setwd("G:/Mi unidad/Agrosavia/FeCa/Fenoma/Análisis/AguaT")
datos<-read.table("evd.csv", header=T, sep=';')
##Librerias
library(lme4)
## Cargando paquete requerido: Matrix
library(lmerTest) # p-values
## Warning: package 'lmerTest' was built under R version 4.4.3
##
## Adjuntando el paquete: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
library(emmeans) # post hoc
## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'
library(dplyr)
##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
##
## Adjuntando el paquete: 'tidyr'
## The following objects are masked from 'package:Matrix':
##
## expand, pack, unpack
library(agricolae)
## Warning: package 'agricolae' was built under R version 4.4.3
library(metan)
## Warning: package 'metan' was built under R version 4.4.3
## |=========================================================|
## | Multi-Environment Trial Analysis (metan) v1.19.0 |
## | Author: Tiago Olivoto |
## | Type 'citation('metan')' to know how to cite metan |
## | Type 'vignette('metan_start')' for a short tutorial |
## | Visit 'https://bit.ly/metanpkg' for a complete tutorial |
## |=========================================================|
##
## Adjuntando el paquete: 'metan'
## The following object is masked from 'package:tidyr':
##
## replace_na
## The following object is masked from 'package:dplyr':
##
## recode_factor
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
##Convertir a factor
datos$gen <- factor(datos$gen)
datos$municipio <- factor(datos$municipio)
datos$mun <- factor(datos$mun)
datos$reg <- factor(datos$reg)
#Modelo 0
modelo <- lm (log(DE) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(DE)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 1.14718 0.163883 20.9406 < 2.2e-16 ***
## mun 9 0.93604 0.104004 13.2894 1.345e-15 ***
## gen:mun 60 1.61423 0.026904 3.4377 4.414e-10 ***
## Residuals 154 1.20522 0.007826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm ((DE) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: (DE)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 1262150 180307 23.3914 < 2.2e-16 ***
## mun 9 953687 105965 13.7469 4.542e-16 ***
## gen:mun 60 1720915 28682 3.7209 3.223e-11 ***
## Residuals 154 1187073 7708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##Contrastes a posteriori
#Genotipos
g<-emmeans(modelo, pairwise ~ gen)
## NOTE: Results may be misleading due to involvement in interactions
g
## $emmeans
## gen emmean SE df lower.CL upper.CL
## CNCH12 1015 16 154 984 1047
## CNCH13 1154 16 154 1122 1185
## FBO1 986 16 154 954 1017
## FCHI8 nonEst NA NA NA NA
## FEAR5 1045 16 154 1014 1077
## FGI4 1070 16 154 1039 1102
## FMA7 969 16 154 937 1001
## FSV1 nonEst NA NA NA NA
##
## Results are averaged over the levels of: mun
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -138.1 22.7 154 -6.092 <.0001
## CNCH12 - FBO1 29.8 22.7 154 1.315 0.7766
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -29.7 22.7 154 -1.310 0.7791
## CNCH12 - FGI4 -54.7 22.7 154 -2.414 0.1577
## CNCH12 - FMA7 46.5 22.7 154 2.050 0.3194
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 167.9 22.7 154 7.407 <.0001
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 108.4 22.7 154 4.782 0.0001
## CNCH13 - FGI4 83.4 22.7 154 3.678 0.0043
## CNCH13 - FMA7 184.6 22.7 154 8.142 <.0001
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -59.5 22.7 154 -2.625 0.0975
## FBO1 - FGI4 -84.5 22.7 154 -3.729 0.0036
## FBO1 - FMA7 16.7 22.7 154 0.735 0.9772
## FBO1 - FSV1 nonEst NA NA NA NA
## FCHI8 - FEAR5 nonEst NA NA NA NA
## FCHI8 - FGI4 nonEst NA NA NA NA
## FCHI8 - FMA7 nonEst NA NA NA NA
## FCHI8 - FSV1 nonEst NA NA NA NA
## FEAR5 - FGI4 -25.0 22.7 154 -1.104 0.8790
## FEAR5 - FMA7 76.2 22.7 154 3.360 0.0124
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 101.2 22.7 154 4.464 0.0002
## FGI4 - FSV1 nonEst NA NA NA NA
## FMA7 - FSV1 nonEst NA NA NA NA
##
## Results are averaged over the levels of: mun
## Note: contrasts are still on the ( scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## P value adjustment: tukey method for comparing a family of 6 estimates
pwpp(g, type = "response")
## Warning: `aes_()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`
## ℹ The deprecated feature was likely used in the emmeans package.
## Please report the issue at <https://github.com/rvlenth/emmeans/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_segment()`).
#Municipios
m<-emmeans(modelo, pairwise ~ mun)
## NOTE: Results may be misleading due to involvement in interactions
m
## $emmeans
## mun emmean SE df lower.CL upper.CL
## CH 954 17.9 154 918 989
## Gig 915 17.9 154 880 951
## Htc 1030 17.9 154 994 1065
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
## RiN 952 17.9 154 917 988
## SnV 955 17.9 154 920 991
## Tam 1057 17.9 154 1021 1092
## ViG 1002 17.9 154 966 1037
## Yac 1131 17.9 154 1096 1166
##
## Results are averaged over the levels of: gen
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CH - Gig 38.62 25.3 154 1.524 0.7934
## CH - Htc -75.92 25.3 154 -2.995 0.0619
## CH - Jam nonEst NA NA NA NA
## CH - PtR nonEst NA NA NA NA
## CH - RiN 1.38 25.3 154 0.054 1.0000
## CH - SnV -1.42 25.3 154 -0.056 1.0000
## CH - Tam -102.71 25.3 154 -4.052 0.0020
## CH - ViG -47.67 25.3 154 -1.881 0.5662
## CH - Yac -177.08 25.3 154 -6.987 <.0001
## Gig - Htc -114.54 25.3 154 -4.519 0.0003
## Gig - Jam nonEst NA NA NA NA
## Gig - PtR nonEst NA NA NA NA
## Gig - RiN -37.25 25.3 154 -1.470 0.8223
## Gig - SnV -40.04 25.3 154 -1.580 0.7616
## Gig - Tam -141.33 25.3 154 -5.576 <.0001
## Gig - ViG -86.29 25.3 154 -3.405 0.0187
## Gig - Yac -215.71 25.3 154 -8.511 <.0001
## Htc - Jam nonEst NA NA NA NA
## Htc - PtR nonEst NA NA NA NA
## Htc - RiN 77.29 25.3 154 3.050 0.0534
## Htc - SnV 74.50 25.3 154 2.939 0.0718
## Htc - Tam -26.79 25.3 154 -1.057 0.9645
## Htc - ViG 28.25 25.3 154 1.115 0.9528
## Htc - Yac -101.17 25.3 154 -3.992 0.0025
## Jam - PtR nonEst NA NA NA NA
## Jam - RiN nonEst NA NA NA NA
## Jam - SnV nonEst NA NA NA NA
## Jam - Tam nonEst NA NA NA NA
## Jam - ViG nonEst NA NA NA NA
## Jam - Yac nonEst NA NA NA NA
## PtR - RiN nonEst NA NA NA NA
## PtR - SnV nonEst NA NA NA NA
## PtR - Tam nonEst NA NA NA NA
## PtR - ViG nonEst NA NA NA NA
## PtR - Yac nonEst NA NA NA NA
## RiN - SnV -2.79 25.3 154 -0.110 1.0000
## RiN - Tam -104.08 25.3 154 -4.107 0.0016
## RiN - ViG -49.04 25.3 154 -1.935 0.5292
## RiN - Yac -178.46 25.3 154 -7.041 <.0001
## SnV - Tam -101.29 25.3 154 -3.997 0.0025
## SnV - ViG -46.25 25.3 154 -1.825 0.6043
## SnV - Yac -175.67 25.3 154 -6.931 <.0001
## Tam - ViG 55.04 25.3 154 2.172 0.3747
## Tam - Yac -74.38 25.3 154 -2.935 0.0728
## ViG - Yac -129.42 25.3 154 -5.106 <.0001
##
## Results are averaged over the levels of: gen
## Note: contrasts are still on the ( scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## P value adjustment: tukey method for comparing a family of 8 estimates
pwpp(m, type = "response")
## Warning: Removed 34 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 34 rows containing missing values or values outside the scale range
