In pvrqualitasag/MeatValueIndex:

knitr::opts_chunk$set(echo = TRUE)

library(dplyr)

1. Computing Economic Value For Beef Breed

Genetic Standard Deviations

Economic values can also be given in terms of a change of one genetic standard deviation. The used estimates for this parameter are

l_gen_sd <- list(CCc = 0.6336,
                 CCa = 0.6335,
                 CFc = 0.3474,
                 CFa = 0.3609,
                 CWc = 0.0557,
                 CWa = 0.1395)

Carcass conformation adults (CCa)

### # prices
vec_price_cca <- c(7.526960,7.938872,8.450784,8.800000,9.137304,9.392693,9.642693)

AN

n_mean_cca_an <- 5.62
n_sd_cca_an <- 1
vec_count_cca_an <- c(0,17,248,3463,8320,8874,5806)
vec_freq_cca_an <- vec_count_cca_an / sum(vec_count_cca_an)

(ev_cca_an <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_an,
                        pn_sd = n_sd_cca_an,
                        pvec_class_freq = vec_freq_cca_an,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cca,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CCa,
                        pb_verbose = TRUE))

AU

n_mean_cca_au <- 6.72
n_sd_cca_au <- 0.58
vec_count_cca_au <- c(0,0,1,20,125,485,2230)
vec_freq_cca_au <- vec_count_cca_au / sum(vec_count_cca_au)

(ev_cca_au <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_au,
                        pn_sd = n_sd_cca_au,
                        pvec_class_freq = vec_freq_cca_au,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cca,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CCa,
                        pb_verbose = TRUE))

CH

n_mean_cca_ch <- 6.65
n_sd_cca_ch <- 0.65
vec_count_cca_ch <- c(0,0,6,53,234,929,2210)
vec_freq_cca_ch <- vec_count_cca_ch / sum(vec_count_cca_ch)

(ev_cca_ch <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_ch,
                        pn_sd = n_sd_cca_ch,
                        pvec_class_freq = vec_freq_cca_ch,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cca,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CCa,
                        pb_verbose = TRUE))

LM

n_mean_cca_lm <- 6.56
n_sd_cca_lm <- 0.7
vec_count_cca_lm <- c(2,11,57,1041,6629,21068,56252)
vec_freq_cca_lm <- vec_count_cca_lm / sum(vec_count_cca_lm)

(ev_cca_lm <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_lm,
                        pn_sd = n_sd_cca_lm,
                        pvec_class_freq = vec_freq_cca_lm,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cca,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CCa,
                        pb_verbose = TRUE))

Carcass conformation calves (CCc)

### # prices
vec_price_ccc <- c(11.2,12.7,13.6,14.2,14.7,15.2,15.7)

AN

n_mean_ccc_an <- 5.48
n_sd_ccc_an <- 0.99
vec_count_ccc_an <- c(2,2,35,280,634,707,297)
vec_freq_ccc_an <- vec_count_ccc_an / sum(vec_count_ccc_an)

(ev_ccc_an <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_an,
                        pn_sd = n_sd_ccc_an,
                        pvec_class_freq = vec_freq_ccc_an,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_ccc,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CCc,
                        pb_verbose = TRUE))

AU

n_mean_ccc_au <- 6.33
n_sd_ccc_au <- 1.04
vec_count_ccc_au <- c(0,1,0,3,6,14,36)
vec_freq_ccc_au <- vec_count_ccc_au / sum(vec_count_ccc_au)

(ev_ccc_au <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_au,
                        pn_sd = n_sd_ccc_au,
                        pvec_class_freq = vec_freq_ccc_au,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_ccc,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CCc,
                        pb_verbose = TRUE))

CH

n_mean_ccc_ch <- 6.32
n_sd_ccc_ch <- 1.04
vec_count_ccc_ch <- c(0,0,1,3,5,9,31)
vec_freq_ccc_ch <- vec_count_ccc_ch / sum(vec_count_ccc_ch)

(ev_ccc_ch <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_ch,
                        pn_sd = n_sd_ccc_ch,
                        pvec_class_freq = vec_freq_ccc_ch,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_ccc,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CCc,
                        pb_verbose = TRUE))

