Description Usage Arguments Details Value References See Also Examples
Plots the benchmark heterogeneity priors inducing the specified model fits fits.bm
and the corresponding marginal benchmark posteriors for different parameters in the
NNHM.
Also displays the actual heterogeneity priors inducing the specified actual model fits fits.actual
and the corresponding marginal posteriors.
All bayesmeta fits should be based on the same data set.
1 2 3 4 5 6 7 8 9 10 11  plot_RA_fits(fits.actual, fits.bm, type="pri.tau", xlim, ylim,
legend=FALSE, pos.legend="topright",
legend.tau.prior=c(), bty="o",
col.actual=c("red","lightpink3","darkgreen","green",
"violetred")[1:length(fits.actual)],
col.bm=c("cyan","black","blue","darkgray",
"dodgerblue")[1:length(fits.bm)],
lty.actual=rep(2, times=length(col.actual)),
lty.bm=rep(1, times=length(col.bm)),
lwd.actual=rep(2, times=length(col.actual)),
lwd.bm=rep(2, times=length(col.bm)))

fits.actual 
a list of model fits (max. 5 fits) of class bayesmeta, computed with
the 
fits.bm 
a list of model fits (max. 5 fits) of class bayesmeta, computed with
the 
type 
specifies if heterogeneity priors or marginal posterior densities for
a given parameter should be plotted.
Options are 
xlim 
a vector of two real numbers. Limits of the xaxis. (First number >= 0 for densities for tau.) 
ylim 
a vector of two real nonnegative numbers. Limits of the yaxis. 
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 with one
entry for each fit in 
bty 
the type of box to be drawn around the legend. The allowed values are "o" (the default) and "n". 
col.actual 
vector of color specifications for the lines representing the actual fits in

col.bm 
vector of color specifications for the lines representing the benchmark fits in

lty.actual 
vector of line type specifications for the lines representing the actual fits in

lty.bm 
vector of line type specifications for the lines representing the benchmark fits in

lwd.actual 
numeric vector specifying the width of the lines representing the actual fits in

lwd.bm 
numeric vector specifying the width of the lines representing the benchmark fits in

Two alternative suggestions for posterior benchmarks are provided
in Ott et al. (2021, Section 3.4) and its Supplementary Material (Section 2.5) and they
can be computed using the functions fit_models_RA
and fit_models_RA_5bm
,
respectively.
No return value, produces graphical output only.
Ott, M., Plummer, M., Roos, M. How vague is vague? How informative is informative? Reference analysis for Bayesian metaanalysis. Manuscript revised for Statistics in Medicine. 2021.
Ott, M., Plummer, M., Roos, M. Supplementary Material: How vague is vague? How informative is informative? Reference analysis for Bayesian metaanalysis. Revised for Statistics in Medicine. 2021.
bayesmeta
in the package bayesmeta,
fit_models_RA
, plot_RA
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26  # for aurigular acupuncture (AA) data set with two
# actual halfnormal and halfCauchy heterogeneity priors
data(aa)
# compute the model fits
fits < fit_models_RA(df=aa, tau.prior=list(function(t)dhalfnormal(t, scale=1),
function(t)dhalfcauchy(t, scale=1)))
# plot the HN0 benchmark prior (do not show the improper J benchmark)
fits.bm.pri < fits[1]
# benchmark fits under HN0 and J priors
fits.bm.post < fits[1:2]
fits.actual < fits[3:4]
# prior densities
plot_RA_fits(fits.actual=fits.actual, fits.bm=fits.bm.pri,
type="pri.tau", xlim=c(0, 2), ylim=c(0, 3),
legend=TRUE,
legend.tau.prior=c("HN(1)", "HC(1)", "HN0"))
# marginal posterior for the effect mu
plot_RA_fits(fits.actual=fits.actual, fits.bm=fits.bm.post,
type="post.mu", xlim=c(1.5, 1.5), ylim=c(0, 3),
legend=TRUE,
legend.tau.prior=c("HN(1)", "HC(1)",
"HN0", "J"))

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.