Publications by R on The broken bridge between biologists and statisticians
Pairwise comparisons in nonlinear regression
Pairwise comparisons are one of the most debated topic in agricultural research: they are very often used and, sometimes, abused, in literature. I have nothing against the appropriate use of this very useful technique and, for those who are interested, some colleagues and I have given a bunch of (hopefully) useful suggestions in a paper, a few year...
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AMMI analyses for multi-environment studies
Again into a subject that is rather important for most agronomists, i.e. the selection of crop varieties. All farmers are perfectly aware that crop performances are affected both by the genotype and by the environment. These two effects are not purely additive and they often show a significant interaction. By this word, we mean that a genotype can...
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Fitting threshold models to seed germination data
In previous posts we have shown that we can use time-to-event curves to describe the germination pattern of a seed population (see here). We have also shown that these curves can be modified to include the effects of external/internal factors/covariates, such as the genotype, the species, the humidity content and temperature in the substrate (see h...
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Multi-environment split-plot experiments
Have you made a split-plot field experiment? Have you repeated such an experiment in two (or more) years/locations? Have you run into troubles, because the reviewer told you that your ANOVA model was invalid? If so, please, stop for awhile and read: this post might help you understand what was wrong with your analyses. Motivating example Let’s...
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Should I say ”there is no difference” or ”the difference is not significant”?
In a recent manuscript we wrote a sentence similar to the following: “On average, the genotype A gave a yield of 12.4 tons per hectare, while the genotype B gave 10.6 tons per hectare and such a difference was not significant (P = 0.20)”. Perhaps I should point out that we were talking about maize yields… One of the reviewers complained tha...
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Meta-analysis for a single study. Is it possible?
We all know that the word meta-analysis encompasses a body of statistical techniques to combine quantitative evidence from several independent studies. However, I have recently discovered that meta-analytic methods can also be used to analyse the results of a single research project. That happened a few months ago, when I was reading a paper from...
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Analysing seed germination and emergence data with R (a tutorial). Part 7
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/. All these posts exapand on a paper that we have recently published in the Journal ‘Weed Science’; please follow this link to the paper. Exploring the results of a time-to-event fit: model parameters In the ...
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My first experience with blogdown
This is my first day at work with blogdown. I must admit it is pretty overwhelming at the beginning … I thought that it might be useful to write down a few notes, to summarise my steps ahead, during the learning process. I do not work with blogdown everyday and I tend to forget things quite easily. Therefore, these notes may help me recap how f...
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Going back to the basics: the correlation coefficient
A measure of joint variability In statistics, dependence or association is any statistical relationship, whether causal or not, between two random variables or bivariate data. It is often measured by the Pearson correlation coefficient: \[\rho _{X,Y} =\textrm{corr} (X,Y) = \frac {\textrm{cov}(X,Y) }{ \sigma_X \sigma_Y } = \frac{ \sum_{1 = 1}^n [(...
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Drowning in a glass of water: variance-covariance and correlation matrices
One of the easiest tasks in R is to get correlations between each pair of variables in a dataset. As an example, let’s take the first four columns in the ‘mtcars’ dataset, that is available within R. Getting the variances-covariances and the correlations is straightforward. data(mtcars) matr <- mtcars[,1:4] #Covariances cov(matr) ## ...
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