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(VLA) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(VLA)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 0.55561 0.079372 36.032 < 2.2e-16 ***
## mun 9 0.40395 0.044883 20.375 < 2.2e-16 ***
## gen:mun 60 1.38226 0.023038 10.458 < 2.2e-16 ***
## Residuals 154 0.33923 0.002203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm ((VLA) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: (VLA)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 12.7190 1.81700 36.529 < 2.2e-16 ***
## mun 9 9.2843 1.03158 20.739 < 2.2e-16 ***
## gen:mun 60 30.7800 0.51300 10.313 < 2.2e-16 ***
## Residuals 154 7.6601 0.04974
## ---
## 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 4.52 0.0407 154 4.44 4.60
## CNCH13 4.67 0.0407 154 4.59 4.75
## FBO1 4.74 0.0407 154 4.66 4.82
## FCHI8 nonEst NA NA NA NA
## FEAR5 5.02 0.0407 154 4.94 5.10
## FGI4 5.13 0.0407 154 5.05 5.21
## FMA7 4.65 0.0407 154 4.57 4.73
## 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 -0.1436 0.0576 154 -2.494 0.1324
## CNCH12 - FBO1 -0.2189 0.0576 154 -3.801 0.0028
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -0.4979 0.0576 154 -8.647 <.0001
## CNCH12 - FGI4 -0.6049 0.0576 154 -10.504 <.0001
## CNCH12 - FMA7 -0.1309 0.0576 154 -2.274 0.2112
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 -0.0753 0.0576 154 -1.308 0.7805
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -0.3543 0.0576 154 -6.153 <.0001
## CNCH13 - FGI4 -0.4613 0.0576 154 -8.011 <.0001
## CNCH13 - FMA7 0.0127 0.0576 154 0.220 0.9999
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -0.2790 0.0576 154 -4.846 <.0001
## FBO1 - FGI4 -0.3860 0.0576 154 -6.703 <.0001
## FBO1 - FMA7 0.0880 0.0576 154 1.528 0.6471
## 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 -0.1070 0.0576 154 -1.858 0.4324
## FEAR5 - FMA7 0.3670 0.0576 154 6.373 <.0001
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 0.4740 0.0576 154 8.231 <.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 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 4.72 0.0455 154 4.63 4.81
## Gig 4.76 0.0455 154 4.67 4.85
## Htc 4.83 0.0455 154 4.74 4.92
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
## RiN 4.73 0.0455 154 4.64 4.82
## SnV 4.62 0.0455 154 4.53 4.71
## Tam 5.21 0.0455 154 5.12 5.30
## ViG 4.44 0.0455 154 4.35 4.53
## Yac 4.78 0.0455 154 4.69 4.87
##
## 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 -0.0478 0.0644 154 -0.743 0.9955
## CH - Htc -0.1104 0.0644 154 -1.715 0.6775
## CH - Jam nonEst NA NA NA NA
## CH - PtR nonEst NA NA NA NA
## CH - RiN -0.0111 0.0644 154 -0.172 1.0000
## CH - SnV 0.0975 0.0644 154 1.514 0.7990
## CH - Tam -0.4940 0.0644 154 -7.674 <.0001
## CH - ViG 0.2756 0.0644 154 4.281 0.0008
## CH - Yac -0.0668 0.0644 154 -1.038 0.9678
## Gig - Htc -0.0626 0.0644 154 -0.972 0.9777
## Gig - Jam nonEst NA NA NA NA
## Gig - PtR nonEst NA NA NA NA
## Gig - RiN 0.0367 