Function reference • metapsyTools

Preparation Module

Functions to prepare the data and calculate effect sizes.

checkDataFormat(): Check data format

checkConflicts(): Check for (potential) data format conflicts

filterPoolingData(): Filter data to be pooled for meta-analysis

filterPriorityRule(): Filter data based on a priority rule

calculateEffectSizes(): Calculate effect sizes

Analysis Module

Functions to run meta-analyses.

runMetaAnalysis(): Run different types of meta-analyses

correctPublicationBias(): Correct the effect size for publication bias/small-study effects

subgroupAnalysis(): Run subgroup analyses

metaRegression(): Meta-Regression method for objects of class 'runMetaAnalysis'

metaRegression(<meta>): Meta-Regression method for objects of class 'runMetaAnalysis'

metaRegression(<rma>): Meta-Regression method for objects of class 'runMetaAnalysis'

metaRegression(<runMetaAnalysis>): Meta-Regression method for objects of class 'runMetaAnalysis'

exploreStudies(): Explore included treatments and comparisons

createStudyTable(): Create study table

createRobSummary(): Create a summary risk of bias plot

Effect Size Calculators

Default plug-in functions used by calculateEffectSizes.

g.m.sd(): Calculate Hedges' g using means and standard deviations

g.change.m.sd(): Calculate Hedges' g using within-group change data

g.binary(): Calculate Hedges' g using binary outcome data

g.precalc(): Forward pre-calculated values of Hedges' g

rr.precalc(): Forward pre-calculated log-risk ratios

rr.binary(): Calculate the log-risk ratio using binary outcome data

S3 Methods & Helper Functions

Additional functionality for core functions.

blup(<runMetaAnalysis>): Best Linear Unbiased Predictions (BLUPs) for 'runMetaAnalysis' models.

blup(): blup: Empirical Bayes estimates

eb(<runMetaAnalysis>): Best Linear Unbiased Predictions (BLUPs) for 'runMetaAnalysis' models.

eb(): eb: Empirical Bayes estimates

imputeResponse(): Impute response rates based on continuous outcome data

metapsyFindOutliers(): Find Statistical Outliers in a Meta-Analysis

metapsyInfluenceAnalysis(): Influence Diagnostics

profile(<runMetaAnalysis>): Profile Likelihood Plots for 'runMetaAnalysis' models.

proportionMID(): Calculate the proportion of true effect sizes above a meaningful threshold

addTrialArmInfo(): Add information that varies between trial arms as extra columns to your meta-analysis dataset

plot(<proportionMID>): Plot method for objects of class 'proportionMID'

plot(<runMetaAnalysis>): Plot method for objects of class 'runMetaAnalysis'

plot(<subgroupAnalysis>): Plot method for objects of class 'runMetaAnalysis'

print(<checkConflicts>): Print method for the 'checkConflicts' function

print(<exploreStudies>): Print method for objects of class 'exploreStudies'

print(<proportionMID>): Print method for objects of class 'proportionMID'

print(<runMetaAnalysis>) print(<runMetaAnalysis>): Print method for objects of class 'runMetaAnalysis'

print(<subgroupAnalysis>): Print method for objects of class 'subgroupAnalysis'

summary(<runMetaAnalysis>): Show details of 'runMetaAnalysis' class objects

`data<-`() `which.run<-`() `es.measure<-`() `es.type<-`() `es.var<-`() `se.var<-`() `es.binary.raw.vars<-`() `method.tau<-`() `i2.ci.threelevel<-`() `nsim.boot<-`() `hakn<-`() `study.var<-`() `arm.var.1<-`() `arm.var.2<-`() `measure.var<-`() `low.rob.filter<-`() `method.tau.ci<-`() `which.combine<-`() `which.combine.var<-`() `which.outliers<-`() `which.influence<-`() `which.rob<-`() `nnt.cer<-`() `rho.within.study<-`() `phi.within.study<-`() `w1.var<-`() `w2.var<-`() `vcov<-`() `near.pd<-`() `use.rve<-`() `html<-`() `lower.is.better<-`() `selmodel.steps<-`() rerun(): Replacement functions for "runMetaAnalysis" results objects

Datasets

depressionPsyCtr: The 'depressionPsyCtr' dataset