In pvrqualitasag/MeatValueIndex:
Disclaimer
This notebook gives an overview over different weighting strategies between economic values of traits of different animal categories.
Background
In beef cattle there are three different carcass performance traits.
tbl_trait <- tibble::data_frame(Abbreviation = c("CC", "CF", "CW"),
Trait = c("Carcass Conformation",
"Carcass Fat",
"Carcass Weight"))
knitr::kable( tbl_trait,
booktabs = TRUE,
longtable = TRUE )
Both traits are available in two animal categories
tbl_category <- tibble::data_frame(Abbreviation = c("c", "a"),
Category = c("calf", "adult"))
knitr::kable( tbl_category,
booktabs = TRUE,
longtable = TRUE )
n_nr_trait <- nrow(tbl_category) * nrow(tbl_trait)
Combining both, results in the following matrix of a total of r n_nr_trait traits
tbl_trait_matrix <- tibble::data_frame(Trait = c("CC", "CF", "CW"),
Calf = c("CCc", "CFc", "CWc"),
Adult = c("CCa", "CFa", "CWa"))
knitr::kable( tbl_trait_matrix,
booktabs = TRUE,
longtable = TRUE )
1. Economic Values
library(dplyr)
#' #1. Computing Economic Value For Dual 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)
#'
#' ###OB
## ------------------------------------------------------------------------
n_mean_cca_ob <- 5.20
n_sd_cca_ob <- 1.02
vec_count_cca_ob <- c(4,43,218,1150,2106,1905,522)
vec_freq_cca_ob <- vec_count_cca_ob / sum(vec_count_cca_ob)
#'
## ---- include=FALSE------------------------------------------------------
(ev_cca_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_ob,
pn_sd = n_sd_cca_ob,
pvec_class_freq = vec_freq_cca_ob,
pvec_threshold = NULL,
pvec_price = vec_price_cca,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCa,
pb_verbose = TRUE))
#'
#' ###BV
## ------------------------------------------------------------------------
n_mean_cca_bv <- 4.78
n_sd_cca_bv <- 1.27
vec_count_cca_bv <- c(273,1562,5982,17808,14303,11438,5875)
vec_freq_cca_bv <- vec_count_cca_bv / sum(vec_count_cca_bv)
#'
## ---- include=FALSE------------------------------------------------------
(ev_cca_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_bv,
pn_sd = n_sd_cca_bv,
pvec_class_freq = vec_freq_cca_bv,
pvec_threshold = NULL,
pvec_price = vec_price_cca,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCa,
pb_verbose = TRUE))
#'
#' ###SI
## ------------------------------------------------------------------------
n_mean_cca_si <- 5.83
n_sd_cca_si <- 0.9
vec_count_cca_si <- c(4,38,377,3994,13917,24738,13535)
vec_freq_cca_si <- vec_count_cca_si / sum(vec_count_cca_si)
#'
## ---- include=FALSE------------------------------------------------------
(ev_cca_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_si,
pn_sd = n_sd_cca_si,
pvec_class_freq = vec_freq_cca_si,
pvec_threshold = NULL,
pvec_price = vec_price_cca,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCa,
pb_verbose = TRUE))
#'
#' ###SF
## ------------------------------------------------------------------------
n_mean_cca_sf <- 4.57
n_sd_cca_sf <- 1.18
vec_count_cca_sf <- c(165,1189,4814,11545,10445,6303,1755)
vec_freq_cca_sf <- vec_count_cca_sf / sum(vec_count_cca_sf)
#'
## ---- include=FALSE------------------------------------------------------
(ev_cca_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_sf,
pn_sd = n_sd_cca_sf,
pvec_class_freq = vec_freq_cca_sf,
pvec_threshold = NULL,
pvec_price = vec_price_cca,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCa,
pb_verbose = TRUE))
#'
#' ###MO
## ------------------------------------------------------------------------
n_mean_cca_mo <- 5.39
n_sd_cca_mo <- 0.94
vec_count_cca_mo <- c(10,33,194,1341,3511,3730,979)
vec_freq_cca_mo <- vec_count_cca_mo / sum(vec_count_cca_mo)
#'
## ---- include=FALSE------------------------------------------------------
(ev_cca_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_mo,
pn_sd = n_sd_cca_mo,
pvec_class_freq = vec_freq_cca_mo,
pvec_threshold = NULL,
pvec_price = vec_price_cca,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCa,
pb_verbose = TRUE))
#'
#' ##Carcass conformation calves (CCc)
#'
## ------------------------------------------------------------------------
### # prices
vec_price_ccc <- c(11.2,12.7,13.6,14.2,14.7,15.2,15.7)
#'
#' ###OB
## ------------------------------------------------------------------------
n_mean_ccc_ob <- 4.98
n_sd_ccc_ob <- 1.01
vec_count_ccc_ob <- c(11,94,451,2266,3486,2376,472)
vec_freq_ccc_ob <- vec_count_ccc_ob / sum(vec_count_ccc_ob)
#'
## ---- include=FALSE------------------------------------------------------
(ev_ccc_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_ob,
pn_sd = n_sd_ccc_ob,
pvec_class_freq = vec_freq_ccc_ob,
pvec_threshold = NULL,
pvec_price = vec_price_ccc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCc,
pb_verbose = TRUE))
#'
#' ###BV
## ------------------------------------------------------------------------
n_mean_ccc_bv <- 4.08
n_sd_ccc_bv <- 1.08
vec_count_ccc_bv <- c(1759,10739,42522,94581,40759,15082,4855)
vec_freq_ccc_bv <- vec_count_ccc_bv / sum(vec_count_ccc_bv)
#'
## ---- include=FALSE------------------------------------------------------
(ev_ccc_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_bv,
pn_sd = n_sd_ccc_bv,
pvec_class_freq = vec_freq_ccc_bv,
pvec_threshold = NULL,
pvec_price = vec_price_ccc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCc,
pb_verbose = TRUE))
#'
#' ###SI
## ------------------------------------------------------------------------
n_mean_ccc_si <- 5.33
n_sd_ccc_si <- 1.02
vec_count_ccc_si <- c(16,64,247,1620,3359,3374,1087)
vec_freq_ccc_si <- vec_count_ccc_si / sum(vec_count_ccc_si)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_ccc_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_si,
pn_sd = n_sd_ccc_si,
pvec_class_freq = vec_freq_ccc_si,
pvec_threshold = NULL,
pvec_price = vec_price_ccc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCc,
pb_verbose = TRUE))
#'
#' ###SF
## ------------------------------------------------------------------------
n_mean_ccc_sf <- 3.88
n_sd_ccc_sf <- 1.08
vec_count_ccc_sf <- c(645,4027,13631,21453,9150,3054,626)
vec_freq_ccc_sf <- vec_count_ccc_sf / sum(vec_count_ccc_sf)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_ccc_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_sf,
pn_sd = n_sd_ccc_sf,
pvec_class_freq = vec_freq_ccc_sf,
pvec_threshold = NULL,
pvec_price = vec_price_ccc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCc,
pb_verbose = TRUE))
#'
#' ###MO
## ------------------------------------------------------------------------
n_mean_ccc_mo <- 4.73
n_sd_ccc_mo <- 1.11
vec_count_ccc_mo <- c(14,57,232,774,920,523,118)
vec_freq_ccc_mo <- vec_count_ccc_mo / sum(vec_count_ccc_mo)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_ccc_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_mo,
pn_sd = n_sd_ccc_mo,
pvec_class_freq = vec_freq_ccc_mo,
pvec_threshold = NULL,
pvec_price = vec_price_ccc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CCc,
pb_verbose = TRUE))
#'
#'
#'
