I am pleased to announce that my Introductory Fisheries Analyses with R (IFAR) book has been published, almost two weeks ahead of schedule. Details about the book (and companion website) are here and it can be purchased from CRC Press (at a ...

I ended the last post with some pretty plots of air temperature change within and between years in the Central England Temperature series. The elephant in the room1 at the end of that post was is the change in the within year (seasonal) effect over time statistically significant? This is the question I’ll try to answer,...

In a series of irregular posts1 I’ve looked at how additive models can be used to fit non-linear models to time series. Up to now I’ve looked at models that included a single non-linear trend, as well as a model that included a within-year (or seasonal) part and a trend part. In this trend plus season model it...

Before going into complex model building, looking at data relation is a sensible step to understand how your different variable interact together. Correlation look at trends shared between two variables, and regression look at causal relation between a predictor (independent variable) and a response (dependent) variable. Correlation As mentioned above correlation look at global movement

(This article was first published on Win-Vector Blog » R, and kindly contributed to R-bloggers) The following article is getting quite a lot of press right now: David Just and Brian Wansink (2015). Fast Food, Soft Drink, and Candy Intake is Unrelated to Body Mass Index for 95% of American Adults. Obesity Science & Practice, forthcoming (upcoming in a...

I've released BayesFactor 0.9.12-2 to CRAN; it should be available on all platforms now. The changes include:Added feature allowing fine-tuning of priors on a per-effect basis: see new argument rscaleEffects of lmBF, anovaBF, and generalTestBFFixed bug...

Logistic regression is a method for fitting a regression curve, y = f(x), when y is a categorical variable. The typical use of this model is predicting y given a set of predictors x. The predictors can be continuous, categorical or a mix of both. The categorical variable y, in general, can assume different values.

This is the third post in the longitudinal data series. Previously, we introduced what longitudinal data is, how we can convert between long and wide format data-sets, and a basic multilevel model for analysis. Apparently, the basic multilevel model is not quite enough to analyse our imaginary randomised controlled trial (RCT) data-set. This post is

Where to start to start? I was recently asked by a colleague manager from another organisation what direction they could give to a staff member interested in building skills in the whole “big data” thing. A search of the web shows hundreds if not thousands of sites and blog posts aimed at budding data scientists, but most of them...

Logistic regression is a technique that is well suited for examining the relationship between a categorical response variable and one or more categorical or continuous predictor variables. The model is generally presented in the following format, where β refers to the parameters and x represents the independent variables. log(odds)=β0+β1∗x1+...+βn∗xn The log(odds), or log-odds ratio, is defined

e-mails with the latest R posts.

(You will not see this message again.)