Computes the estimate, standard error, and confidence interval for an average of any type of parameter from two or more studies.
meta.ave.gen(alpha, est, se, bystudy = TRUE)alpha level for 1-alpha confidence
vector of parameter estimates
vector of standard errors
logical to also return each study estimate (TRUE) or not
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
est <- c(.022, .751, .421, .287, .052, .146, .562, .904)
se <- c(.124, .464, .102, .592, .864, .241, .252, .318)
meta.ave.gen(.05, est, se, bystudy = TRUE)
#> Estimate SE LL UL
#> Average 0.393125 0.1561622 0.08705266 0.6991973
#> Study 1 0.022000 0.1240000 -0.22103553 0.2650355
#> Study 2 0.751000 0.4640000 -0.15842329 1.6604233
#> Study 3 0.421000 0.1020000 0.22108367 0.6209163
#> Study 4 0.287000 0.5920000 -0.87329868 1.4472987
#> Study 5 0.052000 0.8640000 -1.64140888 1.7454089
#> Study 6 0.146000 0.2410000 -0.32635132 0.6183513
#> Study 7 0.562000 0.2520000 0.06808908 1.0559109
#> Study 8 0.904000 0.3180000 0.28073145 1.5272685
# Should return:
# Estimate SE LL UL
# Average 0.393125 0.1561622 0.08705266 0.6991973
# Study 1 0.022000 0.1240000 -0.22103553 0.2650355
# Study 2 0.751000 0.4640000 -0.15842329 1.6604233
# Study 3 0.421000 0.1020000 0.22108367 0.6209163
# Study 4 0.287000 0.5920000 -0.87329868 1.4472987
# Study 5 0.052000 0.8640000 -1.64140888 1.7454089
# Study 6 0.146000 0.2410000 -0.32635132 0.6183513
# Study 7 0.562000 0.2520000 0.06808908 1.0559109
# Study 8 0.904000 0.3180000 0.28073145 1.5272685