ESTANDARIZACION Y MODELOS DE CRECIMIENTO
Laura Valbuena
1/9/2020
- DATOS
ESTANDARIZACION DE LOS DATOS
tmed = sort(datos$Tmed)
tmed## [1] 22.5 22.7 22.8 22.9 22.9 22.9 22.9 23.0 23.1 23.1 23.1 23.1 23.1 23.1
## [15] 23.2 23.2 23.2 23.2 23.3 23.3 23.3 23.3 23.3 23.3 23.4 23.4 23.4 23.4
## [29] 23.4 23.4 23.4 23.4 23.5 23.5 23.5 23.5 23.5 23.5 23.5 23.5 23.5 23.5
## [43] 23.5 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.6 23.7 23.7
## [57] 23.7 23.7 23.7 23.7 23.7 23.7 23.7 23.8 23.8 23.8 23.8 23.8 23.8 23.8
## [71] 23.8 23.8 23.8 23.8 23.8 23.8 23.9 23.9 24.0 24.0 24.0 24.0 24.0 24.0
## [85] 24.0 24.0 24.0 24.0 24.1 24.1 24.1 24.1 24.1 24.1 24.1 24.1 24.1 24.1
## [99] 24.1 24.1 24.2 24.2 24.2 24.2 24.2 24.2 24.2 24.2 24.2 24.2 24.3 24.3
## [113] 24.3 24.3 24.3 24.3 24.3 24.3 24.3 24.3 24.3 24.3 24.4 24.4 24.4 24.4
## [127] 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.4 24.5
## [141] 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.5 24.5
## [155] 24.5 24.5 24.5 24.5 24.5 24.5 24.6 24.6 24.6 24.6 24.6 24.6 24.6 24.6
## [169] 24.6 24.6 24.6 24.6 24.6 24.6 24.6 24.6 24.6 24.6 24.6 24.6 24.6 24.6
## [183] 24.6 24.6 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.7
## [197] 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.7 24.8 24.8 24.8 24.8
## [211] 24.8 24.8 24.8 24.8 24.8 24.8 24.8 24.8 24.8 24.8 24.8 24.8 24.9 24.9
## [225] 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9
## [239] 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9 24.9
## [253] 24.9 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0
## [267] 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0
## [281] 25.0 25.0 25.0 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1
## [295] 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1 25.1
## [309] 25.1 25.1 25.1 25.1 25.1 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2
## [323] 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2
## [337] 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.3 25.3 25.3 25.3 25.3 25.3 25.3
## [351] 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3
## [365] 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.3 25.4 25.4 25.4 25.4
## [379] 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4
## [393] 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4 25.4
## [407] 25.4 25.4 25.4 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5
## [421] 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5
## [435] 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5
## [449] 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5 25.5
## [463] 25.5 25.5 25.5 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6
## [477] 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6
## [491] 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6
## [505] 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.6 25.7 25.7 25.7 25.7 25.7 25.7
## [519] 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7
## [533] 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7
## [547] 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7 25.7
## [561] 25.7 25.7 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8
## [575] 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8
## [589] 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8
## [603] 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.8
## [617] 25.8 25.8 25.8 25.8 25.8 25.8 25.8 25.9 25.9 25.9 25.9 25.9 25.9 25.9
## [631] 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9
## [645] 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9
## [659] 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9
## [673] 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9 25.9
## [687] 25.9 25.9 25.9 25.9 