search for: spiegelhalt

...If there isn't such a function a first attempt for a "selfmade" plot would be to tell boxplot not to plot boxes (sounds silly isn't it?). I've tried the option "boxwex=0" but the result is unsatisfactory. An example for a league table can be found in Marshall, Spiegelhalter [1998], Reliability of league tables of in vitro fertilisation clinics, BMJ1998;316:1701-1705, you may find it at http://bmj.bmjjournals.com Thanks in advance Eik Vettorazzi

This is a question of clarification. IN 2009 Higgins, Thompson and Spiegelhalter (J R Statist Soc A 172:137-159) gave WinBUGs code to get credible intervals from random effects meta analysis for the prediction interval of a new study. It appears that the predict.rma function creates approximate credible intervals (pending a function revision by the author) for that purpose....

Greetings all, I'm a newcomer to Bayesian stats, and I'm trying to calculate the Deviance Information Criterion "by hand" from some MCMC output. However, having consulted several sources, I am left confused as to the exact terms to use. The most common formula can be written as DIC = 2*Mean(Deviance over the whole sampled posterior distribution) - Deviance(Mean

...lt, see Lin, X. & Breslow N.E. (1996) in JASA. For empirical demonstartion, see Carlin et al (2001) in Biostatistics. (3)Bayesian approach. See for example, Clayton, D. G. (1996). Generalized linear mixed models. In Markov Chain Monte Carlo in Practice, Ed W.R. Gilks, S. Richardson & D.J. Spiegelhalter, pp. 275-301. London: Chapman and Hall. (4)REML estimation in GLMM. I will have a paper in June issue of Bioemtrika. REML estimation has the same asymptotic property as standard MLE but less bias for finite samples. Of course, one can argue that Bayesian formulation automatically generates REML...

....36-1 of survival package by adding censor=FALSE argument to survfit.coxph * Added type="ccterms" to various predict methods * Made type="ccterms" the default for partial g-indexes in gIndex, i.e., combine all indirectly related (through interactions) terms * Added Spiegelhalter calibration test to val.prob * Added a check in cph to trigger an error if strata() is used in formula * Fixed drawing of polygon for shaded confidence bands for survplot.survfit (thanks to Patrick Breheny <patrick.breheny at uky.edu>) * Changed default adjust.subtitle in bplo...

....36-1 of survival package by adding censor=FALSE argument to survfit.coxph * Added type="ccterms" to various predict methods * Made type="ccterms" the default for partial g-indexes in gIndex, i.e., combine all indirectly related (through interactions) terms * Added Spiegelhalter calibration test to val.prob * Added a check in cph to trigger an error if strata() is used in formula * Fixed drawing of polygon for shaded confidence bands for survplot.survfit (thanks to Patrick Breheny <patrick.breheny at uky.edu>) * Changed default adjust.subtitle in bplo...

Dear all, I am trying to learn Bayesian inference and Bayesian data analysis, I am new in the field. Would any experts on the list recommend any good sites or materials for beginners? My approach is to learn and understand the theory first, then program on my own using R, though I see there are already packages. appreciate any help, thanks in advance!

Hi, I'm attending summer School at UCLA (IPAM) on "probabilistics models of cognition". I have been an R-user since v. 1.4.1, but was trained in the frequentist tradition (as most psychologists!). I found that all faculty here use matlab and Murphy's bayes net toolbox. I have not had the need to use matlab before, and would love to stick to R for graphics models and

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems

Hello. Douglas Bates has explained in a previous posting to R why he does not output residual degrees of freedom, F values and probabilities in the mixed model (lmer) function: because the usual degrees of freedom (obs - fixed df -1) are not exact and are really only upper bounds. I am interpreting what he said but I am not a professional statistician, so I might be getting this wrong... Does