Module2.md
In allogamous/EnvRtype: Envirotyping Tools for Crop Research and Genetic Improvment
Environmental Characterization Module
- Environmental characterization is a fundamental step to understand how the environment regulates the phenotypic expression and adaptation of the genotypes under different growing conditions. For this reason, based on envirotyping (environmental + typing) studies alredy published (see references), we provide mechanisms that enable the typing of environmental factors in terms of frequency of occurrence. We have also developed functions for collecting environmental factors and organizing them as covariates to be used in reaction norm studies.
### Environmental Covariables (ECs, W matrix)
> * Environmental variables can be used as indicators of the quality of an environment (experiment, location). A double entry table (*q* environments x *k* environmental factors) can be built as suggested by Jarquin et al (2014). Hereafther we will refer to this matrix as **W**, and therefore, it will be obtained by the function *W_matrix*:
```{r}
W_matrix(env.data = df.clim,by.interval = F)
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> * Different statistics can be used, as in *summaryWTH*. The statistic argument is used to select between *mean* or *quantile*. If the selection is made in quantile, the complementary argument *prob* is used to choose the quantiles to be used.
wzxhzdk:1
> * We can perform a Quality Control (QC) based on the maximum sd tolered.
wzxhzdk:2
> * We can perform a Quality Control (QC) based on the maximum sd tolered.
wzxhzdk:3
> * Create for specific variables. To do this, insert the name of the variables in the *id.var* argument.
wzxhzdk:4
> * Or even combine with summaryWTH by using *is.processed=TRUE*:
```{r}
data<-summaryWTH(df.clim,env.id = 'env',statistic = 'quantile')
W_matrix(env.data = data,is.processed = T)
wzxhzdk:5
> * Typologies can be defined across different time intervals by setting the argument *by.interval = TRUE* (generic time intervals)
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> * Typologies can be defined across **specific** time intervals by setting the argument *by.interval = TRUE* and and defining the time windows (in days after begining of the data) using the function *time.window*
```{r}
env_typing(env.data = df.clim,env.id = 'env',var.id='T2M',by.interval = T,time.window = c(0,15,35,65,90,120))
wzxhzdk:7
> * The two-way table of typologies can be plotted based on this [code](https://raw.githubusercontent.com/allogamous/EnvRtype/master/plot.R)
### **Example 1 : Air Temperature (**T2M**)**
**Option 1: facet by developmental stages**
**Option 2: envirotypes as a combination of environmental factor x cardinal class x developmental stage**
**Option 3: envirotypes per environnment**
### **Example 2 : Solar Radiation (**SRAD**)**
**Option 1: facet by developmental stages**
**Option 2: envirotypes as a combination of environmental factor x cardinal class x developmental stage**
**Option 3: envirotypes per environnment**
### **Example 3 : Valour Pressure Defict (**VPD**)**
**Option 1: facet by developmental stages**
**Option 2: envirotypes as a combination of environmental factor x cardinal class x developmental stage**
**Option 3: envirotypes per environnment**
> * in *var.id* you can put the names of the desirable variables:
```{r}
env_typing(env.data = df.clim,var.id = c('T2M','PRECTOT','WS2M'),env.id='env',by.interval = T)
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> * These cardinals must respect ecophysiological limits for each crop, germplasm or region. For that, we recommend looking for ecophysiology literature and crop growth modeling, such as Soltani and Sinclar (2012) [**Modeling physiology of crop development, growth and yield**](https://www.amazon.com.br/Modeling-Physiology-Development-Growth-Yield/dp/1845939700); However, we do not always have ecophysiological information about the best possible cardinals ... so we use quantiles!
> * If quantiles = NULL, 1%, 25%, 50%, 99% is assumed
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> * All analyses can also be run considering centered on the mean and scaled x ~ N (0.1)
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allogamous/EnvRtype documentation built on June 11, 2025, 5:57 a.m.
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Environmental Characterization Module
- Environmental characterization is a fundamental step to understand how the environment regulates the phenotypic expression and adaptation of the genotypes under different growing conditions. For this reason, based on envirotyping (environmental + typing) studies alredy published (see references), we provide mechanisms that enable the typing of environmental factors in terms of frequency of occurrence. We have also developed functions for collecting environmental factors and organizing them as covariates to be used in reaction norm studies.
### Environmental Covariables (ECs, W matrix)
> * Environmental variables can be used as indicators of the quality of an environment (experiment, location). A double entry table (*q* environments x *k* environmental factors) can be built as suggested by Jarquin et al (2014). Hereafther we will refer to this matrix as **W**, and therefore, it will be obtained by the function *W_matrix*:
```{r}
W_matrix(env.data = df.clim,by.interval = F)
wzxhzdk:0
> * Different statistics can be used, as in *summaryWTH*. The statistic argument is used to select between *mean* or *quantile*. If the selection is made in quantile, the complementary argument *prob* is used to choose the quantiles to be used.
wzxhzdk:1
> * We can perform a Quality Control (QC) based on the maximum sd tolered.
wzxhzdk:2
> * We can perform a Quality Control (QC) based on the maximum sd tolered.
wzxhzdk:3
> * Create for specific variables. To do this, insert the name of the variables in the *id.var* argument.
wzxhzdk:4
> * Or even combine with summaryWTH by using *is.processed=TRUE*:
```{r}
data<-summaryWTH(df.clim,env.id = 'env',statistic = 'quantile')
W_matrix(env.data = data,is.processed = T)
wzxhzdk:5
> * Typologies can be defined across different time intervals by setting the argument *by.interval = TRUE* (generic time intervals)
wzxhzdk:6
> * Typologies can be defined across **specific** time intervals by setting the argument *by.interval = TRUE* and and defining the time windows (in days after begining of the data) using the function *time.window*
```{r}
env_typing(env.data = df.clim,env.id = 'env',var.id='T2M',by.interval = T,time.window = c(0,15,35,65,90,120))
wzxhzdk:7
> * The two-way table of typologies can be plotted based on this [code](https://raw.githubusercontent.com/allogamous/EnvRtype/master/plot.R)
### **Example 1 : Air Temperature (**T2M**)**
**Option 1: facet by developmental stages**
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