Official data for the COVID epidemic in Italy are availabe at https://github.com/pcm-dpc. The number of known active cases over time show a first wave of infections over March-June and a second one, much higher, started in October 2020 and still ongoing:
Did you think Krypto the Superdog was cool? Try Crypto the R package! (You didn’t think Krypto was cool? It’s ok.)
Some time ago I created an R package for encrypting/decrypting text messages. I called it Crypto. Continue reading
[Download the simulated data and the R script used in the following example]
Many biological variables depend on the size of the organisms and on the environmental temperature. For example, large organisms tend to grow more slowly, and live longer, than small ones. On the other hand, organisms tend to grow faster in warm climates compared to cold ones. Moreover, small organisms and organisms from warm climates consume more resources (per mass unit) than large organisms and those living in cold climates.
The effect of body mass and temperature at the individual level reflects at the ecological level, in variables such as the intrinsic growth rate of populations, the strength of competition and predator-prey interactions (e.g. Vucic-Pestic et al. 2011), and so on.
Scientists suggested that these patterns are explained by the role that body mass and temperature play on the metabolism of individuals, namely the ensemble of the chemical reactions that keep them alive. In 2004, Brown et al. summarized and formalized what they called the Metabolic Theory of Ecology (MTE), centered on the following equation: Continue reading
Some time ago I wrote about how to fit a linear model and interpret its summary table in R. At the time I used an example in which the response variable depended on two explanatory variables and on their interaction. It was a rather specific article, in which I overlooked some essential steps in the process of selection and interpretation of a statistical model. In the present article and in the relative worked examples I will expand the topic a bit, explaining 1) how to select the most parsimonious model relatively to a dataset using function anova() and 2) how to use summary(), together with relevel(), for testing for significant differences between pairs of experimental treatments (R script here). Continue reading