fcr_get.RdThis function calculates the Feed Conversion Ratio (FCR) based on average daily gain (ADG) and average daily feed intake (ADFI) results. It also calculates a corrected FCR if stage information is available.
fcr_get(adg_res, adfi_res)A list containing ADG results, including 'adg_info' and 'adg_data' from adg_get().
A list containing ADFI results, including 'adfi_info' and 'adfi_data' from adfi_get().
A list containing:
A data.table with FCR results, including 'fcr' and 'cor_fcr' (if applicable)
The original ADG info data.table
The original ADG data
The original ADFI info data.table
The original ADFI data
This function assumes that 'adg_info' and 'adfi_info' are data.tables with columns 'responder' and 'location'.
The function expects 'corrected_dfi' and 'lm_slope' columns in the joined data for FCR calculation.
The corrected FCR calculation is only performed if a 'stage' column is present in the joined data.
The variables 'min_weight_cut' and 'max_weight_cut' should be defined in the global environment or passed as parameters for the corrected FCR calculation.
nedap_csv_data <- mintyr::nedap
adg_results <- adg_get(data = nedap_csv_data)
#> • There are no duplicate responders in different locations.
#> • The removing of weight < 15kg will not delete responder.
#> • Removing records of missing will delete responders: 1
#> • Deleted responders:
#> c("15964")
#> • Running RANSAC Robust Regression:
#> • RANSAC Robust Regression succeeded!
#> • The outliers detected by Robust model will not delete responder.
#> • All responders' begin_test_weight are less than or equal to 60kg.
#> • Removing end_test_weight <85kg records will delete responders: 1
#> • Deleted responders:
#> c("15967")
#> • Running Simple Linear Regression
#> • Calculate ADG using Simple Linear Regression succeeded!
adfi_results <- adfi_get(data = nedap_csv_data, adg_res = adg_results)
#> • There are no duplicate responders in different locations.
#> • Successfully generated the following 3 variables:
#> - FIV:feed intake per visit;
#> - OTV:occupation time per visit;
#> - FRV:feeding rate per visit;
#> • Successfully generated 10 error types from 3 variables:
#> - FIV-lo; FIV-hi; FIV-0; OTV-lo; OTV-hi; FRV-hi-FIV-lo; FRV-hi-strict; FRV-hi; FRV-0; FRV-lo;
#> • Running linear mixed model with equation:
#> dfi_right_part ~ otd_2 + otd_6 + otd_9 + otd_10 + otv_hi_p + frv_hi_fiv_lo_p + frv_lo_p + location + lm_slope + weight + (1 | responder)
fcr_results <- fcr_get(adg_res = adg_results, adfi_res = adfi_results)
head(fcr_results$fcr_res)
#> Key: <responder, location>
#> responder location start_date_origin min_weight_origin end_date_origin
#> <char> <char> <Date> <num> <Date>
#> 1: 13913 101 2024-02-19 21866.22 2024-05-25
#> 2: 13918 101 2024-02-19 16529.06 2024-05-25
#> 3: 13935 102 2024-02-19 21414.80 2024-05-25
#> 4: 13954 101 2024-02-20 30876.24 2024-05-25
#> 5: 13996 101 2024-02-19 22753.31 2024-05-25
#> 6: 14260 102 2024-02-19 27757.93 2024-05-25
#> max_weight_origin r_squared lm_slope test_days origin_dfi corrected_dfi
#> <num> <num> <num> <int> <num> <num>
#> 1: 132895.4 0.9914949 1180.5834 95 2562.365 2577.739
#> 2: 121102.8 0.9917870 1089.6744 96 2319.814 2305.710
#> 3: 138871.4 0.9976035 1233.5471 96 2703.371 2706.317
#> 4: 118602.7 0.9781564 941.2985 95 2317.260 2308.288
#> 5: 142956.6 0.9957625 1281.2277 96 2909.309 2921.329
#> 6: 142547.7 0.9962627 1221.0429 96 2846.546 2819.889
#> fcr
#> <num>
#> 1: 2.183445
#> 2: 2.115962
#> 3: 2.193931
#> 4: 2.452238
#> 5: 2.280101
#> 6: 2.309410
head(fcr_results$fcr_summary)
#> traits N min max median mean sd cv
#> <char> <char> <char> <char> <char> <char> <char> <char>
#> 1: min_weight_origin 28 7.09 30.88 22.22 21.52 4.87 22.64%
#> 2: max_weight_origin 28 105.51 146.80 135.95 132.66 10.57 7.97%
#> 3: lm_slope 28 907.51 1321.20 1203.42 1173.66 109.56 9.33%
#> 4: test_days 28 95.00 96.00 96.00 95.68 0.48 0.50%
#> 5: origin_dfi 28 1867.24 3361.74 2823.68 2739.91 357.71 13.06%
#> 6: corrected_dfi 28 1878.89 3336.57 2817.39 2737.01 353.74 12.92%