Generalized Linear Models – Poisson Regression

June 26th, 2011

The Generalized Linear Model (GLM) allows us to model responses with distributions other than the Normal distribution, which is one of the assumptions underlying linear regression as used in many cases. When data is counts of events (or items) then a discrete distribution is more appropriate is usually more appropriate than approximating with a continuous distribution, especially as our counts should be bounded below at zero. Negative counts do not make sense. Read the rest of this entry »

Variable selection using automatic methods

May 22nd, 2010

When 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, 2010

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. Read the rest of this entry »

Simple Linear Regression

April 23rd, 2010

One of the most frequent used techniques in statistics is linear regression where we investigate the potential relationship between a variable of interest (often called the response variable but there are many other names in use) and a set of one of more variables (known as the independent variables or some other term). Unsurprisingly there are flexible facilities in R for fitting a range of linear models from the simple case of a single variable to more complex relationships. Read the rest of this entry »

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