gMLE.nn {BayesGOF}

Normal-Normal Parameter Estimation

Description

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

Usage

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

Arguments

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 \tau^2: "DL" uses Dersimonian and Lard technique, "SJ" uses Sidik-Jonkman, "REML" uses restricted maximum likelihood, and "MoM" uses a method of moments technique.

Value

estimate

Vector with both estimated \hat{\mu} and \hat{\tau}^2.

mu.hat

Estimated \hat{\mu}.

tau.sq

Estimated \hat{\tau}^2.

method

User-selected method.

Author(s)

Doug Fletcher

References

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.

Examples

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