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 9 1.23802 0.137558 17.5769 < 2.2e-16 ***
## mun 9 0.89157 0.099063 12.6581 6.164e-15 ***
## gen:mun 58 1.56785 0.027032 3.4541 5.309e-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 9 1381570 153508 19.915 < 2.2e-16 ***
## mun 9 903241 100360 13.020 2.569e-15 ***
## gen:mun 58 1651941 28482 3.695 5.904e-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
## CHCH13 nonEst NA NA NA NA
## CNCH12 1015 16 154 984 1047
## CNCH13 nonEst NA NA NA NA
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 nonEst NA NA NA NA
## CNCH12 - CNH13 nonEst NA NA NA NA
## CNCH12 - FBO1 29.8 22.7 154 1.315 0.6826
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -29.7 22.7 154 -1.310 0.6853
## CNCH12 - FGI4 -54.7 22.7 154 -2.414 0.1169
## CNCH12 - FMA7 46.5 22.7 154 2.050 0.2476
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - CNH13 nonEst NA NA NA NA
## CNCH13 - FBO1 nonEst NA NA NA NA
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 nonEst NA NA NA NA
## CNCH13 - FGI4 nonEst NA NA NA NA
## CNCH13 - FMA7 nonEst NA NA NA NA
## CNCH13 - FSV1 nonEst NA NA NA NA
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -59.5 22.7 154 -2.625 0.0708
## FBO1 - FGI4 -84.5 22.7 154 -3.729 0.0025
## FBO1 - FMA7 16.7 22.7 154 0.735 0.9479
## 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 0.8041
## FEAR5 - FMA7 76.2 22.7 154 3.360 0.0086
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 101.2 22.7 154 4.464 0.0001
## 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 5 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 70 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 70 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 asymp.LCL asymp.UCL
## CH nonEst NA NA NA NA
## Gig nonEst NA NA NA NA
## Htc nonEst NA NA NA NA
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
## RiN nonEst NA NA NA NA
## SnV nonEst NA NA NA NA
## Tam nonEst NA NA NA NA
## ViG nonEst NA NA NA NA
## Yac nonEst NA NA NA NA
##
## 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 z.ratio p.value
## CH - Gig nonEst NA NA NA NA
## CH - Htc nonEst NA NA NA NA
## CH - Jam nonEst NA NA NA NA
## CH - PtR nonEst NA NA NA NA
## CH - RiN nonEst NA NA NA NA
## CH - SnV nonEst NA NA NA NA
## CH - Tam nonEst NA NA NA NA
## CH - ViG nonEst NA NA NA NA
## CH - Yac nonEst NA NA NA NA
## Gig - Htc nonEst NA NA NA NA
## Gig - Jam nonEst NA NA NA NA
## Gig - PtR nonEst NA NA NA NA
## Gig - RiN nonEst NA NA NA NA
## Gig - SnV nonEst NA NA NA NA
## Gig - Tam nonEst NA NA NA NA
## Gig - ViG nonEst NA NA NA NA
## Gig - Yac nonEst NA NA NA NA
## Htc - Jam nonEst NA NA NA NA
## Htc - PtR nonEst NA NA NA NA
## Htc - RiN nonEst NA NA NA NA
## Htc - SnV nonEst NA NA NA NA
## Htc - Tam nonEst NA NA NA NA
## Htc - ViG nonEst NA NA NA NA
## Htc - Yac nonEst NA NA NA NA
## 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 nonEst NA NA NA NA
## RiN - Tam nonEst NA NA NA NA
## RiN - ViG nonEst NA NA NA NA
## RiN - Yac nonEst NA NA NA NA
## SnV - Tam nonEst NA NA NA NA
## SnV - ViG nonEst NA NA NA NA
## SnV - Yac nonEst NA NA NA NA
## Tam - ViG nonEst NA NA NA NA
## Tam - Yac nonEst NA NA NA NA
## ViG - Yac nonEst NA NA NA NA
##
## 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 2 estimates
pwpp(m, type = "response")
## Warning in min(pvtmp): ningún argumento finito para min; retornando Inf
## Warning in max(pvtmp): ningun argumento finito para max; retornando -Inf
## Warning: Removed 90 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 90 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
## CHCH13 nonEst NA NA NA NA
## CNCH12 1024 50.7 154 924 1124
## CNCH13 1083 50.7 154 983 1183
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 nonEst NA NA NA NA