LM

n_mean_ccc_lm <- 6.55
n_sd_ccc_lm <- 0.77
vec_count_ccc_lm <- c(2,2,15,73,273,809,2472)
vec_freq_ccc_lm <- vec_count_ccc_lm / sum(vec_count_ccc_lm)

(ev_ccc_lm <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_lm,
                        pn_sd = n_sd_ccc_lm,
                        pvec_class_freq = vec_freq_ccc_lm,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_ccc,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CCc,
                        pb_verbose = TRUE))

Carcass fatness adults (CFa)

### # prices
vec_price_cfa <- c(-0.9000000,
-0.3000000,
0.0000000,
-0.3926929,
-0.8480817)

AN

n_mean_cfa_an <- 3.09
n_sd_cfa_an <- 0.74
vec_count_cfa_an <- c(803,3415,15694,6330,486)
vec_freq_cfa_an <- vec_count_cfa_an / sum(vec_count_cfa_an)

(ev_cfa_an <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_an,
                        pn_sd = n_sd_cfa_an,
                        pvec_class_freq = vec_freq_cfa_an,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cfa,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CFa,
                        pb_verbose = TRUE))

AU

n_mean_cfa_au <- 2.65
n_sd_cfa_au <- 0.69
vec_count_cfa_au <- c(164,860,1664,169,4)
vec_freq_cfa_au <- vec_count_cfa_au / sum(vec_count_cfa_au)

(ev_cfa_au <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_au,
                        pn_sd = n_sd_cfa_au,
                        pvec_class_freq = vec_freq_cfa_au,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cfa,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CFa,
                        pb_verbose = TRUE))

CH

n_mean_cfa_ch <- 2.63
n_sd_cfa_ch <- 0.71
vec_count_cfa_ch <- c(302,1354,2581,292,3)
vec_freq_cfa_ch <- vec_count_cfa_ch / sum(vec_count_cfa_ch)

(ev_cfa_ch <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_ch,
                        pn_sd = n_sd_cfa_ch,
                        pvec_class_freq = vec_freq_cfa_ch,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cfa,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CFa,
                        pb_verbose = TRUE))

LM

n_mean_cfa_lm <- 2.71
n_sd_cfa_lm <- 0.71
vec_count_cfa_lm <- c(5106,21966,50669,7172,147)
vec_freq_cfa_lm <- vec_count_cfa_lm / sum(vec_count_cfa_lm)

(ev_cfa_lm <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_lm,
                        pn_sd = n_sd_cfa_lm,
                        pvec_class_freq = vec_freq_cfa_lm,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cfa,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CFa,
                        pb_verbose = TRUE))

Carcass fatness calves (CFc)

### # prices
vec_price_cfc <- c(-1.5,
-0.6,
0.0,
-0.4,
-1.0)

AN

n_mean_cfc_an <- 2.51
n_sd_cfc_an <- 0.76
vec_count_cfc_an <- c(187,706,936,126,2)
vec_freq_cfc_an <- vec_count_cfc_an / sum(vec_count_cfc_an)

(ev_cfc_an <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_an,
                        pn_sd = n_sd_cfc_an,
                        pvec_class_freq = vec_freq_cfc_an,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cfc,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CFc,
                        pb_verbose = TRUE))

AU

n_mean_cfc_au <- 1.88
n_sd_cfc_au <- 0.78
vec_count_cfc_au <- c(22,23,15,0,0)
vec_freq_cfc_au <- vec_count_cfc_au / sum(vec_count_cfc_au)

(ev_cfc_au <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_au,
                        pn_sd = n_sd_cfc_au,
                        pvec_class_freq = vec_freq_cfc_au,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cfc,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CFc,
                        pb_verbose = TRUE))

CH

n_mean_cfc_ch <- 2.06
n_sd_cfc_ch <- 0.91
vec_count_cfc_ch <- c(18,12,19,1,0)
vec_freq_cfc_ch <- vec_count_cfc_ch / sum(vec_count_cfc_ch)

(ev_cfc_ch <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_ch,
                        pn_sd = n_sd_cfc_ch,
                        pvec_class_freq = vec_freq_cfc_ch,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cfc,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CFc,
                        pb_verbose = TRUE))

LM

n_mean_cfc_lm <- 2.21
n_sd_cfc_lm <- 0.77
vec_count_cfc_lm <- c(730,1495,1358,63,0)
vec_freq_cfc_lm <- vec_count_cfc_lm / sum(vec_count_cfc_lm)