#' ##Carcass fatness adults (CFa)
## ------------------------------------------------------------------------
### # prices
vec_price_cfa <- c(-0.9000000,
-0.3000000,
0.0000000,
-0.3926929,
-0.8480817)
#'
#' ###OB
## ------------------------------------------------------------------------
n_mean_cfa_ob <- 2.88
n_sd_cfa_ob <- 0.58
vec_count_cfa_ob <- c(161,889,4429,448,21)
vec_freq_cfa_ob <- vec_count_cfa_ob / sum(vec_count_cfa_ob)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfa_ob<- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_ob,
pn_sd = n_sd_cfa_ob,
pvec_class_freq = vec_freq_cfa_ob,
pvec_threshold = NULL,
pvec_price = vec_price_cfa,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CFa,
pb_verbose = TRUE))
#'
#' ###BV
## ------------------------------------------------------------------------
n_mean_cfa_bv <- 2.85
n_sd_cfa_bv <- 0.6
vec_count_cfa_bv <- c(1704,9941,41215,4167,214)
vec_freq_cfa_bv <- vec_count_cfa_bv / sum(vec_count_cfa_bv)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfa_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_bv,
pn_sd = n_sd_cfa_bv,
pvec_class_freq = vec_freq_cfa_bv,
pvec_threshold = NULL,
pvec_price = vec_price_cfa,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CFa,
pb_verbose = TRUE))
#'
#' ###SI
## ------------------------------------------------------------------------
n_mean_cfa_si <- 2.82
n_sd_cfa_si <- 0.55
vec_count_cfa_si <- c(1259,10942,41326,3004,72)
vec_freq_cfa_si <- vec_count_cfa_si / sum(vec_count_cfa_si)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfa_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_si,
pn_sd = n_sd_cfa_si,
pvec_class_freq = vec_freq_cfa_si,
pvec_threshold = NULL,
pvec_price = vec_price_cfa,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CFa,
pb_verbose = TRUE))
#'
#' ###SF
## ------------------------------------------------------------------------
n_mean_cfa_sf <- 2.87
n_sd_cfa_sf <- 0.55
vec_count_cfa_sf <- c(787,5694,27214,2442,79)
vec_freq_cfa_sf <- vec_count_cfa_sf / sum(vec_count_cfa_sf)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfa_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_sf,
pn_sd = n_sd_cfa_sf,
pvec_class_freq = vec_freq_cfa_sf,
pvec_threshold = NULL,
pvec_price = vec_price_cfa,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CFa,
pb_verbose = TRUE))
#'
#' ###MO
## ------------------------------------------------------------------------
n_mean_cfa_mo <- 2.68
n_sd_cfa_mo <- 0.6
vec_count_cfa_mo <- c(373,2721,6420,280,4)
vec_freq_cfa_mo <- vec_count_cfa_mo / sum(vec_count_cfa_mo)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfa_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_mo,
pn_sd = n_sd_cfa_mo,
pvec_class_freq = vec_freq_cfa_mo,
pvec_threshold = NULL,
pvec_price = vec_price_cfa,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CFa,
pb_verbose = TRUE))
#'
#'
#'
#' ##Carcass fatness calves (CFc)
## ------------------------------------------------------------------------
### # prices
vec_price_cfc <- c(-1.5,
-0.6,
0.0,
-0.4,
-1.0)
#'
#' ###OB
## ------------------------------------------------------------------------
n_mean_cfc_ob <- 2.62
n_sd_cfc_ob <- 0.69
vec_count_cfc_ob <- c(632,2671,5379,471,3)
vec_freq_cfc_ob <- vec_count_cfc_ob / sum(vec_count_cfc_ob)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfc_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_ob,
pn_sd = n_sd_cfc_ob,
pvec_class_freq = vec_freq_cfc_ob,
pvec_threshold = NULL,
pvec_price = vec_price_cfc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CFc,
pb_verbose = TRUE))
#'
#' ###BV
## ------------------------------------------------------------------------
n_mean_cfc_bv <- 2.68
n_sd_cfc_bv <- 0.67
vec_count_cfc_bv <- c(12790,52626,133521,11316,44)
vec_freq_cfc_bv <- vec_count_cfc_bv / sum(vec_count_cfc_bv)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfc_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_bv,
pn_sd = n_sd_cfc_bv,
pvec_class_freq = vec_freq_cfc_bv,
pvec_threshold = NULL,
pvec_price = vec_price_cfc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CFc,
pb_verbose = TRUE))
#'
#' ###SI
## ------------------------------------------------------------------------
n_mean_cfc_si <- 2.66
n_sd_cfc_si <- 0.7
vec_count_cfc_si <- c(691,2522,5974,578,2)
vec_freq_cfc_si <- vec_count_cfc_si / sum(vec_count_cfc_si)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfc_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_si,
pn_sd = n_sd_cfc_si,
pvec_class_freq = vec_freq_cfc_si,
pvec_threshold = NULL,
pvec_price = vec_price_cfc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CFc,
pb_verbose = TRUE))
#'
#' ###SF
## ------------------------------------------------------------------------
n_mean_cfc_sf <- 2.76
n_sd_cfc_sf <- 0.65
vec_count_cfc_sf <- c(2475,11464,35025,3602,19)
vec_freq_cfc_sf <- vec_count_cfc_sf / sum(vec_count_cfc_sf)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfc_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_sf,
pn_sd = n_sd_cfc_sf,
pvec_class_freq = vec_freq_cfc_sf,
pvec_threshold = NULL,
pvec_price = vec_price_cfc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$CFc,
pb_verbose = TRUE))
#'
#' ###MO
## ------------------------------------------------------------------------
n_mean_cfc_mo <- 2.64
n_sd_cfc_mo <- 0.67
vec_count_cfc_mo <- c(191,676,1675,96,0)
vec_freq_cfc_mo <- vec_count_cfc_mo / sum(vec_count_cfc_mo)
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cfc_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_mo,
pn_sd = n_sd_cfc_mo,
pvec_class_freq = vec_freq_cfc_mo,
pvec_threshold = NULL,
pvec_price = vec_price_cfc,
pn_delta_mean = .1,
pn_gen_sd = l_gen_sd$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
#'
#' ### OB
## ------------------------------------------------------------------------
n_mean_cwa_ob <- 2.61 * n_scale_fact_cwa
n_sd_cwa_ob <- 0.41 * n_scale_fact_cwa
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwa_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_ob,
pn_sd = n_sd_cwa_ob,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwa,
pvec_price = vec_price_cwa,
pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa,
pn_delta_mean = .01 * n_scale_fact_cwa))
#'
#' ### BV
## ------------------------------------------------------------------------
n_mean_cwa_bv <- 2.77 * n_scale_fact_cwa
n_sd_cwa_bv <- 0.36 * n_scale_fact_cwa
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwa_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_bv,
pn_sd = n_sd_cwa_bv,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwa,
pvec_price = vec_price_cwa,
pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa,
pn_delta_mean = .01 * n_scale_fact_cwa))
#'
#'
#' ### SI
## ------------------------------------------------------------------------
n_mean_cwa_si <- 2.79 * n_scale_fact_cwa
n_sd_cwa_si <- 0.42 * n_scale_fact_cwa
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwa_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_si,
