Publications by R on The broken bridge between biologists and statisticians
Stabilising transformations: how do I present my results?
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, t...
5037 sym R (1971 sym/6 pcs) 2 img 2 tbl
Stabilising transformations: how do I present my results?
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, t...
5037 sym R (1971 sym/6 pcs) 2 img 2 tbl
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 failure-time data, phenological data, time-to-remissio...
13129 sym R (7585 sym/11 pcs) 4 img 1 tbl
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 failure-time data, phenological data, time-to-remissio...
13129 sym R (7585 sym/11 pcs) 4 img 1 tbl
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 failure-time data, phenological data, time-to-remissio...
13129 sym R (7585 sym/11 pcs) 4 img 1 tbl
Germination data and time-to-event methods: comparing germination curves
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 appropri...
9800 sym R (3824 sym/8 pcs) 4 img
Germination data and time-to-event methods: comparing germination curves
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 appropri...
9800 sym R (3824 sym/8 pcs) 4 img
Germination data and time-to-event methods: comparing germination curves
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 appropri...
9800 sym R (3727 sym/8 pcs) 4 img
Fitting ‘complex’ mixed models with ‘nlme’. Example #1
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 the ‘nlme’ package...
8096 sym R (6513 sym/9 pcs)
Fitting ‘complex’ mixed models with ‘nlme’. Example #1
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 the ‘nlme’ package...
8096 sym R (6513 sym/9 pcs)