## (`geom_segment()`).
#Interacción
gm<-emmeans(modelo, pairwise ~ gen|mun)
gm
## $emmeans
## mun = CH:
## gen emmean SE df lower.CL upper.CL
## CNCH12 1024 50.7 154 924 1124
## CNCH13 1083 50.7 154 983 1183
## FBO1 917 50.7 154 817 1017
## FCHI8 988 50.7 154 888 1088
## FEAR5 881 50.7 154 781 981
## FGI4 964 50.7 154 864 1064
## FMA7 988 50.7 154 888 1088
## FSV1 786 50.7 154 686 886
##
## mun = Gig:
## gen emmean SE df lower.CL upper.CL
## CNCH12 1107 50.7 154 1007 1207
## CNCH13 833 50.7 154 733 933
## FBO1 869 50.7 154 769 969
## FCHI8 774 50.7 154 674 874
## FEAR5 1048 50.7 154 948 1148
## FGI4 929 50.7 154 829 1029
## FMA7 905 50.7 154 805 1005
## FSV1 857 50.7 154 757 957
##
## mun = Htc:
## gen emmean SE df lower.CL upper.CL
## CNCH12 1083 50.7 154 983 1183
## CNCH13 1298 50.7 154 1198 1398
## FBO1 929 50.7 154 829 1029
## FCHI8 929 50.7 154 829 1029
## FEAR5 1024 50.7 154 924 1124
## FGI4 1131 50.7 154 1031 1231
## FMA7 905 50.7 154 805 1005
## FSV1 941 50.7 154 841 1041
##
## mun = Jam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 1012 50.7 154 912 1112
## CNCH13 1452 50.7 154 1352 1552
## FBO1 1048 50.7 154 948 1148
## FCHI8 nonEst NA NA NA NA
## FEAR5 917 50.7 154 817 1017
## FGI4 1071 50.7 154 971 1171
## FMA7 1083 50.7 154 983 1183
## FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## gen emmean SE df lower.CL upper.CL
## CNCH12 1047 50.7 154 947 1147
## CNCH13 940 50.7 154 840 1040
## FBO1 988 50.7 154 888 1088
## FCHI8 nonEst NA NA NA NA
## FEAR5 1095 50.7 154 995 1195
## FGI4 1202 50.7 154 1102 1302
## FMA7 1083 50.7 154 983 1183
## FSV1 1095 50.7 154 995 1195
##
## mun = RiN:
## gen emmean SE df lower.CL upper.CL
## CNCH12 976 50.7 154 876 1076
## CNCH13 964 50.7 154 864 1064
## FBO1 1000 50.7 154 900 1100
## FCHI8 845 50.7 154 745 945
## FEAR5 1071 50.7 154 971 1171
## FGI4 1024 50.7 154 924 1124
## FMA7 822 50.7 154 722 922
## FSV1 917 50.7 154 817 1017
##
## mun = SnV:
## gen emmean SE df lower.CL upper.CL
## CNCH12 869 50.7 154 769 969
## CNCH13 1262 50.7 154 1162 1362
## FBO1 881 50.7 154 781 981
## FCHI8 845 50.7 154 745 945
## FEAR5 1071 50.7 154 971 1171
## FGI4 1012 50.7 154 912 1112
## FMA7 952 50.7 154 852 1052
## FSV1 750 50.7 154 650 850
##
## mun = Tam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 964 50.7 154 864 1064
## CNCH13 1179 50.7 154 1079 1279
## FBO1 1071 50.7 154 971 1171
## FCHI8 929 50.7 154 829 1029
## FEAR5 1059 50.7 154 959 1159
## FGI4 1178 50.7 154 1078 1278
## FMA7 988 50.7 154 888 1088
## FSV1 1083 50.7 154 983 1183
##
## mun = ViG:
## gen emmean SE df lower.CL upper.CL
## CNCH12 1047 50.7 154 947 1147
## CNCH13 1155 50.7 154 1055 1255
## FBO1 1000 50.7 154 900 1100
## FCHI8 988 50.7 154 888 1088
## FEAR5 1000 50.7 154 900 1100
## FGI4 1036 50.7 154 936 1136
## FMA7 917 50.7 154 817 1017
## FSV1 869 50.7 154 769 969
##
## mun = Yac:
## gen emmean SE df lower.CL upper.CL
## CNCH12 1024 50.7 154 924 1124
## CNCH13 1369 50.7 154 1269 1469
## FBO1 1155 50.7 154 1055 1255
## FCHI8 1024 50.7 154 924 1124
## FEAR5 1286 50.7 154 1186 1386
## FGI4 1155 50.7 154 1055 1255
## FMA7 1047 50.7 154 947 1147
## FSV1 988 50.7 154 888 1088
##
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## mun = CH:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -59.667 71.7 154 -0.832 0.9910
## CNCH12 - FBO1 107.000 71.7 154 1.493 0.8103
## CNCH12 - FCHI8 35.333 71.7 154 0.493 0.9997
## CNCH12 - FEAR5 143.000 71.7 154 1.995 0.4888
## CNCH12 - FGI4 59.333 71.7 154 0.828 0.9913
## CNCH12 - FMA7 35.667 71.7 154 0.498 0.9997
## CNCH12 - FSV1 238.000 71.7 154 3.320 0.0243
## CNCH13 - FBO1 166.667 71.7 154 2.325 0.2870
## CNCH13 - FCHI8 95.000 71.7 154 1.325 0.8880
## CNCH13 - FEAR5 202.667 71.7 154 2.827 0.0959
## CNCH13 - FGI4 119.000 71.7 154 1.660 0.7128
## CNCH13 - FMA7 95.333 71.7 154 1.330 0.8861
## CNCH13 - FSV1 297.667 71.7 154 4.152 0.0014
## FBO1 - FCHI8 -71.667 71.7 154 -1.000 0.9739
## FBO1 - FEAR5 36.000 71.7 154 0.502 0.9996
## FBO1 - FGI4 -47.667 71.7 154 -0.665 0.9978
## FBO1 - FMA7 -71.333 71.7 154 -0.995 0.9746
## FBO1 - FSV1 131.000 71.7 154 1.827 0.6025
## FCHI8 - FEAR5 107.667 71.7 154 1.502 0.8054
## FCHI8 - FGI4 24.000 71.7 154 0.335 1.0000
## FCHI8 - FMA7 0.333 71.7 154 0.005 1.0000
## FCHI8 - FSV1 202.667 71.7 154 2.827 0.0959
## FEAR5 - FGI4 -83.667 71.7 154 -1.167 0.9400
## FEAR5 - FMA7 -107.333 71.7 154 -1.497 0.8079
## FEAR5 - FSV1 95.000 71.7 154 1.325 0.8880
## FGI4 - FMA7 -23.667 71.7 154 -0.330 1.0000
## FGI4 - FSV1 178.667 71.7 154 2.492 0.2066
## FMA7 - FSV1 202.333 71.7 154 2.823 0.0970
##
## mun = Gig:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 274.000 71.7 154 3.822 0.0046
## CNCH12 - FBO1 238.667 71.7 154 3.329 0.0236
## CNCH12 - FCHI8 333.333 71.7 154 4.650 0.0002
## CNCH12 - FEAR5 59.667 71.7 154 0.832 0.9910
## CNCH12 - FGI4 178.667 71.7 154 2.492 0.2066
## CNCH12 - FMA7 202.667 71.7 154 2.827 0.0959
## CNCH12 - FSV1 250.000 71.7 154 3.487 0.0144
## CNCH13 - FBO1 -35.333 71.7 154 -0.493 0.9997
## CNCH13 - FCHI8 59.333 71.7 154 0.828 0.9913
## CNCH13 - FEAR5 -214.333 71.7 154 -2.990 0.0628
## CNCH13 - FGI4 -95.333 71.7 154 -1.330 0.8861
## CNCH13 - FMA7 -71.333 71.7 154 -0.995 0.9746
## CNCH13 - FSV1 -24.000 71.7 154 -0.335 1.0000
## FBO1 - FCHI8 94.667 71.7 154 1.321 0.8898
## FBO1 - FEAR5 -179.000 71.7 154 -2.497 0.2046
## FBO1 - FGI4 -60.000 71.7 154 -0.837 0.9907
## FBO1 - FMA7 -36.000 71.7 154 -0.502 0.9996
## FBO1 - FSV1 11.333 71.7 154 0.158 1.0000
## FCHI8 - FEAR5 -273.667 71.7 154 -3.818 0.0047
## FCHI8 - FGI4 -154.667 71.7 154 -2.158 0.3834
## FCHI8 - FMA7 -130.667 71.7 154 -1.823 0.6057
## FCHI8 - FSV1 -83.333 71.7 154 -1.162 0.9412
## FEAR5 - FGI4 119.000 71.7 154 1.660 0.7128
## FEAR5 - FMA7 143.000 71.7 154 1.995 0.4888
## FEAR5 - FSV1 190.333 71.7 154 2.655 0.1448
## FGI4 - FMA7 24.000 71.7 154 0.335 1.0000
## FGI4 - FSV1 71.333 71.7 154 0.995 0.9746
## FMA7 - FSV1 47.333 71.7 154 0.660 0.9979
##
## mun = Htc:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -214.667 71.7 154 -2.995 0.0620
## CNCH12 - FBO1 154.333 71.7 154 2.153 0.3863
## CNCH12 - FCHI8 154.333 71.7 154 2.153 0.3863
## CNCH12 - FEAR5 59.333 71.7 154 0.828 0.9913
## CNCH12 - FGI4 -47.667 71.7 154 -0.665 0.9978
## CNCH12 - FMA7 178.000 71.7 154 2.483 0.2106
## CNCH12 - FSV1 142.333 71.7 154 1.986 0.4950
## CNCH13 - FBO1 369.000 71.7 154 5.147 <.0001
## CNCH13 - FCHI8 369.000 71.7 154 5.147 <.0001
## CNCH13 - FEAR5 274.000 71.7 154 3.822 0.0046
## CNCH13 - FGI4 167.000 71.7 154 2.330 0.2845
## CNCH13 - FMA7 392.667 71.7 154 5.478 <.0001
## CNCH13 - FSV1 357.000 71.7 154 4.980 <.0001
## FBO1 - FCHI8 0.000 71.7 154 0.000 1.0000
## FBO1 - FEAR5 -95.000 71.7 154 -1.325 0.8880
## FBO1 - FGI4 -202.000 71.7 154 -2.818 0.0981
## FBO1 - FMA7 23.667 71.7 154 0.330 1.0000
## FBO1 - FSV1 -12.000 71.7 154 -0.167 1.0000
## FCHI8 - FEAR5 -95.000 71.7 154 -1.325 0.8880
## FCHI8 - FGI4 -202.000 71.7 154 -2.818 0.0981
## FCHI8 - FMA7 23.667 71.7 154 0.330 1.0000
## FCHI8 - FSV1 -12.000 71.7 154 -0.167 1.0000
## FEAR5 - FGI4 -107.000 71.7 154 -1.493 0.8103
## FEAR5 - FMA7 118.667 71.7 154 1.655 0.7157
## FEAR5 - FSV1 83.000 71.7 154 1.158 0.9424
## FGI4 - FMA7 225.667 71.7 154 3.148 0.0404
## FGI4 - FSV1 190.000 71.7 154 2.650 0.1463
## FMA7 - FSV1 -35.667 71.7 154 -0.498 0.9997
##
## mun = Jam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -440.333 71.7 154 -6.143 <.0001
## CNCH12 - FBO1 -35.667 71.7 154 -0.498 0.9962
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 95.333 71.7 154 1.330 0.7680
## CNCH12 - FGI4 -59.333 71.7 154 -0.828 0.9620
## CNCH12 - FMA7 -71.000 71.7 154 -0.990 0.9204
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 404.667 71.7 154 5.645 <.0001
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 535.667 71.7 154 7.472 <.0001
## CNCH13 - FGI4 381.000 71.7 154 5.315 <.0001
## CNCH13 - FMA7 369.333 71.7 154 5.152 <.0001
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 131.000 71.7 154 1.827 0.4513
## FBO1 - FGI4 -23.667 71.7 154 -0.330 0.9995
## FBO1 - FMA7 -35.333 71.7 154 -0.493 0.9964
## FBO1 - FSV1 nonEst NA NA NA NA
## FCHI8 - FEAR5 nonEst NA NA NA NA
## FCHI8 - FGI4 nonEst NA NA NA NA
## FCHI8 - FMA7 nonEst NA NA NA NA
## FCHI8 - FSV1 nonEst NA NA NA NA
## FEAR5 - FGI4 -154.667 71.7 154 -2.158 0.2639
## FEAR5 - FMA7 -166.333 71.7 154 -2.320 0.1923
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 -11.667 71.7 154 -0.163 1.0000
## FGI4 - FSV1 nonEst NA NA NA NA
## FMA7 - FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 107.000 71.7 154 1.493 0.7488
## CNCH12 - FBO1 59.333 71.7 154 0.828 0.9817
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -48.000 71.7 154 -0.670 0.9940
## CNCH12 - FGI4 -155.000 71.7 154 -2.162 0.3223
## CNCH12 - FMA7 -36.000 71.7 154 -0.502 0.9988
## CNCH12 - FSV1 -48.000 71.7 154 -0.670 0.9940
## CNCH13 - FBO1 -47.667 71.7 154 -0.665 0.9943
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -155.000 71.7 154 -2.162 0.3223
## CNCH13 - FGI4 -262.000 71.7 154 -3.655 0.0064
## CNCH13 - FMA7 -143.000 71.7 154 -1.995 0.4222
## CNCH13 - FSV1 -155.000 71.7 154 -2.162 0.3223
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -107.333 71.7 154 -1.497 0.7461
## FBO1 - FGI4 -214.333 71.7 154 -2.990 0.0497
## FBO1 - FMA7 -95.333 71.7 154 -1.330 0.8369
## FBO1 - FSV1 -107.333 71.7 154 -1.497 0.7461
## FCHI8 - FEAR5 nonEst NA NA NA NA
## FCHI8 - FGI4 nonEst NA NA NA NA
## FCHI8 - FMA7 nonEst NA NA NA NA
## FCHI8 - FSV1 nonEst NA NA NA NA
## FEAR5 - FGI4 -107.000 71.7 154 -1.493 0.7488
## FEAR5 - FMA7 12.000 71.7 154 0.167 1.0000
## FEAR5 - FSV1 0.000 71.7 154 0.000 1.0000
## FGI4 - FMA7 119.000 71.7 154 1.660 0.6436
## FGI4 - FSV1 107.000 71.7 154 1.493 0.7488
## FMA7 - FSV1 -12.000 71.7 154 -0.167 1.0000
##
## mun = RiN:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 12.000 71.7 154 0.167 1.0000
## CNCH12 - FBO1 -23.667 71.7 154 -0.330 1.0000
## CNCH12 - FCHI8 131.000 71.7 154 1.827 0.6025
## CNCH12 - FEAR5 -95.000 71.7 154 -1.325 0.8880
## CNCH12 - FGI4 -47.667 71.7 154 -0.665 0.9978
## CNCH12 - FMA7 154.667 71.7 154 2.158 0.3834
## CNCH12 - FSV1 59.667 71.7 154 0.832 0.9910
## CNCH13 - FBO1 -35.667 71.7 154 -0.498 0.9997
## CNCH13 - FCHI8 119.000 71.7 154 1.660 0.7128
## CNCH13 - FEAR5 -107.000 71.7 154 -1.493 0.8103
## CNCH13 - FGI4 -59.667 71.7 154 -0.832 0.9910
## CNCH13 - FMA7 142.667 71.7 154 1.990 0.4919
## CNCH13 - FSV1 47.667 71.7 154 0.665 0.9978
## FBO1 - FCHI8 154.667 71.7 154 2.158 0.3834
## FBO1 - FEAR5 -71.333 71.7 154 -0.995 0.9746
## FBO1 - FGI4 -24.000 71.7 154 -0.335 1.0000
## FBO1 - FMA7 178.333 71.7 154 2.488 0.2086
## FBO1 - FSV1 83.333 71.7 154 1.162 0.9412
## FCHI8 - FEAR5 -226.000 71.7 154 -3.153 0.0399
## FCHI8 - FGI4 -178.667 71.7 154 -2.492 0.2066
## FCHI8 - FMA7 23.667 71.7 154 0.330 1.0000
## FCHI8 - FSV1 -71.333 71.7 154 -0.995 0.9746
## FEAR5 - FGI4 47.333 71.7 154 0.660 0.9979
## FEAR5 - FMA7 249.667 71.7 154 3.483 0.0146
## FEAR5 - FSV1 154.667 71.7 154 2.158 0.3834
## FGI4 - FMA7 202.333 71.7 154 2.823 0.0970
## FGI4 - FSV1 107.333 71.7 154 1.497 0.8079
## FMA7 - FSV1 -95.000 71.7 154 -1.325 0.8880
##
## mun = SnV:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -392.333 71.7 154 -5.473 <.0001
## CNCH12 - FBO1 -11.333 71.7 154 -0.158 1.0000
## CNCH12 - FCHI8 24.333 71.7 