## CNCH12 1107 50.7 154 1007 1207
## CNCH13 833 50.7 154 733 933
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 nonEst NA NA NA NA
## CNCH12 1083 50.7 154 983 1183
## CNCH13 1298 50.7 154 1198 1398
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 nonEst NA NA NA NA
## CNCH12 1012 50.7 154 912 1112
## CNCH13 1452 50.7 154 1352 1552
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 nonEst NA NA NA NA
## CNCH12 1047 50.7 154 947 1147
## CNCH13 940 50.7 154 840 1040
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 964 50.7 154 864 1064
## CNCH12 976 50.7 154 876 1076
## CNCH13 nonEst NA NA NA NA
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 nonEst NA NA NA NA
## CNCH12 869 50.7 154 769 969
## CNCH13 1262 50.7 154 1162 1362
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 nonEst NA NA NA NA
## CNCH12 964 50.7 154 864 1064
## CNCH13 nonEst NA NA NA NA
## CNH13 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
## CHCH13 nonEst NA NA NA NA
## CNCH12 1047 50.7 154 947 1147
## CNCH13 1155 50.7 154 1055 1255
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 nonEst NA NA NA NA
## CNCH12 1024 50.7 154 924 1124
## CNCH13 1369 50.7 154 1269 1469
## CNH13 nonEst NA NA NA NA
## 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 -59.667 71.7 154 -0.832 0.9910
## CNCH12 - CNH13 nonEst NA NA NA NA
## 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 - CNH13 nonEst NA NA NA NA
## 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
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - FSV1 nonEst NA NA NA NA
## 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 274.000 71.7 154 3.822 0.0046
## CNCH12 - CNH13 nonEst NA NA NA NA
## 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 - CNH13 nonEst NA NA NA NA
## 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
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - FSV1 nonEst NA NA NA NA
## 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 -214.667 71.7 154 -2.995 0.0620
## CNCH12 - CNH13 nonEst NA NA NA NA
## 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 - CNH13 nonEst NA NA NA NA
## 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
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - FSV1 nonEst NA NA NA NA
## 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 -440.333 71.7 154 -6.143 <.0001
## CNCH12 - CNH13 nonEst NA NA NA NA
## 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 - CNH13 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
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 107.000 71.7 154 1.493 0.7488
## CNCH12 - CNH13 nonEst NA NA NA NA
## 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 - CNH13 nonEst NA NA NA NA
## 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
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - FSV1 nonEst NA NA NA NA
## 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
## CHCH13 - CNCH12 -12.000 71.7 154 -0.167 1.0000
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 -35.667 71.7 154 -0.498 0.9997
## CHCH13 - FCHI8 119.000 71.7 154 1.660 0.7128
## CHCH13 - FEAR5 -107.000 71.7 154 -1.493 0.8103
## CHCH13 - FGI4 -59.667 71.7 154 -0.832 0.9910
## CHCH13 - FMA7 142.667 71.7 154 1.990 0.4919
## CHCH13 - FSV1 47.667 71.7 154 0.665 0.9978
## CNCH12 - CNCH13 nonEst NA NA NA NA
## CNCH12 - CNH13 nonEst NA NA NA NA
## 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 - CNH13 nonEst NA NA NA NA
## CNCH13 - FBO1 nonEst NA NA NA NA
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 nonEst NA NA NA NA