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0
## [701] 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0
## [715] 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0
## [729] 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0
## [743] 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.0 26.1 26.1 26.1 26.1 26.1 26.1
## [757] 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1
## [771] 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1
## [785] 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1 26.1
## [799] 26.1 26.1 26.1 26.1 26.1 26.1 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2
## [813] 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2
## [827] 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2
## [841] 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2
## [855] 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2 26.2
## [869] 26.2 26.2 26.2 26.2 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3
## [883] 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3
## [897] 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3
## [911] 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3
## [925] 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3 26.3
## [939] 26.3 26.3 26.3 26.3 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4
## [953] 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4
## [967] 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4
## [981] 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4 26.4
## [995] 26.4 26.4 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5
## [1009] 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5
## [1023] 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5
## [1037] 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5 26.5
## [1051] 26.5 26.5 26.5 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6
## [1065] 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6
## [1079] 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6
## [1093] 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6
## [1107] 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.6 26.7 26.7
## [1121] 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7
## [1135] 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7
## [1149] 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7
## [1163] 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7
## [1177] 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.7 26.8 26.8 26.8
## [1191] 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8
## [1205] 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8
## [1219] 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8
## [1233] 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8
## [1247] 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.8 26.9 26.9
## [1261] 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9
## [1275] 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9
## [1289] 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9
## [1303] 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9 26.9
## [1317] 26.9 26.9 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0
## [1331] 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0
## [1345] 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0
## [1359] 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0
## [1373] 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.0 27.1
## [1387] 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1
## [1401] 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1
## [1415] 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1
## [1429] 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.1 27.2 27.2
## [1443] 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2