154 0.339 1.0000
## CNCH12 - FEAR5 -202.000 71.7 154 -2.818 0.0981
## CNCH12 - FGI4 -142.667 71.7 154 -1.990 0.4919
## CNCH12 - FMA7 -82.667 71.7 154 -1.153 0.9436
## CNCH12 - FSV1 119.333 71.7 154 1.665 0.7098
## CNCH13 - FBO1 381.000 71.7 154 5.315 <.0001
## CNCH13 - FCHI8 416.667 71.7 154 5.812 <.0001
## CNCH13 - FEAR5 190.333 71.7 154 2.655 0.1448
## CNCH13 - FGI4 249.667 71.7 154 3.483 0.0146
## CNCH13 - FMA7 309.667 71.7 154 4.320 0.0007
## CNCH13 - FSV1 511.667 71.7 154 7.138 <.0001
## FBO1 - FCHI8 35.667 71.7 154 0.498 0.9997
## FBO1 - FEAR5 -190.667 71.7 154 -2.660 0.1433
## FBO1 - FGI4 -131.333 71.7 154 -1.832 0.5993
## FBO1 - FMA7 -71.333 71.7 154 -0.995 0.9746
## FBO1 - FSV1 130.667 71.7 154 1.823 0.6057
## FCHI8 - FEAR5 -226.333 71.7 154 -3.157 0.0394
## FCHI8 - FGI4 -167.000 71.7 154 -2.330 0.2845
## FCHI8 - FMA7 -107.000 71.7 154 -1.493 0.8103
## FCHI8 - FSV1 95.000 71.7 154 1.325 0.8880
## FEAR5 - FGI4 59.333 71.7 154 0.828 0.9913
## FEAR5 - FMA7 119.333 71.7 154 1.665 0.7098
## FEAR5 - FSV1 321.333 71.7 154 4.483 0.0004
## FGI4 - FMA7 60.000 71.7 154 0.837 0.9907
## FGI4 - FSV1 262.000 71.7 154 3.655 0.0083
## FMA7 - FSV1 202.000 71.7 154 2.818 0.0981
##
## mun = Tam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -214.333 71.7 154 -2.990 0.0628
## CNCH12 - FBO1 -107.000 71.7 154 -1.493 0.8103
## CNCH12 - FCHI8 35.667 71.7 154 0.498 0.9997
## CNCH12 - FEAR5 -95.000 71.7 154 -1.325 0.8880
## CNCH12 - FGI4 -214.000 71.7 154 -2.985 0.0636
## CNCH12 - FMA7 -24.000 71.7 154 -0.335 1.0000
## CNCH12 - FSV1 -119.000 71.7 154 -1.660 0.7128
## CNCH13 - FBO1 107.333 71.7 154 1.497 0.8079
## CNCH13 - FCHI8 250.000 71.7 154 3.487 0.0144
## CNCH13 - FEAR5 119.333 71.7 154 1.665 0.7098
## CNCH13 - FGI4 0.333 71.7 154 0.005 1.0000
## CNCH13 - FMA7 190.333 71.7 154 2.655 0.1448
## CNCH13 - FSV1 95.333 71.7 154 1.330 0.8861
## FBO1 - FCHI8 142.667 71.7 154 1.990 0.4919
## FBO1 - FEAR5 12.000 71.7 154 0.167 1.0000
## FBO1 - FGI4 -107.000 71.7 154 -1.493 0.8103
## FBO1 - FMA7 83.000 71.7 154 1.158 0.9424
## FBO1 - FSV1 -12.000 71.7 154 -0.167 1.0000
## FCHI8 - FEAR5 -130.667 71.7 154 -1.823 0.6057
## FCHI8 - FGI4 -249.667 71.7 154 -3.483 0.0146
## FCHI8 - FMA7 -59.667 71.7 154 -0.832 0.9910
## FCHI8 - FSV1 -154.667 71.7 154 -2.158 0.3834
## FEAR5 - FGI4 -119.000 71.7 154 -1.660 0.7128
## FEAR5 - FMA7 71.000 71.7 154 0.990 0.9752
## FEAR5 - FSV1 -24.000 71.7 154 -0.335 1.0000
## FGI4 - FMA7 190.000 71.7 154 2.650 0.1463
## FGI4 - FSV1 95.000 71.7 154 1.325 0.8880
## FMA7 - FSV1 -95.000 71.7 154 -1.325 0.8880
##
## mun = ViG:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -107.667 71.7 154 -1.502 0.8054
## CNCH12 - FBO1 47.333 71.7 154 0.660 0.9979
## CNCH12 - FCHI8 59.000 71.7 154 0.823 0.9916
## CNCH12 - FEAR5 47.333 71.7 154 0.660 0.9979
## CNCH12 - FGI4 11.667 71.7 154 0.163 1.0000
## CNCH12 - FMA7 130.667 71.7 154 1.823 0.6057
## CNCH12 - FSV1 178.333 71.7 154 2.488 0.2086
## CNCH13 - FBO1 155.000 71.7 154 2.162 0.3805
## CNCH13 - FCHI8 166.667 71.7 154 2.325 0.2870
## CNCH13 - FEAR5 155.000 71.7 154 2.162 0.3805
## CNCH13 - FGI4 119.333 71.7 154 1.665 0.7098
## CNCH13 - FMA7 238.333 71.7 154 3.325 0.0240
## CNCH13 - FSV1 286.000 71.7 154 3.990 0.0025
## FBO1 - FCHI8 11.667 71.7 154 0.163 1.0000
## FBO1 - FEAR5 0.000 71.7 154 0.000 1.0000
## FBO1 - FGI4 -35.667 71.7 154 -0.498 0.9997
## FBO1 - FMA7 83.333 71.7 154 1.162 0.9412
## FBO1 - FSV1 131.000 71.7 154 1.827 0.6025
## FCHI8 - FEAR5 -11.667 71.7 154 -0.163 1.0000
## FCHI8 - FGI4 -47.333 71.7 154 -0.660 0.9979
## FCHI8 - FMA7 71.667 71.7 154 1.000 0.9739
## FCHI8 - FSV1 119.333 71.7 154 1.665 0.7098
## FEAR5 - FGI4 -35.667 71.7 154 -0.498 0.9997
## FEAR5 - FMA7 83.333 71.7 154 1.162 0.9412
## FEAR5 - FSV1 131.000 71.7 154 1.827 0.6025
## FGI4 - FMA7 119.000 71.7 154 1.660 0.7128
## FGI4 - FSV1 166.667 71.7 154 2.325 0.2870
## FMA7 - FSV1 47.667 71.7 154 0.665 0.9978
##
## mun = Yac:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -345.000 71.7 154 -4.813 0.0001
## CNCH12 - FBO1 -131.000 71.7 154 -1.827 0.6025
## CNCH12 - FCHI8 0.333 71.7 154 0.005 1.0000
## CNCH12 - FEAR5 -261.667 71.7 154 -3.650 0.0084
## CNCH12 - FGI4 -130.667 71.7 154 -1.823 0.6057
## CNCH12 - FMA7 -23.333 71.7 154 -0.325 1.0000
## CNCH12 - FSV1 36.000 71.7 154 0.502 0.9996
## CNCH13 - FBO1 214.000 71.7 154 2.985 0.0636
## CNCH13 - FCHI8 345.333 71.7 154 4.817 0.0001
## CNCH13 - FEAR5 83.333 71.7 154 1.162 0.9412
## CNCH13 - FGI4 214.333 71.7 154 2.990 0.0628
## CNCH13 - FMA7 321.667 71.7 154 4.487 0.0004
## CNCH13 - FSV1 381.000 71.7 154 5.315 <.0001
## FBO1 - FCHI8 131.333 71.7 154 1.832 0.5993
## FBO1 - FEAR5 -130.667 71.7 154 -1.823 0.6057
## FBO1 - FGI4 0.333 71.7 154 0.005 1.0000
## FBO1 - FMA7 107.667 71.7 154 1.502 0.8054
## FBO1 - FSV1 167.000 71.7 154 2.330 0.2845
## FCHI8 - FEAR5 -262.000 71.7 154 -3.655 0.0083
## FCHI8 - FGI4 -131.000 71.7 154 -1.827 0.6025
## FCHI8 - FMA7 -23.667 71.7 154 -0.330 1.0000
## FCHI8 - FSV1 35.667 71.7 154 0.498 0.9997
## FEAR5 - FGI4 131.000 71.7 154 1.827 0.6025
## FEAR5 - FMA7 238.333 71.7 154 3.325 0.0240
## FEAR5 - FSV1 297.667 71.7 154 4.152 0.0014
## FGI4 - FMA7 107.333 71.7 154 1.497 0.8079
## FGI4 - FSV1 166.667 71.7 154 2.325 0.2870
## FMA7 - FSV1 59.333 71.7 154 0.828 0.9913
##
## Note: contrasts are still on the ( scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## P value adjustment: tukey method for varying family sizes
# Modelo 1
modelo <- lmer(log(DE) ~ gen +
(1|mun) +
(1|mun:gen),
data = datos)
