Package index • vcmeta

Meta-analysis

Overall effect sizes and CIs across groups of studies

meta.ave.agree(): Confidence interval for an average G-index agreement coefficient

meta.ave.cor.gen(): Confidence interval for an average correlation of any type

meta.ave.cor(): Confidence interval for an average Pearson or partial correlation

meta.ave.cronbach(): Confidence interval for an average Cronbach alpha reliability

meta.ave.gen.cc(): Confidence interval for an average effect size using a constant coefficient model

meta.ave.gen.log(): Exponentiated confidence interval for an average of log-transformed parameters

meta.ave.gen.rc(): Confidence interval for an average effect size using a random coefficient model

meta.ave.gen(): Confidence interval for an average of any parameter

meta.ave.mean.ps(): Confidence interval for an average mean difference from paired-samples studies

meta.ave.mean2(): Confidence interval for an average mean difference from 2-group studies

meta.ave.meanratio.ps(): Confidence interval for an average mean ratio from paired-samples studies

meta.ave.meanratio2(): Confidence interval for an average mean ratio from 2-group studies

meta.ave.oddsratio(): Confidence interval for average odds ratio from 2-group studies

meta.ave.path(): Confidence interval for an average slope coefficient in a general linear model or a path model.

meta.ave.pbcor(): Confidence interval for an average point-biserial correlation

meta.ave.plot(): Forest plot for average effect sizes

meta.ave.prop.ps(): Confidence interval for an average proportion difference in paired-samples studies

meta.ave.prop2(): Confidence interval for an average proportion difference in 2-group studies

meta.ave.propratio2(): Confidence interval for an average proportion ratio from 2-group studies

meta.ave.semipart(): Confidence interval for an average semipartial correlation

meta.ave.slope(): Confidence interval for an average slope coefficient

meta.ave.spear(): Confidence interval for an average Spearman correlation

meta.ave.stdmean.ps(): Confidence interval for an average standardized mean difference from paired-samples studies

meta.ave.stdmean2(): Confidence interval for an average standardized mean difference from 2-group studies

meta.ave.var(): Confidence interval for an average variance

Meta-analysis of categorical moderators

Estimate differences between linear contrasts of groups of studies

meta.lc.agree(): Confidence interval for a linear contrast of G-index coefficients

meta.lc.gen(): Confidence interval for a linear contrast of effect sizes

meta.lc.mean.ps(): Confidence interval for a linear contrast of mean differences from paired-samples studies

meta.lc.mean1(): Confidence interval for a linear contrast of means

meta.lc.mean2(): Confidence interval for a linear contrast of mean differences from 2-group studies

meta.lc.meanratio.ps(): Confidence interval for a log-linear contrast of mean ratios from paired-samples studies

meta.lc.meanratio2(): Confidence interval for a log-linear contrast of mean ratios from 2-group studies

meta.lc.oddsratio(): Confidence interval for a log-linear contrast of odds ratios

meta.lc.prop.ps(): Confidence interval for a linear contrast of proportion differences in paired-samples studies

meta.lc.prop1(): Confidence interval for a linear contrast of proportions

meta.lc.prop2(): Confidence interval for a linear contrast of proportion differences in 2-group studies

meta.lc.propratio2(): Confidence interval for a log-linear contrast of proportion ratios from 2-group studies

meta.lc.stdmean.ps(): Confidence interval for a linear contrast of standardized mean differences from paired-samples studies

meta.lc.stdmean2(): Confidence interval for a linear contrast of standardized mean differences from 2-group studies

Meta-regression

Estimate relationships between quantitative predictors and effect sizes in groups of studies

meta.lm.agree(): Meta-regression analysis for G agreement indices

meta.lm.cor.gen(): Meta-regression analysis for correlations

meta.lm.cor(): Meta-regression analysis for Pearson or partial correlations

meta.lm.cronbach(): Meta-regression analysis for Cronbach reliabilities

meta.lm.gen(): Meta-regression analysis for any type of effect size

meta.lm.mean.ps(): Meta-regression analysis for paired-samples mean differences

meta.lm.mean1(): Meta-regression analysis for 1-group means

meta.lm.mean2(): Meta-regression analysis for 2-group mean differences

meta.lm.meanratio.ps(): Meta-regression analysis for paired-samples log mean ratios

meta.lm.meanratio2(): Meta-regression analysis for 2-group log mean ratios

meta.lm.oddsratio(): Meta-regression analysis for odds ratios

meta.lm.prop.ps(): Meta-regression analysis for paired-samples proportion differences

meta.lm.prop1(): Meta-regression analysis for 1-group proportions

meta.lm.prop2(): Meta-regression analysis for 2-group proportion differences

meta.lm.propratio2(): Meta-regression analysis for 2-group proportion ratios

meta.lm.semipart(): Meta-regression analysis for semipartial correlations

meta.lm.spear(): Meta-regression analysis for Spearman correlations

meta.lm.stdmean.ps(): Meta-regression analysis for paired-samples standardized mean differences

meta.lm.stdmean2(): Meta-regression analysis for 2-group standardized mean differences

