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

# Author: R on The broken bridge between biologists and statisticians

#### AMMI analyses for GE interactions

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians.
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 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 give particularly good/bad performances ... Read More

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

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. Models based on the distribution of germination time How can we rework Equation 1 to predict the proportion of germinated seeds, as a function of time and water potential? One line of attack follows the proposal we made in a relatively recent paper (Onofri at al., 2018). We started from the idea that the time course of the proportion of germinated seeds ((P)) is expected to increase over time, according to a S-shaped curve, such as the usual log-logistic cumulative probability function (other cumulative distribution functions can be ... Read More

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

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. 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. And, as a long-time R user, I have mainly incorporated in my workflow all the functions from the base R implementation. But now, the tidyverse is with us! Well, as far as I know, the tidyverse has been around ... Read More

#### Nonlinear combinations of model parameters in regression

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. 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 is smoother, for beginners. One problem with ‘nls()’ is that, in contrast to ‘drm()’, it is not specifically tailored to the needs of biologists or students in biology. Therefore, now and then, I have to build some helper ... Read More

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

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. 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 as a split-plot, in a randomised complete block design. Genotypes would be randomly allocated to main plots, while fertilisation systems would be randomly allocated to sub-plots. As usual in agricultural research, the experiment should be repeated in different years, in order to explore the environmental variability ... Read More

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

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. 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 the ‘nlme’ package, as the ‘lmer’ package does not easily permit such manipulations. The ‘nlme’ package is fully described in Pinheiro and Bates (2000). Of course, the ‘asreml’ package can be used, but, unfortunately, this is not freeware. Coding mixed models in ... Read More

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

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. 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 from an appropriate example: a few years ago, some collegues studied the germination behaviour for seeds of a plant species (Verbascum arcturus, BTW…), in different conditions. In detail, they considered the factorial combination of two storage periods (LONG and SHORT ... Read More

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

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. 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 failure-time data, phenological data, time-to-remission data… the first point is: germination data are time-to-event data. You may wonder: what’s the matter with time-to-event data? Do they have anything special? With few exceptions, all time-to-event data are affected by a certain ... Read More

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

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. 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. Yes, there might be more advanced and elegant solutions, but stabilising transformations are suggested in most traditional biometry books, they are very straightforward to apply and they do not require any specific statistical software. I do not think ... Read More

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

Feed: R-bloggers. Author: R on The broken bridge between biologists and statisticians. 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 = AQ + BW + C] where A, B and C are three coefficients. The standard error for this combination can be obtained as: [ sigma_Z = sqrt{ A^2 sigma^2_Q + B^2 sigma^2_W + 2AB sigma_{QW} }] In biology, ... Read More

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