anova(modelo)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## gen 0.31414 0.044878 7 60.277 5.7344 4.112e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ranova(modelo)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## log(DE) ~ gen + (1 | mun) + (1 | mun:gen)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 162.33 -302.66
## (1 | mun) 10 157.39 -294.77 9.884 1 0.001667 **
## (1 | mun:gen) 10 143.65 -267.31 37.351 1 9.866e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(DE ~ 1 +
(1|gen) +
(1|mun) +
(1|gen:mun),
data = datos)
ranova(modelo_blup)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## DE ~ (1 | gen) + (1 | mun) + (1 | gen:mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -1420.4 2850.8
## (1 | gen) 4 -1428.2 2864.4 15.579 1 7.914e-05 ***
## (1 | mun) 4 -1425.1 2858.3 9.497 1 0.002059 **
## (1 | gen:mun) 4 -1441.6 2891.2 42.399 1 7.442e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CNCH12 3.161265
## CNCH13 119.189052
## FBO1 -21.875868
## FCHI8 -70.574404
## FEAR5 28.114380
## FGI4 49.146692
## FMA7 -35.878739
## FSV1 -71.282378
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 1014.8653
## CNCH13 1130.8931
## FBO1 989.8282
## FCHI8 941.1296
## FEAR5 1039.8184
## FGI4 1060.8507
## FMA7 975.8253
## FSV1 940.4217
#Blups Parcela
blups$mun
## (Intercept)
## CH -42.902787
## Gig -71.537662
## Htc 13.378490
## Jam 42.192867
## PtR 30.588936
## RiN -43.922151
## SnV -41.852532
## Tam 33.240653
## ViG -7.564817
## Yac 88.379005
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## CH 968.8012
## Gig 940.1664
## Htc 1025.0825
## Jam 1053.8969
## PtR 1042.2930
## RiN 967.7819
## SnV 969.8515
## Tam 1044.9447
## ViG 1004.1392
## Yac 1100.0830
#Blups interacción
blups$`gen:mun`
## (Intercept)
## CNCH12:CH 37.7704347
## CNCH12:Gig 119.8078956
## CNCH12:Htc 39.9999945
## CNCH12:Jam -32.9154606
## CNCH12:PtR 1.3727060
## CNCH12:RiN 3.9375921
## CNCH12:SnV -75.7389147
## CNCH12:Tam -61.1967764
## CNCH12:ViG 29.2444250
## CNCH12:Yac -57.8887952
## CNCH13:CH -3.4019581
## CNCH13:Gig -165.1115160
## CNCH13:Htc 112.0567363
## CNCH13:Jam 203.9931611
## CNCH13:PtR -161.5514445
## CNCH13:RiN -89.5880565
## CNCH13:SnV 126.1052007
## CNCH13:Tam 10.6164619
## CNCH13:ViG 23.1365384
## CNCH13:Yac 109.3778211
## FBO1:CH -22.1043040
## FBO1:Gig -36.2507317
## FBO1:Htc -54.4522434
## FBO1:Jam 11.4293052
## FBO1:PtR -23.6810301
## FBO1:RiN 39.5162313
## FBO1:SnV -49.1699056
## FBO1:Tam 35.2577417
## FBO1:ViG 12.9568155
## FBO1:Yac 56.0979760
## FCHI8:CH 65.8237455
## FCHI8:Gig -69.8309363
## FCHI8:Htc -18.8774497
## FCHI8:RiN -37.8946060
## FCHI8:SnV -39.6499879
## FCHI8:Tam -33.3869691
## FCHI8:ViG 40.0089862
## FCHI8:Yac -4.2676153
## FEAR5:CH -84.9210875
## FEAR5:Gig 57.9922522
## FEAR5:Htc -21.5721454
## FEAR5:Jam -120.7859801
## FEAR5:PtR 18.2086980
## FEAR5:RiN 55.1075798
## FEAR5:SnV 53.5957014
## FEAR5:Tam -10.0267887
## FEAR5:ViG -23.5615883
## FEAR5:Yac 115.0329502
## FGI4:CH -39.1660308
## FGI4:Gig -44.3028283
## FGI4:Htc 41.2281574
## FGI4:Jam -23.1646746
## FGI4:PtR 81.0090008
## FGI4:RiN 5.1657550
## FGI4:SnV -5.1122499
## FGI4:Tam 61.5396407
## FGI4:ViG -12.8710378
## FGI4:Yac 3.9717431
## FMA7:CH 40.2346930
## FMA7:Gig 0.2768927
## FMA7:Htc -61.5117483
## FMA7:Jam 47.4699226
## FMA7:PtR 56.1902200
## FMA7:RiN -80.5289046
## FMA7:SnV 13.1690915
## FMA7:Tam -15.1453889
## FMA7:ViG -37.6898186
## FMA7:Yac -12.3244147
## FSV1:CH -81.7092093
## FSV1:Gig -8.4378750
## FSV1:Htc -9.5941407
## FSV1:PtR 90.8190781
## FSV1:RiN 14.7323287
## FSV1:SnV -108.5313073
## FSV1:Tam 80.1158445
## FSV1:ViG -46.6480898
## FSV1:Yac -29.8053090
fixef(modelo_blup)[1] + blups$`gen:mun`
## (Intercept)
## CNCH12:CH 1049.4745
## CNCH12:Gig 1131.5119
## CNCH12:Htc 1051.7040
## CNCH12:Jam 978.7886
## CNCH12:PtR 1013.0767
## CNCH12:RiN 1015.6416
## CNCH12:SnV 935.9651
## CNCH12:Tam 950.5073
## CNCH12:ViG 1040.9485
## CNCH12:Yac 953.8152
## CNCH13:CH 1008.3021
## CNCH13:Gig 846.5925
## CNCH13:Htc 1123.7608
## CNCH13:Jam 1215.6972
## CNCH13:PtR 850.1526
## CNCH13:RiN 922.1160
## CNCH13:SnV 1137.8092
## CNCH13:Tam 1022.3205
## CNCH13:ViG 1034.8406
## CNCH13:Yac 1121.0818
## FBO1:CH 989.5997
## FBO1:Gig 975.4533
## FBO1:Htc 957.2518
## FBO1:Jam 1023.1333
## FBO1:PtR 988.0230
## FBO1:RiN 1051.2203
## FBO1:SnV 962.5341
## FBO1:Tam 1046.9618
## FBO1:ViG 1024.6608
## FBO1:Yac 1067.8020
## FCHI8:CH 1077.5278
## FCHI8:Gig 941.8731
## FCHI8:Htc 992.8266
## FCHI8:RiN 973.8094
## FCHI8:SnV 972.0540
## FCHI8:Tam 978.3171
## FCHI8:ViG 1051.7130
## FCHI8:Yac 1007.4364
## FEAR5:CH 926.7829
## FEAR5:Gig 1069.6963
## FEAR5:Htc 990.1319
## FEAR5:Jam 890.9180
## FEAR5:PtR 1029.9127
## FEAR5:RiN 1066.8116
## FEAR5:SnV 1065.2997
## FEAR5:Tam 1001.6772
## FEAR5:ViG 988.1424
## FEAR5:Yac 1126.7370
## FGI4:CH 972.5380
## FGI4:Gig 967.4012
## FGI4:Htc 1052.9322
## FGI4:Jam 988.5394
## FGI4:PtR 1092.7130
## FGI4:RiN 1016.8698
## FGI4:SnV 1006.5918
## FGI4:Tam 1073.2437
## FGI4:ViG 998.8330
## FGI4:Yac 1015.6758
## FMA7:CH 1051.9387
## FMA7:Gig 1011.9809
## FMA7:Htc 950.1923
## FMA7:Jam 1059.1740
## FMA7:PtR 1067.8942
## FMA7:RiN 931.1751
## FMA7:SnV 1024.8731
## FMA7:Tam 996.5586
## FMA7:ViG 974.0142
## FMA7:Yac 999.3796
## FSV1:CH 929.9948
## FSV1:Gig 1003.2662
## FSV1:Htc 1002.1099
## FSV1:PtR 1102.5231
## FSV1:RiN 1026.4364
## FSV1:SnV 903.1727
## FSV1:Tam 1091.8199
## FSV1:ViG 965.0559
## FSV1:Yac 981.8987
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 3.161265
## 2 CNCH13 119.189052
## 3 FBO1 -21.875868
## 4 FCHI8 -70.574404
## 5 FEAR5 28.114380
## 6 FGI4 49.146692
## 7 FMA7 -35.878739
## 8 FSV1 -71.282378
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 CH -42.902787
