Evaluate Nonlinear Model Logistics, Gompertz, and Weibull: Study Case on Calcium and Phosphorus Requirements of Laying Hen

A. PENGHIMPUNAN DAN EDITING DATA

A.1. Pengumpulan Data

Akses basis data kebutuhan kalsium fosfor
- Hendrix Genetics
- Lohmann Tierzucht

# 1. pemasukkan data

Data <- read_excel("Data_2019_Prosiding-ISTAP.xls", sheet = "Data-Kumulatif")
Age <- Data$`Umur (minggu)`
Company <- Data$Perusahaan
Breeds <- Data$Breeds
Body_weight <- Data$`BB_(g/minggu)`
Feed_consumption <- Data$`Konsumsi_(g/minggu)`
Calcium <- Data$`%_Ca`
Total_phosphorus <- Data$`%_P`
Available_phosphorus <- Data$`%_P-Ter.`
Data <- data.frame(Age, Company, Breeds, Body_weight, Feed_consumption, Calcium, Total_phosphorus, Available_phosphorus)

kable(Data) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
  scroll_box(height = "500px")

Age	Company	Breeds	Body_weight	Feed_consumption	Calcium	Total_phosphorus	Available_phosphorus
8	HG	BaB	709.5	350	1.00	0.70	0.42
9	HG	BaB	814.5	378	0.95	0.58	0.36
10	HG	BaB	918.0	406	0.95	0.58	0.36
11	HG	BaB	1019.5	427	0.95	0.58	0.36
12	HG	BaB	1117.0	469	0.95	0.58	0.36
13	HG	BaB	1211.5	490	0.95	0.58	0.36
14	HG	BaB	1300.5	511	0.95	0.58	0.36
15	HG	BaB	1383.5	532	0.95	0.58	0.36
16	HG	BaB	1459.5	574	0.95	0.58	0.36
17	HG	BaB	1529.5	609	2.05	0.65	0.42
18	HG	BaB	1592.0	609	2.05	0.65	0.42
19	HG	BaB	1646.0	651	2.05	0.65	0.42
20	HG	BaB	1693.0	742	2.05	0.65	0.42
21	HG	BaB	1722.0	742	2.05	0.65	0.42
22	HG	BaB	1750.0	777	2.05	0.65	0.42
23	HG	BaB	1773.0	798	2.05	0.65	0.42
24	HG	BaB	1795.0	805	2.05	0.65	0.42
25	HG	BaB	1814.0	805	2.05	0.65	0.42
26	HG	BaB	1827.0	805	2.05	0.65	0.42
27	HG	BaB	1839.0	805	2.05	0.65	0.42
28	HG	BaB	1850.0	805	2.05	0.65	0.42
29	HG	BaB	1860.0	805	2.05	0.65	0.42
30	HG	BaB	1869.0	805	2.05	0.65	0.42
31	HG	BaB	1877.0	805	2.05	0.65	0.42
32	HG	BaB	1885.0	805	2.05	0.65	0.42
33	HG	BaB	1892.0	805	2.05	0.65	0.42
34	HG	BaB	1898.0	805	2.05	0.65	0.42
35	HG	BaB	1904.0	805	2.05	0.65	0.42
8	HG	BaW	602.0	322	1.00	0.70	0.42
9	HG	BaW	686.0	343	0.95	0.58	0.36
10	HG	BaW	770.5	371	0.95	0.58	0.36
11	HG	BaW	854.5	392	0.95	0.58	0.36
12	HG	BaW	937.0	434	0.95	0.58	0.36
13	HG	BaW	1017.5	455	0.95	0.58	0.36
14	HG	BaW	1095.0	476	0.95	0.58	0.36
15	HG	BaW	1169.5	497	0.95	0.58	0.36
16	HG	BaW	1240.0	518	0.95	0.58	0.36
17	HG	BaW	1305.5	546	2.05	0.65	0.42
18	HG	BaW	1366.0	546	2.05	0.65	0.42
19	HG	BaW	1420.0	602	2.05	0.65	0.42
20	HG	BaW	1468.0	693	2.05	0.65	0.42
21	HG	BaW	1504.0	693	2.05	0.65	0.42
22	HG	BaW	1542.0	735	2.05	0.65	0.42
23	HG	BaW	1566.0	756	2.05	0.65	0.42
24	HG	BaW	1580.0	770	2.05	0.65	0.42
25	HG	BaW	1594.0	770	2.05	0.65	0.42
26	HG	BaW	1607.0	777	2.05	0.65	0.42
27	HG	BaW	1619.0	777	2.05	0.65	0.42
28	HG	BaW	1631.0	777	2.05	0.65	0.42
29	HG	BaW	1641.0	784	2.05	0.65	0.42
30	HG	BaW	1651.0	784	2.05	0.65	0.42
31	HG	BaW	1660.0	784	2.05	0.65	0.42
32	HG	BaW	1668.0	784	2.05	0.65	0.42
33	HG	BaW	1676.0	791	2.05	0.65	0.42
34	HG	BaW	1683.0	791	2.05	0.65	0.42
35	HG	BaW	1690.0	791	2.05	0.65	0.42
8	HG	BB	702.5	350	1.00	0.70	0.42
9	HG	BB	806.0	378	0.95	0.58	0.36
10	HG	BB	909.0	406	0.95	0.58	0.36
11	HG	BB	1009.0	427	0.95	0.58	0.36
12	HG	BB	1106.5	469	0.95	0.58	0.36
13	HG	BB	1199.0	490	0.95	0.58	0.36
14	HG	BB	1287.0	511	0.95	0.58	0.36
15	HG	BB	1369.5	532	0.95	0.58	0.36
16	HG	BB	1445.0	567	0.95	0.58	0.36
17	HG	BB	1514.0	609	2.05	0.65	0.42
18	HG	BB	1576.0	609	2.05	0.65	0.42
19	HG	BB	1630.0	651	2.05	0.65	0.42
20	HG	BB	1676.0	742	2.05	0.65	0.42
21	HG	BB	1713.0	742	2.05	0.65	0.42
22	HG	BB	1750.0	770	2.05	0.65	0.42
23	HG	BB	1767.0	791	2.05	0.65	0.42
24	HG	BB	1782.0	798	2.05	0.65	0.42
25	HG	BB	1796.0	798	2.05	0.65	0.42
26	HG	BB	1809.0	798	2.05	0.65	0.42
27	HG	BB	1821.0	798	2.05	0.65	0.42
28	HG	BB	1831.0	798	2.05	0.65	0.42
29	HG	BB	1841.0	798	2.05	0.65	0.42
30	HG	BB	1850.0	798	2.05	0.65	0.42
31	HG	BB	1858.0	805	2.05	0.65	0.42
32	HG	BB	1866.0	805	2.05	0.65	0.42
33	HG	BB	1873.0	805	2.05	0.65	0.42
34	HG	BB	1879.0	805	2.05	0.65	0.42
35	HG	BB	1885.0	805	2.05	0.65	0.42
8	HG	BBL	773.0	350	1.00	0.70	0.42
9	HG	BBL	887.0	378	0.95	0.58	0.36
10	HG	BBL	1000.0	406	0.95	0.58	0.36
11	HG	BBL	1110.5	427	0.95	0.58	0.36
12	HG	BBL	1217.5	448	0.95	0.58	0.36
13	HG	BBL	1320.0	469	0.95	0.58	0.36
14	HG	BBL	1416.5	490	0.95	0.58	0.36
15	HG	BBL	1506.5	511	0.95	0.58	0.36
16	HG	BBL	1590.5	532	0.95	0.58	0.36
17	HG	BBL	1666.5	553	2.05	0.65	0.42
18	HG	BBL	1734.0	595	2.05	0.65	0.42
19	HG	BBL	1794.0	658	2.05	0.65	0.42
20	HG	BBL	1845.0	777	2.05	0.65	0.42
21	HG	BBL	1887.0	777	2.05	0.65	0.42
22	HG	BBL	1921.0	798	2.05	0.65	0.42
23	HG	BBL	1946.0	805	2.05	0.65	0.42
24	HG	BBL	1963.0	812	2.05	0.65	0.42
25	HG	BBL	1977.0	812	2.05	0.65	0.42
26	HG	BBL	1991.0	819	2.05	0.65	0.42
27	HG	BBL	2004.0	826	2.05	0.65	0.42
28	HG	BBL	2015.0	826	2.05	0.65	0.42
29	HG	BBL	2026.0	833	2.05	0.65	0.42
30	HG	BBL	2036.0	833	2.05	0.65	0.42
31	HG	BBL	2045.0	840	2.05	0.65	0.42
32	HG	BBL	2053.0	840	2.05	0.65	0.42
33	HG	BBL	2061.0	840	2.05	0.65	0.42
34	HG	BBL	2068.0	847	2.05	0.65	0.42
35	HG	BBL	2074.0	847	2.05	0.65	0.42
8	HG	BW	587.0	294	1.00	0.70	0.42
9	HG	BW	669.0	329	0.95	0.58	0.36
10	HG	BW	751.5	357	0.95	0.58	0.36
11	HG	BW	833.0	378	0.95	0.58	0.36
12	HG	BW	914.0	399	0.95	0.58	0.36
13	HG	BW	992.5	420	0.95	0.58	0.36
14	HG	BW	1069.0	434	0.95	0.58	0.36
15	HG	BW	1142.5	455	0.95	0.58	0.36
16	HG	BW	1211.5	476	0.95	0.58	0.36
17	HG	BW	1276.0	497	2.05	0.65	0.42
18	HG	BW	1335.5	539	2.05	0.65	0.42
19	HG	BW	1388.0	574	2.05	0.65	0.42
20	HG	BW	1435.0	665	2.05	0.65	0.42
21	HG	BW	1473.0	665	2.05	0.65	0.42
22	HG	BW	1503.0	707	2.05	0.65	0.42
23	HG	BW	1519.0	728	2.05	0.65	0.42
24	HG	BW	1535.0	735	2.05	0.65	0.42
25	HG	BW	1550.0	742	2.05	0.65	0.42
26	HG	BW	1564.0	742	2.05	0.65	0.42
27	HG	BW	1577.0	749	2.05	0.65	0.42
28	HG	BW	1589.0	749	2.05	0.65	0.42
29	HG	BW	1601.0	749	2.05	0.65	0.42
30	HG	BW	1611.0	756	2.05	0.65	0.42
31	HG	BW	1620.0	756	2.05	0.65	0.42
32	HG	BW	1629.0	756	2.05	0.65	0.42
33	HG	BW	1637.0	756	2.05	0.65	0.42
34	HG	BW	1645.0	763	2.05	0.65	0.42
35	HG	BW	1652.0	763	2.05	0.65	0.42
8	HG	DB	702.5	350	1.00	0.70	0.42
9	HG	DB	806.0	364	0.95	0.58	0.36
10	HG	DB	909.0	406	0.95	0.58	0.36
11	HG	DB	1009.0	427	0.95	0.58	0.36
12	HG	DB	1106.5	448	0.95	0.58	0.36
13	HG	DB	1199.0	469	0.95	0.58	0.36
14	HG	DB	1287.0	490	0.95	0.58	0.36
15	HG	DB	1369.5	511	0.95	0.58	0.36
16	HG	DB	1445.0	532	0.95	0.58	0.36
17	HG	DB	1514.0	560	2.05	0.65	0.42
18	HG	DB	1576.0	602	2.05	0.65	0.42
19	HG	DB	1630.0	644	2.05	0.65	0.42
20	HG	DB	1678.0	735	2.05	0.65	0.42
