Blog Archives

How do we combine errors, in biology? The delta method

How do we combine errors, in biology? The delta method

In a recent post I have shown that we can build linear combinations of model parameters (see here ). For example, if we have two parameter estimates, say Q and W, with standard errors respectively equal to \(\sigma_Q\) and \(\sigma_W\), we can build a linear combination as follows: \ where A, B and C are...

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Dealing with correlation in designed field experiments: part II

With field experiments, studying the correlation between the observed traits may not be an easy task. Indeed, in these experiments, subjects are not independent, but they are grouped by treatment factors (e.g., genotypes or weed control methods) or by blocking factors (e.g., blocks, plots, main-plots). I have dealt with this problem in a previous post and I gave a...

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Dealing with correlation in designed field experiments: part I

Observations are grouped When we have recorded two traits in different subjects, we can be interested in describing their joint variability, by using the Pearson’s correlation coefficient. That’s ok, altough we have to respect some basic assumptions (e.g. linearity) that have been detailed elsewhere (see here). Problems may arise when we need to test the hypothesis that the correlation coefficient is...

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How do we combine errors? The linear case

In our research work, we usually fit models to experimental data. Our aim is to estimate some biologically relevant parameters, together with their standard errors. Very often, these parameters are interesting in themselves, as they represent means, differences, rates or other important descriptors. In other cases, we use those estimates to derive further indices, by way of some appropriate...

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Some everyday data tasks: a few hints with R

We all work with data frames and it is important that we know how we can reshape them, as necessary to meet our needs. I think that there are, at least, four routine tasks that we need to be able to accomplish: subsetting sorting casting melting Obviously, there is a wide array of possibilities; I’ll just mention a few, which I regularly use. Subsetting the...

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Drowning in a glass of water: variance-covariance and correlation matrices

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 corr...

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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: \ }{ \sigma_X \sigma_Y }\] Other measures of...

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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...

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