Construction of Selection Index.
seliNDRIx: Your Package Description Here
buld+_b— output: github_document —
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("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%
# Example using random function # Read the data 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%
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.
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