Transformation Analysis in R
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Transformation models describe conditional distributions in a simple yet powerful and extensible way. This class of models relies on a parametric family of distributions characterised by their transformation function. Well-known classics, such as the normal linear regression models, binary and polytomous logistic regression, or Weibull and Cox regression models can all be understood as special transformation models. This course highlights the connections between these models in very simple terms. Furthermore, a general form of the likelihood allowing arbitrary forms of random censoring and truncation will be introduced. Finally, model estimation using the R add-on packages mlt and tram will be illustrated by regression models for binary, ordered, continuous, and potentially censored response variables.
Who should attend
Novice and advanced R users from all professional groups.