The R Commander GUI can be used to perform classical hypothesis testing. There are menu options to undertake the variants on the t-test as well as tests on proportions or equality of variances for two samples of data. Read the rest of this entry »
R Commander – hypothesis testing
June 16th, 2010R Commander – data manipulation and summaries
June 14th, 2010Previously we considered the R Commander interface as a simple GUI for the R statistical software system. Here we will look at how to undertake data manipulation and creating basic statistical summaries of data sets. Read the rest of this entry »
R Commander – a good introductory GUI for R
June 1st, 2010The R software is very powerful and flexible but one of the complaints of new users is that the learning curve is steep and can be daunting. There have been various projects to create GUIs for R with varying levels of sophistication, one of which is R Commander by John Fox. Read the rest of this entry »
Variable selection using automatic methods
May 22nd, 2010When we have a set of data with a small number of variables we can easily use a manual approach to identifying a good set of variables and the form they take in our statistical model. In other situations we may have a large number of potentially important variables and it soon becomes a time consuming effort to follow a manual variable selection process. In this case we may consider using automatic subset selection tools to remove some of the burden of the task. Read the rest of this entry »
Linear regression models with robust parameter estimation
May 15th, 2010There 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. Read the rest of this entry »
Manual variable selection using the dropterm function
May 12th, 2010When 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 out. Read the rest of this entry »
Book Review – Modern Applied Statistics with S by W. N. Venables and B. D. Ripley (Springer 2003)
May 9th, 2010[amazonshowcase_dc70f73bfc2c3691d95f6a642e207132]
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 the Statistical Methods covered in each Chapter is important and there are the book includes many examples of using a wide range of techniques. Read the rest of this entry »
Using the update function during variable selection
May 9th, 2010When 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 fitted and to specify the terms that we want to add or remove as opposed to a copy and paste and manually editing the formula to our needs. Read the rest of this entry »