pn_sd = n_sd_cwa_si,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwa,
pvec_price = vec_price_cwa,
pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa,
pn_delta_mean = .01 * n_scale_fact_cwa))
#'
#' ### SF
## ------------------------------------------------------------------------
n_mean_cwa_sf <- 2.88 * n_scale_fact_cwa
n_sd_cwa_sf <- 0.32 * n_scale_fact_cwa
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwa_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_sf,
pn_sd = n_sd_cwa_sf,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwa,
pvec_price = vec_price_cwa,
pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa,
pn_delta_mean = .01 * n_scale_fact_cwa))
#'
#' ### MO
## ------------------------------------------------------------------------
n_mean_cwa_mo <- 2.99 * n_scale_fact_cwa
n_sd_cwa_mo <- 0.25 * n_scale_fact_cwa
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwa_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_mo,
pn_sd = n_sd_cwa_mo,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwa,
pvec_price = vec_price_cwa,
pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa,
pn_delta_mean = .01 * n_scale_fact_cwa))
#'
#'
#'
#' ##Carcass weight calves (CWc)
## ------------------------------------------------------------------------
n_scale_fact_cwc <- 100
vec_price_cwc <- seq(0.0,-1.1,-0.1);vec_price_cwc
vec_thre_cwc <- seq(1.4, 1.5, 0.01) * n_scale_fact_cwc
vec_thre_cwc
#'
#' ### OB
## ------------------------------------------------------------------------
n_mean_cwc_ob <- 1.25 * n_scale_fact_cwc
n_sd_cwc_ob <- 0.13 * n_scale_fact_cwc
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwc_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_ob,
pn_sd = n_sd_cwc_ob,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwc,
pvec_price = vec_price_cwc,
pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc,
pn_delta_mean = .01 * n_scale_fact_cwc))
#'
#' ### BV
## ------------------------------------------------------------------------
n_mean_cwc_bv <- 1.26 * n_scale_fact_cwc
n_sd_cwc_bv <- 0.14 * n_scale_fact_cwc
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwc_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_bv,
pn_sd = n_sd_cwc_bv,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwc,
pvec_price = vec_price_cwc,
pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc,
pn_delta_mean = .01 * n_scale_fact_cwc))
#'
#'
#' ### SI
## ------------------------------------------------------------------------
n_mean_cwc_si <- 1.27 * n_scale_fact_cwc
n_sd_cwc_si <- 0.13 * n_scale_fact_cwc
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwc_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_si,
pn_sd = n_sd_cwc_si,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwc,
pvec_price = vec_price_cwc,
pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc,
pn_delta_mean = .01 * n_scale_fact_cwc))
#'
#' ### SF
## ------------------------------------------------------------------------
n_mean_cwc_sf <- 1.24 * n_scale_fact_cwc
n_sd_cwc_sf <- 0.13 * n_scale_fact_cwc
#'
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwc_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_sf,
pn_sd = n_sd_cwc_sf,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwc,
pvec_price = vec_price_cwc,
pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc,
pn_delta_mean = .01 * n_scale_fact_cwc))
#'
#' ### MO
## ------------------------------------------------------------------------
n_mean_cwc_mo <- 1.28 * n_scale_fact_cwc
n_sd_cwc_mo <- 0.15 * n_scale_fact_cwc
#'
## ---- include=FALSE------------------------------------------------------
(ev_cwc_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_mo,
pn_sd = n_sd_cwc_mo,
pvec_class_freq = NULL,
pvec_threshold = vec_thre_cwc,
pvec_price = vec_price_cwc,
pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc,
pn_delta_mean = .01 * n_scale_fact_cwc))
tbl_ev_result_ev_per_gen_sd <- tibble::data_frame(Traits = c("cca", "ccc", "cfa", "cfc", "cwa", "cwc"),
OB = c(ev_cca_ob$ev_per_gen_sd,
ev_ccc_ob$ev_per_gen_sd,
ev_cfa_ob$ev_per_gen_sd,
ev_cfc_ob$ev_per_gen_sd,
ev_cwa_ob$ev_per_gen_sd,
ev_cwc_ob$ev_per_gen_sd),
BV = c(ev_cca_bv$ev_per_gen_sd,
ev_ccc_bv$ev_per_gen_sd,
ev_cfa_bv$ev_per_gen_sd,
ev_cfc_bv$ev_per_gen_sd,
ev_cwa_bv$ev_per_gen_sd,
ev_cwc_bv$ev_per_gen_sd),
SI = c(ev_cca_si$ev_per_gen_sd,
ev_ccc_si$ev_per_gen_sd,
ev_cfa_si$ev_per_gen_sd,
ev_cfc_si$ev_per_gen_sd,
ev_cwa_si$ev_per_gen_sd,
ev_cwc_si$ev_per_gen_sd),
SF = c(ev_cca_sf$ev_per_gen_sd,
ev_ccc_sf$ev_per_gen_sd,
ev_cfa_sf$ev_per_gen_sd,
ev_cfc_sf$ev_per_gen_sd,
ev_cwa_sf$ev_per_gen_sd,
ev_cwc_sf$ev_per_gen_sd),
MO = c(ev_cca_mo$ev_per_gen_sd,
ev_ccc_mo$ev_per_gen_sd,
ev_cfa_mo$ev_per_gen_sd,
ev_cfc_mo$ev_per_gen_sd,
ev_cwa_mo$ev_per_gen_sd,
ev_cwc_mo$ev_per_gen_sd))
#' ###2.1) Computing Relative Economic Factors For All Categories
## ---- echo=FALSE---------------------------------------------------------
### # 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)
#'
#' ###2.2) Computing Relative Economic Factors For Adults
#' Computing the factors for different animal categories separately can be done with two separate function calls.We start with the category "addults"
## ---- echo=FALSE---------------------------------------------------------
### # adults
vec_row_idx_adult <- c(1,3,5)
tbl_rel_factors_adult <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_gen_sd[vec_row_idx_adult,],
pb_first_col_trait_name = TRUE)
#'
#' ###2.3) Computing Relative Economic Factors For Calves
#' The same is done for the category "calves"
## ---- echo=FALSE---------------------------------------------------------
### # adults
vec_row_idx_calves <- c(2,4,6)
tbl_rel_factors_calves <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_gen_sd[vec_row_idx_calves,],
pb_first_col_trait_name = TRUE)
#'
#' ##3. Importance of calves versus adults for each population
## ---- include=FALSE------------------------------------------------------
tbl_number_calves_adults <- tibble::data_frame(Categories = c("adults", "calves"),
OB = c(5948,
9156),
BV = c(57241,
210297),
SI = c(56603,
9767),
SF = c(36216,
52585),
MO = c(9798,
2638))
#'
## ---- include=FALSE------------------------------------------------------
### # 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)
### # adjust dimensions of proportion matrix to be consistent with ev tbl.
(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))
#' ### Szenario B) The relative factors are weighted with animal categories to get weighted relative factors (File name: weighted_economic_value_relative.csv)
## ---- include=FALSE------------------------------------------------------
tbl_weighted_rel_factors <- MeatValueIndex::weight_economic_value(ptbl_economic_value = tbl_rel_factors,
ptbl_weight = tbl_proportion4eachtrait,
pb_first_col_trait_name = TRUE)
Economic values for all r n_nr_trait traits were computed using a simplified profit function leading to the following results.