## 2 Gig -71.537662
## 3 Htc 13.378490
## 4 Jam 42.192867
## 5 PtR 30.588936
## 6 RiN -43.922151
## 7 SnV -41.852532
## 8 Tam 33.240653
## 9 ViG -7.564817
## 10 Yac 88.379005
#Tabla blup_gen_mun
blup_gen_mun <- ranef(modelo_blup)$`gen:mun` %>%
tibble::rownames_to_column("gen:mun") %>%
rename(BLUP = `(Intercept)`)
blup_gen_mun
## gen:mun BLUP
## 1 CNCH12:CH 37.7704347
## 2 CNCH12:Gig 119.8078956
## 3 CNCH12:Htc 39.9999945
## 4 CNCH12:Jam -32.9154606
## 5 CNCH12:PtR 1.3727060
## 6 CNCH12:RiN 3.9375921
## 7 CNCH12:SnV -75.7389147
## 8 CNCH12:Tam -61.1967764
## 9 CNCH12:ViG 29.2444250
## 10 CNCH12:Yac -57.8887952
## 11 CNCH13:CH -3.4019581
## 12 CNCH13:Gig -165.1115160
## 13 CNCH13:Htc 112.0567363
## 14 CNCH13:Jam 203.9931611
## 15 CNCH13:PtR -161.5514445
## 16 CNCH13:RiN -89.5880565
## 17 CNCH13:SnV 126.1052007
## 18 CNCH13:Tam 10.6164619
## 19 CNCH13:ViG 23.1365384
## 20 CNCH13:Yac 109.3778211
## 21 FBO1:CH -22.1043040
## 22 FBO1:Gig -36.2507317
## 23 FBO1:Htc -54.4522434
## 24 FBO1:Jam 11.4293052
## 25 FBO1:PtR -23.6810301
## 26 FBO1:RiN 39.5162313
## 27 FBO1:SnV -49.1699056
## 28 FBO1:Tam 35.2577417
## 29 FBO1:ViG 12.9568155
## 30 FBO1:Yac 56.0979760
## 31 FCHI8:CH 65.8237455
## 32 FCHI8:Gig -69.8309363
## 33 FCHI8:Htc -18.8774497
## 34 FCHI8:RiN -37.8946060
## 35 FCHI8:SnV -39.6499879
## 36 FCHI8:Tam -33.3869691
## 37 FCHI8:ViG 40.0089862
## 38 FCHI8:Yac -4.2676153
## 39 FEAR5:CH -84.9210875
## 40 FEAR5:Gig 57.9922522
## 41 FEAR5:Htc -21.5721454
## 42 FEAR5:Jam -120.7859801
## 43 FEAR5:PtR 18.2086980
## 44 FEAR5:RiN 55.1075798
## 45 FEAR5:SnV 53.5957014
## 46 FEAR5:Tam -10.0267887
## 47 FEAR5:ViG -23.5615883
## 48 FEAR5:Yac 115.0329502
## 49 FGI4:CH -39.1660308
## 50 FGI4:Gig -44.3028283
## 51 FGI4:Htc 41.2281574
## 52 FGI4:Jam -23.1646746
## 53 FGI4:PtR 81.0090008
## 54 FGI4:RiN 5.1657550
## 55 FGI4:SnV -5.1122499
## 56 FGI4:Tam 61.5396407
## 57 FGI4:ViG -12.8710378
## 58 FGI4:Yac 3.9717431
## 59 FMA7:CH 40.2346930
## 60 FMA7:Gig 0.2768927
## 61 FMA7:Htc -61.5117483
## 62 FMA7:Jam 47.4699226
## 63 FMA7:PtR 56.1902200
## 64 FMA7:RiN -80.5289046
## 65 FMA7:SnV 13.1690915
## 66 FMA7:Tam -15.1453889
## 67 FMA7:ViG -37.6898186
## 68 FMA7:Yac -12.3244147
## 69 FSV1:CH -81.7092093
## 70 FSV1:Gig -8.4378750
## 71 FSV1:Htc -9.5941407
## 72 FSV1:PtR 90.8190781
## 73 FSV1:RiN 14.7323287
## 74 FSV1:SnV -108.5313073
## 75 FSV1:Tam 80.1158445
## 76 FSV1:ViG -46.6480898
## 77 FSV1:Yac -29.8053090
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 1009.7329 1009.7329 1009.7329 1084.5883 1084.5883 1084.5883 924.8211
## [8] 924.8211 924.8211 964.0506 964.0506 964.0506 911.9945 911.9945
## [15] 911.9945 978.7819 978.7819 978.7819 973.1572 973.1572 973.1572
## [22] 815.8097 815.8097 815.8097 1063.1355 1063.1355 1063.1355 894.2439
## [29] 894.2439 894.2439 882.0398 882.0398 882.0398 799.7610 799.7610
## [36] 799.7610 1026.2730 1026.2730 1026.2730 945.0102 945.0102 945.0102
## [43] 904.5645 904.5645 904.5645 860.4461 860.4461 860.4461 1068.2438
## [50] 1068.2438 1068.2438 1256.3283 1256.3283 1256.3283 948.7544 948.7544
## [57] 948.7544 935.6307 935.6307 935.6307 1031.6248 1031.6248 1031.6248
## [64] 1115.4574 1115.4574 1115.4574 927.6920 927.6920 927.6920 944.2060
## [71] 944.2060 944.2060 1024.1427 1024.1427 1024.1427 1377.0791 1377.0791
## [78] 1377.0791 1043.4503 1043.4503 1043.4503 961.2253 961.2253 961.2253
## [85] 1079.8789 1079.8789 1079.8789 1065.4881 1065.4881 1065.4881 1046.8269
## [92] 1046.8269 1046.8269 999.9306 999.9306 999.9306 996.7361 996.7361
## [99] 996.7361 1088.6160 1088.6160 1088.6160 1172.4487 1172.4487 1172.4487
## [106] 1062.6044 1062.6044 1062.6044 1061.8297 1061.8297 1061.8297 997.3829
## [113] 997.3829 997.3829 974.8807 974.8807 974.8807 985.4222 985.4222
## [120] 985.4222 859.3129 859.3129 859.3129 1051.0038 1051.0038 1051.0038
## [127] 1022.0943 1022.0943 1022.0943 851.3742 851.3742 851.3742 911.2318
## [134] 911.2318 911.2318 897.2738 897.2738 897.2738 1215.1457 1215.1457
## [141] 1215.1457 898.8057 898.8057 898.8057 859.6271 859.6271 859.6271
## [148] 1051.5616 1051.5616 1051.5616 1013.8859 1013.8859 1013.8859 947.1418
## [155] 947.1418 947.1418 790.0378 790.0378 790.0378 986.9092 986.9092
## [162] 986.9092 1174.7502 1174.7502 1174.7502 1058.3266 1058.3266 1058.3266
## [169] 940.9833 940.9833 940.9833 1063.0323 1063.0323 1063.0323 1155.6310
## [176] 1155.6310 1155.6310 993.9206 993.9206 993.9206 1053.7781 1053.7781
## [183] 1053.7781 1036.5449 1036.5449 1036.5449 1146.4648 1146.4648 1146.4648
## [190] 995.2202 995.2202 995.2202 973.5738 973.5738 973.5738 1008.6920
## [197] 1008.6920 1008.6920 1040.4149 1040.4149 1040.4149 930.5707 930.5707
## [204] 930.5707 886.2087 886.2087 886.2087 1045.3555 1045.3555 1045.3555
## [211] 1328.6499 1328.6499 1328.6499 1134.3051 1134.3051 1134.3051 1025.2410
## [218] 1025.2410 1025.2410 1243.2304 1243.2304 1243.2304 1153.2015 1153.2015
## [225] 1153.2015 1051.8799 1051.8799 1051.8799 998.9953 998.9953 998.9953
#Visualizar Blups gen
ggplot(blup_gen, aes(x=reorder(gen, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Genotipo") +
coord_flip()
#Visualizar Blups mun
ggplot(blup_mun, aes(x=reorder(mun, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Municipio") +
coord_flip()
#Visualizar Blups gen:mun
ggplot(blup_gen_mun, aes(x=reorder(`gen:mun`, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Gen * Mun") +
coord_flip()
##Componentes DE varianza-heredabilidades
vc <- as.data.frame(VarCorr(modelo_blup))
vc
## grp var1 var2 vcov sdcor
## 1 gen:mun (Intercept) <NA> 6964.978 83.45644
## 2 mun (Intercept) <NA> 3416.077 58.44722
## 3 gen (Intercept) <NA> 5011.981 70.79534
## 4 Residual <NA> <NA> 7708.259 87.79669
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## gen:mun (Intercept) 83.456
