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Update data-terminology.qmd #7

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Expand Up @@ -10,7 +10,7 @@ While the utilization of digital or remote sensing technology for disease measur

![Different approaches used to obtain estimates or measures of plant disease. RGB = red, green, blue; MSI = multispectral imaging; HSI = hyperspectral imaging.](imgs/disease_measure.png){#fig-disease_measure style="margin: 10px" fig-align="center" width="490"}

inally, while developing new or refining existing disease assessment methods, it is crucial to evaluate the reliability of the assessments made by different raters or instruments, as well as their accuracy---specifically, how close the estimations or measurements are to the reference (or gold standard) values. Several methods are available for assessing the reliability, precision, and accuracy of these estimates or measurements (see [definitions](data-accuracy.html)). The choice of methods depends on the objective of the work, but largely on the type or nature of the data. These considerations will be further discussed.
Finally, while developing new or refining existing disease assessment methods, it is crucial to evaluate the reliability of the assessments made by different raters or instruments, as well as their accuracy---specifically, how close the estimations or measurements are to the reference (or gold standard) values. Several methods are available for assessing the reliability, precision, and accuracy of these estimates or measurements (see [definitions](data-accuracy.html)). The choice of methods depends on the objective of the work, but largely on the type or nature of the data. These considerations will be further discussed.

## Disease variables

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When conducting studies in epidemiology, specifically plant diseases, researchers often collect data on the number of diseased plants, infected plant parts, or individual symptoms, such as lesions. These variables are counted in whole numbers - 1, 2, 3, etc., making them discrete variables. Discrete variables contrast with continuous variables that can take any value within a defined range and can include fractions or decimals. In addition to being discrete, these variables are also non-negative, meaning they cannot take negative values. After all, you can't have a negative number of diseased plants or lesions. Given these characteristics, a suitable distribution to model such data is the Poisson distribution. This distribution is particularly suitable for counting the number of times an event occurs in a given time or space.

In R, we can used the `rpois()` function to obtain 100 random observations following a Poisson distribution. For such, we need to inform the number of observation (n = 100) and `lambda`, the vector of means.
In R, we can use the `rpois()` function to obtain 100 random observations following a Poisson distribution. For such, we need to inform the number of observation (n = 100) and `lambda`, the vector of means.

```{r}
poisson5 <- rpois(100, lambda = 10)
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