Newsletter issue of 'StatNews' distributed by the Cornell Statistical Consulting Unit on survival analysis.
Newsletter issue of 'StatNews' distributed by the Cornell Statistical Consulting Unit on survival analysis.
R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
This page supports an in-class exercise that highlights several key Bayesian concepts. The scenario is as follows: a large paper bag contains pieces of candy with wrappings of different color, and we are interested in learning about the unknown proportion of yellow-wrapped pieces of candy. After completing the exercises, we will be familiar with the following concepts and ideas: probability distributions can quantify degree of belief, prior distribution, posterior distribution, sequential updating, conjugacy, Cromwell’s Rule (http://en.wikipedia.org/wiki/Cromwell's_rule), the data overwhelm the prior, Bayes factors, Savage-Dickey density ratio, sensitivity analysis, coherence.
Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).
Adjust regression parameters to bend and shift a two-dimensional polynomial surface.
Check how your Bayes factor conclusion depends on the r-scale parameter.
This Shiny app implements the p-curve (Simonsohn, Nelson, & Simmons, 2014; see http://www.p-curve.com) in its previous ("app2") and the current version ("app3"), the R-Index and the Test of Insufficient Variance, TIVA (Schimmack, 2014; see http://www.r-index.org/), and tests whether p values are reported correctly.
When does a significant p-value indicate a true effect? This app will help with understanding the Positive Predictive Value (PPV) of a p-value.
This app is based on Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. http://doi.org/10.1371/journal.pmed.0020124
Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?
Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.