(ev_cfc_lm <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_lm,
                        pn_sd = n_sd_cfc_lm,
                        pvec_class_freq = vec_freq_cfc_lm,
                        pvec_threshold = NULL,
                        pvec_price = vec_price_cfc,
                        pn_delta_mean = .1,
                        pn_gen_sd = l_gen_sd$CFc,
                        pb_verbose = TRUE))

Carcass weight adults (CWa)

 n_scale_fact_cwa <- 100
vec_price_cwa <- c(0.0,
-0.1,
-0.2,
-0.3,
-0.5,
-0.7,
-0.9,
-1.2,
-1.4,
-1.6,
-1.8)
vec_thre_cwa <- c(2.9,
3.0,
3.1,
3.2,
3.3,
3.4,
3.5,
3.6,
3.7,
3.8) * n_scale_fact_cwa

AN

n_mean_cwa_an <- 2.32 * n_scale_fact_cwa
n_sd_cwa_an <- 0.53 * n_scale_fact_cwa

(ev_cwa_an <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_an,
                        pn_sd = n_sd_cwa_an,
                        pvec_class_freq = NULL,
                        pvec_threshold = vec_thre_cwa,
                        pvec_price = vec_price_cwa,
                        pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa,
                        pn_delta_mean = .01 * n_scale_fact_cwa))

AU

n_mean_cwa_au <- 2.77 * n_scale_fact_cwa
n_sd_cwa_au <- 0.54 * n_scale_fact_cwa

(ev_cwa_au <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_au,
                        pn_sd = n_sd_cwa_au,
                        pvec_class_freq = NULL,
                        pvec_threshold = vec_thre_cwa,
                        pvec_price = vec_price_cwa,
                        pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa,
                        pn_delta_mean = .01 * n_scale_fact_cwa))

CH

n_mean_cwa_ch <- 2.91 * n_scale_fact_cwa
n_sd_cwa_ch <- 0.55 * n_scale_fact_cwa

(ev_cwa_ch <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_ch,
                        pn_sd = n_sd_cwa_ch,
                        pvec_class_freq = NULL,
                        pvec_threshold = vec_thre_cwa,
                        pvec_price = vec_price_cwa,
                        pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa,
                        pn_delta_mean = .01 * n_scale_fact_cwa))

LM

n_mean_cwa_lm <- 2.42 * n_scale_fact_cwa
n_sd_cwa_lm <- 0.45 * n_scale_fact_cwa

(ev_cwa_lm <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_lm,
                        pn_sd = n_sd_cwa_lm,
                        pvec_class_freq = NULL,
                        pvec_threshold = vec_thre_cwa,
                        pvec_price = vec_price_cwa,
                        pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa,
                        pn_delta_mean = .01 * n_scale_fact_cwa))

Carcass weight calves (CWc)

n_scale_fact_cwc <- 100
vec_price_cwc <- seq(0.0,-1.1,-0.1);vec_price_cwc
vec_thre_cwc <- seq(1.4, 1.5, 0.01) * n_scale_fact_cwc
vec_thre_cwc

AN

n_mean_cwc_an <- 1.23 * n_scale_fact_cwc
n_sd_cwc_an <- 0.14 * n_scale_fact_cwc

(ev_cwc_an <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_an,
                        pn_sd = n_sd_cwc_an,
                        pvec_class_freq = NULL,
                        pvec_threshold = vec_thre_cwc,
                        pvec_price = vec_price_cwc,
                        pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc,
                        pn_delta_mean = .01 * n_scale_fact_cwc))

AU

n_mean_cwc_au <- 1.25 * n_scale_fact_cwc
n_sd_cwc_au <- 0.18 * n_scale_fact_cwc

(ev_cwc_au <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_au,
                        pn_sd = n_sd_cwc_au,
                        pvec_class_freq = NULL,
                        pvec_threshold = vec_thre_cwc,
                        pvec_price = vec_price_cwc,
                        pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc,
                        pn_delta_mean = .01 * n_scale_fact_cwc))

