plot_RA | R Documentation |

Plots the proper half-normal benchmark heterogeneity prior proposed in Ott et al. (2021)
and the corresponding marginal benchmark posteriors for different parameters in the
NNHM. Also displays the marginal Jeffreys reference posterior,
which is induced by an improper reference prior.
Displays the specified actual heterogeneity priors in `tau.prior`

and the corresponding marginal posteriors as well.

```
plot_RA(df, tau.prior = list(),
type = "pri.tau", improper.prior = NULL, show.sigma.i = FALSE,
xlim, ylim, legend = FALSE,
pos.legend = "topright", legend.tau.prior = c(),
xlab = NULL, bty = "o",
scale.hn0 = 1/500, mu.mean = 0, mu.sd = 4)
```

`df` |
data frame with one column "y" containing the (transformed) effect estimates for the individual studies and one column "sigma" containing the standard errors of these estimates. |

`tau.prior` |
list (maximum length 5) of prior specifications, which are |

`type` |
specifies if heterogeneity priors or marginal posterior densities for
a given parameter should be plotted.
Options are |

`improper.prior` |
vector of integers specifying the positions of the improper priors in the list |

`show.sigma.i` |
logical. Specifies if the standard errors of the estimates for the individual studies (the entries of the column "sigma" in the data frame |

`xlim` |
a vector of two real numbers. Limits of the x-axis. (First number >= 0 for densities for tau.) |

`ylim` |
a vector of two real non-negative numbers. Limits of the y-axis. |

`legend` |
logical. Specifies if a legend should be added to the plot. Defaults to |

`pos.legend` |
a character string specifing the position of the legend in the plot.
Options are all the keywords accepted by
the |

`legend.tau.prior` |
a vector of character strings or expressions of the same length
as the list |

`xlab` |
x-axis label. If unspecified or set to NULL (the default), a default label specifying the paramter of interest is used. |

`bty` |
the type of box to be drawn around the legend. The allowed values are "o" (the default) and "n". |

`scale.hn0` |
scale parameter of the half-normal benchmark prior (usually a small number to obtain an anti-conservative benchmark which has most of its probability mass close to 0). Defaults to 0.002. |

`mu.mean` |
mean of the normal prior for the effect mu. |

`mu.sd` |
standard deviation of the normal prior for the effect mu. |

The two heterogeneity benchmark priors used are introduced in Ott et al. (2021, Section 3.4) and they are denoted by HN0 and J. Note that the latter Jeffreys reference prior J is improper, but leads to a proper posterior if there are at least two studies in the meta-analysis data set.

The default values for `mu.mean`

and `mu.sd`

are suitable for effects mu on the log odds (ratio) scale.

If you wish to adapt the colors, line types or line widths of the plotted densities,
we suggest to compute the models fits first by using the function `fit_models_RA`

and then to apply the more flexible function `plot_RA_fits`

operating on model fits of class `bayesmeta`

.

No return value, produces graphical output only.

Ott, M., Plummer, M., Roos, M. (2021). How vague is vague?
How informative is informative? Reference analysis for
Bayesian meta-analysis. *Statistics in Medicine* **40**, 4505–4521.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.9076")}

`plot_RA_fits`

,
`plot_RA_5bm`

,
`fit_models_RA`

```
# for aurigular acupuncture (AA) data set with two
# actual half-normal and half-Cauchy heterogeneity priors
data(aa)
# it takes a few seconds to run each of the following functions
# prior densities
plot_RA(df=aa, tau.prior=list(function(t)dhalfnormal(t, scale=1),
function(t)dhalfcauchy(t, scale=1)),
type="pri.tau", xlim=c(0,2), ylim=c(0,3), legend=TRUE,
legend.tau.prior=c("HN(1)", "HC(1)"))
# marginal posterior for the effect mu
plot_RA(df=aa, tau.prior=list(function(t)dhalfnormal(t, scale=1),
function(t)dhalfcauchy(t, scale=1)),
type="post.mu", xlim=c(-1.5,2), ylim=c(0,3), legend=TRUE,
legend.tau.prior=c("HN(1)", "HC(1)"))
```

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