Meta-sub

Estimate differences in relationships found in groups of studies

meta.sub.cor(): Confidence interval for a subgroup difference in average Pearson or partial correlations

meta.sub.cronbach(): Confidence interval for a subgroup difference in average Cronbach reliabilities

meta.sub.gen(): Confidence interval for a subgroup difference in average effect size

meta.sub.pbcor(): Confidence interval for a subgroup difference in average point-biserial correlations

meta.sub.semipart(): Confidence interval for a subgroup difference in average semipartial correlations

meta.sub.spear(): Confidence interval for a subgroup difference in average Spearman correlations

Replication

Compare original and replication results

replicate.agree(): Compares and combines G-index of agreement in original and follow-up studies

replicate.cor.gen(): Compares and combines any type of correlation in original and follow-up studies

replicate.cor(): Compares and combines Pearson or partial correlations in original and follow-up studies

replicate.cronbach(): Compares and combines Cronbach reliablity in original and follow-up studies

replicate.gen(): Compares and combines effect sizes in original and follow-up studies

replicate.mean.ps(): Compares and combines paired-samples mean differences in original and follow-up studies

replicate.mean1(): Compares and combines single mean in original and follow-up studies

replicate.mean2(): Compares and combines 2-group mean differences in original and follow-up studies

replicate.oddsratio(): Compares and combines odds ratios in original and follow-up studies

replicate.plot(): Plot to compare estimates from original and follow-up studies

replicate.prop.ps(): Compares and combines paired-samples proportion differences in original and follow-up studies

replicate.prop1(): Compares and combines single proportions in original and follow-up studies

replicate.prop2(): Compares and combines 2-group proportion differences in original and follow-up studies

replicate.propratio2(): Compares and combines 2-group proportion ratios in original and follow-up studies

replicate.slope(): Compares and combines slope coefficients in original and follow-up studies

replicate.spear(): Compares and combines Spearman correlations in original and follow-up studies

replicate.stdmean.ps(): Compares and combines paired-samples standardized mean differences in original and follow-up studies

replicate.stdmean2(): Compares and combines 2-group standardized mean differences in original and follow-up studies

Standard errors

Standard errors for various effects

se.agree(): Computes the estimate and standard error for a G-index of agreement

se.ave.cor.nonover(): Computes the standard error for the average of two Pearson correlations with no variables in common that have been estimated from the same sample

se.ave.cor.over(): Computes the standard error for the average of two Pearson correlations with one variable in common that have been estimated from the same sample

se.ave.mean2.dep(): Computes the standard error for the average of 2-group mean differences from two parallel measurement response variables in the same sample

se.biphi(): Computes the standard error for a biserial-phi correlation

se.bscor(): Computes the standard error for a biserial correlation

se.cohen(): Computes the standard error for Cohen's d

se.cor(): Computes the standard error for a Pearson or partial correlation

se.mean.ps(): Computes the standard error for a paired-samples mean difference

se.mean2(): Computes the standard error for a 2-group mean difference

se.meanratio.ps(): Computes the standard error for a paired-samples log mean ratio

se.meanratio2(): Computes the standard error for a 2-group log mean ratio

se.oddsratio(): Computes the standard error for a log odds ratio

se.pbcor(): Computes the standard error for a point-biserial correlation

se.prop.ps(): Computes the estimate and standard error for a paired-samples proportion difference

se.prop2(): Computes the estimate and standard error for a 2-group proportion difference

se.propratio.ps(): Computes the estimate and standard error for a paired-samples log proportion ratio

se.propratio2(): Computes the estimate and standard error for a 2-group log proportion ratio

se.semipart(): Computes the standard error for a semipartial correlation

se.slope(): Computes a slope and standard error

se.spear(): Computes the standard error for a Spearman correlation

se.stdmean.ps(): Computes the standard error for a paired-samples standardized mean difference

se.stdmean2(): Computes the standard error for a 2-group standardized mean difference

se.tetra(): Computes the standard error for a tetrachoric correlation approximation

Miscellaneous

Additional functions

cor.from.t(): Computes a Pearson correlation between paired measurements from a paired-samples t statistic

meta.chitest(): Computes a chi-square test of effect-size homogeneity

stdmean2.from.t(): Computes Cohen's d from pooled-variance t statistic

table.from.odds(): Computes the cell frequencies in a 2x2 table using the marginal proportions and odds ratio

table.from.phi(): Computes the cell frequencies in a 2x2 table using the marginal proportions and phi correlation