0.0644 154 0.571 0.9992
## Gig - SnV 0.1453 0.0644 154 2.257 0.3245
## Gig - Tam -0.4462 0.0644 154 -6.931 <.0001
## Gig - ViG 0.3235 0.0644 154 5.024 <.0001
## Gig - Yac -0.0190 0.0644 154 -0.295 1.0000
## Htc - Jam nonEst NA NA NA NA
## Htc - PtR nonEst NA NA NA NA
## Htc - RiN 0.0993 0.0644 154 1.543 0.7829
## Htc - SnV 0.2079 0.0644 154 3.229 0.0320
## Htc - Tam -0.3836 0.0644 154 -5.959 <.0001
## Htc - ViG 0.3860 0.0644 154 5.996 <.0001
## Htc - Yac 0.0436 0.0644 154 0.677 0.9975
## 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 0.1085 0.0644 154 1.686 0.6963
## RiN - Tam -0.4830 0.0644 154 -7.501 <.0001
## RiN - ViG 0.2867 0.0644 154 4.453 0.0004
## RiN - Yac -0.0558 0.0644 154 -0.866 0.9886
## SnV - Tam -0.5915 0.0644 154 -9.187 <.0001
## SnV - ViG 0.1782 0.0644 154 2.767 0.1111
## SnV - Yac -0.1643 0.0644 154 -2.552 0.1822
## Tam - ViG 0.7697 0.0644 154 11.955 <.0001
## Tam - Yac 0.4272 0.0644 154 6.635 <.0001
## ViG - Yac -0.3425 0.0644 154 -5.319 <.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 4.53 0.129 154 4.28 4.79
## CNCH13 5.24 0.129 154 4.99 5.50
## FBO1 4.54 0.129 154 4.29 4.80
## FCHI8 4.52 0.129 154 4.26 4.77
## FEAR5 4.83 0.129 154 4.58 5.09
## FGI4 5.45 0.129 154 5.20 5.70
## FMA7 4.29 0.129 154 4.03 4.54
## FSV1 4.32 0.129 154 4.06 4.57
##
## mun = Gig:
## gen emmean SE df lower.CL upper.CL
## CNCH12 4.69 0.129 154 4.44 4.95
## CNCH13 4.86 0.129 154 4.60 5.11
## FBO1 4.13 0.129 154 3.88 4.39
## FCHI8 4.27 0.129 154 4.02 4.52
## FEAR5 5.42 0.129 154 5.17 5.68
## FGI4 5.34 0.129 154 5.08 5.59
## FMA7 5.08 0.129 154 4.83 5.33
## FSV1 4.32 0.129 154 4.06 4.57
##
## mun = Htc:
## gen emmean SE df lower.CL upper.CL
## CNCH12 4.39 0.129 154 4.14 4.64
## CNCH13 4.84 0.129 154 4.58 5.09
## FBO1 5.23 0.129 154 4.97 5.48
## FCHI8 4.68 0.129 154 4.42 4.93
## FEAR5 4.61 0.129 154 4.35 4.86
## FGI4 5.38 0.129 154 5.12 5.63
## FMA7 4.69 0.129 154 4.43 4.94
## FSV1 4.81 0.129 154 4.55 5.06
##
## mun = Jam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 4.64 0.129 154 4.38 4.89
## CNCH13 4.81 0.129 154 4.55 5.06
## FBO1 4.89 0.129 154 4.64 5.14
## FCHI8 nonEst NA NA NA NA
## FEAR5 5.00 0.129 154 4.74 5.25
## FGI4 3.91 0.129 154 3.66 4.17
## FMA7 4.59 0.129 154 4.34 4.85
## FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## gen emmean SE df lower.CL upper.CL
## CNCH12 3.71 0.129 154 3.46 3.97
## CNCH13 4.49 0.129 154 4.23 4.74
## FBO1 4.53 0.129 154 4.28 4.79
## FCHI8 nonEst NA NA NA NA
## FEAR5 4.69 0.129 154 4.43 4.94
## FGI4 5.25 0.129 154 5.00 5.51
## FMA7 4.62 0.129 154 4.36 4.87
## FSV1 4.96 0.129 154 4.71 5.22
##
## mun = RiN:
## gen emmean SE df lower.CL upper.CL
## CNCH12 4.78 0.129 154 4.52 5.03
## CNCH13 4.10 0.129 154 3.84 4.35
## FBO1 4.54 0.129 154 4.28 4.79
## FCHI8 4.14 0.129 154 3.88 4.39
## FEAR5 5.23 0.129 154 4.97 5.48
## FGI4 5.75 0.129 154 5.50 6.01
## FMA7 4.20 0.129 154 3.95 4.46
## FSV1 5.08 0.129 154 4.83 5.34
##
## mun = SnV:
## gen