## [1457] 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2 27.2
## [1471] 27.2 27.2 27.2 27.2 27.2 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3
## [1485] 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3
## [1499] 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.3 27.4
## [1513] 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4
## [1527] 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4
## [1541] 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.4 27.5 27.5 27.5
## [1555] 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5
## [1569] 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5 27.5
## [1583] 27.5 27.5 27.5 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.6
## [1597] 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.6 27.7 27.7 27.7 27.7 27.7
## [1611] 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7
## [1625] 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.8 27.8 27.8 27.8 27.8
## [1639] 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.8 27.9 27.9 27.9
## [1653] 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9 27.9
## [1667] 27.9 27.9 27.9 28.0 28.0 28.0 28.0 28.0 28.0 28.0 28.0 28.0 28.0 28.0
## [1681] 28.0 28.0 28.0 28.0 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1 28.1
## [1695] 28.1 28.1 28.1 28.2 28.2 28.2 28.2 28.2 28.2 28.2 28.2 28.2 28.2 28.3
## [1709] 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3 28.3
## [1723] 28.3 28.4 28.4 28.4 28.4 28.4 28.4 28.4 28.4 28.4 28.4 28.4 28.5 28.5
## [1737] 28.5 28.5 28.5 28.5 28.5 28.5 28.5 28.5 28.6 28.6 28.6 28.6 28.6 28.7
## [1751] 28.7 28.8 28.8 28.8 28.8 28.9 28.9 28.9 28.9 28.9 29.0 29.0 29.0 29.0
## [1765] 29.0 29.1 29.1 29.1 29.1 29.1 29.2 29.2 29.2 29.3 29.4 29.4hrel = sort(datos$RHUM, decreasing=TRUE)
hrel## [1] 99.96 99.79 99.75 99.54 99.50 99.38 99.30 99.29 99.29 99.25 99.25 99.25
## [13] 99.25 99.17 99.08 99.04 99.00 99.00 98.96 98.92 98.88 98.88 98.86 98.79
## [25] 98.71 98.67 98.67 98.50 98.42 98.42 98.38 98.29 98.29 98.24 98.21 98.08
## [37] 98.08 98.04 98.04 98.00 98.00 98.00 97.96 97.96 97.92 97.92 97.92 97.91
## [49] 97.91 97.88 97.83 97.83 97.83 97.83 97.79 97.79 97.75 97.75 97.71 97.71
## [61] 97.71 97.71 97.71 97.63 97.63 97.63 97.58 97.58 97.54 97.54 97.52 97.50
## [73] 97.50 97.42 97.42 97.42 97.38 97.33 97.33 97.33 97.25 97.25 97.25 97.25
## [85] 97.25 97.21 97.21 97.21 97.21 97.17 97.13 97.09 97.08 97.08 97.08 97.08
## [97] 97.04 97.00 97.00 97.00 97.00 96.95 96.92 96.88 96.88 96.88 96.88 96.88
## [109] 96.88 96.84 96.83 96.83 96.83 96.83 96.79 96.75 96.75 96.75 96.71 96.71
## [121] 96.71 96.63 96.63 96.58 96.58 96.58 96.58 96.58 96.58 96.54 96.54 96.54
## [133] 96.54 96.52 96.50 96.50 96.50 96.50 96.50 96.50 96.47 96.46 96.46 96.42
## [145] 96.38 96.38 96.38 96.38 96.38 96.33 96.33 96.33 96.33 96.33 96.29 96.29
## [157] 96.29 96.25 96.25 96.25 96.20 96.17 96.17 96.17 96.13 96.13 96.13 96.13
## [169] 96.13 96.13 96.13 96.13 96.08 96.08 96.04 96.04 96.04 96.04 96.00 96.00
## [181] 96.00 95.96 95.92 95.92 95.92 95.92 95.88 95.88 95.88 95.88 95.83 95.83
## [193] 95.79 95.79 95.79 95.75 95.71 95.67 95.67 95.67 95.67 95.63 95.63 95.63
## [205] 95.60 95.58 95.58 95.58 95.58 95.57 95.54 95.54 95.54 95.50 95.50 95.50
## [217] 95.46 95.46 95.42 95.42 95.41 95.38 95.38 95.33 95.33 95.33 95.33 95.33
## [229] 95.33 95.33 95.33 95.33 95.29 95.29 95.25 95.17 95.17 95.13 95.13 95.13
## [241] 95.08 95.06 95.04 95.04 95.04 95.04 95.04 95.04 95.04 95.00 95.00 94.96
## [253] 94.96 94.94 94.92 94.92 94.92 94.88 94.88 94.88 94.88 94.87 94.84 94.83
## [265] 94.83 94.83 94.79 94.79 94.79 94.79 94.79 94.79 94.78 94.75 94.75 94.74
## [277] 94.71 94.71 94.71 94.71 94.63 94.63 94.60 94.58 94.58 94.58 94.58 94.58
## [289] 94.58 94.58 