## mun (Intercept) 58.447
## gen (Intercept) 70.795
## Residual 87.797
varG <- vc$vcov[vc$grp=="gen"]
varGE <- vc$vcov[vc$grp=="gen:mun"]
varE <- vc$vcov[vc$grp=="Residual"]
e <- 10
r <- 4
# Heredabilidad genotipos y plasticidad
#H2 genotipos
H2g <- varG / (varG + varGE/e + varE/(r*e))
H2g
## [1] 0.8493177
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## [1] 1.180268
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CNCH12 3.161265
## CNCH13 119.189052
## FBO1 -21.875868
## FCHI8 -70.574404
## FEAR5 28.114380
## FGI4 49.146692
## FMA7 -35.878739
## FSV1 -71.282378
##Predicho DE carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## CNCH13 119.189052 1130.8931
## FGI4 49.146692 1060.8507
## FEAR5 28.114380 1039.8184
## CNCH12 3.161265 1014.8653
## FBO1 -21.875868 989.8282
## FMA7 -35.878739 975.8253
## FCHI8 -70.574404 941.1296
## FSV1 -71.282378 940.4217
# Visualización ranking predichos
blup_gen$gen <- rownames(blup_gen)
ggplot(blup_gen, aes(x=reorder(gen,pred), y=pred))+
geom_point(size=3)+
coord_flip()+
ylab("Fenotipo predicho (BLUP)")+
xlab("Genotipo")
### Análisis G×E (estabilidad)
## matriz genotipo × parcela.
mat <- datos %>%
group_by(gen,mun) %>%
summarise(DE=mean(DE)) %>%
pivot_wider(names_from=mun,
values_from=DE)
## `summarise()` has grouped output by 'gen'. You can override using the `.groups`
## argument.
##Convertir a matriz
mat2 <- as.matrix(mat[,-1])
rownames(mat2) <- mat$gen
### Estabilidad con Metan
modelo_metan <- gge(datos, mun, gen, DE)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $DE
## $coordgen
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 41.48105 -78.81267 -290.01427 116.361314 -99.39826 -70.70874
## [2,] -310.22376 117.08579 14.18520 7.873116 -35.94789 -63.84142
## [3,] 35.24082 21.91989 81.92205 -112.201389 -156.24603 -61.29307
## [4,] 99.85708 176.85688 -60.04062 -121.014791 -48.07850 219.48626
## [5,] -18.75608 -246.77569 27.30343 -207.489747 51.59921 -18.58862
## [6,] -54.42631 -118.75828 83.25829 185.868973 60.42577 223.29152
## [7,] 59.86420 101.21135 -40.30235 -7.860260 297.59854 -100.26998
## [8,] 146.96300 27.27273 183.68825 138.462783 -69.95283 -128.07595
## [,7] [,8]
## [1,] 0.02652862 131.1815
## [2,] -69.86442971 131.1815
## [3,] 266.96001662 131.1815
## [4,] -102.34783027 131.1815
## [5,] -111.37577854 131.1815
## [6,] 91.67735537 131.1815
## [7,] 79.54624055 131.1815
## [8,] -154.62210263 131.1815
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -153.65034 82.54215 -153.4147105 7.331290 30.570206 49.757498
## [2,] 14.21652 -236.23843 -155.4667890 29.939605 17.428678 -74.079309
## [3,] -324.76475 -49.75158 -45.6947251 109.275259 -52.231692 26.156165
## [4,] -377.57708 187.39414 13.7885135 79.461417 5.415977 -69.064938
## [5,] 59.14935 -148.55836 55.6069652 113.117369 98.812100 39.300084
## [6,] -83.35684 -189.84698 30.9126780 -12.922251 -83.659156 6.982388
## [7,] -398.56894 -53.35775 0.2599472 -74.943049 100.498075 10.816962
## [8,] -163.82670 -59.42938 148.9074510 81.800340 -16.325422 0.810978
## [9,] -198.00783 -10.47932 -76.9821513 -7.315532 -62.518592 46.612219
## [10,] -318.99036 -95.68793 70.3864943 -126.984963 -5.375556 -9.551618
## [,7] [,8]
## [1,] 41.324998 -6.027151e-14
## [2,] 6.318039 -6.202640e-14
## [3,] -58.149986 7.114386e-15
## [4,] 22.449234 1.166277e-13
## [5,] 14.004476 1.562564e-13
## [6,] 30.390156 9.545450e-14
## [7,] -16.939928 -6.728357e-14
## [8,] 26.380600 -2.371368e-13
## [9,] 19.574299 2.385757e-14
## [10,] 3.128985 7.605453e-14
##
## $eigenvalues
## [1] 7.803541e+02 4.170970e+02 2.951031e+02 2.462809e+02 1.872493e+02
## [6] 1.318807e+02 9.015869e+01 3.489622e-13
##
## $totalvar
## [1] 991246
##
## $varexpl
## [1] 61.43 17.55 8.79 6.12 3.54 1.75 0.82 0.00
##
## $labelgen
## [1] "CNCH12" "CNCH13" "FBO1" "FCHI8" "FEAR5" "FGI4" "FMA7" "FSV1"
##
## $labelenv
## [1] "CH" "Gig" "Htc" "Jam" "PtR" "RiN" "SnV" "Tam" "ViG" "Yac"
##
## $labelaxes
## [1] "PC1" "PC2" "PC3" "PC4" "PC5" "PC6" "PC7" "PC8"
##
## $ge_mat
## CH Gig Htc Jam PtR RiN
## CNCH12 69.83333 192.12500 53.250000 -56.162020 -3.780505 23.87500
## CNCH13 129.50000 -81.87500 267.916667 384.171314 -110.780505 11.87500
## FBO1 -37.16667 -46.54167 -101.083333 -20.495353 -63.113838 47.54167
## FCHI8 34.50000 -141.20833 -101.083333 -88.191963 -94.203134 -107.12500
## FEAR5 -73.16667 132.45833 -6.083333 -151.495353 44.219495 118.87500
## FGI4 10.50000 13.45833 100.916667 3.171314 151.219495 71.54167
## FMA7 34.16667 -10.54167 -124.750000 14.837980 32.219495 -130.79167
## FSV1 -168.16667 -57.87500 -89.083333 -85.835919 44.219495 -35.79167
## SnV Tam ViG Yac
## CNCH12 -85.91667 -92.208333 45.83333 -106.91667
## CNCH13 306.41667 122.125000 153.50000 238.08333
## FBO1 -74.58333 14.791667 -1.50000 24.08333
## FCHI8 -110.25000 -127.875000 -13.16667 -107.25000
## FEAR5 116.08333 2.791667 -1.50000 154.75000
## FGI4 56.75000 121.791667 34.16667 23.75000
## FMA7 -3.25000 -68.208333 -84.83333 -83.58333
## FSV1 -205.25000 26.791667 -132.50000 -142.91667
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 0.002695147
##
## $grand_mean
## [1] 1011.473
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 1015.4667 1153.5667 985.6667 925.8881 1045.1667 1070.2000 969.0000 926.8326
##
## $mean_env
## CH Gig Htc Jam PtR RiN SnV Tam
## 953.8333 915.2083 1029.7500 1068.1620 1051.1138 952.4583 955.2500 1056.5417
## ViG Yac
## 1001.5000 1130.9167
##
## $scale_val
## CH Gig Htc Jam PtR RiN SnV Tam
## 91.90799 111.42482 135.63089 164.52899 86.98141 86.25874 159.38480 92.73532
## ViG Yac
## 85.99096 137.38392
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración DE variables
