Replacement functions for "runMetaAnalysis" results objects

Once a model has been fitted using runMetaAnalysis, replacement functions are defined for each function argument. This allows to quickly tweak one or more analysis settings, which are implemented once the rerun function is called.

Usage

data(x) <- value
which.run(x) <- value
es.measure(x) <- value
es.type(x) <- value
es.var(x) <- value
se.var(x) <- value
es.binary.raw.vars(x) <- value
method.tau(x) <- value
i2.ci.threelevel(x) <- value
nsim.boot(x) <- value
hakn(x) <- value
study.var(x) <- value
arm.var.1(x) <- value
arm.var.2(x) <- value
measure.var(x) <- value
low.rob.filter(x) <- value
method.tau.ci(x) <- value
which.combine(x) <- value
which.combine.var(x) <- value
which.outliers(x) <- value
which.influence(x) <- value
which.rob(x) <- value
nntCer(x) <- value
# S3 method for class 'runMetaAnalysis'
nnt(x) <- value
rho.within.study(x) <- value
phi.within.study(x) <- value
power.within.study(x) <- value
w1.var(x) <- value
w2.var(x) <- value
time.var(x) <- value
vcov(x) <- value
near.pd(x) <- value
use.rve(x) <- value
html(x) <- value
lower.is.better(x) <- value
selmodelSteps(x) <- value
# S3 method for class 'runMetaAnalysis'
selmodel(x) <- value
rerun(m)

Arguments

x

Object of class runMetaAnalysis.

value

Value of one of the arguments in runMetaAnalysis or correctPublicationBias

m

(Adapted) object of class runMetaAnalysis.

Details

nntCer / nntCer<- and selmodelSteps / selmodelSteps<- are also available as S3 methods nnt, nnt<-, selmodel, selmodel<- for runMetaAnalysis objects. The function time.var (and time.var<-) is a regular replacement function, not an S3 method for time.

See also

runMetaAnalysis, correctPublicationBias

Examples

if (FALSE) { # \dontrun{
data("depressionPsyCtr")
depressionPsyCtr %>%
  checkDataFormat() %>%
  checkConflicts() %>%
  calculateEffectSizes() %>% 
  filterPoolingData(condition_arm2 %in% 
                      c("wl", "other ctr")) -> data

m <- runMetaAnalysis(data, "combined")

# Compare results when other tau^2 estimator is used
method.tau(m) <- "DL"
rerun(m)
} # }