Profile likelihood is an interesting theory to visualize and compute confidence interval for estimators (see e.g. Venzon & Moolgavkar (1988)). As we will use is, we will plot But more generally, it is possible to consider where . Then (...

Summer Program In Data Analysis (SPIDA): May 24th – June 1st, 2012 In its thirteenth season this year, ISR’s Summer Program in Data Analysis focuses on linear models, beginning with “standard” regression through generalized linear models, and extending to mixed or multilevel models, linear and non-linear and generalized, which incorporate two or more hierarchical levels of data or longitudinal...

(This article was first published on twotorials by anthony damico, and kindly contributed to R-bloggers) To leave a comment for the author, please follow the link and comment on his blog: twotorials by anthony damico. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL,...

(This article was first published on twotorials by anthony damico, and kindly contributed to R-bloggers) To leave a comment for the author, please follow the link and comment on his blog: twotorials by anthony damico. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL,...

If you're a Twitter user like me, you're probably familiar with the way that conversations can easily by tracked by following the #hashtag that participants include in the tweets to label the topic. But what causes some topics to take off, and others to die on the vine? Does the use of retweets (copying another users tweet to your...

If you can write the likelihood function for your model, MHadaptive will take care of the rest (ie. all that MCMC business). I wrote this R package to simplify the estimation of posterior distributions of arbitrary models. Here’s how it works: 1) Define your model (ie the likelihood * prior). In this example, lets build