## CNCH13 - FGI4 nonEst NA NA NA NA
## CNCH13 - FMA7 nonEst NA NA NA NA
## CNCH13 - FSV1 nonEst NA NA NA NA
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - FSV1 nonEst NA NA NA NA
## 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 -392.333 71.7 154 -5.473 <.0001
## CNCH12 - CNH13 nonEst NA NA NA NA
## 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 - CNH13 nonEst NA NA NA NA
## 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
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - FSV1 nonEst NA NA NA NA
## 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 nonEst NA NA NA NA
## CNCH12 - CNH13 -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 - CNH13 nonEst NA NA NA NA
## CNCH13 - FBO1 nonEst NA NA NA NA
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 nonEst NA NA NA NA
## CNCH13 - FGI4 nonEst NA NA NA NA
## CNCH13 - FMA7 nonEst NA NA NA NA
## CNCH13 - FSV1 nonEst NA NA NA NA
## CNH13 - FBO1 107.333 71.7 154 1.497 0.8079
## CNH13 - FCHI8 250.000 71.7 154 3.487 0.0144
## CNH13 - FEAR5 119.333 71.7 154 1.665 0.7098
## CNH13 - FGI4 0.333 71.7 154 0.005 1.0000
## CNH13 - FMA7 190.333 71.7 154 2.655 0.1448
## CNH13 - 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 -107.667 71.7 154 -1.502 0.8054
## CNCH12 - CNH13 nonEst NA NA NA NA
## 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 - CNH13 nonEst NA NA NA NA
## 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
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - FSV1 nonEst NA NA NA NA
## 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
## CHCH13 - CNCH12 nonEst NA NA NA NA
## CHCH13 - CNCH13 nonEst NA NA NA NA
## CHCH13 - CNH13 nonEst NA NA NA NA
## CHCH13 - FBO1 nonEst NA NA NA NA
## CHCH13 - FCHI8 nonEst NA NA NA NA
## CHCH13 - FEAR5 nonEst NA NA NA NA
## CHCH13 - FGI4 nonEst NA NA NA NA
## CHCH13 - FMA7 nonEst NA NA NA NA
## CHCH13 - FSV1 nonEst NA NA NA NA
## CNCH12 - CNCH13 -345.000 71.7 154 -4.813 0.0001
## CNCH12 - CNH13 nonEst NA NA NA NA
## 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 - CNH13 nonEst NA NA NA NA
## 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
## CNH13 - FBO1 nonEst NA NA NA NA
## CNH13 - FCHI8 nonEst NA NA NA NA
## CNH13 - FEAR5 nonEst NA NA NA NA
## CNH13 - FGI4 nonEst NA NA NA NA
## CNH13 - FMA7 nonEst NA NA NA NA
## CNH13 - FSV1 nonEst NA NA NA NA
## 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 0.32952 0.036613 9 59.192 4.6784 0.0001062 ***
## ---
## 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> 13 160.70 -295.41
## (1 | mun) 12 156.17 -288.34 9.067 1 0.002602 **
## (1 | mun:gen) 12 142.23 -260.45 36.953 1 1.21e-09 ***
## ---
## 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 -1420.3 2850.7
## (1 | gen) 4 -1428.2 2864.4 15.690 1 7.460e-05 ***
## (1 | mun) 4 -1425.1 2858.2 9.501 1 0.002054 **
## (1 | gen:mun) 4 -1440.8 2889.5 40.817 1 1.672e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CHCH13 -7.770110
## CNCH12 -2.920796
## CNCH13 128.110432
## CNH13 44.115438
## FBO1 -28.312784
## FCHI8 -78.166693
## FEAR5 22.385984
## FGI4 43.716390
## FMA7 -42.514120
## FSV1 -78.643741
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CHCH13 1011.1244
## CNCH12 1015.9737
## CNCH13 1147.0049
## CNH13 1063.0099
## FBO1 990.5817
## FCHI8 940.7278
## FEAR5 1041.2805
## FGI4 1062.6109
## FMA7 976.3804
## FSV1 940.2508
#Blups Parcela
blups$mun
## (Intercept)
## CH -44.878186
## Gig -73.521229
## Htc 11.419143
## Jam 39.732525
## PtR 28.393769
## RiN -33.302268
## SnV -43.827632
## Tam 39.072965
## ViG -9.530138
## Yac 86.441050
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## CH 974.0163
