Author: R on The broken bridge between biologists and statisticians

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/ In the previous post we have shown that time-to-event curves (e.g., germination or emergence curves) can be used to describe the time course of germinations/emergences for a seed lot (this post). We have also seen that the effects of experimental factors on seed germination can be accounted for by coding a different time-to-event curve for each factor level (this post). In this post, we would like to consider the ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/ Very often, seed scientists need to compare the germination behavior of different seed populations, e.g., different plant species, or one single plant species submitted to different temperatures, light conditions, priming treatments and so on. How should such a comparison be performed? For example, if we have submitted several seed samples to different environmental conditions, how do we decide whether the germinative response is affected by those environmental conditions? If ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. Principal Component Analysis (PCA) is perhaps the most widespread multivariate technique in biology and it is used to summarise the results of experiments in a wide range of disciplines, from agronomy to botany, from entomology to plant pathology. Whenever possible, the results are presented by way of a biplot, an ubiquitous type of graph with a formidable descriptive value. Indeed, carefully drawn biplots can be used to represent, altogether, the experimental subjects, the experimental variables and their reciprocal relationships (distances and correlations). However, biplots are not created equal ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. In this post I am revisiting the concept of Principal Component Analysis (PCA). You might say that there is no need for that, as the Internet is full with posts relating to such a rather old technique. However, I feel that, in those posts, the theoretical aspects are either too deeply rooted in maths or they are skipped altogether, so that the main emphasis is on interpreting the output of an R function. I think that both approaches may not be suitable for biologists: the first one may ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. This is a follow-up post. If you are interested in other posts of this series, please go to: https://www.statforbiology.com/tags/drcte/ The first thing we should consider before working through this tutorial is the structure of germination/emergence data. To our experience, seed scientists are used to storing their datasets in several formats, that may not be immediately usable with the ‘drcte’ and ‘drc’ packages, which this tutorial is built upon. The figure below shows some of the possible formats that I have often encountered in my consulting work. Both the ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. Seed germination and emergence data describe the time until the event of interest occurs and, therefore, they can be put together in the wide group of time-to-event data. You may wonder: what’s the matter with time-to-event data? Do they have anything special that needs our attention? The answer is, definitely, yes! Indeed, with very few exceptions, time-to-event data are affected by a peculiar form of uncertainty, which takes the name of censoring. It relates to the fact that, due to the typical monitoring schedule, the exact time-to-event may ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. In general, undergraduate students in biology/ecology courses tend to consider the derivatives as a very abstract entity, with no real usefulness in the everyday life. In my work as a teacher, I have often tried to fight against such an attitude, by providing convincing examples on how we can use the derivatives to get a better understanding about the changes on a given system. In this post I’ll tell you about a recent situation where I was involved with derivatives. A few weeks ago, a colleague of mine ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. In a recent post I presented several equations and just as many self-starting functions for nonlinear regression analyses in R. Today, I would like to build upon that post and present some further equations, relating to the so-called threshold models. But, … what are threshold models? In some instances, we need to describe relationships where the response variable changes abruptly, following a small change in the predictor. A typical threshold model looks like that in the Figure below, where we see three threshold levels: (X = 5.5): at ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. In recent times, a few colleagues at my Department and I have devoted some research effort to data management for diallel mating experiments, which we have summarised in a paper (Onofri et al., 2020) and a series of five blog posts (see here). A final topic that remains to be covered relates to the frequent possibility that these diallel experiments are repeated across years and/or locations. How should the resulting dataset be analysed? We will start from a multi-environment full diallel experiment with 5 parental lines, in four ... Read More

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. Another post for this series about diallel mating experiments. So far, we have published a paper in Plant Breeding (Onofri et al., 2020), where we presented lmDiallel, a new R package to fit diallel models. We followed up this paper with a series of four blog posts, giving more detail about the package (see here), about the Hayman’s models type 1 (see here) and type 2 (see here) and about the Griffing’s family of models (see here). In this post we are going to talk about the Gardner-Eberarth ... Read More