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

In dgbonett/vcmeta: Varying Coefficient Meta-Analysis

Confidence interval for an average Spearman correlation

Description

Computes the estimate, standard error, and confidence interval for an average Spearman correlation from two or more studies. The Spearman correlation is preferred to the Pearson correlation if the relation between the two quantitative variables is monotonic rather than linear or if the bivariate normality assumption is not plausible.

Usage

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

Arguments

alpha	alpha level for 1-alpha confidence
n	vector of sample sizes
cor	vector of estimated Spearman correlations
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(150, 200, 300, 200, 350)
cor <- c(.14, .29, .16, .21, .23)
meta.ave.spear(.05, n, cor, bystudy = TRUE)

# Should return:
#         Estimate         SE          LL        UL
# Average    0.206 0.02944265  0.14763960 0.2629309
# Study 1    0.140 0.08031750 -0.02151639 0.2943944
# Study 2    0.290 0.06492643  0.15476515 0.4145671
# Study 3    0.160 0.05635101  0.04689807 0.2690514
# Study 4    0.210 0.06776195  0.07187439 0.3402225
# Study 5    0.230 0.05069710  0.12690280 0.3281809

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