Modeling

R Modeling

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Linear regression, logistic regression

  • Multiple linear regression with r
  • Interpreting regression coefficients; finding a parsimonious model 

Generalized linear models

  • Logistic regression with r
  • Multiple regression and logistic regression as special cases of the generalized linear model
  • The need for a different model when the response variable is binary, the logistic transform and fitting the model to some simple examples, deviance residuals
  • The Poisson model for count data.
  • The problem of overdispersion 

Analyzing longitudinal data using r

  • Examples of longitudinal data
  • Mixed-effects models for longitudinal data
  • Simple graphics for longitudinal data and simple inference using the summary measure approach
  • The ‘long-form’ of longitudinal data 

Generalized estimating equations

  • Modeling the correlational structure of the repeated measurements
  • The dropout problem
  • The generalized estimating equation approach for non-normal response variables in longitudinal data