##Blup gen
blup_gen <- ranef(modelo_blup)$gen
media <- fixef(modelo_blup)[1]
blup_gen$BLUP_C <- media + blup_gen[,1]
blup_gen$gen <- rownames(blup_gen)
blup_gen <- blup_gen[,c("gen","BLUP_C")]
blup_gen
## gen BLUP_C
## CNCH12 CNCH12 1014.8653
## CNCH13 CNCH13 1130.8931
## FBO1 FBO1 989.8282
## FCHI8 FCHI8 941.1296
## FEAR5 FEAR5 1039.8184
## FGI4 FGI4 1060.8507
## FMA7 FMA7 975.8253
## FSV1 FSV1 940.4217
##Plasticidad usando Fisher environments (joint regression)
#índice (creando valores DE x para definir env = promedio DE tasas en c/parcela)
indice_env <- datos %>%
group_by(mun) %>%
summarise(env = mean(DE))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas DE reacción joint regression env
ggplot(datos, aes(x = env, y = DE,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (local)",
y = expression(Fenotipo)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
#visualización Normas DE reacción clima local
ggplot(datos, aes(x = E, y = DE,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (Estrés)",
y = expression(Fenotipo)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
## plasticidad joint
# modelo factores fijos
mod_plas_lm <- lm(DE ~ gen*env,
data=datos)
emtrends(mod_plas_lm, "gen", var = "env")
## gen env.trend SE df lower.CL upper.CL
## CNCH12 0.0487 0.300 215 -0.54274 0.64
## CNCH13 1.8223 0.300 215 1.23088 2.41
## FBO1 1.0585 0.300 215 0.46714 1.65
## FCHI8 0.8772 0.345 215 0.19773 1.56
## FEAR5 0.5942 0.300 215 0.00277 1.19
## FGI4 1.0719 0.300 215 0.48051 1.66
## FMA7 0.8742 0.300 215 0.28284 1.47
## FSV1 1.2764 0.327 215 0.63097 1.92
##
## Confidence level used: 0.95
# modelo blup factores aleatorios
modelo_plasticidad <- lmer(DE ~ env +
(env|gen) +
(1|mun),
data=datos)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 1.49264 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
pend <- ranef(modelo_plasticidad)$gen
pend$gen <- rownames(pend)
plasticidad <- pend[,c("gen","env")]
colnames(plasticidad)[2] <- "Pendiente"
#plasticidad Estrés
# modelo factores fijos
mod_plas2_lm <- lm(DE ~ gen*E,
data=datos)
emtrends(mod_plas2_lm, "gen", var = "E")
## gen E.trend SE df lower.CL upper.CL
## CNCH12 263.4 418 215 -561 1087.4
## CNCH13 -571.8 418 215 -1396 252.2
## FBO1 -351.7 418 215 -1176 472.3
## FCHI8 -403.9 442 215 -1275 467.5
## FEAR5 -511.8 418 215 -1336 312.2
## FGI4 48.1 418 215 -776 872.2
## FMA7 -735.6 418 215 -1560 88.5
## FSV1 356.3 424 215 -479 1191.3
##
## Confidence level used: 0.95
#Modelo factores aleatorios
modelo_plasticidad2 <- lmer(DE ~ E +
(E|gen) +
(1|mun),
data=datos)
pend2 <- ranef(modelo_plasticidad2)$gen
pend2$gen <- rownames(pend)
plasticidad2 <- pend2[,c("gen","E")]
colnames(plasticidad2)[2] <- "Pendiente2"
##Tabla selección MGIDI 1
tabla_sel <- blup_gen %>%
left_join(plasticidad, by="gen")
mgidi_mod <- mgidi(tabla_sel,
ideotype = c("h, h"))
##
## -------------------------------------------------------------------------------
## Principal Component Analysis
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## PC Eigenvalues `Variance (%)` `Cum. variance (%)`
## <chr> <dbl> <dbl> <dbl>
## 1 PC1 1.79 89.7 89.7
## 2 PC2 0.21 10.3 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C 0.95 0.9 0.1
## 2 Pendiente 0.95 0.9 0.1
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.8973986
## -------------------------------------------------------------------------------
## Selection differential
## -------------------------------------------------------------------------------
## # A tibble: 2 × 8
## VAR Factor Xo Xs SD SDperc sense goal
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 BLUP_C FA1 1.01e+ 3 1131. 119. 1.18e 1 increase 100
## 2 Pendiente FA1 1.30e-13 0.317 0.317 2.45e14 increase 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## CNCH13
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 CNCH13 0.0000000001
## 2 FGI4 1.40
## 3 FEAR5 1.99
## 4 FBO1 2.32
## 5 FMA7 2.63
## 6 CNCH12 2.68
## 7 FSV1 2.83
## 8 FCHI8 3.05
#SLAáfico Selección 1
plot(mgidi_mod)
##Tabla selección MGIDI 2 estrés
tabla_sel2 <- blup_gen %>%
left_join(plasticidad, by="gen") %>%
left_join(plasticidad2, by="gen")
mgidi_mod2<-mgidi(tabla_sel2,
ideotype = c("h, h, l"))
##
## -------------------------------------------------------------------------------
## Principal Component Analysis
## -------------------------------------------------------------------------------
## # A tibble: 3 × 4
## PC Eigenvalues `Variance (%)` `Cum. variance (%)`
## <chr> <dbl> <dbl> <dbl>
## 1 PC1 2.54 84.8 84.8
## 2 PC2 0.28 9.23 94.0
## 3 PC3 0.18 5.99 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 3 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C -0.94 0.88 0.12
## 2 Pendiente -0.91 0.83 0.17
## 3 Pendiente2 -0.91 0.83 0.17
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.8478191
## -------------------------------------------------------------------------------
## Selection differential
## -------------------------------------------------------------------------------
## # A tibble: 3 × 8
## VAR Factor Xo Xs SD SDperc sense goal
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 BLUP_C FA1 1.01e+ 3 1131. 119. 1.18e 1 increase 100
## 2 Pendiente FA1 1.30e-13 0.317 0.317 2.45e14 increase 100
## 3 Pendiente2 FA1 4.19e-12 -159. -159. -3.79e15 decrease 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## CNCH13
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod2$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 CNCH13 0.0000000001
## 2 FGI4 1.57
## 3 FEAR5 1.75
## 4 FBO1 2.26
## 5 FMA7 2.31
## 6 CNCH12 2.74
## 7 FCHI8 2.87
## 8 FSV1 3.16
#SLAáfico Selección 1
plot(mgidi_mod2)
