Discussing with a non statistician colleague, it seems that the logistic regression is not intuitive; Some basics questions like : - Why don't use the linear model? - What's logistic function? - How can we compute by hand, step by step t...

The registration for R/Finance 2013 -- which will take place May 17 and 18 in Chicago -- is NOW OPEN!Building on the success of the previous conferences in 2009, 2010, 2011 and 2012, we expect more than 250 attendees from around the world. R users from...

The annoucement below just went to the R-SIG-Finance list. More information is as usual at the R / Finance page: Now open for registrations: R / Finance 2013: Applied Finance with R May 17 and 18, 2013 Chicago, IL, USA The registration for R/Fin...

Next topic on logistic regression: the exact and the conditional logistic regressions. Exact logistic regression When the dataset is very small or severely unbalanced, maximum likelihood estimates of coefficients may be biased. An alternative is to use exact logistic regression, available in R with the elrm package. Its syntax is based on an events/trials formulation.

Third part on logistic regression (first here, second here). Two steps in assessing the fit of the model: first is to determine if the model fits using summary measures of goodness of fit or by assessing the predictive ability of the model; second is to deterime if there’s any observations that do not fit the

Second part on logistic regression (first one here). We used in the previous post a likelihood ratio test to compare a full and null model. The same can be done to compare a full and nested model to test the contribution of any subset of parameters: Interpretation of coefficients Note: Dohoo do not report the

We continue to explore the book Veterinary Epidemiologic Research and today we’ll have a look at generalized linear models (GLM), specifically the logistic regression (chapter 16). In veterinary epidemiology, often the outcome is dichotomous (yes/no), representing the presence or absence of disease or mortality. We code 1 for the presence of the outcome and 0

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