## Gig 945.3733
## Htc 1030.3137
## Jam 1058.6270
## PtR 1047.2883
## RiN 985.5922
## SnV 975.0669
## Tam 1057.9675
## ViG 1009.3644
## Yac 1105.3356
#Blups interacción
blups$`gen:mun`
## (Intercept)
## CHCH13:RiN -9.786252
## CNCH12:CH 38.140868
## CNCH12:Gig 119.622643
## CNCH12:Htc 40.343518
## CNCH12:Jam -31.709296
## CNCH12:PtR 2.151754
## CNCH12:RiN -4.598361
## CNCH12:SnV -74.591637
## CNCH12:Tam -65.813404
## CNCH12:ViG 29.665913
## CNCH12:Yac -56.890665
## CNCH13:CH -13.634808
## CNCH13:Gig -174.231410
## CNCH13:Htc 101.021834
## CNCH13:Jam 192.692357
## CNCH13:PtR -170.542192
## CNCH13:SnV 114.985554
## CNCH13:ViG 12.714699
## CNCH13:Yac 98.345738
## CNH13:Tam 55.562251
## FBO1:CH -21.066530
## FBO1:Gig -35.110189
## FBO1:Htc -53.204668
## FBO1:Jam 12.589345
## FBO1:PtR -22.473019
## FBO1:RiN 30.994165
## FBO1:SnV -47.947063
## FBO1:Tam 30.238257
## FBO1:ViG 13.747256
## FBO1:Yac 56.573227
## FCHI8:CH 67.097817
## FCHI8:Gig -67.622276
## FCHI8:Htc -17.035178
## FCHI8:RiN -45.048499
## FCHI8:SnV -37.654083
## FCHI8:Tam -37.098292
## FCHI8:ViG 41.452467
## FCHI8:Yac -2.540880
## FEAR5:CH -83.967320
## FEAR5:Gig 57.973590
## FEAR5:Htc -21.063698
## FEAR5:Jam -119.234859
## FEAR5:PtR 18.615903
## FEAR5:RiN 45.964741
## FEAR5:SnV 53.600994
## FEAR5:Tam -15.250303
## FEAR5:ViG -23.035188
## FEAR5:Yac 114.590707
## FGI4:CH -38.741763
## FGI4:Gig -43.837470
## FGI4:Htc 41.090416
## FGI4:Jam -22.498119
## FGI4:PtR 80.770018
## FGI4:RiN -3.851463
## FGI4:SnV -4.921325
## FGI4:Tam 55.609928
## FGI4:ViG -12.634093
## FGI4:Yac 4.073532
## FMA7:CH 40.989695
## FMA7:Gig 1.311363
## FMA7:Htc -60.071857
## FMA7:Jam 48.527224
## FMA7:PtR 56.995438
## FMA7:RiN -88.085178
## FMA7:SnV 14.109162
## FMA7:Tam -19.675836
## FMA7:ViG -36.408674
## FMA7:Yac -11.236770
## FSV1:CH -79.592697
## FSV1:Gig -6.817037
## FSV1:Htc -7.982959
## FSV1:PtR 91.913941
## FSV1:RiN 7.050624
## FSV1:SnV -106.231394
## FSV1:Tam 75.459966
## FSV1:ViG -44.778911
## FSV1:Yac -28.071286
fixef(modelo_blup)[1] + blups$`gen:mun`
## (Intercept)
## CHCH13:RiN 1009.1083
## CNCH12:CH 1057.0354
## CNCH12:Gig 1138.5172
## CNCH12:Htc 1059.2380
## CNCH12:Jam 987.1852
## CNCH12:PtR 1021.0463
## CNCH12:RiN 1014.2961
## CNCH12:SnV 944.3029
## CNCH12:Tam 953.0811
## CNCH12:ViG 1048.5604
## CNCH12:Yac 962.0038
## CNCH13:CH 1005.2597
## CNCH13:Gig 844.6631
## CNCH13:Htc 1119.9163
## CNCH13:Jam 1211.5869
## CNCH13:PtR 848.3523
## CNCH13:SnV 1133.8801
## CNCH13:ViG 1031.6092
## CNCH13:Yac 1117.2402
## CNH13:Tam 1074.4568
## FBO1:CH 997.8280
## FBO1:Gig 983.7843
## FBO1:Htc 965.6898
## FBO1:Jam 1031.4839
## FBO1:PtR 996.4215
## FBO1:RiN 1049.8887
## FBO1:SnV 970.9474
## FBO1:Tam 1049.1328
## FBO1:ViG 1032.6418
## FBO1:Yac 1075.4677
## FCHI8:CH 1085.9923
## FCHI8:Gig 951.2722
## FCHI8:Htc 1001.8593
## FCHI8:RiN 973.8460
## FCHI8:SnV 981.2404
## FCHI8:Tam 981.7962
## FCHI8:ViG 1060.3470
## FCHI8:Yac 1016.3536
## FEAR5:CH 934.9272
## FEAR5:Gig 1076.8681
## FEAR5:Htc 997.8308
## FEAR5:Jam 899.6596
## FEAR5:PtR 1037.5104
## FEAR5:RiN 1064.8592
## FEAR5:SnV 1072.4955
## FEAR5:Tam 1003.6442
## FEAR5:ViG 995.8593
## FEAR5:Yac 1133.4852
## FGI4:CH 980.1527
## FGI4:Gig 975.0570
## FGI4:Htc 1059.9849
## FGI4:Jam 996.3964
## FGI4:PtR 1099.6645
## FGI4:RiN 1015.0430
## FGI4:SnV 1013.9732
## FGI4:Tam 1074.5044
## FGI4:ViG 1006.2604
## FGI4:Yac 1022.9680
## FMA7:CH 1059.8842
## FMA7:Gig 1020.2059
## FMA7:Htc 958.8227
## FMA7:Jam 1067.4217
## FMA7:PtR 1075.8899
## FMA7:RiN 930.8093
## FMA7:SnV 1033.0037
## FMA7:Tam 999.2187
## FMA7:ViG 982.4858
## FMA7:Yac 1007.6577
## FSV1:CH 939.3018
## FSV1:Gig 1012.0775
## FSV1:Htc 1010.9115
## FSV1:PtR 1110.8084
## FSV1:RiN 1025.9451
## FSV1:SnV 912.6631
## FSV1:Tam 1094.3545
## FSV1:ViG 974.1156
## FSV1:Yac 990.8232
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CHCH13 -7.770110