emmean SE df lower.CL upper.CL
## CNCH12 4.40 0.129 154 4.15 4.66
## CNCH13 4.49 0.129 154 4.24 4.75
## FBO1 4.60 0.129 154 4.35 4.85
## FCHI8 4.31 0.129 154 4.06 4.57
## FEAR5 5.09 0.129 154 4.84 5.35
## FGI4 4.58 0.129 154 4.32 4.83
## FMA7 4.83 0.129 154 4.58 5.09
## FSV1 4.64 0.129 154 4.38 4.89
##
## mun = Tam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 5.11 0.129 154 4.86 5.37
## CNCH13 5.04 0.129 154 4.78 5.29
## FBO1 6.34 0.129 154 6.09 6.60
## FCHI8 4.58 0.129 154 4.32 4.83
## FEAR5 5.24 0.129 154 4.98 5.49
## FGI4 5.43 0.129 154 5.17 5.68
## FMA7 4.60 0.129 154 4.34 4.85
## FSV1 5.35 0.129 154 5.10 5.61
##
## mun = ViG:
## gen emmean SE df lower.CL upper.CL
## CNCH12 4.87 0.129 154 4.62 5.13
## CNCH13 4.11 0.129 154 3.86 4.37
## FBO1 3.99 0.129 154 3.74 4.25
## FCHI8 3.56 0.129 154 3.31 3.81
## FEAR5 4.88 0.129 154 4.62 5.13
## FGI4 4.61 0.129 154 4.35 4.86
## FMA7 5.25 0.129 154 4.99 5.50
## FSV1 4.25 0.129 154 4.00 4.51
##
## mun = Yac:
## gen emmean SE df lower.CL upper.CL
## CNCH12 4.10 0.129 154 3.84 4.35
## CNCH13 4.69 0.129 154 4.43 4.94
## FBO1 4.62 0.129 154 4.36 4.87
## FCHI8 4.59 0.129 154 4.33 4.84
## FEAR5 5.23 0.129 154 4.97 5.48
## FGI4 5.58 0.129 154 5.33 5.84
## FMA7 4.39 0.129 154 4.14 4.65
## FSV1 5.07 0.129 154 4.81 5.32
##
## 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 -0.7107 0.182 154 -3.903 0.0035
## CNCH12 - FBO1 -0.0117 0.182 154 -0.064 1.0000
## CNCH12 - FCHI8 0.0147 0.182 154 0.081 1.0000
## CNCH12 - FEAR5 -0.2983 0.182 154 -1.638 0.7264
## CNCH12 - FGI4 -0.9170 0.182 154 -5.036 <.0001
## CNCH12 - FMA7 0.2457 0.182 154 1.349 0.8783
## CNCH12 - FSV1 0.2177 0.182 154 1.195 0.9322
## CNCH13 - FBO1 0.6990 0.182 154 3.839 0.0044
## CNCH13 - FCHI8 0.7253 0.182 154 3.983 0.0026
## CNCH13 - FEAR5 0.4123 0.182 154 2.264 0.3202
## CNCH13 - FGI4 -0.2063 0.182 154 -1.133 0.9485
## CNCH13 - FMA7 0.9563 0.182 154 5.252 <.0001
## CNCH13 - FSV1 0.9283 0.182 154 5.098 <.0001
## FBO1 - FCHI8 0.0263 0.182 154 0.145 1.0000
## FBO1 - FEAR5 -0.2867 0.182 154 -1.574 0.7649
## FBO1 - FGI4 -0.9053 0.182 154 -4.972 <.0001
## FBO1 - FMA7 0.2573 0.182 154 1.413 0.8500
## FBO1 - FSV1 0.2293 0.182 154 1.259 0.9122
## FCHI8 - FEAR5 -0.3130 0.182 154 -1.719 0.6750
## FCHI8 - FGI4 -0.9317 0.182 154 -5.116 <.0001
## FCHI8 - FMA7 0.2310 0.182 154 1.269 0.9090
## FCHI8 - FSV1 0.2030 0.182 154 1.115 0.9527
## FEAR5 - FGI4 -0.6187 0.182 154 -3.397 0.0191
## FEAR5 - FMA7 0.5440 0.182 154 2.987 0.0632
## FEAR5 - FSV1 0.5160 0.182 154 2.834 0.0943
## FGI4 - FMA7 1.1627 0.182 154 6.385 <.0001
## FGI4 - FSV1 1.1347 0.182 154 6.231 <.0001
## FMA7 - FSV1 -0.0280 0.182 154 -0.154 1.0000
##
## mun = Gig:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.1640 0.182 154 -0.901 0.9856
## CNCH12 - FBO1 0.5577 0.182 154 3.062 0.0515
## CNCH12 - FCHI8 0.4227 0.182 154 2.321 0.2891
## CNCH12 - FEAR5 -0.7300 0.182 154 -4.009 0.0024
## CNCH12 - FGI4 -0.6443 0.182 154 -3.538 0.0122
