In pvrqualitasag/MeatValueIndex:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Input

Indices are computed using weighting factors or economic values. Both are read from csv-formatted files. The second type of input corresponds to a space-separated text file containing predicted breeding values for all animals.

### # specify a given file
s_factor_input_path <- system.file("extdata", "economic_values", "weighted_economic_value_relative.csv",
                                   package = "MeatValueIndex")
### # reading economic values
tbl_ev_factors <- readr::read_csv(file = s_factor_input_path)
tbl_ev_factors

The second input are the breeding values

### # specify path to breeding value file
s_bv_input_path <- system.file("extdata", "breeding_values", 
                               "rawSolutionsQualitas_Mitrassenbasis.txt_pubUpdated_OBBVSISFMO_60",
                                package = "MeatValueIndex")
### # reading s_bv_input_path
tbl_bv <- readr::read_delim(file = s_bv_input_path, delim = " ")
tbl_bv

Computation

We start with joining the factors to the breeding values

library(dplyr)
tbl_bv_ev <- tbl_bv %>% inner_join(tbl_ev_factors, by = c("trait" = "Trait", "breed" = "Breed")) %>% select(-c(pubCode,base))
tbl_bv_ev <- tbl_bv_ev %>% mutate(index=estimate*Ev)
tbl_index <- tbl_bv_ev %>% group_by(idaTvd) %>% summarise(IndexSum = sum(index))
tbl_index

Computing Correlations

Correlations between index values and breeding values of single traits are computed as follows

tbl_cor_result <- tibble::data_frame(Trait = c("cca", "ccc", "cfa", "cfc", "cwa", "cwc"),
                                     OB = c(rep(NA, 6)),
                                     BV = c(rep(NA, 6)),
                                     SI = c(rep(NA, 6)),
                                     SF = c(rep(NA, 6)),
                                     MO = c(rep(NA, 6)))

tbl_cor_result

vec_breed <- colnames(tbl_cor_result)
vec_trait <- tbl_cor_result[,1][[1]]
for (bidx in 2:ncol(tbl_cor_result)){
  b <- vec_breed[bidx]
  cat("Computation for breed: ", b, "\n")
  for (tidx in seq_along(vec_trait)){
    t <- vec_trait[tidx]
    cat("Use trait: ", t, "\n")
    tbl_cor_result[tidx, bidx] <- MeatValueIndex::compute_correlation(ptbl_bv = tbl_bv,
                                                                      ptbl_index = tbl_index,
                                                                      ps_breed = b,
                                                                      ps_trait = t)
  }
}
tbl_cor_result
knitr::kable(tbl_cor_result, booktabs = TRUE)