selindrix

To construct the selection index for multi-trait selection of animals

https://github.com/venkatesanraja/selindrix

Repository

To construct the selection index for multi-trait selection of animals

README.Rmd

---
output: github_document
title: "seliNDRIx: Your Package Description Here"
---
buld+_b---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# seliNDRIx




The goal of seliNDRIx is to construct the selection index from the animal breeding data using the mixed and random sire models.

## Installation

You can install the development version of seliNDRIx like so:

``` r
# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?
install.packages("seliNDRIx")
require(seliNDRIx)
## Example
This is a basic example which shows you how to solve a common problem:

library(seliNDRIx)

# Example using mixed function
# Read the data
 data("data", package = "seliNDRIx")
 # Define your parameters
 traits <- c("tmy", "py", "fatyield")
 fixed <- c("farm", "soc", "poc")
 random <- c("sire")
 economic_values <- c(1, 0.85, 0.65)

 # Run the analysis
 results <- mixed_si(
 data = data,
 traits = traits,
 fixed = fixed,
 random = random,
 economic_values = economic_values
 )
 results
 # To calculate the overall selection index for each animal
 SI <- c(results$SelectionIndex)  # Selection index estimates (weights) for traits
 traits <- c("tmy", "py", "fatyield")  # Define the trait columns to use
 overall_index <- function(data, SI, traits) {
 # Ensure the number of weights matches the number of trait columns
  if (length(SI) != length(traits)) {
    stop("The number of weights must match the number of trait columns.")
  }
  # Select only the defined trait columns and calculate the index
  data %>%
    rowwise() %>%
    mutate(Index = sum(c_across(all_of(traits)) * SI)) %>%
    ungroup()
 }
 # Calculate the selection index
 result3 <- overall_index(data, SI, traits)
 # Print the result
 print(result3)

 # Select the top 20% of animals with the highest selection index values
 top20 <- result3 %>%
  arrange(desc(Index)) %>%  # Sort by Index in descending order
  slice_head(prop = 0.2)    # Select the top 20%

 write.csv(top20, "D:/Top20.csv", row.names = FALSE)
 # Example using random function
 # Read the data
 data("data", package = "seliNDRIx")
 # Run the analysis
 results2 <- random_si(data,
 traits = c("tmy", "py", "fatyield"),
 economic_values = c(1, 0.85, 0.65))
 results2
 # To calculate the overall selection index for each animal
 SI <- c(results$SelectionIndex)  # Selection index estimates (weights) for traits
 traits <- c("tmy", "py", "fatyield")  # Define the trait columns to use
 overall_index <- function(data, SI, traits) {
 # Ensure the number of weights matches the number of trait columns
  if (length(SI) != length(traits)) {
    stop("The number of weights must match the number of trait columns.")
  }
  # Select only the defined trait columns and calculate the index
  data %>%
    rowwise() %>%
    mutate(Index = sum(c_across(all_of(traits)) * SI)) %>%
    ungroup()
 }

 # Calculate the selection index
 result3 <- overall_index(data, SI, traits)

 # Print the result
 print(result3)

 # Select the top 20% of animals with the highest selection index values
 top20 <- result3 %>%
  arrange(desc(Index)) %>%  # Sort by Index in descending order
  slice_head(prop = 0.2)    # Select the top 20%

 write.csv(top20, "D:/Top20.csv", row.names = FALSE)

What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so:


You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this.

You can also embed plots, for example:

In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.

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