## CNCH12 - FMA7 -0.3877 0.182 154 -2.129 0.4013
## CNCH12 - FSV1 0.3753 0.182 154 2.061 0.4448
## CNCH13 - FBO1 0.7217 0.182 154 3.963 0.0028
## CNCH13 - FCHI8 0.5867 0.182 154 3.222 0.0326
## CNCH13 - FEAR5 -0.5660 0.182 154 -3.108 0.0453
## CNCH13 - FGI4 -0.4803 0.182 154 -2.638 0.1506
## CNCH13 - FMA7 -0.2237 0.182 154 -1.228 0.9223
## CNCH13 - FSV1 0.5393 0.182 154 2.962 0.0677
## FBO1 - FCHI8 -0.1350 0.182 154 -0.741 0.9956
## FBO1 - FEAR5 -1.2877 0.182 154 -7.071 <.0001
## FBO1 - FGI4 -1.2020 0.182 154 -6.601 <.0001
## FBO1 - FMA7 -0.9453 0.182 154 -5.191 <.0001
## FBO1 - FSV1 -0.1823 0.182 154 -1.001 0.9737
## FCHI8 - FEAR5 -1.1527 0.182 154 -6.330 <.0001
## FCHI8 - FGI4 -1.0670 0.182 154 -5.859 <.0001
## FCHI8 - FMA7 -0.8103 0.182 154 -4.450 0.0004
## FCHI8 - FSV1 -0.0473 0.182 154 -0.260 1.0000
## FEAR5 - FGI4 0.0857 0.182 154 0.470 0.9998
## FEAR5 - FMA7 0.3423 0.182 154 1.880 0.5668
## FEAR5 - FSV1 1.1053 0.182 154 6.070 <.0001
## FGI4 - FMA7 0.2567 0.182 154 1.409 0.8517
## FGI4 - FSV1 1.0197 0.182 154 5.599 <.0001
## FMA7 - FSV1 0.7630 0.182 154 4.190 0.0012
##
## mun = Htc:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.4470 0.182 154 -2.455 0.2232
## CNCH12 - FBO1 -0.8390 0.182 154 -4.607 0.0002
## CNCH12 - FCHI8 -0.2887 0.182 154 -1.585 0.7585
## CNCH12 - FEAR5 -0.2160 0.182 154 -1.186 0.9348
## CNCH12 - FGI4 -0.9870 0.182 154 -5.420 <.0001
## CNCH12 - FMA7 -0.2967 0.182 154 -1.629 0.7320
## CNCH12 - FSV1 -0.4180 0.182 154 -2.295 0.3029
## CNCH13 - FBO1 -0.3920 0.182 154 -2.153 0.3864
## CNCH13 - FCHI8 0.1583 0.182 154 0.869 0.9883
## CNCH13 - FEAR5 0.2310 0.182 154 1.269 0.9090
## CNCH13 - FGI4 -0.5400 0.182 154 -2.965 0.0671
## CNCH13 - FMA7 0.1503 0.182 154 0.826 0.9914
## CNCH13 - FSV1 0.0290 0.182 154 0.159 1.0000
## FBO1 - FCHI8 0.5503 0.182 154 3.022 0.0575
## FBO1 - FEAR5 0.6230 0.182 154 3.421 0.0177
## FBO1 - FGI4 -0.1480 0.182 154 -0.813 0.9922
## FBO1 - FMA7 0.5423 0.182 154 2.978 0.0648
## FBO1 - FSV1 0.4210 0.182 154 2.312 0.2940
## FCHI8 - FEAR5 0.0727 0.182 154 0.399 0.9999
## FCHI8 - FGI4 -0.6983 0.182 154 -3.835 0.0044
## FCHI8 - FMA7 -0.0080 0.182 154 -0.044 1.0000
## FCHI8 - FSV1 -0.1293 0.182 154 -0.710 0.9966
## FEAR5 - FGI4 -0.7710 0.182 154 -4.234 0.0010
## FEAR5 - FMA7 -0.0807 0.182 154 -0.443 0.9998
## FEAR5 - FSV1 -0.2020 0.182 154 -1.109 0.9540
## FGI4 - FMA7 0.6903 0.182 154 3.791 0.0052
## FGI4 - FSV1 0.5690 0.182 154 3.125 0.0432
## FMA7 - FSV1 -0.1213 0.182 154 -0.666 0.9977
##
## mun = Jam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.1700 0.182 154 -0.934 0.9372
## CNCH12 - FBO1 -0.2520 0.182 154 -1.384 0.7367
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -0.3610 0.182 154 -1.982 0.3572
## CNCH12 - FGI4 0.7240 0.182 154 3.976 0.0015
## CNCH12 - FMA7 0.0440 0.182 154 0.242 0.9999
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 -0.0820 0.182 154 -0.450 0.9976
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -0.1910 0.182 154 -1.049 0.9004