21	HG	DB	1713.0	735	2.05	0.65	0.42
22	HG	DB	1750.0	763	2.05	0.65	0.42
23	HG	DB	1767.0	784	2.05	0.65	0.42
24	HG	DB	1782.0	784	2.05	0.65	0.42
25	HG	DB	1796.0	791	2.05	0.65	0.42
26	HG	DB	1809.0	791	2.05	0.65	0.42
27	HG	DB	1821.0	791	2.05	0.65	0.42
28	HG	DB	1831.0	791	2.05	0.65	0.42
29	HG	DB	1841.0	791	2.05	0.65	0.42
30	HG	DB	1850.0	791	2.05	0.65	0.42
31	HG	DB	1858.0	791	2.05	0.65	0.42
32	HG	DB	1866.0	791	2.05	0.65	0.42
33	HG	DB	1873.0	791	2.05	0.65	0.42
34	HG	DB	1879.0	791	2.05	0.65	0.42
35	HG	DB	1885.0	791	2.05	0.65	0.42
8	HG	DW	594.0	294	1.00	0.70	0.42
9	HG	DW	677.0	336	0.95	0.58	0.36
10	HG	DW	760.0	357	0.95	0.58	0.36
11	HG	DW	842.0	378	0.95	0.58	0.36
12	HG	DW	923.0	399	0.95	0.58	0.36
13	HG	DW	1002.0	420	0.95	0.58	0.36
14	HG	DW	1079.0	441	0.95	0.58	0.36
15	HG	DW	1152.0	455	0.95	0.58	0.36
16	HG	DW	1221.5	476	0.95	0.58	0.36
17	HG	DW	1287.0	504	2.05	0.65	0.42
18	HG	DW	1345.5	539	2.05	0.65	0.42
19	HG	DW	1401.0	574	2.05	0.65	0.42
20	HG	DW	1450.0	672	2.05	0.65	0.42
21	HG	DW	1493.0	672	2.05	0.65	0.42
22	HG	DW	1529.0	714	2.05	0.65	0.42
23	HG	DW	1550.0	735	2.05	0.65	0.42
24	HG	DW	1567.0	742	2.05	0.65	0.42
25	HG	DW	1579.0	742	2.05	0.65	0.42
26	HG	DW	1591.0	749	2.05	0.65	0.42
27	HG	DW	1601.0	749	2.05	0.65	0.42
28	HG	DW	1611.0	749	2.05	0.65	0.42
29	HG	DW	1621.0	756	2.05	0.65	0.42
30	HG	DW	1630.0	756	2.05	0.65	0.42
31	HG	DW	1638.0	756	2.05	0.65	0.42
32	HG	DW	1646.0	756	2.05	0.65	0.42
33	HG	DW	1653.0	763	2.05	0.65	0.42
34	HG	DW	1659.0	763	2.05	0.65	0.42
35	HG	DW	1665.0	763	2.05	0.65	0.42
8	HG	HB	702.5	350	1.00	0.70	0.42
9	HG	HB	806.0	364	0.95	0.58	0.36
10	HG	HB	909.0	406	0.95	0.58	0.36
11	HG	HB	1009.0	427	0.95	0.58	0.36
12	HG	HB	1106.5	448	0.95	0.58	0.36
13	HG	HB	1199.0	469	0.95	0.58	0.36
14	HG	HB	1369.5	490	0.95	0.58	0.36
15	HG	HB	1445.0	511	0.95	0.58	0.36
16	HG	HB	1514.0	532	0.95	0.58	0.36
17	HG	HB	1576.0	553	2.05	0.65	0.42
18	HG	HB	1630.0	588	2.05	0.65	0.42
19	HG	HB	1676.0	651	2.05	0.65	0.42
20	HG	HB	1713.0	735	2.05	0.65	0.42
21	HG	HB	1750.0	735	2.05	0.65	0.42
22	HG	HB	1767.0	756	2.05	0.65	0.42
23	HG	HB	1782.0	777	2.05	0.65	0.42
24	HG	HB	1796.0	784	2.05	0.65	0.42
25	HG	HB	1809.0	791	2.05	0.65	0.42
26	HG	HB	1821.0	791	2.05	0.65	0.42
27	HG	HB	1831.0	791	2.05	0.65	0.42
28	HG	HB	1841.0	791	2.05	0.65	0.42
29	HG	HB	1850.0	791	2.05	0.65	0.42
30	HG	HB	1858.0	791	2.05	0.65	0.42
31	HG	HB	1866.0	791	2.05	0.65	0.42
32	HG	HB	1873.0	791	2.05	0.65	0.42
33	HG	HB	1879.0	791	2.05	0.65	0.42
34	HG	HB	1885.0	791	2.05	0.65	0.42
35	HG	HB	1891.0	791	2.05	0.65	0.42
8	HG	HW	587.0	308	1.00	0.70	0.42
9	HG	HW	669.0	329	0.95	0.58	0.36
10	HG	HW	751.5	350	0.95	0.58	0.36
11	HG	HW	833.0	371	0.95	0.58	0.36
12	HG	HW	914.0	392	0.95	0.58	0.36
13	HG	HW	993.0	413	0.95	0.58	0.36
14	HG	HW	1069.0	434	0.95	0.58	0.36
15	HG	HW	1142.5	455	0.95	0.58	0.36
16	HG	HW	1211.5	476	0.95	0.58	0.36
17	HG	HW	1276.0	497	2.05	0.65	0.42
18	HG	HW	1335.5	532	2.05	0.65	0.42
19	HG	HW	1388.0	574	2.05	0.65	0.42
20	HG	HW	1434.0	665	2.05	0.65	0.42
21	HG	HW	1473.0	665	2.05	0.65	0.42
22	HG	HW	1503.0	700	2.05	0.65	0.42
23	HG	HW	1520.0	728	2.05	0.65	0.42
24	HG	HW	1536.0	735	2.05	0.65	0.42
25	HG	HW	1551.0	735	2.05	0.65	0.42
26	HG	HW	1565.0	742	2.05	0.65	0.42
27	HG	HW	1577.0	742	2.05	0.65	0.42
28	HG	HW	1589.0	742	2.05	0.65	0.42
29	HG	HW	1600.0	749	2.05	0.65	0.42
30	HG	HW	1610.0	749	2.05	0.65	0.42
31	HG	HW	1620.0	749	2.05	0.65	0.42
32	HG	HW	1629.0	749	2.05	0.65	0.42
33	HG	HW	1637.0	756	2.05	0.65	0.42
34	HG	HW	1644.0	756	2.05	0.65	0.42
35	HG	HW	1651.0	756	2.05	0.65	0.42
8	HG	IB	702.5	350	1.00	0.70	0.42
9	HG	IB	806.0	378	0.95	0.58	0.36
10	HG	IB	909.0	406	0.95	0.58	0.36
11	HG	IB	1009.0	427	0.95	0.58	0.36
12	HG	IB	1106.5	448	0.95	0.58	0.36
13	HG	IB	1199.0	469	0.95	0.58	0.36
14	HG	IB	1287.0	490	0.95	0.58	0.36
15	HG	IB	1369.5	511	0.95	0.58	0.36
16	HG	IB	1445.0	532	0.95	0.58	0.36
17	HG	IB	1514.0	553	2.05	0.65	0.42
18	HG	IB	1576.0	588	2.05	0.65	0.42
19	HG	IB	1630.0	623	2.05	0.65	0.42
20	HG	IB	1630.0	728	2.05	0.65	0.42
21	HG	IB	1676.0	728	2.05	0.65	0.42
22	HG	IB	1713.0	770	2.05	0.65	0.42
23	HG	IB	1750.0	784	2.05	0.65	0.42
24	HG	IB	1767.0	784	2.05	0.65	0.42
25	HG	IB	1782.0	784	2.05	0.65	0.42
26	HG	IB	1796.0	784	2.05	0.65	0.42
27	HG	IB	1809.0	784	2.05	0.65	0.42
28	HG	IB	1821.0	784	2.05	0.65	0.42
29	HG	IB	1831.0	784	2.05	0.65	0.42
30	HG	IB	1841.0	784	2.05	0.65	0.42
31	HG	IB	1850.0	784	2.05	0.65	0.42
32	HG	IB	1858.0	784	2.05	0.65	0.42
33	HG	IB	1866.0	784	2.05	0.65	0.42
34	HG	IB	1873.0	784	2.05	0.65	0.42
35	HG	IB	1878.0	784	2.05	0.65	0.42
8	HG	IW	602.0	343	1.00	0.70	0.42
9	HG	IW	686.0	371	0.95	0.58	0.36
10	HG	IW	770.0	392	0.95	0.58	0.36
11	HG	IW	853.5	413	0.95	0.58	0.36
12	HG	IW	936.5	434	0.95	0.58	0.36
13	HG	IW	1016.5	455	0.95	0.58	0.36
14	HG	IW	1094.5	476	0.95	0.58	0.36
15	HG	IW	1169.0	497	0.95	0.58	0.36
16	HG	IW	1240.0	518	0.95	0.58	0.36
17	HG	IW	1306.5	546	2.05	0.65	0.42
18	HG	IW	1367.5	595	2.05	0.65	0.42
19	HG	IW	1423.0	651	2.05	0.65	0.42
20	HG	IW	1472.0	728	2.05	0.65	0.42
21	HG	IW	1514.0	728	2.05	0.65	0.42
22	HG	IW	1548.0	756	2.05	0.65	0.42
23	HG	IW	1568.0	770	2.05	0.65	0.42
24	HG	IW	1587.0	770	2.05	0.65	0.42
25	HG	IW	1600.0	777	2.05	0.65	0.42
26	HG	IW	1612.0	777	2.05	0.65	0.42
27	HG	IW	1623.0	777	2.05	0.65	0.42
28	HG	IW	1633.0	784	2.05	0.65	0.42
29	HG	IW	1643.0	784	2.05	0.65	0.42
30	HG	IW	1652.0	784	2.05	0.65	0.42
31	HG	IW	1661.0	791	2.05	0.65	0.42
32	HG	IW	1669.0	791	2.05	0.65	0.42
33	HG	IW	1676.0	791	2.05	0.65	0.42
34	HG	IW	1683.0	791	2.05	0.65	0.42
35	HG	IW	1689.0	791	2.05	0.65	0.42
8	HG	SB	686.0	350	1.00	0.70	0.42
9	HG	SB	787.0	378	0.95	0.58	0.36
10	HG	SB	887.0	406	0.95	0.58	0.36
11	HG	SB	984.5	427	0.95	0.58	0.36
12	HG	SB	1079.0	448	0.95	0.58	0.36
13	HG	SB	1169.0	469	0.95	0.58	0.36
14	HG	SB	1254.5	490	0.95	0.58	0.36
15	HG	SB	1334.5	511	0.95	0.58	0.36
16	HG	SB	1408.0	532	0.95	0.58	0.36
17	HG	SB	1475.0	553	2.05	0.65	0.42
18	HG	SB	1536.5	588	2.05	0.65	0.42
19	HG	SB	1536.0	623	2.05	0.65	0.42
20	HG	SB	1590.0	714	2.05	0.65	0.42
21	HG	SB	1638.0	714	2.05	0.65	0.42
22	HG	SB	1669.0	756	2.05	0.65	0.42
23	HG	SB	1700.0	777	2.05	0.65	0.42
24	HG	SB	1718.0	777	2.05	0.65	0.42
25	HG	SB	1734.0	777	2.05	0.65	0.42
26	HG	SB	1748.0	777	2.05	0.65	0.42
27	HG	SB	1762.0	777	2.05	0.65	0.42
28	HG	SB	1774.0	777	2.05	0.65	0.42
29	HG	SB	1785.0	777	2.05	0.65	0.42
30	HG	SB	1795.0	777	2.05	0.65	0.42
31	HG	SB	1804.0	777	2.05	0.65	0.42
32	HG	SB	1813.0	777	2.05	0.65	0.42
33	HG	SB	1820.0	777	2.05	0.65	0.42
34	HG	SB	1827.0	777	2.05	0.65	0.42
