adfi_get.RdADFI get from pig performance test station csv data
adfi_get(data, adg_res)A data frame or data table containing the nedap or fire pig performance test data to be processed. Columns must include 'visit_time', 'location', 'responder', 'feed_intake'.
ADG results from function of adg_get().
A list containing:
dfi_info: A data.table containing DFI statistics
dfi_data: A data.table containing processed sample data
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)
head(adfi_results$adfi_info)
#> Key: <responder, location>
#> responder location test_days origin_dfi corrected_dfi
#> <char> <char> <int> <num> <num>
#> 1: 13913 101 95 2562.365 2577.739
#> 2: 13918 101 96 2319.814 2305.710
#> 3: 13935 102 96 2703.371 2706.317
#> 4: 13954 101 95 2317.260 2308.288
#> 5: 13996 101 96 2909.309 2921.329
#> 6: 14260 102 96 2846.546 2819.889