94.51 94.50 94.50 94.50 94.50 94.50 94.50 94.50 94.46 94.42
## [301] 94.38 94.38 94.38 94.35 94.34 94.33 94.33 94.33 94.33 94.33 94.29 94.29
## [313] 94.29 94.29 94.26 94.25 94.25 94.25 94.25 94.25 94.25 94.25 94.25 94.25
## [325] 94.21 94.21 94.21 94.21 94.17 94.13 94.13 94.13 94.13 94.08 94.08 94.08
## [337] 94.08 94.04 94.04 94.04 94.00 94.00 93.96 93.96 93.96 93.96 93.96 93.96
## [349] 93.93 93.92 93.92 93.92 93.92 93.92 93.92 93.92 93.92 93.88 93.88 93.88
## [361] 93.88 93.88 93.86 93.83 93.83 93.83 93.83 93.83 93.83 93.79 93.79 93.79
## [373] 93.79 93.79 93.79 93.78 93.75 93.71 93.71 93.71 93.71 93.71 93.67 93.67
## [385] 93.67 93.67 93.63 93.63 93.63 93.63 93.63 93.58 93.58 93.58 93.58 93.58
## [397] 93.58 93.58 93.57 93.54 93.54 93.54 93.52 93.50 93.50 93.46 93.46 93.42
## [409] 93.38 93.33 93.33 93.33 93.29 93.29 93.29 93.29 93.28 93.26 93.25 93.25
## [421] 93.25 93.25 93.25 93.21 93.21 93.21 93.17 93.17 93.13 93.13 93.13 93.13
## [433] 93.13 93.13 93.08 93.08 93.08 93.08 93.08 93.05 93.04 93.04 93.04 93.04
## [445] 93.04 93.00 93.00 93.00 93.00 93.00 92.97 92.96 92.96 92.96 92.96 92.92
## [457] 92.92 92.92 92.92 92.92 92.92 92.92 92.91 92.88 92.88 92.83 92.81 92.80
## [469] 92.80 92.79 92.79 92.79 92.79 92.79 92.75 92.75 92.75 92.71 92.71 92.69
## [481] 92.67 92.67 92.67 92.63 92.58 92.58 92.58 92.58 92.58 92.58 92.57 92.54
## [493] 92.54 92.54 92.54 92.54 92.54 92.50 92.50 92.50 92.50 92.50 92.50 92.50
## [505] 92.50 92.47 92.46 92.46 92.46 92.42 92.42 92.41 92.38 92.38 92.38 92.38
## [517] 92.37 92.33 92.31 92.30 92.29 92.29 92.29 92.29 92.25 92.25 92.25 92.22
## [529] 92.22 92.21 92.21 92.21 92.21 92.21 92.17 92.17 92.17 92.17 92.17 92.17
## [541] 92.14 92.13 92.13 92.13 92.13 92.13 92.13 92.13 92.08 92.08 92.08 92.08
## [553] 92.08 92.08 92.08 92.03 92.00 92.00 92.00 92.00 91.96 91.96 91.96 91.94
## [565] 91.92 91.92 91.92 91.88 91.88 91.88 91.88 91.88 91.88 91.87 91.83 91.83
## [577] 91.83 91.83 91.83 91.79 91.79 91.79 91.79 91.79 91.79 91.75 91.75 91.75
## [589] 91.75 91.75 91.75 91.74 91.71 91.71 91.71 91.71 91.71 91.71 91.71 91.67
## [601] 91.67 91.67 91.67 91.67 91.66 91.65 91.65 91.63 91.63 91.63 91.63 91.63
## [613] 91.63 91.58 91.58 91.58 91.58 91.54 91.54 91.54 91.54 91.54 91.54 91.50
## [625] 91.50 91.50 91.50 91.50 91.50 91.49 91.49 91.48 91.43 91.42 91.42 91.42
## [637] 91.42 91.38 91.38 91.38 91.35 91.35 91.34 91.33 91.33 91.33 91.33 91.33
## [649] 91.33 91.33 91.33 91.33 91.33 91.33 91.33 91.29 91.29 91.26 91.25 91.25
## [661] 91.25 91.22 91.21 91.21 91.21 91.21 91.21 91.17 91.17 91.17 91.17 91.17
## [673] 91.13 91.13 91.13 91.13 91.13 91.13 91.09 91.08 91.08 91.08 91.08 91.08
## [685] 91.08 91.04 91.04 91.04 91.03 91.02 91.00 91.00 91.00 91.00 90.96 90.96
## [697] 90.96 90.96 90.92 90.92 90.92 90.88 90.88 90.88 90.88 90.88 90.88 90.85
## [709] 90.83 90.83 90.83 90.83 90.82 90.81 90.79 90.79 90.79 90.79 90.79 90.79
## [721] 90.79 90.79 90.79 90.77 90.76 90.75 90.75 90.75 90.75 90.75 90.75 90.75
## [733] 90.75 90.74 90.71 90.71 90.71 90.71 90.71 90.71 90.71 90.69 90.69 90.67
## [745] 90.67 90.67 90.67 90.65 90.63 90.63 90.60 90.58 90.58 90.57 90.55 90.54
## [757] 90.54 90.54 90.54 90.54 90.53 90.50 90.50 90.50 90.50 90.46 90.46 90.46
## [769] 90.46 90.46 90.44 90.43 90.42 90.42 90.38 90.38 90.35 90.33 90.33 90.33
## [781] 90.30 90.29 90.29 90.29 90.25 90.25 90.25 90.25 90.25 90.25 90.25 90.21
## [793] 90.21 90.21 90.21 90.19 90.19 90.17 90.17 90.17 90.13 90.13 90.13 90.13
## [805] 90.13 90.13 90.13 90.08 90.08 90.08 90.04 90.04 90.04 90.04 90.04 90.00
## [817] 90.00 90.00 90.00 90.00 90.00 90.00 89.96 89.96 89.96 89.96 89.96 89.96
## [829] 89.94 89.92 89.92 89.91 89.88 89.88 89.86 89.83 89.83 89.83 89.83 89.83
## [841] 89.83 89.83 89.81 89.76 89.75 