizontal"

ref_line

Numeric vector of positions for reference lines, by default no reference lines are drawn

Value

A ggplot object.

Details

Plotting is handled by ggplot2 and the stats and geoms provided in the ggdist package. As a result, the output is very flexible. Any plotting stats provided by ggdist may be used, via the argument stat. The default uses ggdist::stat_pointinterval(), to produce medians and 95% Credible Intervals with 66% inner bands. Additional arguments in ... are passed to the ggdist stat, to customise the output. For example, to produce means and Credible Intervals, specify point_interval = mean_qi. To produce an 80% Credible Interval with no inner band, specify .width = c(0, 0.8).

Alternative stats can be specified to produce different summaries. For example, specify stat = "[half]eye" to produce (half) eye plots, or stat = "histinterval" to produce histograms with intervals.

A full list of options and examples is found in the ggdist vignette vignette("slabinterval", package = "ggdist").

A ggplot object is returned which can be further modified through the usual ggplot2 functions to add further aesthetics, geoms, themes, etc.

Examples

# NOT RUN {
## Smoking cessation
# Set up network of smoking cessation data
head(smoking)

smk_net <- set_agd_arm(smoking,
                       study = studyn,
                       trt = trtc,
                       r = r,
                       n = n,
                       trt_ref = "No intervention")

# Print details
smk_net

# }
# NOT RUN {
# Fitting a random effects model
smk_fit_RE <- nma(smk_net,
                  trt_effects = "random",
                  prior_intercept = normal(scale = 100),
                  prior_trt = normal(scale = 100),
                  prior_het = normal(scale = 5))

smk_fit_RE
# }
# NOT RUN {
# }
# NOT RUN {
# Produce relative effects
smk_releff_RE <- relative_effects(smk_fit_RE)
plot(smk_releff_RE, ref_line = 0)

# Customise plot options
plot(smk_releff_RE, ref_line = 0, stat = "halfeye")

# Further customisation is possible with ggplot commands
plot(smk_releff_RE, ref_line = 0, stat = "halfeye", slab_alpha = 0.6) +
  ggplot2::aes(slab_fill = ifelse(..x.. < 0, "darkred", "grey60"))

# Produce posterior ranks
smk_rank_RE <- posterior_ranks(smk_fit_RE, lower_better = FALSE)
plot(smk_rank_RE)

# Produce rank probabilities
smk_rankprob_RE <- posterior_rank_probs(smk_fit_RE, lower_better = FALSE)
plot(smk_rankprob_RE)

# Produce cumulative rank probabilities
smk_cumrankprob_RE <- posterior_rank_probs(smk_fit_RE, lower_better = FALSE,
                                           cumulative = TRUE)
plot(smk_cumrankprob_RE)

#' # Further customisation is possible with ggplot commands
plot(smk_cumrankprob_RE) +
  ggplot2::facet_null() +
  ggplot2::aes(colour = Treatment)
# }