Summary Version 0.8.0 of the Rcpp package was released to CRAN today. This release marks another milestone in the ongoing redesign of the package, and underlying C++ library. Overview Rcpp is an R package and C++ library that facilitates integr...
1041 search results for "regression"
R Package ‘rms’ for Regression Modeling
If you attended Frank Harrell's Regression Modeling Strategies course a few weeks ago, you got a chance to see the rms package for R in action. Frank's rms package does regression modeling, testing, estimation, validation, graphics, prediction, and ty...
Example 7.36: Propensity score stratification
In examples 7.34 and 7.35 we described methods using propensity scores to account for possible confounding factors in an observational study.In addition to adjusting for the propensity score in a multiple regression and matching on the propensity score...
An economist explains: Why I use R
Economist and R blogger JD Long gave a talk last week (as part of the vconf.org project) about why he uses R to do statistical forecasts of agricultural yield for the reinsurance company he works for. I couldn't make the live session, but a replay is now available. The audio's a bit choppy, but if you've every struggled with...
Book Review – Modern Applied Statistics with S by W. N. Venables and B. D. Ripley (Springer 2003)
Order this book from Amazon Modern Applied Statistics with S (Fourth Edition) is one of the oldest and most popular books on Applied Statistics using R and S-plus. A large number of topics in Applied Statistics are covered in this book and it is certainly not for the faint hearted. A sound knowledge of
Using the update function during variable selection
When fitting statistical models to data where there are multiple variables we are often interested in adding or removing terms from our model and in cases where there are a large number of terms it can be quicker to use the update function to start with a formula from a model that we have already
Knowing whether a time-series has been differenced appropriately in order to make it stationary
Hello everybody,
Today I would like to make you learn a simple method (and of-course using R) how to identify whether a time-series has been differenced appropriately while making it stationary.
Suppose, you have made a series stationary by differencing it, now in order to know whether it is neither over nor under differenced subject the current series against next level...
Today I would like to make you learn a simple method (and of-course using R) how to identify whether a time-series has been differenced appropriately while making it stationary.
Suppose, you have made a series stationary by differencing it, now in order to know whether it is neither over nor under differenced subject the current series against next level...
Knowing whether a time-series has been differenced appropriately in order to make it stationary
Hello everybody,
Today I would like to make you learn a simple method (and of-course using R) how to identify whether a time-series has been differenced appropriately while making it stationary.
Suppose, you have made a series stationary by differencing it, now in order to know whether it is neither over nor under differenced subject the current series against next level...
Today I would like to make you learn a simple method (and of-course using R) how to identify whether a time-series has been differenced appropriately while making it stationary.
Suppose, you have made a series stationary by differencing it, now in order to know whether it is neither over nor under differenced subject the current series against next level...
Bayes vs. SAS
Glancing perchance at the back of my Amstat News, I was intrigued by the SAS advertisement Bayesian Methods Specify Bayesian analysis for ANOVA, logistic regression, Poisson regression, accelerated failure time models and Cox regression through the GENMOD, LIFEREG and PHREG procedures. Analyze a wider variety of models with the MCMC procedure, a general purpose Bayesian 
Mixed linear model approach adapted for genome-wide association studies
A few weeks ago I covered an R package for efficient mixed model regression that is capable of simultaneously accounting for both population stratification and relatedness to compute unbiased estimates of standard errors and p-values for genetic associ...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