89.75 89.74 89.72 89.71 89.71 89.71 89.71
## [853] 89.70 89.69 89.67 89.67 89.67 89.67 89.67 89.67 89.67 89.63 89.63 89.63
## [865] 89.63 89.63 89.63 89.63 89.61 89.60 89.60 89.58 89.58 89.58 89.56 89.55
## [877] 89.54 89.52 89.47 89.46 89.44 89.42 89.42 89.42 89.42 89.42 89.42 89.42
## [889] 89.38 89.38 89.38 89.38 89.38 89.38 89.33 89.33 89.33 89.33 89.33 89.33
## [901] 89.33 89.33 89.33 89.29 89.29 89.29 89.29 89.29 89.29 89.26 89.25 89.21
## [913] 89.21 89.20 89.17 89.17 89.17 89.17 89.13 89.13 89.13 89.13 89.13 89.13
## [925] 89.13 89.13 89.08 89.08 89.04 89.04 89.04 89.04 89.00 89.00 89.00 89.00
## [937] 89.00 89.00 88.99 88.96 88.96 88.95 88.92 88.92 88.92 88.92 88.90 88.88
## [949] 88.88 88.88 88.87 88.83 88.83 88.81 88.80 88.75 88.75 88.75 88.74 88.71
## [961] 88.71 88.71 88.71 88.68 88.67 88.67 88.67 88.67 88.67 88.67 88.67 88.67
## [973] 88.64 88.63 88.63 88.60 88.58 88.58 88.58 88.54 88.54 88.54 88.54 88.54
## [985] 88.50 88.50 88.50 88.50 88.50 88.49 88.46 88.46 88.46 88.46 88.42 88.42
## [997] 88.42 88.42 88.42 88.39 88.38 88.35 88.33 88.33 88.33 88.33 88.33 88.33
## [1009] 88.33 88.33 88.32 88.30 88.29 88.28 88.27 88.25 88.25 88.21 88.21 88.21
## [1021] 88.21 88.21 88.21 88.21 88.18 88.17 88.17 88.13 88.13 88.13 88.13 88.12
## [1033] 88.11 88.08 88.08 88.08 88.08 88.08 88.08 88.08 88.05 88.04 88.04 88.04
## [1045] 88.03 88.00 88.00 88.00 88.00 88.00 87.98 87.96 87.96 87.95 87.94 87.92
## [1057] 87.92 87.92 87.88 87.88 87.86 87.85 87.83 87.83 87.83 87.83 87.79 87.79
## [1069] 87.75 87.75 87.75 87.75 87.75 87.71 87.71 87.71 87.71 87.71 87.67 87.67
## [1081] 87.67 87.67 87.63 87.58 87.58 87.58 87.58 87.54 87.54 87.54 87.54 87.54
## [1093] 87.54 87.54 87.50 87.50 87.50 87.50 87.50 87.50 87.46 87.46 87.42 87.39
## [1105] 87.38 87.38 87.38 87.38 87.33 87.33 87.33 87.30 87.29 87.29 87.29 87.26
## [1117] 87.24 87.22 87.21 87.21 87.21 87.17 87.13 87.13 87.13 87.13 87.13 87.08
## [1129] 87.08 87.08 87.04 87.01 86.96 86.96 86.96 86.92 86.92 86.92 86.92 86.88
## [1141] 86.88 86.88 86.83 86.83 86.83 86.79 86.79 86.79 86.79 86.75 86.75 86.75
## [1153] 86.75 86.75 86.73 86.71 86.71 86.71 86.70 86.68 86.67 86.67 86.64 86.63
## [1165] 86.63 86.58 86.58 86.58 86.58 86.58 86.50 86.50 86.46 86.46 86.46 86.42
## [1177] 86.42 86.42 86.42 86.38 86.38 86.33 86.33 86.33 86.33 86.33 86.29 86.29
## [1189] 86.29 86.29 86.29 86.26 86.25 86.25 86.21 86.21 86.21 86.21 86.19 86.17
## [1201] 86.17 86.17 86.17 86.17 86.17 86.13 86.13 86.13 86.13 86.08 86.08 86.08
## [1213] 86.04 86.04 86.04 86.04 86.02 86.02 86.00 86.00 86.00 85.96 85.96 85.96
## [1225] 85.92 85.92 85.92 85.88 85.88 85.83 85.83 85.83 85.83 85.83 85.79 85.79
## [1237] 85.75 85.71 85.71 85.67 85.63 85.63 85.63 85.63 85.58 85.54 85.54 85.51
## [1249] 85.50 85.46 85.46 85.42 85.42 85.42 85.33 85.33 85.25 85.21 85.17 85.17
## [1261] 85.10 85.08 85.08 85.08 85.08 85.08 85.04 85.04 85.04 85.04 85.00 85.00
## [1273] 85.00 85.00 84.96 84.92 84.92 84.88 84.88 84.88 84.83 84.79 84.79 84.79
## [1285] 84.79 84.75 84.71 84.71 84.71 84.71 84.71 84.69 84.67 84.67 84.67 84.63
## [1297] 84.58 84.57 84.54 84.54 84.54 84.52 84.50 84.46 84.46 84.42 84.42 84.38
## [1309] 84.38 84.33 84.33 84.29 84.29 84.29 84.27 84.25 84.21 84.21 84.21 84.17
## [1321] 84.13 84.13 84.13 84.11 84.07 84.04 84.00 84.00 84.00 83.96 83.96 83.96
## [1333] 83.96 83.92 83.92 83.88 83.83 83.83 83.79 83.79 83.77 83.76 83.75 83.75
## [1345] 83.75 83.75 83.71 83.67 83.65 83.63 83.63 83.63 83.63 83.63 83.61 83.58
## [1357] 83.50 83.50 83.50 83.46 83.46 83.42 83.42 83.42 83.42 83.42 83.38 83.38
## [1369] 83.38 83.35 83.33 83.33 83.33 83.33 83.30 83.29 83.29 83.25 83.25 83.21
## [1381] 83.19 83.17 83.17 83.17 83.17 83.14 83.13 83.13 83.13 83.13 83.08 83.04
## [1393] 83.00 83.00 