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"),
OB = c(ev_cca_ob$ev_per_gen_sd,
ev_ccc_ob$ev_per_gen_sd,
ev_cfa_ob$ev_per_gen_sd,
ev_cfc_ob$ev_per_gen_sd,
ev_cwa_ob$ev_per_gen_sd,
ev_cwc_ob$ev_per_gen_sd),
BV = c(ev_cca_bv$ev_per_gen_sd,
ev_ccc_bv$ev_per_gen_sd,
ev_cfa_bv$ev_per_gen_sd,
ev_cfc_bv$ev_per_gen_sd,
ev_cwa_bv$ev_per_gen_sd,
ev_cwc_bv$ev_per_gen_sd),
SI = c(ev_cca_si$ev_per_gen_sd,
ev_ccc_si$ev_per_gen_sd,
ev_cfa_si$ev_per_gen_sd,
ev_cfc_si$ev_per_gen_sd,
ev_cwa_si$ev_per_gen_sd,
ev_cwc_si$ev_per_gen_sd),
SF = c(ev_cca_sf$ev_per_gen_sd,
ev_ccc_sf$ev_per_gen_sd,
ev_cfa_sf$ev_per_gen_sd,
ev_cfc_sf$ev_per_gen_sd,
ev_cwa_sf$ev_per_gen_sd,
ev_cwc_sf$ev_per_gen_sd),
MO = c(ev_cca_mo$ev_per_gen_sd,
ev_ccc_mo$ev_per_gen_sd,
ev_cfa_mo$ev_per_gen_sd,
ev_cfc_mo$ev_per_gen_sd,
ev_cwa_mo$ev_per_gen_sd,
ev_cwc_mo$ev_per_gen_sd))
knitr::kable(tbl_ev_result_ev_per_gen_sd,booktabs = TRUE)
2. Comparison To Values (Szenario A und Szenario A*)
SZENARIO A
Computing Relative Economic Factors For All Categories.
knitr::kable(tbl_rel_factors, booktabs = TRUE)
Computing Relative Economic Factors For All Categories For Szenario A*
Computing relative economic factor within category.
Computing Relative Economic Factors For Adults (table 2.2)
knitr::kable(tbl_rel_factors_adult, booktabs = TRUE)
Computing Relative Economic Factors For Calves (table 2.3)
knitr::kable(tbl_rel_factors_calves, booktabs = TRUE)
SZENARIO A*
To get the relative importance inside of an index, we have to re-scale
tbl_rel_fact_adult_calf <- bind_rows(tbl_rel_factors_adult, tbl_rel_factors_calves)
tbl_rel_fact_adult_calf <- tbl_rel_fact_adult_calf[c(1,4,2,5,3,6),]
tbl_rel_fact_adult_calf_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_rel_fact_adult_calf,
pb_first_col_trait_name = TRUE)
knitr::kable(tbl_rel_fact_adult_calf_rescaled, booktabs = TRUE)
3. Comparison To Values (Szenario B und Szenario B*)
Computing Relative Economic Factors Weithted with proportion of animal categories.
SZENARIO B
The following table shows economic values of r n_nr_trait traits weighted with proportion of animal categories.
knitr::kable( tbl_weighted_rel_factors,
booktabs = TRUE )
The values in the above table are now re-scaled.
tbl_weighted_rel_factors_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_weighted_rel_factors,
pb_first_col_trait_name = TRUE)
knitr::kable( tbl_weighted_rel_factors_rescaled,
booktabs = TRUE )
SZENARIO B*
The above factors are weighted with the proportion of the aniMal categories
tbl_weighted_rel_fact_adult_calf <- MeatValueIndex::weight_economic_value(ptbl_economic_value = tbl_rel_fact_adult_calf_rescaled,
ptbl_weight = tbl_proportion4eachtrait,
pb_first_col_trait_name = TRUE)
tbl_weighted_rel_fact_adult_calf_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_weighted_rel_fact_adult_calf,
pb_first_col_trait_name = TRUE)
knitr::kable(tbl_weighted_rel_fact_adult_calf_rescaled, booktabs = TRUE)
Conclusions
- For traits with a low relative weight (
CFa, CFc, CWa and CWc), the different strategies do not show different results
- The different weighting strategies lead to different relative weights in
CCa and CCc where the weights are high
Prepare Economic Values Input For Selection Response
tbl_ev_result_ev_per_trait_unit <- tibble::data_frame(Traits = c("cca", "ccc", "cfa", "cfc", "cwa", "cwc"),
OB = c(ev_cca_ob$ev_per_trait_unit,
ev_ccc_ob$ev_per_trait_unit,
ev_cfa_ob$ev_per_trait_unit,
ev_cfc_ob$ev_per_trait_unit,
ev_cwa_ob$ev_per_trait_unit,
ev_cwc_ob$ev_per_trait_unit),
BV = c(ev_cca_bv$ev_per_trait_unit,
ev_ccc_bv$ev_per_trait_unit,
ev_cfa_bv$ev_per_trait_unit,
ev_cfc_bv$ev_per_trait_unit,
ev_cwa_bv$ev_per_trait_unit,
ev_cwc_bv$ev_per_trait_unit),
SI = c(ev_cca_si$ev_per_trait_unit,
ev_ccc_si$ev_per_trait_unit,
ev_cfa_si$ev_per_trait_unit,
ev_cfc_si$ev_per_trait_unit,
ev_cwa_si$ev_per_trait_unit,
ev_cwc_si$ev_per_trait_unit),
SF = c(ev_cca_sf$ev_per_trait_unit,
ev_ccc_sf$ev_per_trait_unit,
ev_cfa_sf$ev_per_trait_unit,
ev_cfc_sf$ev_per_trait_unit,
ev_cwa_sf$ev_per_trait_unit,
ev_cwc_sf$ev_per_trait_unit),
MO = c(ev_cca_mo$ev_per_trait_unit,
ev_ccc_mo$ev_per_trait_unit,
ev_cfa_mo$ev_per_trait_unit,
ev_cfc_mo$ev_per_trait_unit,
ev_cwa_mo$ev_per_trait_unit,
ev_cwc_mo$ev_per_trait_unit))
#Äquivalent zu Szenario A per_trait_units
(tbl_rel_factors_per_trait_units <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_trait_unit,
pb_first_col_trait_name = TRUE))
(tbl_rel_factors_adult_per_trait_units <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_trait_unit[vec_row_idx_adult,],
pb_first_col_trait_name = TRUE))
(tbl_rel_factors_calf_per_trait_units <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_trait_unit[vec_row_idx_calves,],
pb_first_col_trait_name = TRUE))
tbl_rel_fact_adult_calf_per_trait_units <- bind_rows(tbl_rel_factors_adult_per_trait_units, tbl_rel_factors_calf_per_trait_units)
(tbl_rel_fact_adult_calf_per_trait_units <- tbl_rel_fact_adult_calf_per_trait_units[c(1,4,2,5,3,6),])
#Äquivalent zu Szenario A* per_trait_units
(tbl_rel_fact_adult_calf_per_trait_units_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_rel_fact_adult_calf_per_trait_units,
pb_first_col_trait_name = TRUE))
(tbl_weighted_rel_factors_per_trait_units <- MeatValueIndex::weight_economic_value(ptbl_economic_value = tbl_rel_factors_per_trait_units,
ptbl_weight = tbl_proportion4eachtrait,
pb_first_col_trait_name = TRUE))
Äquivalent zu Szenario B per_trait_units
tbl_weighted_rel_factors_per_trait_units_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_weighted_rel_factors_per_trait_units,
pb_first_col_trait_name = TRUE)
knitr::kable( tbl_weighted_rel_factors_per_trait_units_rescaled,
booktabs = TRUE )
Äquivalent zu Szenario B* per_trait_units
#Äquivalent zu Szenario B* per_trait_units
tbl_weighted_rel_fact_adult_calf_per_trait_units <- MeatValueIndex::weight_economic_value(ptbl_economic_value = tbl_rel_fact_adult_calf_per_trait_units_rescaled,
ptbl_weight = tbl_proportion4eachtrait,
pb_first_col_trait_name = TRUE)
tbl_weighted_rel_fact_adult_calf_rescaled_per_trait_units <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_weighted_rel_fact_adult_calf_per_trait_units,
pb_first_col_trait_name = TRUE)
knitr::kable(tbl_weighted_rel_fact_adult_calf_rescaled_per_trait_units, booktabs = TRUE)
pvrqualitasag/MeatValueIndex documentation built on May 13, 2019, 4:44 p.m.