35	HG	SB	1834.0	777	2.05	0.65	0.42
8	HG	SBL	773.0	350	1.00	0.70	0.42
9	HG	SBL	887.0	378	0.95	0.58	0.36
10	HG	SBL	1000.0	406	0.95	0.58	0.36
11	HG	SBL	1110.5	427	0.95	0.58	0.36
12	HG	SBL	1217.5	448	0.95	0.58	0.36
13	HG	SBL	1320.0	469	0.95	0.58	0.36
14	HG	SBL	1416.5	490	0.95	0.58	0.36
15	HG	SBL	1506.5	511	0.95	0.58	0.36
16	HG	SBL	1590.5	532	0.95	0.58	0.36
17	HG	SBL	1666.5	553	2.05	0.65	0.42
18	HG	SBL	1734.0	595	2.05	0.65	0.42
19	HG	SBL	1794.0	658	2.05	0.65	0.42
20	HG	SBL	1845.0	777	2.05	0.65	0.42
21	HG	SBL	1887.0	777	2.05	0.65	0.42
22	HG	SBL	1921.0	784	2.05	0.65	0.42
23	HG	SBL	1946.0	791	2.05	0.65	0.42
24	HG	SBL	1963.0	798	2.05	0.65	0.42
25	HG	SBL	1977.0	805	2.05	0.65	0.42
26	HG	SBL	1991.0	805	2.05	0.65	0.42
27	HG	SBL	2004.0	812	2.05	0.65	0.42
28	HG	SBL	2015.0	812	2.05	0.65	0.42
29	HG	SBL	2026.0	819	2.05	0.65	0.42
30	HG	SBL	2036.0	819	2.05	0.65	0.42
31	HG	SBL	2045.0	826	2.05	0.65	0.42
32	HG	SBL	2053.0	826	2.05	0.65	0.42
33	HG	SBL	2061.0	826	2.05	0.65	0.42
34	HG	SBL	2068.0	833	2.05	0.65	0.42
35	HG	SBL	2074.0	833	2.05	0.65	0.42
8	HG	SW	585.5	294	1.00	0.70	0.42
9	HG	SW	667.5	322	0.95	0.58	0.36
10	HG	SW	749.5	343	0.95	0.58	0.36
11	HG	SW	831.0	364	0.95	0.58	0.36
12	HG	SW	911.0	385	0.95	0.58	0.36
13	HG	SW	989.0	406	0.95	0.58	0.36
14	HG	SW	1064.5	420	0.95	0.58	0.36
15	HG	SW	1137.5	441	0.95	0.58	0.36
16	HG	SW	1206.0	462	0.95	0.58	0.36
17	HG	SW	1270.0	483	2.05	0.65	0.42
18	HG	SW	1329.0	518	2.05	0.65	0.42
19	HG	SW	1383.0	560	2.05	0.65	0.42
20	HG	SW	1430.0	644	2.05	0.65	0.42
21	HG	SW	1471.0	644	2.05	0.65	0.42
22	HG	SW	1505.0	679	2.05	0.65	0.42
23	HG	SW	1525.0	707	2.05	0.65	0.42
24	HG	SW	1541.0	714	2.05	0.65	0.42
25	HG	SW	1555.0	714	2.05	0.65	0.42
26	HG	SW	1568.0	721	2.05	0.65	0.42
27	HG	SW	1580.0	721	2.05	0.65	0.42
28	HG	SW	1592.0	721	2.05	0.65	0.42
29	HG	SW	1602.0	728	2.05	0.65	0.42
30	HG	SW	1611.0	728	2.05	0.65	0.42
31	HG	SW	1626.0	728	2.05	0.65	0.42
32	HG	SW	1633.0	728	2.05	0.65	0.42
33	HG	SW	1639.0	735	2.05	0.65	0.42
34	HG	SW	1644.0	735	2.05	0.65	0.42
35	HG	SW	1644.0	735	2.05	0.65	0.42
8	LTZ	LB-Classic	685.0	357	1.00	0.70	0.45
9	LTZ	LB-Classic	782.0	385	0.90	0.58	0.37
10	LTZ	LB-Classic	874.0	406	0.90	0.58	0.37
11	LTZ	LB-Classic	961.0	420	0.90	0.58	0.37
12	LTZ	LB-Classic	1043.0	448	0.90	0.58	0.37
13	LTZ	LB-Classic	1123.0	455	0.90	0.58	0.37
14	LTZ	LB-Classic	1197.0	476	0.90	0.58	0.37
15	LTZ	LB-Classic	1264.0	490	0.90	0.58	0.37
16	LTZ	LB-Classic	1330.0	497	0.90	0.58	0.37
17	LTZ	LB-Classic	1400.0	504	2.00	0.65	0.45
18	LTZ	LB-Classic	1475.0	525	2.00	0.65	0.45
19	LTZ	LB-Classic	1555.0	567	2.00	0.65	0.45
20	LTZ	LB-Classic	1640.0	700	2.00	0.65	0.45
21	LTZ	LB-Classic	1711.0	700	2.00	0.65	0.45
22	LTZ	LB-Classic	1790.0	742	2.00	0.65	0.45
23	LTZ	LB-Classic	1830.0	770	2.00	0.65	0.45
24	LTZ	LB-Classic	1870.0	791	2.00	0.65	0.45
25	LTZ	LB-Classic	1885.0	798	2.00	0.65	0.45
26	LTZ	LB-Classic	1900.0	798	2.00	0.65	0.45
27	LTZ	LB-Classic	1905.0	798	2.00	0.65	0.45
28	LTZ	LB-Classic	1911.0	798	2.00	0.65	0.45
29	LTZ	LB-Classic	1915.0	798	2.00	0.65	0.45
30	LTZ	LB-Classic	1920.0	798	2.00	0.65	0.45
31	LTZ	LB-Classic	1923.0	798	2.00	0.65	0.45
32	LTZ	LB-Classic	1925.0	805	2.00	0.65	0.45
33	LTZ	LB-Classic	1928.0	805	2.00	0.65	0.45
34	LTZ	LB-Classic	1931.0	805	2.00	0.65	0.45
35	LTZ	LB-Classic	1933.0	805	2.00	0.65	0.45
8	LTZ	LB-Lite	670.0	350	1.00	0.70	0.45
9	LTZ	LB-Lite	765.0	378	0.90	0.58	0.37
10	LTZ	LB-Lite	855.0	399	0.90	0.58	0.37
11	LTZ	LB-Lite	940.0	413	0.90	0.58	0.37
12	LTZ	LB-Lite	1020.0	441	0.90	0.58	0.37
13	LTZ	LB-Lite	1098.0	448	0.90	0.58	0.37
14	LTZ	LB-Lite	1171.0	469	0.90	0.58	0.37
15	LTZ	LB-Lite	1236.0	483	0.90	0.58	0.37
16	LTZ	LB-Lite	1301.0	490	0.90	0.58	0.37
17	LTZ	LB-Lite	1369.0	497	2.00	0.65	0.45
18	LTZ	LB-Lite	1443.0	518	2.00	0.65	0.45
19	LTZ	LB-Lite	1521.0	560	2.00	0.65	0.45
20	LTZ	LB-Lite	1604.0	700	2.00	0.65	0.45
21	LTZ	LB-Lite	1673.0	700	2.00	0.65	0.45
22	LTZ	LB-Lite	1751.0	742	2.00	0.65	0.45
23	LTZ	LB-Lite	1790.0	770	2.00	0.65	0.45
24	LTZ	LB-Lite	1829.0	791	2.00	0.65	0.45
25	LTZ	LB-Lite	1844.0	798	2.00	0.65	0.45
26	LTZ	LB-Lite	1858.0	798	2.00	0.65	0.45
27	LTZ	LB-Lite	1863.0	798	2.00	0.65	0.45
28	LTZ	LB-Lite	1869.0	798	2.00	0.65	0.45
29	LTZ	LB-Lite	1873.0	798	2.00	0.65	0.45
30	LTZ	LB-Lite	1878.0	798	2.00	0.65	0.45
31	LTZ	LB-Lite	1881.0	798	2.00	0.65	0.45
32	LTZ	LB-Lite	1883.0	805	2.00	0.65	0.45
33	LTZ	LB-Lite	1886.0	805	2.00	0.65	0.45
34	LTZ	LB-Lite	1889.0	805	2.00	0.65	0.45
35	LTZ	LB-Lite	1891.0	805	2.00	0.65	0.45
8	LTZ	L-Sandy	658.0	329	1.00	0.70	0.45
9	LTZ	L-Sandy	758.0	357	0.90	0.58	0.37
10	LTZ	L-Sandy	853.0	385	0.90	0.58	0.37
11	LTZ	L-Sandy	935.0	413	0.90	0.58	0.37
12	LTZ	L-Sandy	1009.0	434	0.90	0.58	0.37
13	LTZ	L-Sandy	1072.0	455	0.90	0.58	0.37
14	LTZ	L-Sandy	1130.0	476	0.90	0.58	0.37
15	LTZ	L-Sandy	1183.0	497	0.90	0.58	0.37
16	LTZ	L-Sandy	1230.0	518	0.90	0.58	0.37
17	LTZ	L-Sandy	1280.0	539	0.90	0.58	0.37
18	LTZ	L-Sandy	1333.0	560	2.00	0.65	0.45
19	LTZ	L-Sandy	1394.0	588	2.00	0.65	0.45
20	LTZ	L-Sandy	1490.0	742	2.00	0.65	0.45
21	LTZ	L-Sandy	1552.0	742	2.00	0.65	0.45
22	LTZ	L-Sandy	1612.0	770	2.00	0.65	0.45
23	LTZ	L-Sandy	1663.0	791	2.00	0.65	0.45
24	LTZ	L-Sandy	1704.0	798	2.00	0.65	0.45
25	LTZ	L-Sandy	1738.0	798	2.00	0.65	0.45
26	LTZ	L-Sandy	1767.0	798	2.00	0.65	0.45
27	LTZ	L-Sandy	1791.0	798	2.00	0.65	0.45
28	LTZ	L-Sandy	1810.0	798	2.00	0.65	0.45
29	LTZ	L-Sandy	1824.0	798	2.00	0.65	0.45
30	LTZ	L-Sandy	1835.0	798	2.00	0.65	0.45
31	LTZ	L-Sandy	1841.0	805	2.00	0.65	0.45
32	LTZ	L-Sandy	1845.0	805	2.00	0.65	0.45
33	LTZ	L-Sandy	1848.0	805	2.00	0.65	0.45
34	LTZ	L-Sandy	1851.0	805	2.00	0.65	0.45
35	LTZ	L-Sandy	1854.0	735	2.00	0.65	0.45
8	LTZ	L-Silver	690.0	364	1.00	0.70	0.45
9	LTZ	L-Silver	790.0	392	0.90	0.58	0.37
10	LTZ	L-Silver	890.0	420	0.90	0.58	0.37
11	LTZ	L-Silver	980.0	462	0.90	0.58	0.37
12	LTZ	L-Silver	1075.0	483	0.90	0.58	0.37
13	LTZ	L-Silver	1160.0	504	0.90	0.58	0.37
14	LTZ	L-Silver	1240.0	525	0.90	0.58	0.37
15	LTZ	L-Silver	1305.0	546	0.90	0.58	0.37
16	LTZ	L-Silver	1375.0	567	0.90	0.58	0.37
17	LTZ	L-Silver	1445.0	595	2.00	0.65	0.45
18	LTZ	L-Silver	1520.0	623	2.00	0.65	0.45
19	LTZ	L-Silver	1600.0	700	2.00	0.65	0.45
20	LTZ	L-Silver	1680.0	770	2.00	0.65	0.45
21	LTZ	L-Silver	1750.0	770	2.00	0.65	0.45
22	LTZ	L-Silver	1827.0	791	2.00	0.65	0.45
23	LTZ	L-Silver	1866.0	798	2.00	0.65	0.45
24	LTZ	L-Silver	1905.0	798	2.00	0.65	0.45
25	LTZ	L-Silver	1920.0	798	2.00	0.65	0.45
26	LTZ	L-Silver	1935.0	798	2.00	0.65	0.45