## 2 CNCH12 -2.920796
## 3 CNCH13 128.110432
## 4 CNH13 44.115438
## 5 FBO1 -28.312784
## 6 FCHI8 -78.166693
## 7 FEAR5 22.385984
## 8 FGI4 43.716390
## 9 FMA7 -42.514120
## 10 FSV1 -78.643741
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 CH -44.878186
## 2 Gig -73.521229
## 3 Htc 11.419143
## 4 Jam 39.732525
## 5 PtR 28.393769
## 6 RiN -33.302268
## 7 SnV -43.827632
## 8 Tam 39.072965
## 9 ViG -9.530138
## 10 Yac 86.441050
#Tabla blup_gen_mun
blup_gen_mun <- ranef(modelo_blup)$`gen:mun` %>%
tibble::rownames_to_column("gen:mun") %>%
rename(BLUP = `(Intercept)`)
blup_gen_mun
## gen:mun BLUP
## 1 CHCH13:RiN -9.786252
## 2 CNCH12:CH 38.140868
## 3 CNCH12:Gig 119.622643
## 4 CNCH12:Htc 40.343518
## 5 CNCH12:Jam -31.709296
## 6 CNCH12:PtR 2.151754
## 7 CNCH12:RiN -4.598361
## 8 CNCH12:SnV -74.591637
## 9 CNCH12:Tam -65.813404
## 10 CNCH12:ViG 29.665913
## 11 CNCH12:Yac -56.890665
## 12 CNCH13:CH -13.634808
## 13 CNCH13:Gig -174.231410
## 14 CNCH13:Htc 101.021834
## 15 CNCH13:Jam 192.692357
## 16 CNCH13:PtR -170.542192
## 17 CNCH13:SnV 114.985554
## 18 CNCH13:ViG 12.714699
## 19 CNCH13:Yac 98.345738
## 20 CNH13:Tam 55.562251
## 21 FBO1:CH -21.066530
## 22 FBO1:Gig -35.110189
## 23 FBO1:Htc -53.204668
## 24 FBO1:Jam 12.589345
## 25 FBO1:PtR -22.473019
## 26 FBO1:RiN 30.994165
## 27 FBO1:SnV -47.947063
## 28 FBO1:Tam 30.238257
## 29 FBO1:ViG 13.747256
## 30 FBO1:Yac 56.573227
## 31 FCHI8:CH 67.097817
## 32 FCHI8:Gig -67.622276
## 33 FCHI8:Htc -17.035178
## 34 FCHI8:RiN -45.048499
## 35 FCHI8:SnV -37.654083
## 36 FCHI8:Tam -37.098292
## 37 FCHI8:ViG 41.452467
## 38 FCHI8:Yac -2.540880
## 39 FEAR5:CH -83.967320
## 40 FEAR5:Gig 57.973590
## 41 FEAR5:Htc -21.063698
## 42 FEAR5:Jam -119.234859
## 43 FEAR5:PtR 18.615903
## 44 FEAR5:RiN 45.964741
## 45 FEAR5:SnV 53.600994
## 46 FEAR5:Tam -15.250303
## 47 FEAR5:ViG -23.035188
## 48 FEAR5:Yac 114.590707
## 49 FGI4:CH -38.741763
## 50 FGI4:Gig -43.837470
## 51 FGI4:Htc 41.090416
## 52 FGI4:Jam -22.498119
## 53 FGI4:PtR 80.770018
## 54 FGI4:RiN -3.851463
## 55 FGI4:SnV -4.921325
## 56 FGI4:Tam 55.609928
## 57 FGI4:ViG -12.634093
## 58 FGI4:Yac 4.073532
## 59 FMA7:CH 40.989695
## 60 FMA7:Gig 1.311363
## 61 FMA7:Htc -60.071857
## 62 FMA7:Jam 48.527224
## 63 FMA7:PtR 56.995438
## 64 FMA7:RiN -88.085178
## 65 FMA7:SnV 14.109162
## 66 FMA7:Tam -19.675836
## 67 FMA7:ViG -36.408674
## 68 FMA7:Yac -11.236770
## 69 FSV1:CH -79.592697
## 70 FSV1:Gig -6.817037
## 71 FSV1:Htc -7.982959
## 72 FSV1:PtR 91.913941
## 73 FSV1:RiN 7.050624
## 74 FSV1:SnV -106.231394
## 75 FSV1:Tam 75.459966
## 76 FSV1:ViG -44.778911
## 77 FSV1:Yac -28.071286
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 1009.2364 1009.2364 1009.2364 1088.4919 1088.4919 1088.4919 924.6370
## [8] 924.6370 924.6370 962.9474 962.9474 962.9474 912.4350 912.4350
## [15] 912.4350 978.9909 978.9909 978.9909 972.4919 972.4919 972.4919
## [22] 815.7799 815.7799 815.7799 1062.0751 1062.0751 1062.0751 899.2523
## [29] 899.2523 899.2523 881.9503 881.9503 881.9503 799.5843 799.5843
## [36] 799.5843 1025.7329 1025.7329 1025.7329 945.2522 945.2522 945.2522
## [43] 904.1705 904.1705 904.1705 859.9125 859.9125 859.9125 1067.7364
## [50] 1067.7364 1067.7364 1259.4459 1259.4459 1259.4459 948.7962 948.7962
## [57] 948.7962 935.1118 935.1118 935.1118 1031.6359 1031.6359 1031.6359
## [64] 1115.1205 1115.1205 1115.1205 927.7277 927.7277 927.7277 943.6870
## [71] 943.6870 943.6870 1023.9969 1023.9969 1023.9969 1379.4298 1379.4298
## [78] 1379.4298 1042.9036 1042.9036 1042.9036 961.7782 961.7782 961.7782
## [85] 