## CNCH13 - FGI4 0.8940 0.182 154 4.909 <.0001
## CNCH13 - FMA7 0.2140 0.182 154 1.175 0.8480
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -0.1090 0.182 154 -0.599 0.9910
## FBO1 - FGI4 0.9760 0.182 154 5.360 <.0001
## FBO1 - FMA7 0.2960 0.182 154 1.625 0.5830
## 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 1.0850 0.182 154 5.958 <.0001
## FEAR5 - FMA7 0.4050 0.182 154 2.224 0.2328
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 -0.6800 0.182 154 -3.734 0.0035
## 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 -0.7750 0.182 154 -4.256 0.0007
## CNCH12 - FBO1 -0.8177 0.182 154 -4.490 0.0003
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -0.9747 0.182 154 -5.352 <.0001
## CNCH12 - FGI4 -1.5390 0.182 154 -8.451 <.0001
## CNCH12 - FMA7 -0.9020 0.182 154 -4.953 <.0001
## CNCH12 - FSV1 -1.2510 0.182 154 -6.870 <.0001
## CNCH13 - FBO1 -0.0427 0.182 154 -0.234 1.0000
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -0.1997 0.182 154 -1.096 0.9283
## CNCH13 - FGI4 -0.7640 0.182 154 -4.195 0.0009
## CNCH13 - FMA7 -0.1270 0.182 154 -0.697 0.9926
## CNCH13 - FSV1 -0.4760 0.182 154 -2.614 0.1290
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -0.1570 0.182 154 -0.862 0.9775
## FBO1 - FGI4 -0.7213 0.182 154 -3.961 0.0021
## FBO1 - FMA7 -0.0843 0.182 154 -0.463 0.9992
## FBO1 - FSV1 -0.4333 0.182 154 -2.380 0.2144
## 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 -0.5643 0.182 154 -3.099 0.0366
## FEAR5 - FMA7 0.0727 0.182 154 0.399 0.9997
## FEAR5 - FSV1 -0.2763 0.182 154 -1.517 0.7340
## FGI4 - FMA7 0.6370 0.182 154 3.498 0.0107
## FGI4 - FSV1 0.2880 0.182 154 1.582 0.6943
## FMA7 - FSV1 -0.3490 0.182 154 -1.917 0.4726
##
## mun = RiN:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.6820 0.182 154 3.745 0.0061
## CNCH12 - FBO1 0.2437 0.182 154 1.338 0.8828
## CNCH12 - FCHI8 0.6430 0.182 154 3.531 0.0125
## CNCH12 - FEAR5 -0.4463 0.182 154 -2.451 0.2249
## CNCH12 - FGI4 -0.9760 0.182 154 -5.360 <.0001
## CNCH12 - FMA7 0.5743 0.182 154 3.154 0.0398
## CNCH12 - FSV1 -0.3037 0.182 154 -1.668 0.7080
## CNCH13 - FBO1 -0.4383 0.182 154 -2.407 0.2454
## CNCH13 - FCHI8 -0.0390 0.182 154 -0.214 1.0000
## CNCH13 - FEAR5 -1.1283 0.182 154 -6.196 <.0001
## CNCH13 - FGI4 -1.6580 0.182 154 -9.105 <.0001
## CNCH13 - FMA7 -0.1077 0.182 154 -0.591 0.9989
## CNCH13 - FSV1 -0.9857 0.182 154 -5.413 <.0001
## FBO1 - FCHI8 0.3993 0.182 154 2.193 0.3618
## FBO1 - FEAR5 -0.6900 0.182 154 -3.789 0.0052
## FBO1 - FGI4 -1.2197 0.182 154 -6.698 <.0001
## FBO1 - FMA7 0.3307 0.182 154 1.816 0.6103
## FBO1 - FSV1 -0.5473 0.182 154 -3.006 0.0602
## FCHI8 - FEAR5 -1.0893 0.182 154 -5.982 <.0001
## FCHI8 - FGI4 -1.6190 0.182 154 -8.891 <.0001
## FCHI8 - FMA7 -0.0687 0.182 154 -0.377 0.9999
## FCHI8 - FSV1 -0.9467 0.182 154 -5.199 <.0001
## FEAR5 - FGI4 -0.5297 0.182 154 -2.909 0.0779
## FEAR5 - FMA7 1.0207 0.182 154 5.605 <.0001
## FEAR5 - FSV1 0.1427 