One way analysis of variance models can be fitted to data using the R Commander GUI. The general approach is similar to fitting the other types of model in R Commander described in previous posts. Read the rest of this entry »
R Commander – one-way analysis of variance
June 25th, 2010R Commander – logistic regression
June 23rd, 2010We can use the R Commander GUI to fit logistic regression models with one or more explanatory variables. There are also facilities to plot data and consider model diagnostics. The same series of menus as for linear models are used to fit a logistic regression model. Read the rest of this entry »
R Commander – linear regression
June 18th, 2010We can fit various linear regression models using the R Commander GUI which also provides various ways to consider the model diagnostics to determine whether we need to consider a different model. Read the rest of this entry »
R Commander – hypothesis testing
June 16th, 2010The 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 – 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 »
