The R-Sessions are a series of blog entries on using R. A large part consists of an R-manual I once wrote. Other posts include some tricks I found out, as well as entries detailing functions and packages I wrote for ...
1044 search results for "regression"
Linear regression models with robust parameter estimation
There are situations in regression modelling where robust methods could be considered to handle unusual observations that do not follow the general trend of the data set. There are various packages in R that provide robust statistical methods which are summarised on the CRAN Robust Task View. As an example of using robust statistical estimation in
Manual variable selection using the dropterm function
When fitting a multiple linear regression model to data a natural question is whether a model can be simplified by excluding variables from the model. There are automatic procedures for undertaking these tests but some people prefer to follow a more manual approach to variable selection rather than pressing a button and taking what comes
Rcpp 0.8.0
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...
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...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
