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

In dgbonett/vcmeta: Varying Coefficient Meta-Analysis

Confidence interval for an average Pearson or partial correlation

Description

Computes the estimate, standard error, and confidence interval for an average Pearson or partial correlation from two or more studies. The sample correlations must be all Pearson correlations or all partial correlations. Use the meta.ave.gen function to meta-analyze any combination of Pearson, partial, or Spearman correlations.

Usage

meta.ave.cor(alpha, n, cor, s, bystudy = TRUE)

Arguments

alpha	alpha level for 1-alpha confidence
n	vector of sample sizes
cor	vector of estimated correlations
s	number of control variables (set to 0 for Pearson)
bystudy	logical to also return each study estimate (TRUE) or not

Value

Returns a matrix. The first row is the average estimate across all studies. If bystudy is TRUE, there is 1 additional row for each study. The matrix has the following columns:

• Estimate - estimated effect size

• SE - standard error

• LL - lower limit of the confidence interval

• UL - upper limit of the confidence interval

References

\insertRef

Bonett2008avcmeta

Examples

n <- c(55, 190, 65, 35)
cor <- c(.40, .65, .60, .45)
meta.ave.cor(.05, n, cor, 0, bystudy = TRUE)

# Should return:
#         Estimate         SE        LL        UL
# Average    0.525 0.05113361 0.4176678 0.6178816
# Study 1    0.400 0.11430952 0.1506943 0.6014699
# Study 2    0.650 0.04200694 0.5594086 0.7252465
# Study 3    0.600 0.08000000 0.4171458 0.7361686
# Study 4    0.450 0.13677012 0.1373507 0.6811071

dgbonett/vcmeta documentation built on July 12, 2024, 3:12 p.m.