27	LTZ	L-Silver	1940.0	798	2.00	0.65	0.45
28	LTZ	L-Silver	1945.0	798	2.00	0.65	0.45
29	LTZ	L-Silver	1950.0	798	2.00	0.65	0.45
30	LTZ	L-Silver	1955.0	805	2.00	0.65	0.45
31	LTZ	L-Silver	1958.0	805	2.00	0.65	0.45
32	LTZ	L-Silver	1961.0	805	2.00	0.65	0.45
33	LTZ	L-Silver	1964.0	805	2.00	0.65	0.45
34	LTZ	L-Silver	1967.0	735	2.00	0.65	0.45
35	LTZ	L-Silver	1970.0	735	2.00	0.65	0.45
8	LTZ	LSL-Classic	624.0	315	1.00	0.70	0.45
9	LTZ	LSL-Classic	719.0	343	0.90	0.58	0.37
10	LTZ	LSL-Classic	809.0	371	0.90	0.58	0.37
11	LTZ	LSL-Classic	887.0	392	0.90	0.58	0.37
12	LTZ	LSL-Classic	957.0	420	0.90	0.58	0.37
13	LTZ	LSL-Classic	1017.0	448	0.90	0.58	0.37
14	LTZ	LSL-Classic	1072.0	469	0.90	0.58	0.37
15	LTZ	LSL-Classic	1122.0	490	0.90	0.58	0.37
16	LTZ	LSL-Classic	1167.0	511	0.90	0.58	0.37
17	LTZ	LSL-Classic	1214.0	532	0.90	0.58	0.37
18	LTZ	LSL-Classic	1264.0	553	2.00	0.65	0.45
19	LTZ	LSL-Classic	1322.0	588	2.00	0.65	0.45
20	LTZ	LSL-Classic	1386.0	700	2.00	0.65	0.45
21	LTZ	LSL-Classic	1450.0	742	2.00	0.65	0.45
22	LTZ	LSL-Classic	1500.0	770	2.00	0.65	0.45
23	LTZ	LSL-Classic	1540.0	791	2.00	0.65	0.45
24	LTZ	LSL-Classic	1580.0	798	2.00	0.65	0.45
25	LTZ	LSL-Classic	1610.0	798	2.00	0.65	0.45
26	LTZ	LSL-Classic	1630.0	798	2.00	0.65	0.45
27	LTZ	LSL-Classic	1650.0	798	2.00	0.65	0.45
28	LTZ	LSL-Classic	1670.0	798	2.00	0.65	0.45
29	LTZ	LSL-Classic	1690.0	798	2.00	0.65	0.45
30	LTZ	LSL-Classic	1700.0	798	2.00	0.65	0.45
31	LTZ	LSL-Classic	1705.0	805	2.00	0.65	0.45
32	LTZ	LSL-Classic	1710.0	805	2.00	0.65	0.45
33	LTZ	LSL-Classic	1713.0	805	2.00	0.65	0.45
34	LTZ	LSL-Classic	1715.0	805	2.00	0.65	0.45
35	LTZ	LSL-Classic	1718.0	735	2.00	0.65	0.45
8	LTZ	LSL-Extra	666.0	336	1.00	0.70	0.45
9	LTZ	LSL-Extra	767.0	364	0.90	0.58	0.37
10	LTZ	LSL-Extra	863.0	392	0.90	0.58	0.37
11	LTZ	LSL-Extra	946.0	420	0.90	0.58	0.37
12	LTZ	LSL-Extra	1021.0	448	0.90	0.58	0.37
13	LTZ	LSL-Extra	1085.0	469	0.90	0.58	0.37
14	LTZ	LSL-Extra	1144.0	490	0.90	0.58	0.37
15	LTZ	LSL-Extra	1197.0	511	0.90	0.58	0.37
16	LTZ	LSL-Extra	1245.0	532	0.90	0.58	0.37
17	LTZ	LSL-Extra	1295.0	553	0.90	0.58	0.37
18	LTZ	LSL-Extra	1348.0	581	2.00	0.65	0.45
19	LTZ	LSL-Extra	1410.0	616	2.00	0.65	0.45
20	LTZ	LSL-Extra	1478.0	700	2.00	0.65	0.45
21	LTZ	LSL-Extra	1543.0	742	2.00	0.65	0.45
22	LTZ	LSL-Extra	1593.0	770	2.00	0.65	0.45
23	LTZ	LSL-Extra	1633.0	791	2.00	0.65	0.45
24	LTZ	LSL-Extra	1668.0	798	2.00	0.65	0.45
25	LTZ	LSL-Extra	1698.0	798	2.00	0.65	0.45
26	LTZ	LSL-Extra	1723.0	798	2.00	0.65	0.45
27	LTZ	LSL-Extra	1743.0	798	2.00	0.65	0.45
28	LTZ	LSL-Extra	1758.0	798	2.00	0.65	0.45
29	LTZ	LSL-Extra	1768.0	798	2.00	0.65	0.45
30	LTZ	LSL-Extra	1775.0	798	2.00	0.65	0.45
31	LTZ	LSL-Extra	1780.0	805	2.00	0.65	0.45
32	LTZ	LSL-Extra	1783.0	805	2.00	0.65	0.45
33	LTZ	LSL-Extra	1786.0	805	2.00	0.65	0.45
34	LTZ	LSL-Extra	1789.0	805	2.00	0.65	0.45
35	LTZ	LSL-Extra	1792.0	735	2.00	0.65	0.45
8	LTZ	LSL-Lite	635.0	322	1.00	0.70	0.45
9	LTZ	LSL-Lite	735.0	343	0.90	0.58	0.37
10	LTZ	LSL-Lite	825.0	364	0.90	0.58	0.37
11	LTZ	LSL-Lite	894.0	385	0.90	0.58	0.37
12	LTZ	LSL-Lite	959.0	406	0.90	0.58	0.37
13	LTZ	LSL-Lite	1023.0	427	0.90	0.58	0.37
14	LTZ	LSL-Lite	1084.0	448	0.90	0.58	0.37
15	LTZ	LSL-Lite	1143.0	469	0.90	0.58	0.37
16	LTZ	LSL-Lite	1200.0	497	0.90	0.58	0.37
17	LTZ	LSL-Lite	1255.0	525	0.90	0.58	0.37
18	LTZ	LSL-Lite	1306.0	553	2.00	0.65	0.45
19	LTZ	LSL-Lite	1356.0	581	2.00	0.65	0.45
20	LTZ	LSL-Lite	1405.0	700	2.00	0.65	0.45
21	LTZ	LSL-Lite	1452.0	742	2.00	0.65	0.45
22	LTZ	LSL-Lite	1497.0	770	2.00	0.65	0.45
23	LTZ	LSL-Lite	1538.0	791	2.00	0.65	0.45
24	LTZ	LSL-Lite	1575.0	798	2.00	0.65	0.45
25	LTZ	LSL-Lite	1608.0	798	2.00	0.65	0.45
26	LTZ	LSL-Lite	1628.0	798	2.00	0.65	0.45
27	LTZ	LSL-Lite	1639.0	798	2.00	0.65	0.45
28	LTZ	LSL-Lite	1645.0	798	2.00	0.65	0.45
29	LTZ	LSL-Lite	1649.0	798	2.00	0.65	0.45
30	LTZ	LSL-Lite	1651.0	798	2.00	0.65	0.45
31	LTZ	LSL-Lite	1653.0	805	2.00	0.65	0.45
32	LTZ	LSL-Lite	1655.0	805	2.00	0.65	0.45
33	LTZ	LSL-Lite	1657.0	805	2.00	0.65	0.45
34	LTZ	LSL-Lite	1658.0	805	2.00	0.65	0.45
35	LTZ	LSL-Lite	1660.0	735	2.00	0.65	0.45
8	LTZ	LSL-Ultra-Lite	595.0	315	1.00	0.70	0.45
9	LTZ	LSL-Ultra-Lite	685.0	336	0.90	0.58	0.37
10	LTZ	LSL-Ultra-Lite	765.0	357	0.90	0.58	0.37
11	LTZ	LSL-Ultra-Lite	840.0	378	0.90	0.58	0.37
12	LTZ	LSL-Ultra-Lite	910.0	399	0.90	0.58	0.37
13	LTZ	LSL-Ultra-Lite	975.0	427	0.90	0.58	0.37
14	LTZ	LSL-Ultra-Lite	1035.0	448	0.90	0.58	0.37
15	LTZ	LSL-Ultra-Lite	1090.0	469	0.90	0.58	0.37
16	LTZ	LSL-Ultra-Lite	1143.0	497	0.90	0.58	0.37
17	LTZ	LSL-Ultra-Lite	1203.0	525	0.90	0.58	0.37
18	LTZ	LSL-Ultra-Lite	1273.0	553	2.00	0.65	0.45
19	LTZ	LSL-Ultra-Lite	1338.0	581	2.00	0.65	0.45
20	LTZ	LSL-Ultra-Lite	1393.0	700	2.00	0.65	0.45
21	LTZ	LSL-Ultra-Lite	1438.0	742	2.00	0.65	0.45
22	LTZ	LSL-Ultra-Lite	1478.0	770	2.00	0.65	0.45
23	LTZ	LSL-Ultra-Lite	1513.0	791	2.00	0.65	0.45
24	LTZ	LSL-Ultra-Lite	1538.0	798	2.00	0.65	0.45
25	LTZ	LSL-Ultra-Lite	1558.0	798	2.00	0.65	0.45
26	LTZ	LSL-Ultra-Lite	1573.0	798	2.00	0.65	0.45
27	LTZ	LSL-Ultra-Lite	1583.0	798	2.00	0.65	0.45
28	LTZ	LSL-Ultra-Lite	1589.0	798	2.00	0.65	0.45
29	LTZ	LSL-Ultra-Lite	1591.0	798	2.00	0.65	0.45
30	LTZ	LSL-Ultra-Lite	1593.0	798	2.00	0.65	0.45
31	LTZ	LSL-Ultra-Lite	1595.0	805	2.00	0.65	0.45
32	LTZ	LSL-Ultra-Lite	1597.0	805	2.00	0.65	0.45
33	LTZ	LSL-Ultra-Lite	1601.0	805	2.00	0.65	0.45
34	LTZ	LSL-Ultra-Lite	1603.0	805	2.00	0.65	0.45
35	LTZ	LSL-Ultra-Lite	1604.0	735	2.00	0.65	0.45
8	LTZ	LT	697.0	364	1.00	0.70	0.45
9	LTZ	LT	796.0	392	0.90	0.58	0.37
10	LTZ	LT	890.0	413	0.90	0.58	0.37
11	LTZ	LT	978.0	427	0.90	0.58	0.37
12	LTZ	LT	1062.0	448	0.90	0.58	0.37
13	LTZ	LT	1143.0	462	0.90	0.58	0.37
14	LTZ	LT	1219.0	476	0.90	0.58	0.37
15	LTZ	LT	1287.0	483	0.90	0.58	0.37
16	LTZ	LT	1354.0	504	0.90	0.58	0.37
17	LTZ	LT	1426.0	518	2.00	0.65	0.45
18	LTZ	LT	1502.0	539	2.00	0.65	0.45
19	LTZ	LT	1583.0	581	2.00	0.65	0.45
20	LTZ	LT	1670.0	672	2.00	0.65	0.45
21	LTZ	LT	1742.0	700	2.00	0.65	0.45
22	LTZ	LT	1823.0	742	2.00	0.65	0.45
23	LTZ	LT	1868.0	770	2.00	0.65	0.45
24	LTZ	LT	1906.0	791	2.00	0.65	0.45
25	LTZ	LT	1922.0	798	2.00	0.65	0.45
26	LTZ	LT	1935.0	798	2.00	0.65	0.45
27	LTZ	LT	1940.0	798	2.00	0.65	0.45
28	LTZ	LT	1846.0	798	2.00	0.65	0.45
29	LTZ	LT	1951.0	798	2.00	0.65	0.45
30	LTZ	LT	1955.0	798	2.00	0.65	0.45
31	LTZ	LT	1958.0	798	2.00	0.65	0.45
32	LTZ	LT	1961.0	805	2.00	0.65	0.45
33	LTZ	LT	1964.0	805	2.00	0.65	0.45
34	LTZ	LT	1967.0	805	2.00	0.65	0.45
35	LTZ	LT	1970.0	805	2.00	0.65	0.45

