robu  R Documentation 
robu
is used to metaregression models using robust variance
estimation (RVE) methods. robu
can be used to estimate correlated and
hierarchical effects models using the original (Hedges, Tipton and Johnson,
2010) and smallsample corrected (Tipton, 2013) RVE methods. In addition,
robu
contains options for fitting these models using userspecified
weighting schemes (see the Appendix of Tipton (2013) for a discussion of
non efficient weights in RVE).
robu(
formula,
data,
studynum,
var.eff.size,
userweights,
modelweights = c("CORR", "HIER"),
rho = 0.8,
small = TRUE,
...
)
formula 
An object of class 
data 
A data frame, list or environment or an object coercible by as.data.frame to a data frame. 
studynum 
A vector of study numbers to be used in model fitting.

var.eff.size 
A vector of usercalculated effectsize variances. 
userweights 
A vector of userspecified weights if nonefficient weights are of interest. Users interested in nonefficient weights should see the Appendix of Tipton (2013) for a discussion of the role of nonefficient weights in RVE). 
modelweights 
Userspecified model weighting scheme. The two two
avialable options are 
rho 
Userspecified withinstudy effectsize correlation used to fit
correlated ( 
small 

... 
Additional arguments to be passed to the fitting function. 
output 
A data frame containing some combination of the robust coefficient names and values, standard errors, ttest value, confidence intervals, degrees of freedom and statistical significance. 
n 
The number of studies in the sample 
.
k 
The number of effect sizes in the sample 
.
k descriptives 
the minimum 
tau.sq. 

omega.sq. 

I.2 

Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in metaregression with dependent effect size estimates. Research Synthesis Methods. 1(1): 39–65. Erratum in 1(2): 164–165. DOI: 10.1002/jrsm.5
Tipton, E. (in press) Small sample adjustments for robust variance estimation with metaregression. Psychological Methods.
# Load data
data(hierdat)
# SmallSample Corrections  Hierarchical Dependence Model
HierModSm < robu(formula = effectsize ~ binge + followup + sreport
+ age, data = hierdat, studynum = studyid,
var.eff.size = var, modelweights = "HIER", small = TRUE)
print(HierModSm) # Output results
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