Since at some point I had trouble with a conflict between knitr and the latex package textpos, I used the lesser Sweave in Another Experiment with R and Sweave. I ran the Sweave2knitr command and discovered that textpos and knitr play well togeth...
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Contribute to The R Journal with LyX/knitr
(This paragraph is pure rant; feel free to skip it) I have been looking forward to the one-column LaTeX style of The R Journal, and it has arrived eventually. Last time I mentioned "it does not make sense to sell the cooked shrimps"; actually there is ...
Veterinary Epidemiologic Research: Linear Regression
This post will describe linear regression as from the book Veterinary Epidemiologic Research, describing the examples provided with R. Regression analysis is used for modeling the relationship between a single variable Y (the outcome, or dependent variable) measured on a continuous or near-continuous scale and one or more predictor (independent or explanatory variable), X. If 
Large claims, and ratemaking
During the course, we have seen that it is natural to assume that not only the individual claims frequency can be explained by some covariates, but individual costs too. Of course, appropriate families should be considered to model the distribution of the cost , given some covariates .Here is the dataset we’ll use, > sinistre=read.table("http://freakonometrics.free.fr/sinistreACT2040.txt", + header=TRUE,sep=";") > sinistres=sinistre...
Unknown Variance Two-Tailed Test of Population Mean
Question The mean safety audit score of ACME Co. stores in New York (n=200) was 74.3pts February last year. Suppose we decided to sample 22 out of the 200 stores one year later. We find that the sample mean is 78.6pts and the sample standard deviation is 3.2pts. Can we reject the null hypothesis that
Exposure with binomial responses
Last week, we’ve seen how to take into account the exposure to compute nonparametric estimators of several quantities (empirical means, and empirical variances) incorporating exposure. Let us see what can be done if we want to model a binomial response. The model here is the following: , the number of claims on the period is unobserved the number of...
Learning R Using a Chemical Reaction Engineering Book: Part 4
The links to previous parts are listed here. (Part 1, Part 2, Part 3). In this part, I tried to recreate the examples in sections A.3.1 of the computational appendix in the reaction engineering book (by Rawlings and Ekerdt). Solving a … Continue reading →
Pills, half pills and probabilities
Yesterday, I was uploading some old posts to complete the migration (I get back to my old posts, one by one, to check links of pictures, reformating R codes, etc). And I re-discovered a post published amost 2 years ago, on nuns and Hell’s Angels in an airplaine. It reminded me an old probability problem (that might be known...
packed off!!!
Deliverance!!! We have at last completed our book! Bayesian Essentials with R is off my desk! In a final nitty-gritty day of compiling and recompiling the R package bayess and the LaTeX file, we have reached versions that were in par with our expectations. The package has been submitted to CRAN (it has gone back 
Collinearity and stepwise VIF selection
Collinearity, or excessive correlation among explanatory variables, can complicate or prevent the identification of an optimal set of explanatory variables for a statistical model. For example, forward or backward selection of variables could produce inconsistent results, variance partitioning analyses may be unable to identify unique sources of variation, or parameter estimates may include substantial amounts 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
