Description Usage Arguments Value See Also Examples
View source: R/meta_sensitivity.R
Sensitivity analysis assuming different prior distributions for the two main parameters of a Bayesian metaanalysis (i.e., the overall effect and the heterogeneity of effect sizes across studies).
1 2 3 4 5 6 7 8 9 10 11  meta_sensitivity(
y,
SE,
labels,
data,
d_list,
tau_list,
analysis = "bma",
combine_priors = "crossed",
...
)

y 
effect size per study. Can be provided as (1) a numeric vector, (2)
the quoted or unquoted name of the variable in 
SE 
standard error of effect size for each study. Can be a numeric
vector or the quoted or unquoted name of the variable in 
labels 
optional: character values with study labels. Can be a
character vector or the quoted or unquoted name of the variable in

data 
data frame containing the variables for effect size 
d_list 
a 
tau_list 
a 
analysis 
which type of metaanalysis should be performed for analysis? Can be one of the following:

combine_priors 
either 
... 
further arguments passed to the function specified in 
an object of the S3 class meta_sensitivity
, that is, a list of fitted
metaanalysis models. Results can be printed or plotted using
plot.meta_sensitivity()
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  data(towels)
sensitivity < meta_sensitivity(
y = logOR, SE = SE, labels = study, data = towels,
d_list = list(prior("cauchy", c(0, .707)),
prior("norm", c(0, .5)),
prior("norm", c(.5, .3))),
tau_list = list(prior("invgamma", c(1, 0.15), label = "tau"),
prior("gamma", c(1.5, 3), label = "tau")),
analysis = "random",
combine_priors = "crossed")
print(sensitivity, digits = 2)
par(mfrow = c(1,2))
plot(sensitivity, "d", "prior")
plot(sensitivity, "d", "posterior")
plot(sensitivity, "tau", "prior")
plot(sensitivity, "tau", "posterior")

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