0.182 154 0.783 0.9938
## FGI4 - FMA7 1.5503 0.182 154 8.514 <.0001
## FGI4 - FSV1 0.6723 0.182 154 3.692 0.0073
## FMA7 - FSV1 -0.8780 0.182 154 -4.821 0.0001
##
## mun = SnV:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.0900 0.182 154 -0.494 0.9997
## CNCH12 - FBO1 -0.1973 0.182 154 -1.084 0.9594
## CNCH12 - FCHI8 0.0897 0.182 154 0.492 0.9997
## CNCH12 - FEAR5 -0.6900 0.182 154 -3.789 0.0052
## CNCH12 - FGI4 -0.1753 0.182 154 -0.963 0.9789
## CNCH12 - FMA7 -0.4300 0.182 154 -2.361 0.2681
## CNCH12 - FSV1 -0.2350 0.182 154 -1.290 0.9012
## CNCH13 - FBO1 -0.1073 0.182 154 -0.589 0.9990
## CNCH13 - FCHI8 0.1797 0.182 154 0.987 0.9757
## CNCH13 - FEAR5 -0.6000 0.182 154 -3.295 0.0262
## CNCH13 - FGI4 -0.0853 0.182 154 -0.469 0.9998
## CNCH13 - FMA7 -0.3400 0.182 154 -1.867 0.5755
## CNCH13 - FSV1 -0.1450 0.182 154 -0.796 0.9931
## FBO1 - FCHI8 0.2870 0.182 154 1.576 0.7638
## FBO1 - FEAR5 -0.4927 0.182 154 -2.705 0.1288
## FBO1 - FGI4 0.0220 0.182 154 0.121 1.0000
## FBO1 - FMA7 -0.2327 0.182 154 -1.278 0.9058
## FBO1 - FSV1 -0.0377 0.182 154 -0.207 1.0000
## FCHI8 - FEAR5 -0.7797 0.182 154 -4.282 0.0008
## FCHI8 - FGI4 -0.2650 0.182 154 -1.455 0.8296
## FCHI8 - FMA7 -0.5197 0.182 154 -2.854 0.0897
## FCHI8 - FSV1 -0.3247 0.182 154 -1.783 0.6326
## FEAR5 - FGI4 0.5147 0.182 154 2.826 0.0961
## FEAR5 - FMA7 0.2600 0.182 154 1.428 0.8431
## FEAR5 - FSV1 0.4550 0.182 154 2.499 0.2039
## FGI4 - FMA7 -0.2547 0.182 154 -1.398 0.8568
## FGI4 - FSV1 -0.0597 0.182 154 -0.328 1.0000
## FMA7 - FSV1 0.1950 0.182 154 1.071 0.9619
##
## mun = Tam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.0730 0.182 154 0.401 0.9999
## CNCH12 - FBO1 -1.2307 0.182 154 -6.758 <.0001
## CNCH12 - FCHI8 0.5360 0.182 154 2.943 0.0711
## CNCH12 - FEAR5 -0.1260 0.182 154 -0.692 0.9971
## CNCH12 - FGI4 -0.3163 0.182 154 -1.737 0.6630
## CNCH12 - FMA7 0.5133 0.182 154 2.819 0.0979
## CNCH12 - FSV1 -0.2400 0.182 154 -1.318 0.8908
## CNCH13 - FBO1 -1.3037 0.182 154 -7.159 <.0001
## CNCH13 - FCHI8 0.4630 0.182 154 2.543 0.1858
## CNCH13 - FEAR5 -0.1990 0.182 154 -1.093 0.9575
## CNCH13 - FGI4 -0.3893 0.182 154 -2.138 0.3955
## CNCH13 - FMA7 0.4403 0.182 154 2.418 0.2402
## CNCH13 - FSV1 -0.3130 0.182 154 -1.719 0.6750
## FBO1 - FCHI8 1.7667 0.182 154 9.702 <.0001
## FBO1 - FEAR5 1.1047 0.182 154 6.066 <.0001
## FBO1 - FGI4 0.9143 0.182 154 5.021 <.0001
## FBO1 - FMA7 1.7440 0.182 154 9.577 <.0001
## FBO1 - FSV1 0.9907 0.182 154 5.440 <.0001
## FCHI8 - FEAR5 -0.6620 0.182 154 -3.635 0.0088
## FCHI8 - FGI4 -0.8523 0.182 154 -4.681 0.0002
## FCHI8 - FMA7 -0.0227 0.182 154 -0.124 1.0000
## FCHI8 - FSV1 -0.7760 0.182 154 -4.261 0.0009
## FEAR5 - FGI4 -0.1903 0.182 154 -1.045 0.9666
## FEAR5 - FMA7 0.6393 0.182 154 3.511 0.0133
## FEAR5 - FSV1 -0.1140 0.182 154 -0.626 0.9985
## FGI4 - FMA7 0.8297 0.182 154 4.556 0.0003
## FGI4 - FSV1 0.0763 0.182 154 0.419 0.9999
## FMA7 - FSV1 -0.7533 0.182 154 -4.137 0.0015
##
## mun = ViG:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.7577 0.182 154 4.161 0.0013
## CNCH12 - FBO1 0.8780 0.182 154 4.821 0.0001
## CNCH12 - FCHI8 1.3113 0.182 154 7.201 <.0001
## CNCH12 - FEAR5 -0.0060 0.182 154 -0.033 1.0000
## CNCH12 - FGI4 0.2653 0.182 154 1.457 0.8287
## CNCH12 - FMA7 -0.3750 0.182 154 -2.059 0.4460
## CNCH12 - FSV1 0.6180 0.182 154 3.394 0.0193
## CNCH13 - FBO1 0.1203 0.182 154 0.661 0.9978
## CNCH13 - FCHI8 0.5537 0.182 154 3.040 0.0547
## CNCH13 - FEAR5 -0.7637 0.182 154 -4.194 0.0012
## CNCH13 - FGI4 -0.4923 0.182 154 -2.704 0.1294
## CNCH13 - FMA7 -1.1327 0.182 154 -6.220 <.0001
## CNCH13 - FSV1 -0.1397 0.182 154 -0.767 0.9945
## FBO1 - FCHI8 0.4333 0.182 154 2.380 0.2589
## FBO1 - FEAR5 -0.8840 0.182 154 -4.854 0.0001
## FBO1 - FGI4 -0.6127 0.182 154 -3.364 0.0212
## FBO1 - FMA7 -1.2530 0.182 154 -6.881 <.0001
## FBO1 - FSV1 -0.2600 0.182 154 -1.428 0.8431
## FCHI8 - FEAR5 -1.3173 0.182 154 -7.234 <.0001
## FCHI8 - FGI4 -1.0460 0.182 154 -5.744 <.0001
## FCHI8 - FMA7 -1.6863 0.182 154 -9.260 <.0001
## FCHI8 - FSV1 -0.6933 0.182 154 -3.807 0.0049
## FEAR5 - FGI4 0.2713 0.182 154 1.490 0.8117
## FEAR5 - FMA7 -0.3690 0.182 154 -2.026 0.4677
## FEAR5 - FSV1 0.6240 0.182 154 3.427 0.0174
## FGI4 - FMA7 -0.6403 0.182 154 -3.516 0.0131
## FGI4 - FSV1 0.3527 0.182 154 1.937 0.5281
## FMA7 - FSV1 0.9930 0.182 154 5.453 <.0001
##
## mun = Yac:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.5920 0.182 154 -3.251 0.0299
## CNCH12 - FBO1 -0.5200 0.182 154 -2.856 0.0892
## CNCH12 - FCHI8 -0.4897 0.182 154 -2.689 0.1339
## CNCH12 - FEAR5 -1.1310 0.182 154 -6.211 <.0001
## CNCH12 - FGI4 -1.4833 0.182 154 -8.146 <.0001
## CNCH12 - FMA7 -0.2953 0.182 154 -1.622 0.7365
## CNCH12 - FSV1 -0.9710 0.182 154 -5.332 <.0001
## CNCH13 - FBO1 0.0720 0.182 154 0.395 0.9999
## CNCH13 - FCHI8 0.1023 0.182 154 0.562 0.9992
## CNCH13 - FEAR5 -0.5390 0.182 154 -2.960 0.0681
## CNCH13 - FGI4 -0.8913 0.182 154 -4.895 0.0001
## CNCH13 - FMA7 0.2967 0.182 154 1.629 0.7320
## CNCH13 - FSV1 -0.3790 0.182 154 -2.081 0.4317
## FBO1 - FCHI8 0.0303 0.182 154 0.167 1.0000
## FBO1 - FEAR5 -0.6110 0.182 154 -3.355 0.0218
## FBO1 - FGI4 -0.9633 0.182 154 -5.290 <.0001
## FBO1 - FMA7 0.2247 0.182 154 1.234 0.9206
## FBO1 - FSV1 -0.4510 0.182 154 -2.477 0.2134
## FCHI8 - FEAR5 -0.6413 0.182 154 -3.522 0.0129
## FCHI8 - FGI4 -0.9937 0.182 154 -5.457 <.0001
## FCHI8 - FMA7 0.1943 0.182 154 1.067 0.9626
## FCHI8 - FSV1 -0.4813 0.182 154 -2.643 0.1488
## FEAR5 - FGI4 -0.3523 0.182 154 -1.935 0.5293
## FEAR5 - FMA7 0.8357 0.182 154 4.589 0.0002
## FEAR5 - FSV1 0.1600 0.182 154 0.879 0.9876
## FGI4 - FMA7 1.1880 0.182 154 6.524 <.0001
## FGI4 - FSV1 0.5123 0.182 154 2.813 0.0992
## FMA7 - FSV1 -0.6757 0.182 154 -3.710 0.0068
##
## 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(VLA) ~ 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 0.054489 0.0077842 7 60.485 3.5338 0.003024 **
## ---
## 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(VLA) ~ gen + (1 | mun) + (1 | mun:gen)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 268.35 -514.70
## (1 | mun) 10 267.29 -514.58 2.124 1 0.145
## (1 | mun:gen) 10 200.21 -380.41 136.285 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(VLA ~ 1 +
(1|gen) +
