gMLE.nn: Normal-Normal Parameter Estimation
In BayesGOF: Bayesian Modeling via Frequentist Goodness-of-Fit

Description Usage Arguments Value Author(s) References Examples

Computes type-II Maximum likelihood estimates \hat{μ} and \hat{τ}^2 for Normal prior g\simNormal(μ, τ^2).

1	gMLE.nn(value, se, fixed = FALSE, method = c("DL","SJ","REML","MoM"))

`value`	Vector of values.
`se`	Standard error for each value.
`fixed`	When `FALSE`, treats the input as if from a random effects model; otherwise, will treat it as if it a fixed effect.
`method`	Determines the method to find τ^2: `"DL"` uses Dersimonian and Lard technique, `"SJ"` uses Sidik-Jonkman, `"REML"` uses restricted maximum likelihood, and `"MoM"` uses a method of moments technique.

`estimate`	Vector with both estimated \hat{μ} and \hat{τ}^2.
`mu.hat`	Estimated \hat{μ}.
`tau.sq`	Estimated \hat{τ}^2.
`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