| meta.ave.spear {vcmeta} | R Documentation |
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
Bonett DG (2008). “Meta-analytic interval estimation for bivariate correlations.” Psychological Methods, 13(3), 173–181. ISSN 1939-1463, doi:10.1037/a0012868.
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