82.97 82.96 82.95 82.92 82.92 82.92 82.88 82.79 82.79 82.75
## [1405] 82.75 82.71 82.71 82.67 82.67 82.63 82.63 82.58 82.54 82.50 82.50 82.50
## [1417] 82.50 82.46 82.46 82.46 82.42 82.38 82.38 82.38 82.33 82.33 82.33 82.33
## [1429] 82.29 82.29 82.29 82.29 82.25 82.21 82.21 82.21 82.18 82.17 82.15 82.13
## [1441] 82.09 82.08 82.04 82.04 82.04 82.00 81.96 81.92 81.88 81.88 81.86 81.79
## [1453] 81.79 81.75 81.71 81.71 81.67 81.67 81.58 81.58 81.54 81.54 81.54 81.50
## [1465] 81.50 81.42 81.38 81.38 81.33 81.32 81.29 81.25 81.25 81.21 81.21 81.18
## [1477] 81.17 81.17 81.17 81.17 81.13 81.08 81.08 81.00 81.00 81.00 80.92 80.92
## [1489] 80.92 80.88 80.88 80.83 80.83 80.79 80.79 80.79 80.76 80.71 80.71 80.71
## [1501] 80.67 80.67 80.58 80.58 80.46 80.42 80.38 80.38 80.38 80.36 80.32 80.29
## [1513] 80.29 80.29 80.21 80.21 80.21 80.21 80.21 80.17 80.08 80.00 80.00 80.00
## [1525] 79.92 79.88 79.87 79.83 79.83 79.79 79.79 79.75 79.75 79.71 79.64 79.63
## [1537] 79.63 79.63 79.63 79.63 79.58 79.58 79.58 79.50 79.50 79.50 79.50 79.46
## [1549] 79.46 79.40 79.33 79.17 79.13 79.04 79.00 79.00 79.00 78.96 78.96 78.88
## [1561] 78.83 78.75 78.67 78.67 78.67 78.67 78.63 78.63 78.63 78.63 78.61 78.58
## [1573] 78.50 78.46 78.46 78.46 78.42 78.42 78.42 78.38 78.33 78.26 78.25 78.21
## [1585] 78.21 78.17 78.00 77.92 77.88 77.88 77.88 77.83 77.79 77.75 77.71 77.67
## [1597] 77.67 77.67 77.65 77.65 77.63 77.58 77.54 77.52 77.50 77.46 77.44 77.42
## [1609] 77.42 77.38 77.29 77.29 77.25 77.25 77.25 77.21 77.21 77.21 77.21 77.21
## [1621] 77.17 77.17 77.13 77.08 77.04 77.04 76.96 76.96 76.96 76.92 76.88 76.88
## [1633] 76.83 76.79 76.75 76.67 76.67 76.67 76.58 76.58 76.54 76.54 76.50 76.46
## [1645] 76.45 76.25 76.25 76.04 76.04 76.04 76.00 76.00 75.96 75.88 75.83 75.79
## [1657] 75.79 75.75 75.71 75.71 75.67 75.63 75.58 75.54 75.50 75.50 75.48 75.46
## [1669] 75.46 75.38 75.33 75.29 75.25 75.21 75.17 75.17 75.13 75.05 75.04 74.96
## [1681] 74.96 74.88 74.88 74.88 74.83 74.83 74.79 74.74 74.67 74.58 74.33 74.33
## [1693] 74.17 74.00 73.96 73.79 73.75 73.54 73.50 73.25 73.13 73.08 73.00 72.92
## [1705] 72.79 72.63 72.54 72.50 72.46 72.42 72.42 72.38 72.33 72.22 72.04 72.00
## [1717] 71.96 71.91 71.71 71.39 71.38 71.29 71.25 71.21 71.17 71.08 71.04 71.04
## [1729] 71.00 70.92 70.92 70.87 70.79 70.79 70.58 70.54 70.50 70.38 70.25 70.08
## [1741] 69.67 69.33 69.25 69.21 69.17 68.94 68.88 68.83 68.71 68.58 68.55 68.46
## [1753] 68.25 68.21 68.04 67.78 67.75 67.21 67.08 67.08 67.00 66.88 66.79 66.67
## [1765] 66.46 66.33 66.26 65.58 65.21 65.17 65.00 64.88 63.83 63.79 63.63 62.79estandarizacion = function(x){
media = mean(x)
desv = sd(x)
z = (x - media)/desv
return(z)
}
tmede = estandarizacion(tmed)
hrele = estandarizacion(hrel)- GRAFICOS
hist(tmed, col = 'lightgreen', main = 'Distribución de la temperatura media Acacias-Meta', xlab = 'Temperatura media (°C)')
hist(hrel, col = 'cyan', main = 'Distribución de la humedad relativa Acacias-Meta', xlab = 'Humedad relativa (%)')
hist(tmede, col = 'lightgreen', main = 'Distribución de la temperatura media Acacias-Meta (estandarizada)', xlab = 'Temperatura media (°C)')
hist(hrele, col = 'cyan', main = 'Distribución de la humedad relativa Acacias-Meta (estandarizada)', xlab = 'Humedad relativa (%)')
par(bg = 'white', fg = 'blue')
plot(tmed, hrel, pch = 19, cex =0.8,col = 'purple', main = 'Temperatura media vs Humedad relativa', ylab = 'Humedad relativa (%)', xlab = 'Temperatura media (°C)', type = 'l')
par(bg = 'white', fg = 'blue')
plot(tmede, hrele, pch = 19, cex =0.8,col = 'purple', main = 'Temperatura media estandarizada vs Humedad relativa estandarizada', ylab = 'Humedad relativa (%)', xlab = 'Temperatura media (°C)', type = 'l')
- ¿REALISTA O ENGANOSO?