(1|mun) +
(1|gen:mun),
data = datos)
ranova(modelo_blup)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## VLA ~ (1 | gen) + (1 | mun) + (1 | gen:mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -80.351 170.70
## (1 | gen) 4 -83.810 175.62 6.917 1 0.00854 **
## (1 | mun) 4 -81.413 170.83 2.124 1 0.14505
## (1 | gen:mun) 4 -147.823 303.65 134.944 1 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CNCH12 -0.15059969
## CNCH13 -0.04498257
## FBO1 0.01040022
## FCHI8 -0.28579204
## FEAR5 0.21562790
## FGI4 0.29430137
## FMA7 -0.05429885
## FSV1 0.01534367
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 4.576893
## CNCH13 4.682510
## FBO1 4.737893
## FCHI8 4.441701
## FEAR5 4.943121
## FGI4 5.021794
## FMA7 4.673194
## FSV1 4.742837
#Blups Parcela
blups$mun
## (Intercept)
## CH -0.0058398543
## Gig 0.0180328021
## Htc 0.0492668873
## Jam -0.0566239711
## PtR -0.0748911125
## RiN -0.0003083851
## SnV -0.0544793519
## Tam 0.2407264233
## ViG -0.1433987584
## Yac 0.0275153206
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## CH 4.721653
## Gig 4.745526
## Htc 4.776760
## Jam 4.670869
## PtR 4.652602
## RiN 4.727185
## SnV 4.673014
## Tam 4.968219
## ViG 4.584094
## Yac 4.755008
#Blups interacción
blups$`gen:mun`
## (Intercept)
## CNCH12:CH -0.03407639
## CNCH12:Gig 0.08799805
## CNCH12:Htc -0.21364886
## CNCH12:Jam 0.10605717
## CNCH12:PtR -0.71248565
## CNCH12:RiN 0.18286244
## CNCH12:SnV -0.10848110
## CNCH12:Tam 0.26504066
## CNCH12:ViG 0.39554490
## CNCH12:Yac -0.45809321
## CNCH13:CH 0.51252713
## CNCH13:Gig 0.14074131
## CNCH13:Htc 0.09475743
## CNCH13:Jam 0.16422085
## CNCH13:PtR -0.10776322
## CNCH13:RiN -0.52867313
## CNCH13:SnV -0.12258965
## CNCH13:Tam 0.10367745
## CNCH13:ViG -0.38434816
## CNCH13:Yac -0.01869350
## FBO1:CH -0.16898446
## FBO1:Gig -0.56124741
## FBO1:Htc 0.39885839
## FBO1:Jam 0.18826692
## FBO1:PtR -0.11925100
## FBO1:RiN -0.18271450
## FBO1:SnV -0.07565738
## FBO1:Tam 1.23138071
## FBO1:ViG -0.54309061
## FBO1:Yac -0.13377148
## FCHI8:CH 0.07480687
## FCHI8:Gig -0.17170708
## FCHI8:Htc 0.16926661
## FCHI8:RiN -0.27589245
## FCHI8:SnV -0.06735306
## FCHI8:Tam -0.09705012
## FCHI8:ViG -0.66698426
## FCHI8:Yac 0.10640624
## FEAR5:CH -0.09541225
## FEAR5:Gig 0.41663110
## FEAR5:Htc -0.34936489
## FEAR5:Jam 0.10133455
## FEAR5:PtR -0.16282002
## FEAR5:RiN 0.25523021
## FEAR5:SnV 0.18401583
## FEAR5:Tam 0.04801837
## FEAR5:ViG 0.07011426
## FEAR5:Yac 0.23280440
## FGI4:CH 0.39241917
## FGI4:Gig 0.26816574
## FGI4:Htc 0.27608490
## FGI4:Jam -0.94993143
## FGI4:PtR 0.27592650
## FGI4:RiN 0.66265877
## FGI4:SnV -0.35200936
## FGI4:