A.2. Editing Data

Tranformasi dalam bobot badan metabolis (\(BB^{0.75}\))
- Persamaan:
  1. \(Konsumsi\:Kalsium\:(g/ekor/minggu) = ({\frac{\%\:Kalsium\:Ransum}{100}} \times Konsumsi\:Pakan\:(g/ekor/minggu))\)
  2. \(Transformasi\:kalsium\:(BB^{0.75}\:(g)) = (\frac{Konsumsi\:Kalsium\:(g/ekor/minggu)}{Bobot\:Badan\:(g)}) \times (Bobot\:Badan^{0.75}\:(g))\)
Evalusi

# 2. transformasi dalam bobot badan metabolis
Calcium <- ((Calcium/100) * Feed_consumption)
Calcium_BB075 <- (Calcium/Body_weight)*(Body_weight**0.75)
Total_phosphorus <- ((Total_phosphorus/100) * Feed_consumption)
Total_phosphorus_BB075 <- (Total_phosphorus/Body_weight)*(Body_weight**0.75)
Available_phosphorus <- ((Available_phosphorus/100) * Feed_consumption)
Available_phosphorus_BB075 <- (Available_phosphorus/Body_weight)*(Body_weight**0.75)
Calcium_g <- Calcium
Total_phosphorus_g <- Total_phosphorus
Available_phosphorus_g <- Available_phosphorus
Data <- add_column(Data, Calcium_g, .after = "Calcium")
Data <- add_column(Data, Total_phosphorus_g, .after = "Total_phosphorus")
Data <- add_column(Data, Available_phosphorus_g, .after = "Available_phosphorus")
Data <- add_column(Data, Calcium_BB075, .after = "Calcium_g")
Data <- add_column(Data, Total_phosphorus_BB075, .after = "Total_phosphorus_g")
Data <- add_column(Data, Available_phosphorus_BB075, .after = "Available_phosphorus_g")

# 2. transformasi dalam nilai min max
Calcium_transformation <- scale_min_max(Calcium_BB075)
Total_phosphorus_transformation <- scale_min_max(Total_phosphorus_BB075)
Available_phosphorus_transformation <- scale_min_max(Available_phosphorus_BB075)
Data <- add_column(Data, Calcium_transformation, .after = "Calcium_BB075")
Data <- add_column(Data, Total_phosphorus_transformation, .after = "Total_phosphorus_BB075")
Data <- add_column(Data, Available_phosphorus_transformation, .after = "Available_phosphorus_BB075")