La humedad relativa depende fuertemente de la temperatura; a medida que la temperatura aumenta, la humedad relativa tiende a disminuir, por lo que el grafico ilustra muy bien su relacion inversa. De modo que tiene sentido realizar este grafico, y poner la Humedad relativa como efecto de la temperatura.
cor(tmed,hrel,method = 'pearson')## [1] -0.9536624cor(tmed,hrel,method = 'spearman')## [1] -0.9996042Los indices de correlacion muestran correlacion descendiente todo el tiempo y muestra una fuerte linealidad negativa.
- MODELOS DE CRECIMIENTO DE GROWTHMODELS
library(growthmodels)
#BLUMBERG
growth = blumberg(0:100, 10, 2, 0.5)
par(bg = 'white', fg = 'blue')
plot(growth, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Blumberg', xlab = 'tiempo', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p = cor(growth, 0:100, method = 'pearson')
cor_p## [1] 0.7666426cor_s = cor(growth, 0:100, method = 'spearman')
cor_s## [1] 1#BRODY
growth1 = brody(0:10, 10, 5, 0.3)
time1 = brody.inverse(growth1, 10, 5, 0.3)
plot(growth1, time1, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Brody', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p1 = cor(time1, growth1, method = 'pearson')
cor_p1## [1] 0.9277481cor_s1 = cor(time1, growth1, method = 'spearman')
cor_s1## [1] 1#CHAPMAN-RICHARDS
growth2 = chapmanRichards(0:10, 10, 0.5, 0.3, 0.5)
time2 = chapmanRichards.inverse(growth2, 10, 0.5, 0.3, 0.5)
plot(growth2, time2, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Bhapman-Richards', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p2 = cor(time2, growth2, method = 'pearson')
cor_p2## [1] 0.9533679cor_s2 = cor(time2, growth2, method = 'spearman')
cor_s2## [1] 1#GENERALISED LOGISTIC
growth3 = generalisedLogistic(0:10, 5, 10, 0.3, 0.5, 3)
time3 = generalisedLogistic.inverse(growth3, 5, 10, 0.3, 0.5, 3)
plot(growth3, time3, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Generalised Logistic', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p3 = cor(time3, growth3, method = 'pearson')
cor_p3## [1] 0.9805022cor_s3 = cor(time3, growth3, method = 'spearman')
cor_s3## [1] 1#GENERALISED RICHARDS
growth4 = generalisedRichard(0:10, 5, 10, 0.3, 0.5, 1, 3)
time4 = generalisedRichard.inverse(growth4, 5, 10, 0.3, 0.5, 1, 3)
plot(growth4, time4, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Generalised Richards', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p4 = cor(time4, growth4, method = 'pearson')
cor_p4## [1] 0.9973694cor_s4 = cor(time4, growth4, method = 'spearman')
cor_s4## [1] 1#GROMPERTZ
growth5 = gompertz(0:10, 10, 0.5, 0.3)
time5 = gompertz.inverse(growth5, 10, 0.5, 0.3)
plot(growth5, time5, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Grompertz', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p5 = cor(time5, growth5, method = 'pearson')
cor_p5## [1] 0.9479643cor_s5 = cor(time5, growth5, method = 'spearman')
cor_s5## [1] 1#LOGISTIC
growth6 = logistic(0:10, 10, 0.5, 0.3)
time6 = logistic.inverse(growth6, 10, 0.5, 0.3)
plot(growth6, time6, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Logistic', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p6 = cor(time6, growth6, method = 'pearson')
cor_p6## [1] 0.9590087cor_s6 = cor(time6, growth6, method = 'spearman')
cor_s6## [1] 1#LOG-LOGISTIC
growth7 = loglogistic(0:10, 10, 0.5, 0.3)
time7 = loglogistic.inverse(growth7, 10, 0.5, 0.3)
plot(growth7, time7, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Log-Logistic', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p7 = cor(time7, growth7, method = 'pearson')