1079.8453 1079.8453 1079.8453 1064.6401 1064.6401 1064.6401 1046.5192
## [92] 1046.5192 1046.5192 1004.8565 1004.8565 1004.8565 996.5025 996.5025
## [99] 996.5025 1088.2902 1088.2902 1088.2902 1171.7747 1171.7747 1171.7747
## [106] 1061.7696 1061.7696 1061.7696 1060.5585 1060.5585 1060.5585 968.0359
## [113] 968.0359 968.0359 978.0731 978.0731 978.0731 988.2736 988.2736
## [120] 988.2736 862.3770 862.3770 862.3770 1053.9430 1053.9430 1053.9430
## [127] 1025.4572 1025.4572 1025.4572 854.9929 854.9929 854.9929 913.9991
## [134] 913.9991 913.9991 897.5544 897.5544 897.5544 1218.1629 1218.1629
## [141] 1218.1629 898.8070 898.8070 898.8070 859.2461 859.2461 859.2461
## [148] 1051.0539 1051.0539 1051.0539 1013.8619 1013.8619 1013.8619 946.6619
## [155] 946.6619 946.6619 790.1917 790.1917 790.1917 989.2333 989.2333
## [162] 989.2333 1157.6452 1157.6452 1157.6452 1059.8929 1059.8929 1059.8929
## [169] 942.7025 942.7025 942.7025 1065.1032 1065.1032 1065.1032 1157.2938
## [176] 1157.2938 1157.2938 995.7775 995.7775 995.7775 1054.7837 1054.7837
## [183] 1054.7837 1036.1095 1036.1095 1036.1095 1150.1895 1150.1895 1150.1895
## [190] 994.7988 994.7988 994.7988 972.6501 972.6501 972.6501 1008.7152
## [197] 1008.7152 1008.7152 1040.4467 1040.4467 1040.4467 930.4416 930.4416
## [204] 930.4416 885.9417 885.9417 885.9417 1045.5241 1045.5241 1045.5241
## [211] 1331.7917 1331.7917 1331.7917 1133.5960 1133.5960 1133.5960 1024.6280
## [218] 1024.6280 1024.6280 1242.3122 1242.3122 1242.3122 1153.1255 1153.1255
## [225] 1153.1255 1051.5847 1051.5847 1051.5847 998.6205 998.6205 998.6205
#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> 6791.281 82.40923
## 2 mun (Intercept) <NA> 3357.546 57.94434
## 3 gen (Intercept) <NA> 5392.156 73.43130
## 4 Residual <NA> <NA> 7708.270 87.79675
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## gen:mun (Intercept) 82.409
## mun (Intercept) 57.944
## gen (Intercept) 73.431
## 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] 0.860818
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## [1] 1.084178
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CHCH13 -7.770110
## CNCH12 -2.920796
## CNCH13 128.110432
## CNH13 44.115438
## FBO1 -28.312784
## FCHI8 -78.166693
## FEAR5 22.385984
## FGI4 43.716390
## FMA7 -42.514120
## FSV1 -78.643741
##Predicho de carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## CNCH13 128.110432 1147.0049
## CNH13 44.115438 1063.0099
## FGI4 43.716390 1062.6109
## FEAR5 22.385984 1041.2805
## CNCH12 -2.920796 1015.9737
## CHCH13 -7.770110 1011.1244
## FBO1 -28.312784 990.5817
## FMA7 -42.514120 976.3804
## FCHI8 -78.166693 940.7278
## FSV1 -78.643741 940.2508
# Visualización ranking predichos
blup_gen$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,] -22.58236 24.17034 -10.25752 -25.33924 -12.98756 -11.94036
## [2,] -47.00196 -99.18723 273.23175 59.32105 -146.44517 -75.93811
## [3,] 275.71749 164.41538 -16.25280 -21.08302 -54.47837 -69.45943
## [4,] 120.84362 -93.60260 38.22951 93.42568 47.25945 42.99995
## [5,] -43.44968 48.68914 -84.21385 -95.18631 -111.77464 -40.40083
## [6,] -119.30757 167.75762 73.95490 -100.71993 -13.57677 228.10659
## [7,] 14.53674 -215.64436 -75.85574 -239.09067 39.12064 -18.55011
## [8,] 45.01073 -93.14083 -72.80028 160.38295 69.63803 198.14398
## [9,] -79.95917 81.50314 60.94133 16.36012 286.70274 -148.02741
## [10,] -143.80784 15.03941 -186.97729 151.92937 -103.45834 -104.93428
## [,7] [,8] [,9] [,10]
## [1,] -58.15232 56.441206 340.83973 -117.8319
## [2,] -50.87397 49.473796 -49.28469 -117.7991
## [3,] -100.20951 -2.018072 -57.44459 -117.7984
## [4,] 208.94833 -201.609914 54.35534 -117.8078
## [5,] 245.46522 166.729533 -48.07078 -117.7992
## [6,] -28.05645 -108.969964 -45.05612 -117.7995
## [7,] -90.96598 -50.292448 -50.87788 -117.7990
## [8,] -61.36506 180.493934 -52.16825 -117.7989
## [9,] 11.90593 37.456972 -47.18395 -117.7993
## [10,] -76.69618 -127.705043 -45.20765 -117.7995
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 255.08904 -116.837294 -51.221029 -24.228632 -95.208562 -0.710654
