Forest Plot

Creates a forest plot for gMAP analysis objects.

Usage

forest_plot(
  x,
  prob = 0.95,
  est = c("both", "MAP", "Mean", "none"),
  model = c("stratified", "both", "meta"),
  point_est = c("median", "mean"),
  size = 1.25,
  alpha = 0.5
)

Arguments

x: gMAP object.
prob: confidence interval width and probability mass of credible intervals.
est: can be set to one of both (default), MAP, Mean or none. Controls which model estimates are to be included.
model: controls which estimates are displayed per study. Either stratified (default), both or meta.
point_est: shown point estimate. Either median (default) or mean.
size: controls point and linesize.
alpha: transparency of reference line. Setting alpha=0 suppresses the reference line.

Value

The function returns a ggplot2 plot object.

Details

The function creates a forest plot suitable for gMAP analyses. Note that the Meta-Analytic-Predictive prior is included by default in the plot as opposed to only showing the estimated model mean. See the examples below to obtain standard forest plots.

Also note that the plot internally flips the x and y-axis. Therefore, if you want to manipulate the x-axis, you have to give commands affecting the y-axis (see examples).

Customizing ggplot2 plots

The returned plot is a ggplot2 object. Please refer to the "Customizing Plots" vignette which is part of RBesT documentation for an introduction. For simple modifications (change labels, add reference lines, ...) consider the commands found in bayesplot-helpers. For more advanced customizations please use the ggplot2 package directly. A description of the most common tasks can be found in the R Cookbook and a full reference of available commands can be found at the ggplot2 documentation site.

Examples

# we consider the example AS MAP analysis
example(AS)
#> 
#> AS> ## Setting up dummy sampling for fast execution of example
#> AS> ## Please use 4 chains and 20x more warmup & iter in practice
#> AS> .user_mc_options <- options(RBesT.MC.warmup=50, RBesT.MC.iter=100,
#> AS+                             RBesT.MC.chains=2, RBesT.MC.thin=1)
#> 
#> AS> set.seed(34563)
#> 
#> AS> map_AS <- gMAP(cbind(r, n-r) ~ 1 | study,
#> AS+                family=binomial,
#> AS+                data=AS,
#> AS+                tau.dist="HalfNormal", tau.prior=1,
#> AS+                beta.prior=2)
#> Assuming default prior location   for beta: 0
#> Warning: The largest R-hat is 1.11, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Warning: Maximal Rhat > 1.1. Consider increasing RBesT.MC.warmup MCMC parameter.
#> Final MCMC sample equivalent to less than 1000 independent draws.
#> Please consider increasing the MCMC simulation size.
#> 
#> AS> ## Recover user set sampling defaults
#> AS> options(.user_mc_options)

# default forest plot for a gMAP analysis
forest_plot(map_AS)


# standard forest plot (only stratified estimate and Mean)
forest_plot(map_AS, est=c("Mean"), model="stratified")


# to further customize these plots, first load bayesplot and ggplot2
library(bayesplot)
#> This is bayesplot version 1.11.1
#> - Online documentation and vignettes at mc-stan.org/bayesplot
#> - bayesplot theme set to bayesplot::theme_default()
#>    * Does _not_ affect other ggplot2 plots
#>    * See ?bayesplot_theme_set for details on theme setting
library(ggplot2)

# to make plots with red colors, big fonts for presentations, suppress
# the x axis label and add another title (with a subtitle)
color_scheme_set("red")
theme_set(theme_default(base_size=16))
forest_plot(map_AS, size=2) +
   yaxis_title(FALSE) +
     ggtitle("Ankylosing Spondylitis Forest Plot",
             subtitle="Control Group Response Rate")


# the defaults are set with
color_scheme_set("blue")
theme_set(theme_default(base_size=12))