CH

n_mean_cwc_ch <- 1.27 * n_scale_fact_cwc
n_sd_cwc_ch <- 0.15 * n_scale_fact_cwc

(ev_cwc_ch <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_ch,
                        pn_sd = n_sd_cwc_ch,
                        pvec_class_freq = NULL,
                        pvec_threshold = vec_thre_cwc,
                        pvec_price = vec_price_cwc,
                        pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc,
                        pn_delta_mean = .01 * n_scale_fact_cwc))

LM

n_mean_cwc_lm <- 1.30 * n_scale_fact_cwc
n_sd_cwc_lm <- 0.13 * n_scale_fact_cwc

(ev_cwc_lm <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_lm,
                        pn_sd = n_sd_cwc_lm,
                        pvec_class_freq = NULL,
                        pvec_threshold = vec_thre_cwc,
                        pvec_price = vec_price_cwc,
                        pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc,
                        pn_delta_mean = .01 * n_scale_fact_cwc))

Overview of the phenotypic mean

tbl_population_mean <- tibble::data_frame(Traits = c("cca", "ccc", "cfa", "cfc", "cwa", "cwc"),
                                    AN = c(n_mean_cca_an,
                                           n_mean_ccc_an,
                                           n_mean_cfa_an,
                                           n_mean_cfc_an,
                                           n_mean_cwa_an,
                                           n_mean_cwc_an),
                                    AU = c(n_mean_cca_au,
                                           n_mean_ccc_au,
                                           n_mean_cfa_au,
                                           n_mean_cfc_au,
                                           n_mean_cwa_au,
                                           n_mean_cwc_au),
                                    CH = c(n_mean_cca_ch,
                                           n_mean_ccc_ch,
                                           n_mean_cfa_ch,
                                           n_mean_cfc_ch,
                                           n_mean_cwa_ch,
                                           n_mean_cwc_ch),
                                    LM = c(n_mean_cca_lm,
                                           n_mean_ccc_lm,
                                           n_mean_cfa_lm,
                                           n_mean_cfc_lm,
                                           n_mean_cwa_lm,
                                           n_mean_cwc_lm))

knitr::kable(tbl_population_mean,booktabs = TRUE)

Presenting output for economic values

The computed economic values are shown in the following tables:

1.1) Table are presenting economic value in trait unit.

tbl_ev_result_ev_per_trait_unit <- tibble::data_frame(Traits = c("cca", "ccc", "cfa", "cfc", "cwa", "cwc"),
                                    AN = c(ev_cca_an$ev_per_trait_unit, 
                                            ev_ccc_an$ev_per_trait_unit, 
                                            ev_cfa_an$ev_per_trait_unit, 
                                            ev_cfc_an$ev_per_trait_unit, 
                                            ev_cwa_an$ev_per_trait_unit, 
                                            ev_cwc_an$ev_per_trait_unit),
                                    AU = c(ev_cca_au$ev_per_trait_unit, 
                                            ev_ccc_au$ev_per_trait_unit, 
                                            ev_cfa_au$ev_per_trait_unit, 
                                            ev_cfc_au$ev_per_trait_unit, 
                                            ev_cwa_au$ev_per_trait_unit, 
                                            ev_cwc_au$ev_per_trait_unit),
                                    CH = c(ev_cca_ch$ev_per_trait_unit, 
                                            ev_ccc_ch$ev_per_trait_unit, 
                                            ev_cfa_ch$ev_per_trait_unit, 
                                            ev_cfc_ch$ev_per_trait_unit, 
                                            ev_cwa_ch$ev_per_trait_unit, 
                                            ev_cwc_ch$ev_per_trait_unit),
                                    LM = c(ev_cca_lm$ev_per_trait_unit, 
                                            ev_ccc_lm$ev_per_trait_unit, 
                                            ev_cfa_lm$ev_per_trait_unit, 
                                            ev_cfc_lm$ev_per_trait_unit, 
                                            ev_cwa_lm$ev_per_trait_unit, 
                                            ev_cwc_lm$ev_per_trait_unit))

knitr::kable(tbl_ev_result_ev_per_trait_unit,booktabs = TRUE)

1.2) Table are presenting economic value in genetic standard deviation.