## [2,] 169.88350 63.445389 174.379049 14.554246 44.039478 41.862913
## [3,] 28.71747 -266.293200 134.777988 6.657839 -7.583895 -72.908659
## [4,] 346.63250 -36.660085 47.987183 81.382077 -64.285549 30.422672
## [5,] 387.97276 196.498171 18.107350 92.612684 11.578348 -72.234403
## [6,] -10.87532 -183.226301 -47.505930 132.616302 99.738319 33.220438
## [7,] 408.65001 -19.404059 -2.318351 -88.377488 106.675933 -2.774272
## [8,] 242.84397 -28.661840 -140.526852 95.427563 -11.042475 3.816436
## [9,] 221.31498 -9.989604 82.275235 -11.728969 -47.725706 56.223746
## [10,] 344.32301 -63.160104 -80.562871 -126.754934 16.118075 2.229712
## [,7] [,8] [,9] [,10]
## [1,] 19.71780 5.2755350 -6.649239e-10 1.021597e-15
## [2,] 39.03061 1.3048604 -1.636172e-10 3.164226e-14
## [3,] 7.94412 0.6056542 2.386372e-10 2.676113e-15
## [4,] -73.41287 -5.7458014 4.994764e-11 8.695322e-15
## [5,] 20.66657 -2.7788556 -1.404008e-10 -1.431412e-14
## [6,] 14.65622 -3.7705614 -2.734125e-10 -1.405269e-14
## [7,] -45.10006 7.8552301 -2.400356e-11 -7.711303e-15
## [8,] 24.34645 6.6698443 4.898577e-10 2.090107e-14
## [9,] 33.99546 2.0236537 3.378795e-10 -3.700685e-14
## [10,] 31.05825 -11.1338444 1.185359e-10 8.536543e-15
##
## $eigenvalues
## [1] 8.712390e+02 4.091488e+02 2.984912e+02 2.583637e+02 1.982809e+02
## [6] 1.323026e+02 1.129324e+02 1.786346e+01 9.957679e-10 5.853304e-14
##
## $totalvar
## [1] 1152201
##
## $varexpl
## [1] 65.88 14.53 7.73 5.79 3.41 1.52 1.11 0.03 0.00 0.00
##
## $labelgen
## [1] "CHCH13" "CNCH12" "CNCH13" "CNH13" "FBO1" "FCHI8" "FEAR5" "FGI4"
## [9] "FMA7" "FSV1"
##
## $labelenv
## [1] "RiN" "CH" "Gig" "Htc" "Jam" "PtR" "SnV" "Tam" "ViG" "Yac"
##
## $labelaxes
## [1] "PC1" "PC2" "PC3" "PC4" "PC5" "PC6" "PC7" "PC8" "PC9" "PC10"
##
## $ge_mat
## RiN CH Gig Htc Jam PtR
## CHCH13 -18.922241 -20.745523 -21.729536 -18.39270 -19.303064 -26.34251
## CNCH12 -6.922241 60.886898 184.031598 42.51428 -66.259231 -12.05115
## CNCH13 149.561319 120.553565 -89.968402 257.18095 374.074103 -119.05115
## CNH13 96.816613 92.317005 86.476755 104.27844 107.792381 97.64273
## FBO1 16.744425 -46.113102 -54.635069 -111.81905 -30.592564 -71.38449
## FCHI8 -137.922241 25.553565 -149.301736 -111.81905 -102.607962 -107.60881
## FEAR5 88.077759 -82.113102 124.364931 -16.81905 -161.592564 35.94885
## FGI4 40.744425 1.553565 5.364931 90.18095 -6.925897 142.94885
## FMA7 -161.588908 25.220231 -18.635069 -135.48572 4.740769 23.94885
## FSV1 -66.588908 -177.113102 -65.968402 -99.81905 -99.325970 35.94885
## SnV Tam ViG Yac
## CHCH13 -15.35565 -21.729628 -20.67018 -21.29902
## CNCH12 -96.33476 -109.758876 36.14062 -118.41134
## CNCH13 295.99857 162.133968 143.80729 226.58866
## CNH13 98.70043 104.574457 98.21189 113.25638
## FBO1 -85.00143 -2.758876 -11.19271 12.58866
## FCHI8 -120.66810 -145.425543 -22.85938 -118.74467
## FEAR5 105.66524 -14.758876 -11.19271 143.25533
## FGI4 46.33190 104.241124 24.47395 12.25533
## FMA7 -13.66810 -85.758876 -94.52605 -95.07800
## FSV1 -215.66810 9.241124 -142.19271 -154.41134
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 0.002684372
