The Generalized Additive Models for Location, Scale and Shape (GAMLSS) is a recent development which provides a framework with access to a large set of distributions and the ability to model all of the parameters of these distributions as functions of the explanatory variables within a data set. Read the rest of this entry »

# Getting started with GAMLSS

January 19th, 2014# Logistic Regression and Bias Reduction

May 22nd, 2012David Firth published a paper in 1993 on maximum likelihood estimation and the reduction of bias when using this approach. The research in this area appears to provide benefit for logistic regression in small data sets where there is complete of quasi separation. This approach has been implemented for Generalized Linear Models in the **brglm** package. Read the rest of this entry »

# Generalized Linear Models – Poisson Regression

June 26th, 2011The 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 »

# Classification Trees using the rpart function

September 21st, 2010In a previous post on classification trees we considered using the **tree** package to fit a classification tree to data divided into known classes. In this post we will look at the alternative function rpart that is available within the base **R** distribution. Read the rest of this entry »

# Classification Trees

September 18th, 2010Decision trees are applied to situation where data is divided into groups rather than investigating a numerical response and its relationship to a set of descriptor variables. There are various implementations of classification trees in **R** and the some commonly used functions are **rpart** and **tree**. 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 »

# 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 »

# Analysis of Covariance – Extending Simple Linear Regression

April 28th, 2010The simple linear regression model considers the relationship between two variables and in many cases more information will be available that can be used to extend the model. For example, there might be a categorical variable (sometimes known as a covariate) that can be used to divide the data set to fit a separate linear regression to each of the subsets. We will consider how to handle this extension using one of the data sets available within the **R** software package. Read the rest of this entry »