gMLE.nn {BayesGOF} | R Documentation |
Computes type-II Maximum likelihood estimates \hat{\mu}
and \hat{\tau}^2
for Normal prior g\sim
Normal(\mu, \tau^2)
.
gMLE.nn(value, se, fixed = FALSE, method = c("DL","SJ","REML","MoM"))
value |
Vector of values. |
se |
Standard error for each value. |
fixed |
When |
method |
Determines the method to find |
estimate |
Vector with both estimated |
mu.hat |
Estimated |
tau.sq |
Estimated |
method |
User-selected method. |
Doug Fletcher
Marin-Martinez, F. and Sanchez-Meca, J., 2010. "Weighting by inverse variance or by sample size in random-effects meta-analysis," Educational and Psychological Measurement, 70(1), pp. 56-73.
Brown, L.D., 2008. "In-season prediction of batting averages: A field test of empirical Bayes and Bayes methodologies," The Annals of Applied Statistics, pp. 113-152.
Sidik, K. and Jonkman, J.N., 2005. "Simple heterogeneity variance estimation for meta-analysis," Journal of the Royal Statistical Society: Series C (Applied Statistics), 54(2), pp. 367-384.
data(ulcer)
### MLE estimate of alpha and beta
ulcer.mle <- gMLE.nn(ulcer$y, ulcer$se, method = "DL")$estimate
ulcer.mle
ulcer.reml <- gMLE.nn(ulcer$y, ulcer$se, method = "REML")$estimate
ulcer.reml