##
## $grand_mean
## [1] 1024.083
##
## $mean_gen
## CHCH13 CNCH12 CNCH13 CNH13 FBO1 FCHI8 FEAR5 FGI4
## 1003.6341 1015.4667 1176.1710 1124.0898 985.6667 924.9427 1045.1667 1070.2000
## FMA7 FSV1
## 969.0000 926.4933
##
## $mean_env
## RiN CH Gig Htc Jam PtR SnV Tam
## 983.2556 962.7798 923.3017 1040.4857 1078.2592 1059.3845 965.6681 1074.0922
## ViG Yac
## 1011.1927 1142.4113
##
## $scale_val
## RiN CH Gig Htc Jam PtR SnV Tam
## 100.78238 87.38336 102.94705 125.12416 150.49034 84.86224 144.78738 99.67873
## ViG Yac
## 83.39034 127.56613
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración de variables
##Blup gen
blup_gen <- ranef(modelo_blup)$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
## CHCH13 CHCH13 1011.1244
## CNCH12 CNCH12 1015.9737
## CNCH13 CNCH13 1147.0049
## CNH13 CNH13 1063.0099
## FBO1 FBO1 990.5817
## FCHI8 FCHI8 940.7278
## FEAR5 FEAR5 1041.2805
## FGI4 FGI4 1062.6109
## FMA7 FMA7 976.3804
## FSV1 FSV1 940.2508
##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
## CHCH13 nonEst NA NA NA NA
## CNCH12 0.0487 0.300 213 -0.54218 0.639
## CNCH13 1.7474 0.320 213 1.11584 2.379
## CNH13 nonEst NA NA NA NA
## FBO1 1.0585 0.300 213 0.46770 1.649
## FCHI8 0.8772 0.344 213 0.19837 1.556
## FEAR5 0.5942 0.300 213 0.00333 1.185
## FGI4 1.0719 0.300 213 0.48107 1.663
## FMA7 0.8742 0.300 213 0.28340 1.465
## FSV1 1.2764 0.327 213 0.63158 1.921
##
## 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, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -5.4e+00
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
## CHCH13 nonEst NA NA NA NA
## CNCH12 263.4 413 213 -550 1077
## CNCH13 -766.7 492 213 -1736 203
## CNH13 nonEst NA NA NA NA
## FBO1 -351.7 413 213 -1165 462
## FCHI8 -403.9 436 213 -1264 456
## FEAR5 -511.8 413 213 -1325 302
## FGI4 48.1 413 213 -765 862
## FMA7 -735.6 413 213 -1549 78
## FSV1 356.3 418 213 -468 1181
##
## 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 1.99 99.3 99.3
## 2 PC2 0.01 0.7 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C 1 0.99 0.01
## 2 Pendiente 1 0.99 0.01
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.9930015
## -------------------------------------------------------------------------------
## 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 1.02e+ 3 1105. 86.1 8.45e 0 increase 100
## 2 Pendiente FA1 1.22e-13 0.267 0.267 2.19e14 increase 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## CNCH13 CNH13
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod$MGIDI
## # A tibble: 10 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 CNCH13 0.0000000001
## 2 CNH13 1.20
## 3 FGI4 1.38
## 4 FEAR5 1.78
## 5 CHCH13 2.10
## 6 CNCH12 2.24
## 7 FBO1 2.49
## 8 FMA7 2.73
## 9 FSV1 3.22
## 10 FCHI8 3.27
#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 2.48 82.6 82.6
## 2 PC2 0.51 16.9 99.5
## 3 PC3 0.01 0.47 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 3 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C -0.96 0.93 0.07
## 2 Pendiente -0.96 0.93 0.07
## 3 Pendiente2 -0.79 0.62 0.38
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.8261862
## -------------------------------------------------------------------------------
## 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 1.02e+ 3 1105. 86.1 8.45e 0 increase 100
## 2 Pendiente FA1 1.22e-13 0.267 0.267 2.19e14 increase 100
## 3 Pendiente2 FA1 3.29e-12 -68.5 -68.5 -2.08e15 decrease 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## CNCH13 CNH13
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod2$MGIDI
## # A tibble: 10 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 CNCH13 0.0000000001
## 2 CNH13 1.40
## 3 FEAR5 1.64
## 4 FGI4 1.69
## 5 CHCH13 2.18
## 6 FMA7 2.39
## 7 FBO1 2.44
## 8 CNCH12 2.63
## 9 FCHI8 3.07
## 10 FSV1 3.61
#SLAáfico Selección 1
plot(mgidi_mod2)