cor_p7## [1] 0.6321408cor_s7 = cor(time7, growth7, method = 'spearman')
cor_s7## [1] 1#MITCHERLICH
growth8 = mitcherlich(0:10, 10, 0.5, 0.3)
time8 = mitcherlich.inverse(growth8, 10, 0.5, 0.3)
plot(growth8, time8, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Mitcherlich', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p8 = cor(time8, growth8, method = 'pearson')
cor_p8## [1] 0.6515302cor_s8 = cor(time8, growth8, method = 'spearman')
cor_s8## [1] 1#MORGAN MERCER FLODIN
growth9 = mmf(0:10, 10, 0.5, 4, 1)
time9 = mmf.inverse(growth9, 10, 0.5, 4, 1)
plot(growth9, time9, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Mercer Flodin', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p9 = cor(time9, growth9, method = 'pearson')
cor_p9## [1] 0.9397692cor_s9 = cor(time9, growth9, method = 'spearman')
cor_s9## [1] 1#MONOMOLECULAR
growth10 = monomolecular(0:10, 10, 0.5, 0.3)
time10 = monomolecular.inverse(growth10, 10, 0.5, 0.3)
plot(growth10, time10, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Monomolecular', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p10 = cor(time10, growth10, method = 'pearson')
cor_p10## [1] 0.9277481cor_s10 = cor(time10, growth10, method = 'spearman')
cor_s10## [1] 1#NEGATIVE EXPONENTIAL
growth11 = negativeExponential(0:10, 1, 0.3)
time11 = negativeExponential.inverse(growth11, 10, 0.3)
plot(growth11, time11, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Negative Exponential', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p11 = cor(time11, growth11, method = 'pearson')
cor_p11## [1] 0.9999014cor_s11 = cor(time11, growth11, method = 'spearman')
cor_s11## [1] 1#RICHARD
growth12 = richard(0:10, 10, 0.5, 0.3, 0.5)
time12 = richard.inverse(growth12, 10, 0.5, 0.3, 0.5)
plot(growth12, time12, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Richard', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p12 = cor(time12, growth12, method = 'pearson')
cor_p12## [1] 0.970864cor_s12 = cor(time12, growth12, method = 'spearman')
cor_s12## [1] 1#SCHNUTE
growth13 = schnute(0:10, 10, 5, .5, .5)
time13 = schnute.inverse(growth13, 10, 5, .5, .5)
plot(growth13, time13, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Schnute', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p13 = cor(time13, growth13, method = 'pearson')
cor_p13## [1] 0.9359388cor_s13 = cor(time13, growth13, method = 'spearman')
cor_s13## [1] 1#STANNARD
growth14 = stannard(0:10, 1, .2, .1, .5)
time14 = stannard.inverse(growth14, 1, .2, .1, .5)
plot(growth14, time14, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Stannard', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p14 = cor(time14, growth14, method = 'pearson')
cor_p14## [1] 0.9833873cor_s14 = cor(time14, growth14, method = 'spearman')
cor_s14## [1] 1#VON BERTALANFFY
growth15 = vonBertalanffy(0:10, 10, 0.5, 0.3, 0.5)
time15 = vonBertalanffy.inverse(growth15, 10, 0.5, 0.3, 0.5)
plot(growth15, time15, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Von Bertalanffy', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p15 = cor(time15, growth15, method = 'pearson')
cor_p15## [1] 0.9351013cor_s15 = cor(time15, growth15, method = 'spearman')
cor_s15## [1] 1#WEIBULL
growth16 = weibull(0:10, 10, 0.5, 0.3, 0.5)
time16 = weibull.inverse(growth16, 10, 0.5, 0.3, 0.5)
plot(growth16, time16, pch = 19, cex = 0.5, main = 'Modelo de crecimiento Weibull', xlab = 'tiempo',ylab = 'crecimiento', col = 'red')
grid(nx = 10, ny = 10, lwd = 1,col = 'black')
cor_p16 = cor(time16, growth16, method = 'pearson')
cor_p16## [1] 0.9051705cor_s16 = cor(time16, growth16, method = 'spearman')
cor_s16## [1] 1