# Articles by R on The broken bridge between biologists and statisticians

### Building ANOVA-models for long-term experiments in agriculture

August 19, 2020 |

This is the follow-up of a manuscript that we (some colleagues and I) have published in 2016 in the European Journal of Agronomy (Onofri et al., 2016). I thought that it might be a good idea to rework some concepts to make them less formal, simpler to follow and more closely related ...

### Fitting complex mixed models with nlme. Example #5

A Joint Regression model Let’s talk about a very old, but, nonetheless, useful technique. It is widely known that the yield of a genotype in different environments depends on environmental covariates, such as the amount of rainfall in some critical periods of time. Apart from rain, also temperature, wind, ...

### AMMI analyses for GE interactions

The CoViD-19 situation in Italy is little by little improving and I feel a bit more optimistic. It’s time for a new post! I will go back to a subject that is rather important for most agronomists, i.e. the selection of crop varieties. All farmers are perfectly aware ...

### Seed germination: fitting hydro-time models with R

March 22, 2020 |

I am locked at home, due to the COVID-19 emergency in Italy. Luckily I am healthy, but there is not much to do, inside. I thought it might be nice to spend some time to talk about seed germination models and the connections with survival analysis. We all know that ...

### A collection of self-starters for nonlinear regression in R

February 25, 2020 |

Usually, the first step of every nonlinear regression analysis is to select the function $$f$$, which best describes the phenomenon under study. The next step is to fit this function to the observed data, possibly by using some sort of nonlinear least squares algorithms. These algorithms are iterative, in the ...

### Self-starting routines for nonlinear regression models

February 13, 2020 |

In R, the drc package represents one of the main solutions for nonlinear regression and dose-response analyses (Ritz et al., 2015). It comes with a lot of nonlinear models, which are useful to describe several biological processes, from plant growth to bioassays, from herbicide degradation to seed germination. These models are ...

### Some everyday data tasks: a few hints with R (revisited)

January 27, 2020 |

One year ago, I published a post titled ‘Some everyday data tasks: a few hints with R’. In that post, I considered four data tasks, that we all need to accomplish daily, i.e. subsetting sorting casting melting In that post, I used the methods I was more familiar with. ... [Read more...]

### Nonlinear combinations of model parameters in regression

January 8, 2020 |

Nonlinear regression plays an important role in my research and teaching activities. While I often use the ‘drm()’ function in the ‘drc’ package for my research work, I tend to prefer the ‘nls()’ function for teaching purposes, mainly because, in my opinion, the transition from linear models to nonlinear models ...

### Fitting ‘complex’ mixed models with ‘nlme’: Example #4

September 12, 2019 |

Testing for interactions in nonlinear regression Factorial experiments are very common in agriculture and they are usually laid down to test for the significance of interactions between experimental factors. For example, genotype assessments may be performed at two different nitrogen fertilisation levels (e.g. high and low) to understand whether ...

### Fitting ‘complex’ mixed models with ‘nlme’: Example #3

September 12, 2019 |

Accounting for the experimental design in regression analyses In this post, I am not going to talk about real complex models. However, I am going to talk about models that are often overlooked by agronomists and biologists, while they may be necessary in several circumstances, especially with field experiments. The ...

### Fitting ‘complex’ mixed models with ‘nlme’: Example #2

September 12, 2019 |

A repeated split-plot experiment with heteroscedastic errors Let’s imagine a field experiment, where different genotypes of khorasan wheat are to be compared under different nitrogen (N) fertilisation systems. Genotypes require bigger plots, with respect to fertilisation treatments and, therefore, the most convenient choice would be to lay-out the experiment ... [Read more...]

### Fitting ‘complex’ mixed models with ‘nlme’. Example #1

August 19, 2019 |

The environmental variance model Fitting mixed models has become very common in biology and recent developments involve the manipulation of the variance-covariance matrix for random effects and residuals. To the best of my knowledge, within the frame of frequentist methods, the only freeware solution in R should be based on ... [Read more...]

### Germination data and time-to-event methods: comparing germination curves

July 19, 2019 |

Very often, seed scientists need to compare the germination behaviour 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? Let’s take a practical approach and start ...

### Survival analysis and germination data: an overlooked connection

The background Seed germination data describe the time until an event of interest occurs. In this sense, they are very similar to survival data, apart from the fact that we deal with a different (and less sad) event: germination instead of death. But, seed germination data are also similar to ...

### Stabilising transformations: how do I present my results?

June 14, 2019 |

ANOVA is routinely used in applied biology for data analyses, although, in some instances, the basic assumptions of normality and homoscedasticity of residuals do not hold. In those instances, most biologists would be inclined to adopt some sort of stabilising transformations (logarithm, square root, arcsin square root…), prior to ANOVA. ...

### Genotype experiments: fitting a stability variance model with R

Yield stability is a fundamental aspect for the selection of crop genotypes. The definition of stability is rather complex (see, for example, Annichiarico, 2002); in simple terms, the yield is stable when it does not change much from one environment to the other. It is an important trait, that helps farmers ... [Read more...]

### 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: \[Z = ...

### 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 ... [Read more...]

### Dealing with correlation in designed field experiments: part I

April 29, 2019 |

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 ... [Read more...]

### How do we combine errors? The linear case

April 14, 2019 |

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 ... [Read more...]
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