# 2071 search results for "regression"

## R and Bayesian Statistics

November 21, 2013
By

by Joseph Rickert Drew Linzer, the Bayesian statistician who attracted considerable attention last year with his spot-on, R-based forecast of the 2012 presidential election, recently gave a tutorial on Bayesian statistics to the Bay Area useR Group (BARUG). Drew covered quite a bit of ground running R code that showed how to make use of WinBugs, JAGS and Stan,...

## How to format plots for publication using ggplot2 (with some help from Inkscape)

November 20, 2013
By

The following is the code from a presentation made by Rosemary Hartman to the Davis R Users’ Group. I’ve run the code through the spin function in knitr to produce this post. Download the script to walk through here. First, make your plot. I am going to use the data already in R about sleep habits...

## Predicting optimal of iterations and completion time for GBM

November 20, 2013
By

When choosing the hyperparameters for Generalized Boosted Regression Models, two important choices are shrinkage and the number of trees. Generally a smaller shrinkage with more trees produces a better model, but the modeling time significantly increases. Building a model with too many trees that are heavily cut back by cross validation wastes time, while building a model...

## Art of Statistical Inference

November 20, 2013
By

(This article was first published on MATHEMATICS IN MEDICINE, and kindly contributed to R-bloggers) Art of Statistical Inference Art of Statistical Inference This post was written by me a few years ago, when I started learning the art and science of data analysis. It will be a good starter for the amateur data analysts. Introduction What is statistics? There...

## On the use of marginal posteriors in marginal likelihood estimation via importance-sampling

November 19, 2013
By

Perrakis, Ntzoufras, and Tsionas just arXived a paper on marginal likelihood (evidence) approximation (with the above title). The idea behind the paper is to base importance sampling for the evidence on simulations from the product of the (block) marginal posterior distributions. Those simulations can be directly derived from an MCMC output by randomly permuting the

## Simulation (is where it’s happening)

November 18, 2013
By

Jim Silverton wrote to the Allstat mailing list recently: “Hi, Anyone up for a challenge? Suppose we have random variables that are random points on the surface of a sphere. What is the probability that the tetrahedron made by joining these … Continue reading →

## Some Options for Testing Tables

November 18, 2013
By

Contingency tables are a very good way to summarize discrete data.  They are quite easy to construct and reasonably easy to understand. However, there are many nuances with tables and care should be taken when making conclusions related to the data. Here are just a few thoughts on the topic. Dealing with sparse data On

## Visualizing neural networks in R – update

November 14, 2013
By

In my last post I said I wasn’t going to write anymore about neural networks (i.e., multilayer feedforward perceptron, supervised ANN, etc.). That was a lie. I’ve received several requests to update the neural network plotting function described in the original post. As previously explained, R does not provide a lot of options for visualizing

## Calibration of p-value under variable selection: an example

November 14, 2013
By

Very often people report p-values for linear regression estimates after performing variable selection step. Here is a simple simulation that shows that such a procedure might lead to wrong calibration of such tests.Consider a simple data generating pro...

## A slightly different introduction to R, part V: plotting and simulating linear models

November 11, 2013
By

In the last episode (which was quite some time ago) we looked into comparisons of means with linear models. This time, let’s visualise some linear models with ggplot2, and practice another useful R skill, namely how to simulate data from known models. While doing this, we’ll learn some more about the layered structure of a