summary.singletable {mmeta} | R Documentation |
singletable
Summary a model of class singletable
fitted by singletable
.
## S3 method for class 'singletable' summary(object,...)
object |
an object inheriting from class |
... |
additional arguments; currently none is used. |
A list with the following components: posterior mean, posterior median, equal tail CI, and HDR CI.
Luo, S., Chen, Y., Su, X., Chu, H., (2014). mmeta: An R Package for Multivariate Meta-Analysis. Journal of Statistical Software, 56(11), 1-26.
Chen, Y., Luo, S., (2011a). A Few Remarks on "Statistical Distribution of the Difference of Two Proportions' by Nadarajah and Kotz, Statistics in Medicine 2007; 26(18):3518-3523" . Statistics in Medicine, 30(15), 1913-1915.
Chen, Y., Chu, H., Luo, S., Nie, L., and Chen, S. (2014a). Bayesian analysis on meta-analysis of case-control studies accounting for within-study correlation. Statistical Methods in Medical Research, doi: 10.1177/0962280211430889. In press.
Chen, Y., Luo, S., Chu, H., Su, X., and Nie, L. (2014b). An empirical Bayes method for multivariate meta-analysis with an application in clinical trials. Communication in Statistics: Theory and Methods. In press.
Chen, Y., Luo, S., Chu, H., Wei, P. (2013). Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials. Statistics in Biopharmaceutical Research, 5(2), 142-155.
# Inference under Jeffreys prior distribution #single.OR.Jeffreys <- singletable(a1=0.5, b1=0.5, a2=0.5, # b2=0.5, y1=40, n1=96, y2=49, n2=109, # model="Independent", # measure="OR", method="exact") #summary(single.OR.Jeffreys) # Inference under Laplace prior distribution #single.OR.Laplace <- singletable(a1=1, b1=1, a2=1, b2=1, # y1=40, n1=96, y2=49, n2=109, # model="Independent", measure="OR", # method="exact") # Inference under Sarmanov prior distribution with positive correlation #single.OR.Sar1 <- singletable(a1=0.5, b1=0.5, a2=0.5, b2=0.5, # rho=0.5, y1=40, n1=96, y2=49, n2=109, # model="Sarmanov", # measure="OR", method="exact") # Inference under Sarmanov prior distribution with negative correlation #single.OR.Sar2 <- singletable(a1=0.5, b1=0.5, a2=0.5, b2=0.5, # rho=-0.5, y1=40, n1=96, y2=49, n2=109, # model="Sarmanov", # measure="OR", method="exact") # generate a 2X2 panel plot #par(mfrow=c(2,2)) #plot(single.OR.Jeffreys, type="overlap", xlim=c(0.5, 2), # main="Jefferys Prior") #plot(single.OR.Laplace, type="overlap", xlim=c(0.5, 2), # main="Laplace Prior") #plot(single.OR.Sar1, type="overlap", xlim=c(0.5, 2), # main=expression(paste("Sarmanov Prior ",rho," = 0.5"))) #plot(single.OR.Sar2, type="overlap", xlim=c(0.5, 2), # main=expression(paste("Sarmanov Prior ",rho," = -0.5")))