# 2. grafik data
# Ca
breaksx <-c(seq(10, 35, by=5))
labelsx <-as.character(breaksx)
breaksy <-c(seq(0, 18, by=4))
labelsy <-as.character(breaksy)
breaksyp <-c(seq(0, 5, by=1))
labelsyp <-as.character(breaksyp)
breaksypz <-c(seq(0, 3.5, by=0.5))
labelsypz <-as.character(breaksypz)
PlotDataCa <- ggplot(data=Data, aes(x=Age)) +
  geom_point(aes(y=Calcium_g), shape=17, color="black") +
  xlab("Age") +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  ylab("Ca") +
  scale_y_continuous(limits=c(0, 18), breaks=breaksy, labels=labelsy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8)) +
  theme(legend.position="bottom")
plot(PlotDataCa) # plot 1

# TP
PlotDataTP <- ggplot(data=Data, aes(x=Age)) +
  geom_point(aes(y=Total_phosphorus_g), shape=17, color="black") +
  xlab("Age") +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  ylab("TP") +
  scale_y_continuous(limits=c(1, 6), breaks=breaksyp, labels=labelsyp) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8)) +
  theme(legend.position="bottom")
plot(PlotDataTP) # plot 3

# AP
PlotDataAP <- ggplot(data=Data, aes(x=Age)) +
  geom_point(aes(y=Available_phosphorus_g), shape=17, color="black") +
  xlab("Age") +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  ylab("AP") + 
  scale_y_continuous(limits=c(1, 4), breaks=breaksypz, labels=labelsypz) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8)) +
  theme(legend.position="bottom")
plot(PlotDataAP) # plot 3

# arrange
ggsave(filename = "PlotKebubutuhanCa.png", PlotDataCa)

## Saving 7 x 5 in image

ggsave(filename = "PlotKebubutuhanTP.png", PlotDataTP)

## Saving 7 x 5 in image

ggsave(filename = "PlotKebubutuhanAP.png", PlotDataAP)

## Saving 7 x 5 in image

**B. PEMBUATAN, PREDIKSI, FITTING MODEL**

Pembuatan, Prediksi, dan Fitting Model

B.3.a. Pembuatan model

Persamaan Logistics
Persamaan Gompertz
Persamaan Weibull
- Kalsium

# 3.a. pembuatan model
# Ca
LogisCa.ss <- nls(Calcium_transformation ~ SSlogis(Age, Asym, xmid, scal), data = Data)
GompertzCa.ss <- nls(Calcium_transformation ~ SSgompertz(Age, Asym, b2, b3), data = Data)
WeibullCa.ss <- nls(Calcium_transformation ~ SSweibull(Age, Asym, Drop, lrc, pwr), data = Data)
summary(LogisCa.ss)

## 
## Formula: Calcium_transformation ~ SSlogis(Age, Asym, xmid, scal)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## Asym  0.939666   0.003524  266.66   <2e-16 ***
## xmid 17.316932   0.039082  443.10   <2e-16 ***
## scal  1.153850   0.033982   33.95   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06314 on 641 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 2.279e-06

summary(GompertzCa.ss)

## 
## Formula: Calcium_transformation ~ SSgompertz(Age, Asym, b2, b3)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## Asym 9.427e-01  3.643e-03 258.743  < 2e-16 ***
## b2   6.532e+04  2.272e+04   2.875  0.00418 ** 
## b3   5.142e-01  1.054e-02  48.789  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06321 on 641 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 7.167e-06

summary(WeibullCa.ss)

## 
## Formula: Calcium_transformation ~ SSweibull(Age, Asym, Drop, lrc, pwr)
## 
## Parameters:
##        Estimate Std. Error t value Pr(>|t|)    
## Asym   0.933927   0.003481  268.30   <2e-16 ***
## Drop   0.904363   0.006848  132.06   <2e-16 ***
## lrc  -31.877086   1.121245  -28.43   <2e-16 ***
## pwr   11.007687   0.388116   28.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06459 on 640 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 2.172e-06

Total Fosfor

# TP
LogisTP.ss <- nls(Total_phosphorus_transformation ~ SSlogis(Age, Asym, xmid, scal), data = Data)
GompertzTP.ss <- nls(Total_phosphorus_transformation ~ SSgompertz(Age, Asym, b2, b3), data = Data)
WeibullTP.ss <- nls(Total_phosphorus_transformation ~ SSweibull(Age, Asym, Drop, lrc, pwr), data = Data)
summary(LogisTP.ss)

## 
## Formula: Total_phosphorus_transformation ~ SSlogis(Age, Asym, xmid, scal)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## Asym  0.937014   0.005047  185.67   <2e-16 ***
## xmid 17.115195   0.075735  225.99   <2e-16 ***
## scal  2.562650   0.065112   39.36   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07279 on 641 degrees of freedom
## 
## Number of iterations to convergence: 1 
## Achieved convergence tolerance: 4.364e-06

summary(GompertzTP.ss)

## 
## Formula: Total_phosphorus_transformation ~ SSgompertz(Age, Asym, b2, b3)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## Asym  0.955829   0.007235 132.114  < 2e-16 ***
## b2   58.159902   7.732521   7.521 1.84e-13 ***
## b3    0.769918   0.006561 117.356  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0863 on 641 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 2.301e-06

summary(WeibullTP.ss)

## 
## Formula: Total_phosphorus_transformation ~ SSweibull(Age, Asym, Drop, 
##     lrc, pwr)
## 
## Parameters:
##        Estimate Std. Error t value Pr(>|t|)    
## Asym   0.912941   0.003235  282.23   <2e-16 ***
## Drop   0.778273   0.006475  120.19   <2e-16 ***
## lrc  -23.420656   0.745973  -31.40   <2e-16 ***
## pwr    7.953070   0.253499   31.37   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.05555 on 640 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 5.424e-07

Fosfor Tersedia

# AP
LogisAP.ss <- nls(Available_phosphorus_transformation ~ SSlogis(Age, Asym, xmid, scal), data = Data)
GompertzAP.ss <- nls(Available_phosphorus_transformation ~ SSgompertz(Age, Asym, b2, b3), data = Data)
WeibullAP.ss <- nls(Available_phosphorus_transformation ~ SSweibull(Age, Asym, Drop, lrc, pwr), data = Data)
summary(LogisAP.ss)

## 
## Formula: Available_phosphorus_transformation ~ SSlogis(Age, Asym, xmid, 
##     scal)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## Asym  0.875119   0.005417  161.54   <2e-16 ***
## xmid 17.145962   0.084969  201.79   <2e-16 ***
## scal  2.373410   0.073011   32.51   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08085 on 641 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 1.681e-06

summary(GompertzAP.ss)

## 
## Formula: Available_phosphorus_transformation ~ SSgompertz(Age, Asym, b2, 
##     b3)
## 
## Parameters:
##       Estimate Std. Error t value Pr(>|t|)    
## Asym  0.888881   0.007137 124.552  < 2e-16 ***
## b2   95.694675  16.494551   5.802 1.03e-08 ***
## b3    0.748103   0.008115  92.192  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.09126 on 641 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 1.963e-06

summary(WeibullAP.ss)

## 
## Formula: Available_phosphorus_transformation ~ SSweibull(Age, Asym, Drop, 
##     lrc, pwr)
## 
## Parameters:
##        Estimate Std. Error t value Pr(>|t|)    
## Asym   0.854992   0.003986  214.51   <2e-16 ***
## Drop   0.740102   0.008001   92.51   <2e-16 ***
## lrc  -23.996364   1.009560  -23.77   <2e-16 ***
## pwr    8.168486   0.343963   23.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.06917 on 640 degrees of freedom
## 
## Number of iterations to convergence: 0 
## Achieved convergence tolerance: 1.206e-07

B.3.b. Prediksi nilai konsumsi kalsium dan fospor dari model

Kalsium

# 3.b. prediksi model
# Ca
predict.LogisCa.ss <- predict(LogisCa.ss, Age)
predict.GompertzCa.ss <- predict(GompertzCa.ss, Age)
predict.WeibullCa.ss <- predict(WeibullCa.ss, Age)

Total Fosfor

# TP
predict.LogisTP.ss <- predict(LogisTP.ss, Age)
predict.GompertzTP.ss <- predict(GompertzTP.ss, Age)
predict.WeibullTP.ss <- predict(WeibullTP.ss, Age)

Fosfor Tersedia

#AP
predict.LogisAP.ss <- predict(LogisAP.ss, Age)
predict.GompertzAP.ss <- predict(GompertzAP.ss, Age)
predict.WeibullAP.ss <- predict(WeibullAP.ss, Age)

B.3.c. Penambahan data retransformasi

Kalsium

# 3.c. penambahan data
# Ca
Data_colourCa <- data.frame(Age, Calcium_transformation, predict.LogisCa.ss, predict.GompertzCa.ss, predict.WeibullCa.ss)
Data_colourCa <- melt(Data_colourCa, id.vars=c("Age", "Calcium_transformation"), measure.vars=c("predict.LogisCa.ss", "predict.GompertzCa.ss", "predict.WeibullCa.ss"))

## Warning: attributes are not identical across measure variables; they will
## be dropped

Total Fosfor

# TP
Data_colourTP <- data.frame(Age, Total_phosphorus_transformation, predict.LogisTP.ss, predict.GompertzTP.ss, predict.WeibullTP.ss)
Data_colourTP <- melt(Data_colourTP, id.vars=c("Age", "Total_phosphorus_transformation"), measure.vars=c("predict.LogisTP.ss", "predict.GompertzTP.ss", "predict.WeibullTP.ss"))

## Warning: attributes are not identical across measure variables; they will
## be dropped

Fosfor Tersedia

# AP
Data_colourAP <- data.frame(Age, Available_phosphorus_transformation, predict.LogisAP.ss, predict.GompertzAP.ss, predict.WeibullAP.ss)
Data_colourAP <- melt(Data_colourAP, id.vars=c("Age", "Available_phosphorus_transformation"), measure.vars=c("predict.LogisAP.ss", "predict.GompertzAP.ss", "predict.WeibullAP.ss"))