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Disclaimer
This notebook gives an overview over different weighting strategies between economic values of traits of different animal categories.
Background
In beef cattle there are three different carcass performance traits.
tbl_trait <- tibble::data_frame(Abbreviation = c("CC", "CF", "CW"), Trait = c("Carcass Conformation", "Carcass Fat", "Carcass Weight")) knitr::kable( tbl_trait, booktabs = TRUE, longtable = TRUE )
Both traits are available in two animal categories
tbl_category <- tibble::data_frame(Abbreviation = c("c", "a"), Category = c("calf", "adult")) knitr::kable( tbl_category, booktabs = TRUE, longtable = TRUE )
n_nr_trait <- nrow(tbl_category) * nrow(tbl_trait)
Combining both, results in the following matrix of a total of r n_nr_trait traits
tbl_trait_matrix <- tibble::data_frame(Trait = c("CC", "CF", "CW"), Calf = c("CCc", "CFc", "CWc"), Adult = c("CCa", "CFa", "CWa")) knitr::kable( tbl_trait_matrix, booktabs = TRUE, longtable = TRUE )
1. Economic Values
library(dplyr) #' #1. Computing Economic Value For Dual 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) #' #' ###OB ## ------------------------------------------------------------------------ n_mean_cca_ob <- 5.20 n_sd_cca_ob <- 1.02 vec_count_cca_ob <- c(4,43,218,1150,2106,1905,522) vec_freq_cca_ob <- vec_count_cca_ob / sum(vec_count_cca_ob) #' ## ---- include=FALSE------------------------------------------------------ (ev_cca_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_ob, pn_sd = n_sd_cca_ob, pvec_class_freq = vec_freq_cca_ob, pvec_threshold = NULL, pvec_price = vec_price_cca, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCa, pb_verbose = TRUE)) #' #' ###BV ## ------------------------------------------------------------------------ n_mean_cca_bv <- 4.78 n_sd_cca_bv <- 1.27 vec_count_cca_bv <- c(273,1562,5982,17808,14303,11438,5875) vec_freq_cca_bv <- vec_count_cca_bv / sum(vec_count_cca_bv) #' ## ---- include=FALSE------------------------------------------------------ (ev_cca_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_bv, pn_sd = n_sd_cca_bv, pvec_class_freq = vec_freq_cca_bv, pvec_threshold = NULL, pvec_price = vec_price_cca, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCa, pb_verbose = TRUE)) #' #' ###SI ## ------------------------------------------------------------------------ n_mean_cca_si <- 5.83 n_sd_cca_si <- 0.9 vec_count_cca_si <- c(4,38,377,3994,13917,24738,13535) vec_freq_cca_si <- vec_count_cca_si / sum(vec_count_cca_si) #' ## ---- include=FALSE------------------------------------------------------ (ev_cca_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_si, pn_sd = n_sd_cca_si, pvec_class_freq = vec_freq_cca_si, pvec_threshold = NULL, pvec_price = vec_price_cca, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCa, pb_verbose = TRUE)) #' #' ###SF ## ------------------------------------------------------------------------ n_mean_cca_sf <- 4.57 n_sd_cca_sf <- 1.18 vec_count_cca_sf <- c(165,1189,4814,11545,10445,6303,1755) vec_freq_cca_sf <- vec_count_cca_sf / sum(vec_count_cca_sf) #' ## ---- include=FALSE------------------------------------------------------ (ev_cca_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_sf, pn_sd = n_sd_cca_sf, pvec_class_freq = vec_freq_cca_sf, pvec_threshold = NULL, pvec_price = vec_price_cca, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCa, pb_verbose = TRUE)) #' #' ###MO ## ------------------------------------------------------------------------ n_mean_cca_mo <- 5.39 n_sd_cca_mo <- 0.94 vec_count_cca_mo <- c(10,33,194,1341,3511,3730,979) vec_freq_cca_mo <- vec_count_cca_mo / sum(vec_count_cca_mo) #' ## ---- include=FALSE------------------------------------------------------ (ev_cca_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cca_mo, pn_sd = n_sd_cca_mo, pvec_class_freq = vec_freq_cca_mo, pvec_threshold = NULL, pvec_price = vec_price_cca, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCa, pb_verbose = TRUE)) #' #' ##Carcass conformation calves (CCc) #' ## ------------------------------------------------------------------------ ### # prices vec_price_ccc <- c(11.2,12.7,13.6,14.2,14.7,15.2,15.7) #' #' ###OB ## ------------------------------------------------------------------------ n_mean_ccc_ob <- 4.98 n_sd_ccc_ob <- 1.01 vec_count_ccc_ob <- c(11,94,451,2266,3486,2376,472) vec_freq_ccc_ob <- vec_count_ccc_ob / sum(vec_count_ccc_ob) #' ## ---- include=FALSE------------------------------------------------------ (ev_ccc_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_ob, pn_sd = n_sd_ccc_ob, pvec_class_freq = vec_freq_ccc_ob, pvec_threshold = NULL, pvec_price = vec_price_ccc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCc, pb_verbose = TRUE)) #' #' ###BV ## ------------------------------------------------------------------------ n_mean_ccc_bv <- 4.08 n_sd_ccc_bv <- 1.08 vec_count_ccc_bv <- c(1759,10739,42522,94581,40759,15082,4855) vec_freq_ccc_bv <- vec_count_ccc_bv / sum(vec_count_ccc_bv) #' ## ---- include=FALSE------------------------------------------------------ (ev_ccc_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_bv, pn_sd = n_sd_ccc_bv, pvec_class_freq = vec_freq_ccc_bv, pvec_threshold = NULL, pvec_price = vec_price_ccc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCc, pb_verbose = TRUE)) #' #' ###SI ## ------------------------------------------------------------------------ n_mean_ccc_si <- 5.33 n_sd_ccc_si <- 1.02 vec_count_ccc_si <- c(16,64,247,1620,3359,3374,1087) vec_freq_ccc_si <- vec_count_ccc_si / sum(vec_count_ccc_si) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_ccc_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_si, pn_sd = n_sd_ccc_si, pvec_class_freq = vec_freq_ccc_si, pvec_threshold = NULL, pvec_price = vec_price_ccc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCc, pb_verbose = TRUE)) #' #' ###SF ## ------------------------------------------------------------------------ n_mean_ccc_sf <- 3.88 n_sd_ccc_sf <- 1.08 vec_count_ccc_sf <- c(645,4027,13631,21453,9150,3054,626) vec_freq_ccc_sf <- vec_count_ccc_sf / sum(vec_count_ccc_sf) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_ccc_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_sf, pn_sd = n_sd_ccc_sf, pvec_class_freq = vec_freq_ccc_sf, pvec_threshold = NULL, pvec_price = vec_price_ccc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCc, pb_verbose = TRUE)) #' #' ###MO ## ------------------------------------------------------------------------ n_mean_ccc_mo <- 4.73 n_sd_ccc_mo <- 1.11 vec_count_ccc_mo <- c(14,57,232,774,920,523,118) vec_freq_ccc_mo <- vec_count_ccc_mo / sum(vec_count_ccc_mo) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_ccc_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_ccc_mo, pn_sd = n_sd_ccc_mo, pvec_class_freq = vec_freq_ccc_mo, pvec_threshold = NULL, pvec_price = vec_price_ccc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CCc, pb_verbose = TRUE)) #' #' #' #' ##Carcass