tbl_ev_result_ev_per_gen_sd <- tibble::data_frame(Traits = c("cca", "ccc", "cfa", "cfc", "cwa", "cwc"),
                                    AN = c(ev_cca_an$ev_per_gen_sd,
                                            ev_ccc_an$ev_per_gen_sd, 
                                            ev_cfa_an$ev_per_gen_sd, 
                                            ev_cfc_an$ev_per_gen_sd, 
                                            ev_cwa_an$ev_per_gen_sd, 
                                            ev_cwc_an$ev_per_gen_sd),
                                    AU = c(ev_cca_au$ev_per_gen_sd, 
                                            ev_ccc_au$ev_per_gen_sd, 
                                            ev_cfa_au$ev_per_gen_sd, 
                                            ev_cfc_au$ev_per_gen_sd, 
                                            ev_cwa_au$ev_per_gen_sd, 
                                            ev_cwc_au$ev_per_gen_sd),
                                    CH = c(ev_cca_ch$ev_per_gen_sd, 
                                            ev_ccc_ch$ev_per_gen_sd, 
                                            ev_cfa_ch$ev_per_gen_sd, 
                                            ev_cfc_ch$ev_per_gen_sd, 
                                            ev_cwa_ch$ev_per_gen_sd, 
                                            ev_cwc_ch$ev_per_gen_sd),
                                    LM = c(ev_cca_lm$ev_per_gen_sd, 
                                            ev_ccc_lm$ev_per_gen_sd, 
                                            ev_cfa_lm$ev_per_gen_sd, 
                                            ev_cfc_lm$ev_per_gen_sd, 
                                            ev_cwa_lm$ev_per_gen_sd, 
                                            ev_cwc_lm$ev_per_gen_sd))

knitr::kable(tbl_ev_result_ev_per_gen_sd,booktabs = TRUE)

2. Computing Relative Economic Factors

Relative economic factors are defined as the ratio of each economic value on the basis of one genetic standard deviation to the sum of all economic values in a given breed.

### # compute factors with function
tbl_rel_factors <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_gen_sd,
                                                 pb_first_col_trait_name = TRUE)
knitr::kable(tbl_rel_factors, booktabs = TRUE)

3. Importance of calves versus adults for each population

tbl_number_calves_adults <- tibble::data_frame(Categories = c("adults", "calves"),
                                    AN = c(26728,
                                           1957),
                                    AU = c(2861, 
                                          60),
                                    CH = c(4532, 
                                           50),
                                    LM = c(85060, 
                                           3646))

knitr::kable(tbl_number_calves_adults,booktabs = TRUE)

### # Proportion of the slaughtercategories for each breed
tbl_proportion <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_number_calves_adults,
                                                                      pb_first_col_trait_name = TRUE)

knitr::kable(tbl_proportion, booktabs = TRUE)

4. Szenarios

Szenario A) we are using the relative economic factors (table 2, File name: economic_value_relative_BeefBreeds.csv)

knitr::kable(tbl_rel_factors,booktabs = TRUE)

MeatValueIndex::write_ev_to_file(ptbl_economic_value = tbl_rel_factors,
                                 ps_out_path = "economic_value_relative_BeefBreeds.csv",
                                 pb_first_col_trait_name = TRUE)

Szenario B) The relative factors are weighted with animal categories to get weighted relative factors (File name: weighted_economic_value_relative_BeefBreeds.csv)

(tbl_proportion4eachtrait <- bind_rows(tbl_proportion,tbl_proportion,tbl_proportion))
(tbl_proportion4eachtrait <- tbl_proportion4eachtrait[, 2:ncol(tbl_proportion4eachtrait)])
(tbl_proportion4eachtrait <- bind_cols(tbl_rel_factors[,1], tbl_proportion4eachtrait))
(colnames(tbl_proportion4eachtrait) <- colnames(tbl_rel_factors))

tbl_weighted_rel_factors <- MeatValueIndex::weight_economic_value(ptbl_economic_value = tbl_rel_factors,
                                     ptbl_weight = tbl_proportion4eachtrait,
                                     pb_first_col_trait_name = TRUE)

knitr::kable(tbl_weighted_rel_factors,booktabs = TRUE)

MeatValueIndex::write_ev_to_file(ptbl_economic_value = tbl_weighted_rel_factors,
                                ps_out_path = "weighted_economic_value_relative_BeefBreeds.csv",
                                pb_first_col_trait_name = TRUE)

pvrqualitasag/MeatValueIndex documentation built on May 13, 2019, 4:44 p.m.