## Warning: attributes are not identical across measure variables; they will
## be dropped

B.3.d. Fitting model

Kalsium

# 3.d. fitting model
# Ca
breaksx <-c(seq(10, 35, by=5))
labelsx <-as.character(breaksx)
PlotCa <- ggplot(data=Data_colourCa, aes(x=Age, y=value, shape=variable, colour=variable)) +
  geom_point(aes(x=Age, y=Calcium_transformation), shape=20, colour="#8c8c8c") +
  geom_point(aes(Age, value, shape=variable)) +
  geom_line() +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  labs(x="Age(week)", y="Ca (g/hen/week)") +
  scale_shape_manual(name="Nonlinear Model",
                     values=c(20, 8, 18),
                     labels=c("Logistics", "Gompertz", "Weibulle")) +
  scale_colour_manual(name="Nonlinear Model",
                      values=c("red", "green", "blue"),
                      labels=c("Logistics", "Gompertz", "Weibulle")) +
  theme_classic() +
  theme(legend.position="bottom")
plot(PlotCa) # plot 4

Total Fosfor

# TP
PlotTP <- ggplot(data=Data_colourTP, aes(x=Age, y=value, shape=variable, colour=variable)) +
  geom_point(aes(x=Age, y=Total_phosphorus_transformation), shape=20, colour="#8c8c8c") +
  geom_point(aes(Age, value, shape=variable)) +
  geom_line() +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  labs(x="Age(week)", y="TP (g/hen/week)") +
  scale_shape_manual(name="Nonlinear Model",
                     values=c(20, 8, 18),
                     labels=c("Logistics", "Gompertz", "Weibulle")) +
  scale_colour_manual(name="Nonlinear Model",
                      values=c("red", "green", "blue"),
                      labels=c("Logistics", "Gompertz", "Weibulle")) +
  theme_classic() +
  theme(legend.position="bottom")
plot(PlotTP) # plot 5

Fosfor Tersedia

# AP
PlotAP <- ggplot(data=Data_colourAP, aes(x=Age, y=value, shape=variable, colour=variable)) +
  geom_point(aes(x=Age, y=Available_phosphorus_transformation), shape=20, colour="#8c8c8c") +
  geom_point(aes(Age, value, shape=variable)) +
  geom_line() +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  labs(x="Age(week)", y="AP (g/hen/week)") +
  scale_shape_manual(name="Nonlinear Model",
                     values=c(20, 8, 18),
                     labels=c("Logistics", "Gompertz", "Weibulle")) +
  scale_colour_manual(name="Nonlinear Model",
                      values=c("red", "green", "blue"),
                      labels=c("Logistics", "Gompertz", "Weibulle")) +
  theme_classic() +
  theme(legend.position="bottom")
plot(PlotAP) # plot 5

# arrange
ggsave(filename = "PlotModelCa.png", PlotCa)

## Saving 7 x 5 in image

ggsave(filename = "PlotModelTP.png", PlotTP)

## Saving 7 x 5 in image

ggsave(filename = "PlotModelAP.png", PlotAP)

## Saving 7 x 5 in image

# arrange
PlotModel <- ggarrange(PlotCa, PlotTP, PlotAP, labels=c("a.", "b.", "c."), font.label=list(size=12), common.legend = TRUE, legend = "bottom", nrow=1, ncol=3)
plot(PlotModel)

ggsave(filename = "PlotModel.png", PlotModel)

## Saving 7 x 5 in image

4. Prediksi Nilai dari Model

# 4.a. mengabalikan nilai transformasi nilai min max

# Ca
Calcium_predictedLg <- scale_min_max_rev(Calcium_BB075, predict.LogisCa.ss)
Calcium_predictedGp <- scale_min_max_rev(Calcium_BB075, predict.GompertzCa.ss)
Calcium_predictedWb <- scale_min_max_rev(Calcium_BB075, predict.WeibullCa.ss)

# TP
Total_phosphorus_predictedLg <- scale_min_max_rev(Total_phosphorus_BB075, predict.LogisTP.ss)
Total_phosphorus_predictedGp <- scale_min_max_rev(Total_phosphorus_BB075, predict.GompertzTP.ss)
Total_phosphorus_predictedWb <- scale_min_max_rev(Total_phosphorus_BB075, predict.WeibullTP.ss)

# Ca
Available_phosphorus_predictedLg <- scale_min_max_rev(Available_phosphorus_BB075, predict.LogisAP.ss)
Available_phosphorus_predictedGp <- scale_min_max_rev(Available_phosphorus_BB075, predict.GompertzAP.ss)
Available_phosphorus_predictedWb <- scale_min_max_rev(Available_phosphorus_BB075, predict.WeibullAP.ss)

# 4.b. mengabalikan nilai transformasi BB075

# Ca
Calcium_predictedLg <- scale_BB075_rev(Calcium_predictedLg, Body_weight)
Calcium_predictedGp <- scale_BB075_rev(Calcium_predictedGp, Body_weight)
Calcium_predictedWb <- scale_BB075_rev(Calcium_predictedWb, Body_weight)

# TP
Total_phosphorus_predictedLg <- scale_BB075_rev(Total_phosphorus_predictedLg, Body_weight)
Total_phosphorus_predictedGp <- scale_BB075_rev(Total_phosphorus_predictedGp, Body_weight)
Total_phosphorus_predictedWb <- scale_BB075_rev(Total_phosphorus_predictedWb, Body_weight)

# Ca
Available_phosphorus_predictedLg <- scale_BB075_rev(Available_phosphorus_predictedLg, Body_weight)
Available_phosphorus_predictedGp <- scale_BB075_rev(Available_phosphorus_predictedGp, Body_weight)
Available_phosphorus_predictedWb <- scale_BB075_rev(Available_phosphorus_predictedWb, Body_weight)

### 4.c. akumulasi data

# Ca
Data_predictedCa0 <- data.frame(Age, Calcium_BB075, Calcium_predictedLg, Calcium_predictedGp, Calcium_predictedWb)
Data_lineCa <- Data_predictedCa0 %>%
  group_by(Age) %>% 
  summarise(average = mean(Calcium_BB075))
Data_predictedCa <- melt(Data_predictedCa0, id.vars=c("Age", "Calcium_BB075"), measure.vars=c("Calcium_predictedLg", "Calcium_predictedGp", "Calcium_predictedWb"))

## Warning: attributes are not identical across measure variables; they will
## be dropped

# TP
Data_predictedTP0 <- data.frame(Age, Total_phosphorus_BB075, Total_phosphorus_predictedLg, Total_phosphorus_predictedGp, Total_phosphorus_predictedWb)
Data_lineTP0 <- Data_predictedTP0 %>%
  group_by(Age) %>% 
  summarise(average = mean(Total_phosphorus_BB075))
Data_predictedTP <- melt(Data_predictedTP0, id.vars=c("Age", "Total_phosphorus_BB075"), measure.vars=c("Total_phosphorus_predictedLg", "Total_phosphorus_predictedGp", "Total_phosphorus_predictedWb"))

## Warning: attributes are not identical across measure variables; they will
## be dropped

# AP
Data_predictedAP0 <- data.frame(Age, Available_phosphorus_BB075, Available_phosphorus_predictedLg, Available_phosphorus_predictedGp, Available_phosphorus_predictedWb)
Data_lineAP0 <- Data_predictedAP0 %>%
  group_by(Age) %>% 
  summarise(average = mean(Available_phosphorus_BB075))
Data_predictedAP <- melt(Data_predictedAP0, id.vars=c("Age", "Available_phosphorus_BB075"), measure.vars=c("Available_phosphorus_predictedLg", "Available_phosphorus_predictedGp", "Available_phosphorus_predictedWb"))

## Warning: attributes are not identical across measure variables; they will
## be dropped

# 4.c. plot data kebalikan nilai min max

# Ca
breaksx <-c(seq(10, 35, by=5))
labelsx <-as.character(breaksx)
breaksy <-c(seq(0, 16, by=4))
labelsy <-as.character(breaksy)
PlotCaPLg7 <- ggplot(data=Data_predictedCa0, aes(x=Age, y=Calcium_predictedLg)) +
  geom_point(shape=18, color="red") +
  labs(x="Age", y=("Ca")) +
  scale_x_continuous(limits=c(8, 35), labels=labelsx, breaks=breaksx) +
  scale_y_continuous(limits=c(0, 18), labels=labelsy, breaks=breaksy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8))
plot(PlotCaPLg7) # plot 7

PlotCaPGp8 <- ggplot(data=Data_predictedCa0, aes(x=Age, y=Calcium_predictedGp)) +
  geom_point(shape=18, color="green") +
  labs(x="Age", y=("Ca")) +
  scale_x_continuous(limits=c(8, 35), labels=labelsx, breaks=breaksx) +
  scale_y_continuous(limits=c(0, 18), labels=labelsy, breaks=breaksy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8))
plot(PlotCaPGp8) # plot 8

PlotCaPWb9 <- ggplot(data=Data_predictedCa0, aes(x=Age, y=Calcium_predictedWb)) +
  geom_point(shape=18, color="blue") +
  labs(x="Age", y=("Ca")) +
  scale_x_continuous(limits=c(8, 35), labels=labelsx, breaks=breaksx) +
  scale_y_continuous(limits=c(0, 18), labels=labelsy, breaks=breaksy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8))
plot(PlotCaPWb9) # plot 9

# TP
breaksy <-c(seq(0, 5, by=1))
labelsy <-as.character(breaksy)
PlotTPPLg10 <- ggplot(data=Data_predictedCa0, aes(x=Age, y=Total_phosphorus_predictedLg)) +
  geom_point(shape=18, color="red") +
  labs(x="Age", y=("TP")) +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  scale_y_continuous(limits=c(1, 6), breaks=breaksy, labels=labelsy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8))
plot(PlotTPPLg10) # plot 10

PlotTPPGp11 <- ggplot(data=Data_predictedCa0, aes(x=Age, y=Total_phosphorus_predictedGp)) +
  geom_point(shape=18, color="green") +
  labs(x="Age", y=("TP")) +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  scale_y_continuous(limits=c(1, 6), breaks=breaksy, labels=labelsy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8))
plot(PlotTPPGp11) # plot 11