fatness adults (CFa) ## ------------------------------------------------------------------------ ### # prices vec_price_cfa <- c(-0.9000000, -0.3000000, 0.0000000, -0.3926929, -0.8480817) #' #' ###OB ## ------------------------------------------------------------------------ n_mean_cfa_ob <- 2.88 n_sd_cfa_ob <- 0.58 vec_count_cfa_ob <- c(161,889,4429,448,21) vec_freq_cfa_ob <- vec_count_cfa_ob / sum(vec_count_cfa_ob) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfa_ob<- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_ob, pn_sd = n_sd_cfa_ob, pvec_class_freq = vec_freq_cfa_ob, pvec_threshold = NULL, pvec_price = vec_price_cfa, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CFa, pb_verbose = TRUE)) #' #' ###BV ## ------------------------------------------------------------------------ n_mean_cfa_bv <- 2.85 n_sd_cfa_bv <- 0.6 vec_count_cfa_bv <- c(1704,9941,41215,4167,214) vec_freq_cfa_bv <- vec_count_cfa_bv / sum(vec_count_cfa_bv) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfa_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_bv, pn_sd = n_sd_cfa_bv, pvec_class_freq = vec_freq_cfa_bv, pvec_threshold = NULL, pvec_price = vec_price_cfa, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CFa, pb_verbose = TRUE)) #' #' ###SI ## ------------------------------------------------------------------------ n_mean_cfa_si <- 2.82 n_sd_cfa_si <- 0.55 vec_count_cfa_si <- c(1259,10942,41326,3004,72) vec_freq_cfa_si <- vec_count_cfa_si / sum(vec_count_cfa_si) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfa_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_si, pn_sd = n_sd_cfa_si, pvec_class_freq = vec_freq_cfa_si, pvec_threshold = NULL, pvec_price = vec_price_cfa, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CFa, pb_verbose = TRUE)) #' #' ###SF ## ------------------------------------------------------------------------ n_mean_cfa_sf <- 2.87 n_sd_cfa_sf <- 0.55 vec_count_cfa_sf <- c(787,5694,27214,2442,79) vec_freq_cfa_sf <- vec_count_cfa_sf / sum(vec_count_cfa_sf) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfa_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_sf, pn_sd = n_sd_cfa_sf, pvec_class_freq = vec_freq_cfa_sf, pvec_threshold = NULL, pvec_price = vec_price_cfa, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CFa, pb_verbose = TRUE)) #' #' ###MO ## ------------------------------------------------------------------------ n_mean_cfa_mo <- 2.68 n_sd_cfa_mo <- 0.6 vec_count_cfa_mo <- c(373,2721,6420,280,4) vec_freq_cfa_mo <- vec_count_cfa_mo / sum(vec_count_cfa_mo) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfa_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfa_mo, pn_sd = n_sd_cfa_mo, pvec_class_freq = vec_freq_cfa_mo, pvec_threshold = NULL, pvec_price = vec_price_cfa, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CFa, pb_verbose = TRUE)) #' #' #' #' ##Carcass fatness calves (CFc) ## ------------------------------------------------------------------------ ### # prices vec_price_cfc <- c(-1.5, -0.6, 0.0, -0.4, -1.0) #' #' ###OB ## ------------------------------------------------------------------------ n_mean_cfc_ob <- 2.62 n_sd_cfc_ob <- 0.69 vec_count_cfc_ob <- c(632,2671,5379,471,3) vec_freq_cfc_ob <- vec_count_cfc_ob / sum(vec_count_cfc_ob) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfc_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_ob, pn_sd = n_sd_cfc_ob, pvec_class_freq = vec_freq_cfc_ob, pvec_threshold = NULL, pvec_price = vec_price_cfc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CFc, pb_verbose = TRUE)) #' #' ###BV ## ------------------------------------------------------------------------ n_mean_cfc_bv <- 2.68 n_sd_cfc_bv <- 0.67 vec_count_cfc_bv <- c(12790,52626,133521,11316,44) vec_freq_cfc_bv <- vec_count_cfc_bv / sum(vec_count_cfc_bv) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfc_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_bv, pn_sd = n_sd_cfc_bv, pvec_class_freq = vec_freq_cfc_bv, pvec_threshold = NULL, pvec_price = vec_price_cfc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CFc, pb_verbose = TRUE)) #' #' ###SI ## ------------------------------------------------------------------------ n_mean_cfc_si <- 2.66 n_sd_cfc_si <- 0.7 vec_count_cfc_si <- c(691,2522,5974,578,2) vec_freq_cfc_si <- vec_count_cfc_si / sum(vec_count_cfc_si) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfc_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_si, pn_sd = n_sd_cfc_si, pvec_class_freq = vec_freq_cfc_si, pvec_threshold = NULL, pvec_price = vec_price_cfc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CFc, pb_verbose = TRUE)) #' #' ###SF ## ------------------------------------------------------------------------ n_mean_cfc_sf <- 2.76 n_sd_cfc_sf <- 0.65 vec_count_cfc_sf <- c(2475,11464,35025,3602,19) vec_freq_cfc_sf <- vec_count_cfc_sf / sum(vec_count_cfc_sf) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfc_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_sf, pn_sd = n_sd_cfc_sf, pvec_class_freq = vec_freq_cfc_sf, pvec_threshold = NULL, pvec_price = vec_price_cfc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$CFc, pb_verbose = TRUE)) #' #' ###MO ## ------------------------------------------------------------------------ n_mean_cfc_mo <- 2.64 n_sd_cfc_mo <- 0.67 vec_count_cfc_mo <- c(191,676,1675,96,0) vec_freq_cfc_mo <- vec_count_cfc_mo / sum(vec_count_cfc_mo) #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cfc_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cfc_mo, pn_sd = n_sd_cfc_mo, pvec_class_freq = vec_freq_cfc_mo, pvec_threshold = NULL, pvec_price = vec_price_cfc, pn_delta_mean = .1, pn_gen_sd = l_gen_sd$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 #' #' ### OB ## ------------------------------------------------------------------------ n_mean_cwa_ob <- 2.61 * n_scale_fact_cwa n_sd_cwa_ob <- 0.41 * n_scale_fact_cwa #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cwa_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_ob, pn_sd = n_sd_cwa_ob, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwa, pvec_price = vec_price_cwa, pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa, pn_delta_mean = .01 * n_scale_fact_cwa)) #' #' ### BV ## ------------------------------------------------------------------------ n_mean_cwa_bv <- 2.77 * n_scale_fact_cwa n_sd_cwa_bv <- 0.36 * n_scale_fact_cwa #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cwa_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_bv, pn_sd = n_sd_cwa_bv, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwa, pvec_price = vec_price_cwa, pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa, pn_delta_mean = .01 * n_scale_fact_cwa)) #' #' #' ### SI ## ------------------------------------------------------------------------ n_mean_cwa_si <- 2.79 * n_scale_fact_cwa n_sd_cwa_si <- 0.42 * n_scale_fact_cwa #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cwa_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_si, pn_sd = n_sd_cwa_si, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwa, pvec_price = vec_price_cwa, pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa, pn_delta_mean = .01 * n_scale_fact_cwa)) #' #' ### SF ## ------------------------------------------------------------------------ n_mean_cwa_sf <- 2.88 * n_scale_fact_cwa n_sd_cwa_sf <- 0.32 * n_scale_fact_cwa #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cwa_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_sf, pn_sd = n_sd_cwa_sf, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwa, pvec_price = vec_price_cwa, pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa, pn_delta_mean = .01 * n_scale_fact_cwa)) #' #' ### MO ## ------------------------------------------------------------------------ n_mean_cwa_mo <- 2.99 * n_scale_fact_cwa n_sd_cwa_mo <- 0.25 * n_scale_fact_cwa #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cwa_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwa_mo, pn_sd = n_sd_cwa_mo, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwa, pvec_price = vec_price_cwa, pn_gen_sd = l_gen_sd$CWa * n_scale_fact_cwa, pn_delta_mean = .01 * n_scale_fact_cwa)) #' #' #' #' ##Carcass weight calves (CWc) ## ------------------------------------------------------------------------ n_scale_fact_cwc <- 100 vec_price_cwc <- seq(0.0,-1.1,-0.1);vec_price_cwc vec_thre_cwc <- seq(1.4, 1.5, 0.01) * n_scale_fact_cwc vec_thre_cwc #' #' ### OB ## ------------------------------------------------------------------------ n_mean_cwc_ob <- 1.25 * n_scale_fact_cwc n_sd_cwc_ob <- 0.13 * n_scale_fact_cwc #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cwc_ob <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_ob, pn_sd = n_sd_cwc_ob, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwc, pvec_price = vec_price_cwc, pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc, pn_delta_mean = .01 * n_scale_fact_cwc)) #' #' ### BV ## ------------------------------------------------------------------------ n_mean_cwc_bv <- 1.26 * n_scale_fact_cwc n_sd_cwc_bv <- 0.14 * n_scale_fact_cwc #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cwc_bv <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_bv, pn_sd = n_sd_cwc_bv, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwc, pvec_price = vec_price_cwc, pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc, pn_delta_mean = .01 * n_scale_fact_cwc)) #' #' #' ### SI ## ------------------------------------------------------------------------ n_mean_cwc_si <- 1.27 * n_scale_fact_cwc n_sd_cwc_si <- 0.13 * n_scale_fact_cwc #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cwc_si <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_si, pn_sd = n_sd_cwc_si, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwc, pvec_price = vec_price_cwc, pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc, pn_delta_mean = .01 * n_scale_fact_cwc)) #' #' ### SF ## ------------------------------------------------------------------------ n_mean_cwc_sf <- 1.24 * n_scale_fact_cwc n_sd_cwc_sf <- 0.13 * n_scale_fact_cwc #' #' ## ---- include=FALSE------------------------------------------------------ (ev_cwc_sf <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_sf, pn_sd = n_sd_cwc_sf, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwc, pvec_price = vec_price_cwc, pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc, pn_delta_mean = .01 * n_scale_fact_cwc)) #' #' ### MO ## ------------------------------------------------------------------------ n_mean_cwc_mo <- 1.28 * n_scale_fact_cwc n_sd_cwc_mo <- 0.15 * n_scale_fact_cwc #' ## ---- include=FALSE------------------------------------------------------ (ev_cwc_mo <- MeatValueIndex::compute_economic_value( pn_mean = n_mean_cwc_mo, pn_sd = n_sd_cwc_mo, pvec_class_freq = NULL, pvec_threshold = vec_thre_cwc, pvec_price = vec_price_cwc, pn_gen_sd = l_gen_sd$CWc * n_scale_fact_cwc, pn_delta_mean = .01 * n_scale_fact_cwc)) tbl_ev_result_ev_per_gen_sd <- tibble::data_frame(Traits = c("cca", "ccc", "cfa", "cfc", "cwa", "cwc"), OB = c(ev_cca_ob$ev_per_gen_sd, ev_ccc_ob$ev_per_gen_sd, ev_cfa_ob$ev_per_gen_sd, ev_cfc_ob$ev_per_gen_sd, ev_cwa_ob$ev_per_gen_sd, ev_cwc_ob$ev_per_gen_sd), BV = c(ev_cca_bv$ev_per_gen_sd, ev_ccc_bv$ev_per_gen_sd, ev_cfa_bv$ev_per_gen_sd, ev_cfc_bv$ev_per_gen_sd, ev_cwa_bv$ev_per_gen_sd, ev_cwc_bv$ev_per_gen_sd), SI = c(ev_cca_si$ev_per_gen_sd, ev_ccc_si$ev_per_gen_sd, ev_cfa_si$ev_per_gen_sd, ev_cfc_si$ev_per_gen_sd, ev_cwa_si$ev_per_gen_sd, ev_cwc_si$ev_per_gen_sd), SF = c(ev_cca_sf$ev_per_gen_sd, ev_ccc_sf$ev_per_gen_sd, ev_cfa_sf$ev_per_gen_sd, ev_cfc_sf$ev_per_gen_sd, ev_cwa_sf$ev_per_gen_sd, ev_cwc_sf$ev_per_gen_sd), MO = c(ev_cca_mo$ev_per_gen_sd, ev_ccc_mo$ev_per_gen_sd, ev_cfa_mo$ev_per_gen_sd, ev_cfc_mo$ev_per_gen_sd, ev_cwa_mo$ev_per_gen_sd, ev_cwc_mo$ev_per_gen_sd)) #' ###2.1) Computing Relative Economic Factors For All Categories ## ---- echo=FALSE--------------------------------------------------------- ### # 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) #' #' ###2.2) Computing Relative Economic Factors For Adults #' Computing the factors for different animal categories separately can be done with two separate function calls.We start with the category "addults" ## ---- echo=FALSE--------------------------------------------------------- ### # adults vec_row_idx_adult <- c(1,3,5) tbl_rel_factors_adult <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_gen_sd[vec_row_idx_adult,], pb_first_col_trait_name = TRUE) #' #' ###2.3) Computing Relative Economic Factors For Calves #' The same is done for the category "calves" ## ---- echo=FALSE--------------------------------------------------------- ### # adults vec_row_idx_calves <- c(2,4,6) tbl_rel_factors_calves <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_gen_sd[vec_row_idx_calves,], pb_first_col_trait_name = TRUE) #' #' ##3. Importance of calves versus adults for each population ## ---- include=FALSE------------------------------------------------------ tbl_number_calves_adults <- tibble::data_frame(Categories = c("adults", "calves"), OB = c(5948, 9156), BV = c(57241, 210297), SI = c(56603, 9767), SF = c(36216, 52585), MO = c(9798, 2638)) #' ## ---- include=FALSE------------------------------------------------------ ### # 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) ### # adjust dimensions of proportion matrix to be consistent with ev tbl. (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)) #' ### Szenario B) The relative factors are weighted with animal categories to get weighted relative factors (File name: weighted_economic_value_relative.csv) ## ---- include=FALSE------------------------------------------------------ tbl_weighted_rel_factors <- MeatValueIndex::weight_economic_value(ptbl_economic_value = tbl_rel_factors, ptbl_weight = tbl_proportion4eachtrait, pb_first_col_trait_name = TRUE)
Economic values for all r n_nr_trait traits were computed using a simplified profit function leading to the following results.