PlotTPPWb12 <- ggplot(data=Data_predictedCa0, aes(x=Age, y=Total_phosphorus_predictedWb)) +
  geom_point(shape=18, color="blue") +
  labs(x="Age", y=("TP")) +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  scale_y_continuous(limits=c(1, 6), breaks=breaksy, labels=labelsy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8))
plot(PlotTPPWb12) # plot 12

# AP
breaksy <-c(seq(0, 3.5, by=0.5))
labelsy <-as.character(breaksy)
PlotAPPLg13 <- ggplot(data=Data_predictedCa0, aes(x=Age, y=Available_phosphorus_predictedLg)) +
  geom_point(shape=18, color="red") +
  labs(x="Age", y=("AP")) +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  scale_y_continuous(limits=c(1, 4), breaks=breaksy, labels=labelsy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8))
plot(PlotAPPLg13) # plot 13

PlotAPPGp14 <- ggplot(data=Data_predictedCa0, aes(x=Age, y=Available_phosphorus_predictedGp)) +
  geom_point(shape=18, color="green") +
  labs(x="Age", y=("AP")) +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  scale_y_continuous(limits=c(1, 4), breaks=breaksy, labels=labelsy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8))
plot(PlotAPPGp14) # plot 14

PlotAPPWb15 <- ggplot(data=Data_predictedCa0, aes(x=Age, y=Available_phosphorus_predictedWb)) +
  geom_point(shape=18, color="blue") +
  labs(x="Age", y=("AP")) +
  scale_x_continuous(limits=c(8, 35), breaks=breaksx, labels=labelsx) +
  scale_y_continuous(limits=c(1, 4), breaks=breaksy, labels=labelsy) +
  theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), panel.background=element_blank(), axis.line=element_line(colour="black"), axis.title=element_text(size=8))
plot(PlotAPPWb15) # plot 15

# arrange
labelz <- c("a.i.", "a.ii.", "a.iii.", "a.iv.", "b.i.", "b.ii.", "b.iii.", "b.iv.", "c.i.", "c.ii.", "c.iii.", "c.iv.")

PlotPredicted <- ggarrange(PlotDataCa, PlotCaPLg7, PlotCaPGp8, PlotCaPWb9, PlotDataTP, PlotTPPLg10, PlotTPPGp11, PlotTPPWb12, PlotDataAP, PlotAPPLg13, PlotAPPGp14, PlotAPPWb15, labels=labelz, font.label=list(size=8), common.legend=TRUE, legend="bottom", nrow=3, ncol=4)
plot(PlotPredicted)

ggsave(filename = "PlotPredicted.png", PlotPredicted)

## Saving 7 x 5 in image

5. Validasi Model

5.a. Validasi model RMSE, MSE, MAE, dan koefisien korelasi

Kalsium

# 5.a. validasi model RMSE, MSE, MAE, dan kk
# Ca
rmse.LogisCa <- rmse(actual=Calcium_transformation, predicted=predict.LogisCa.ss)
rmse.GompertzCa <- rmse(Calcium_transformation, predict.GompertzCa.ss)
rmse.WeibullCa <- rmse(Calcium_transformation, predict.WeibullCa.ss)

mse.LogisCa <- mse(actual=Calcium_transformation, predicted=predict.LogisCa.ss)
mse.GompertzCa <- mse(Calcium_transformation, predict.GompertzCa.ss)
mse.WeibullCa <- mse(Calcium_transformation, predict.WeibullCa.ss)

mae.LogisCa <- mae(actual=Calcium_transformation, predicted=predict.LogisCa.ss)
mae.GompertzCa <- mae(Calcium_transformation, predict.GompertzCa.ss)
mae.WeibullCa <- mae(Calcium_transformation, predict.WeibullCa.ss)

cc.LogisCa <- cor(Calcium_transformation, predict.LogisCa.ss)
cc.GompertzCa <- cor(Calcium_transformation, predict.GompertzCa.ss)
cc.WeibullCa <- cor(Calcium_transformation, predict.WeibullCa.ss)

Total Fosfor

# TP
rmse.LogisTP <- rmse(Total_phosphorus_transformation, predicted=predict.LogisTP.ss)
rmse.GompertzTP <- rmse(Total_phosphorus_transformation, predict.GompertzTP.ss)
rmse.WeibullTP <- rmse(Total_phosphorus_transformation, predict.WeibullTP.ss)

mse.LogisTP <- mse(Total_phosphorus_transformation, predicted=predict.LogisTP.ss)
mse.GompertzTP <- mse(Total_phosphorus_transformation, predict.GompertzTP.ss)
mse.WeibullTP <- mse(Total_phosphorus_transformation, predict.WeibullTP.ss)

mae.LogisTP <- mae(Total_phosphorus_transformation, predict.LogisTP.ss)
mae.GompertzTP <- mae(Total_phosphorus_transformation, predict.GompertzTP.ss)
mae.WeibullTP <- mae(Total_phosphorus_transformation, predict.WeibullTP.ss)

cc.LogisTP <- cor(Total_phosphorus_transformation, predict.LogisTP.ss)
cc.GompertzTP <- cor(Total_phosphorus_transformation, predict.GompertzTP.ss)
cc.WeibullTP <- cor(Total_phosphorus_transformation, predict.WeibullTP.ss)

Fosfor Tersedia

# AP
rmse.LogisAP <- rmse(Available_phosphorus_transformation, predict.LogisAP.ss)
rmse.GompertzAP <- rmse(Available_phosphorus_transformation, predict.GompertzAP.ss)
rmse.WeibullAP <- rmse(Available_phosphorus_transformation, predict.WeibullAP.ss)

mse.LogisAP <- mse(Available_phosphorus_transformation, predict.LogisAP.ss)
mse.GompertzAP <- mse(Available_phosphorus_transformation, predict.GompertzAP.ss)
mse.WeibullAP <- mse(Available_phosphorus_transformation, predict.WeibullAP.ss)

mae.LogisAP <- mae(Available_phosphorus_transformation, predict.LogisAP.ss)
mae.GompertzAP <- mae(Available_phosphorus_transformation, predict.GompertzAP.ss)
mae.WeibullAP <- mae(Available_phosphorus_transformation, predict.WeibullAP.ss)

cc.LogisAP <- cor(Available_phosphorus_transformation, predict.LogisAP.ss)
cc.GompertzAP <- cor(Available_phosphorus_transformation, predict.GompertzAP.ss)
cc.WeibullAP <- cor(Available_phosphorus_transformation, predict.WeibullAP.ss)

5.b. Tabel validasi

# 5.b. tabel validasi
# Ca
RMSE <- c(rmse.LogisCa, rmse.GompertzCa, rmse.WeibullCa)
MSE <- c(mse.LogisCa, mse.GompertzCa, mse.WeibullCa)
MAE <- c(mae.LogisCa, mae.GompertzCa, mae.WeibullCa)
CC <- c(cc.LogisCa, cc.GompertzCa, cc.WeibullCa)
Models <- c("Logistics", "Gompertz", "Weibull")
Ca.table <- data.frame(Models, RMSE, MSE, MAE, CC)

# TP
RMSE <- c(rmse.LogisTP, rmse.GompertzTP, rmse.WeibullTP)
MSE <- c(mse.LogisTP, mse.GompertzTP, mse.WeibullTP)
MAE <- c(mae.LogisTP, mae.GompertzTP, mae.WeibullTP)
CC <- c(cc.LogisTP, cc.GompertzTP, cc.WeibullTP)
Models <- c("Logistics", "Gompertz", "Weibull")
TP.table <- data.frame(Models, RMSE, MSE, MAE, CC)

# AP
RMSE <- c(rmse.LogisAP, rmse.GompertzAP, rmse.WeibullAP)
MSE <- c(mse.LogisAP, mse.GompertzAP, mse.WeibullAP)
MAE <- c(mae.LogisAP, mae.GompertzAP, mae.WeibullAP)
CC <- c(cc.LogisAP, cc.GompertzAP, cc.WeibullAP)
Models <- c("Logistics", "Gompertz", "Weibull")
AP.table <- data.frame(Models, RMSE, MSE, MAE, CC)

kable(Ca.table) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Models	RMSE	MSE	MAE	CC
Logistics	0.0629886	0.0039676	0.0420092	0.9880670
Gompertz	0.0630578	0.0039763	0.0441593	0.9891457
Weibull	0.0643884	0.0041459	0.0401528	0.9869001

kable(TP.table) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Models	RMSE	MSE	MAE	CC
Logistics	0.0726197	0.0052736	0.0548749	0.9783453
Gompertz	0.0861009	0.0074134	0.0670575	0.9717616
Weibull	0.0553798	0.0030669	0.0424407	0.9862330

kable(AP.table) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Models	RMSE	MSE	MAE	CC
Logistics	0.0806659	0.0065070	0.0665470	0.9703558
Gompertz	0.0910434	0.0082889	0.0763604	0.9651490
Weibull	0.0689550	0.0047548	0.0568673	0.9768222

write.xlsx(x = c("Kalsium"=Ca.table, "Total Fosfor"=TP.table, "Fosfor Tersedia"=AP.table), file = "Data_2019_Tabel validasi.xlsx", row.names=FALSE)

6. Koefisien Persamaan Model

# 6. koefisien persamaan model
Coefficient <- c("Asym", "Drop", "lrc", "pwr")
a <- coef(WeibullCa.ss)
b <- coef(WeibullTP.ss)
c <- coef(WeibullAP.ss)
Data.coefficient <- data.frame(Coefficient, "Calcium"=a, "Total Phosphorus"=b, "Available Phosphorus"=c)

kable(Data.coefficient) %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

	Coefficient	Calcium	Total.Phosphorus	Available.Phosphorus
Asym	Asym	0.9339268	0.9129409	0.8549917
Drop	Drop	0.9043634	0.7782731	0.7401021
lrc	lrc	-31.8770860	-23.4206563	-23.9963641
pwr	pwr	11.0076874	7.9530702	8.1684862

write.xlsx(x = Data.coefficient, file = "Data_2019_Tabel koefisien.xlsx", row.names=FALSE)