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"), OB = c(ev_cca_ob$ev_per_gen_sd, ev_ccc_ob$ev_per_gen_sd, ev_cfa_ob$ev_per_gen_sd, ev_cfc_ob$ev_per_gen_sd, ev_cwa_ob$ev_per_gen_sd, ev_cwc_ob$ev_per_gen_sd), BV = c(ev_cca_bv$ev_per_gen_sd, ev_ccc_bv$ev_per_gen_sd, ev_cfa_bv$ev_per_gen_sd, ev_cfc_bv$ev_per_gen_sd, ev_cwa_bv$ev_per_gen_sd, ev_cwc_bv$ev_per_gen_sd), SI = c(ev_cca_si$ev_per_gen_sd, ev_ccc_si$ev_per_gen_sd, ev_cfa_si$ev_per_gen_sd, ev_cfc_si$ev_per_gen_sd, ev_cwa_si$ev_per_gen_sd, ev_cwc_si$ev_per_gen_sd), SF = c(ev_cca_sf$ev_per_gen_sd, ev_ccc_sf$ev_per_gen_sd, ev_cfa_sf$ev_per_gen_sd, ev_cfc_sf$ev_per_gen_sd, ev_cwa_sf$ev_per_gen_sd, ev_cwc_sf$ev_per_gen_sd), MO = c(ev_cca_mo$ev_per_gen_sd, ev_ccc_mo$ev_per_gen_sd, ev_cfa_mo$ev_per_gen_sd, ev_cfc_mo$ev_per_gen_sd, ev_cwa_mo$ev_per_gen_sd, ev_cwc_mo$ev_per_gen_sd)) knitr::kable(tbl_ev_result_ev_per_gen_sd,booktabs = TRUE)
2. Comparison To Values (Szenario A und Szenario A*)
SZENARIO A
Computing Relative Economic Factors For All Categories.
knitr::kable(tbl_rel_factors, booktabs = TRUE)
Computing Relative Economic Factors For All Categories For Szenario A*
Computing relative economic factor within category.
Computing Relative Economic Factors For Adults (table 2.2)
knitr::kable(tbl_rel_factors_adult, booktabs = TRUE)
Computing Relative Economic Factors For Calves (table 2.3)
knitr::kable(tbl_rel_factors_calves, booktabs = TRUE)
SZENARIO A*
To get the relative importance inside of an index, we have to re-scale
tbl_rel_fact_adult_calf <- bind_rows(tbl_rel_factors_adult, tbl_rel_factors_calves) tbl_rel_fact_adult_calf <- tbl_rel_fact_adult_calf[c(1,4,2,5,3,6),]
tbl_rel_fact_adult_calf_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_rel_fact_adult_calf, pb_first_col_trait_name = TRUE) knitr::kable(tbl_rel_fact_adult_calf_rescaled, booktabs = TRUE)
3. Comparison To Values (Szenario B und Szenario B*)
Computing Relative Economic Factors Weithted with proportion of animal categories.
SZENARIO B
The following table shows economic values of r n_nr_trait traits weighted with proportion of animal categories.
knitr::kable( tbl_weighted_rel_factors, booktabs = TRUE )
The values in the above table are now re-scaled.
tbl_weighted_rel_factors_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_weighted_rel_factors, pb_first_col_trait_name = TRUE) knitr::kable( tbl_weighted_rel_factors_rescaled, booktabs = TRUE )
SZENARIO B*
The above factors are weighted with the proportion of the aniMal categories
tbl_weighted_rel_fact_adult_calf <- MeatValueIndex::weight_economic_value(ptbl_economic_value = tbl_rel_fact_adult_calf_rescaled, ptbl_weight = tbl_proportion4eachtrait, pb_first_col_trait_name = TRUE) tbl_weighted_rel_fact_adult_calf_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_weighted_rel_fact_adult_calf, pb_first_col_trait_name = TRUE) knitr::kable(tbl_weighted_rel_fact_adult_calf_rescaled, booktabs = TRUE)
Conclusions
- For traits with a low relative weight (
CFa,CFc,CWaandCWc), the different strategies do not show different results - The different weighting strategies lead to different relative weights in
CCaandCCcwhere the weights are high
Prepare Economic Values Input For Selection Response
tbl_ev_result_ev_per_trait_unit <- tibble::data_frame(Traits = c("cca", "ccc", "cfa", "cfc", "cwa", "cwc"), OB = c(ev_cca_ob$ev_per_trait_unit, ev_ccc_ob$ev_per_trait_unit, ev_cfa_ob$ev_per_trait_unit, ev_cfc_ob$ev_per_trait_unit, ev_cwa_ob$ev_per_trait_unit, ev_cwc_ob$ev_per_trait_unit), BV = c(ev_cca_bv$ev_per_trait_unit, ev_ccc_bv$ev_per_trait_unit, ev_cfa_bv$ev_per_trait_unit, ev_cfc_bv$ev_per_trait_unit, ev_cwa_bv$ev_per_trait_unit, ev_cwc_bv$ev_per_trait_unit), SI = c(ev_cca_si$ev_per_trait_unit, ev_ccc_si$ev_per_trait_unit, ev_cfa_si$ev_per_trait_unit, ev_cfc_si$ev_per_trait_unit, ev_cwa_si$ev_per_trait_unit, ev_cwc_si$ev_per_trait_unit), SF = c(ev_cca_sf$ev_per_trait_unit, ev_ccc_sf$ev_per_trait_unit, ev_cfa_sf$ev_per_trait_unit, ev_cfc_sf$ev_per_trait_unit, ev_cwa_sf$ev_per_trait_unit, ev_cwc_sf$ev_per_trait_unit), MO = c(ev_cca_mo$ev_per_trait_unit, ev_ccc_mo$ev_per_trait_unit, ev_cfa_mo$ev_per_trait_unit, ev_cfc_mo$ev_per_trait_unit, ev_cwa_mo$ev_per_trait_unit, ev_cwc_mo$ev_per_trait_unit))
#Äquivalent zu Szenario A per_trait_units (tbl_rel_factors_per_trait_units <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_trait_unit, pb_first_col_trait_name = TRUE))
(tbl_rel_factors_adult_per_trait_units <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_trait_unit[vec_row_idx_adult,], pb_first_col_trait_name = TRUE))
(tbl_rel_factors_calf_per_trait_units <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_ev_result_ev_per_trait_unit[vec_row_idx_calves,], pb_first_col_trait_name = TRUE))
tbl_rel_fact_adult_calf_per_trait_units <- bind_rows(tbl_rel_factors_adult_per_trait_units, tbl_rel_factors_calf_per_trait_units) (tbl_rel_fact_adult_calf_per_trait_units <- tbl_rel_fact_adult_calf_per_trait_units[c(1,4,2,5,3,6),])
#Äquivalent zu Szenario A* per_trait_units (tbl_rel_fact_adult_calf_per_trait_units_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_rel_fact_adult_calf_per_trait_units, pb_first_col_trait_name = TRUE))
(tbl_weighted_rel_factors_per_trait_units <- MeatValueIndex::weight_economic_value(ptbl_economic_value = tbl_rel_factors_per_trait_units, ptbl_weight = tbl_proportion4eachtrait, pb_first_col_trait_name = TRUE))
Äquivalent zu Szenario B per_trait_units
tbl_weighted_rel_factors_per_trait_units_rescaled <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_weighted_rel_factors_per_trait_units, pb_first_col_trait_name = TRUE) knitr::kable( tbl_weighted_rel_factors_per_trait_units_rescaled, booktabs = TRUE )
Äquivalent zu Szenario B* per_trait_units
#Äquivalent zu Szenario B* per_trait_units tbl_weighted_rel_fact_adult_calf_per_trait_units <- MeatValueIndex::weight_economic_value(ptbl_economic_value = tbl_rel_fact_adult_calf_per_trait_units_rescaled, ptbl_weight = tbl_proportion4eachtrait, pb_first_col_trait_name = TRUE) tbl_weighted_rel_fact_adult_calf_rescaled_per_trait_units <- MeatValueIndex::get_relative_economic_factors(ptbl_economic_value = tbl_weighted_rel_fact_adult_calf_per_trait_units, pb_first_col_trait_name = TRUE) knitr::kable(tbl_weighted_rel_fact_adult_calf_rescaled_per_trait_units, booktabs = TRUE)
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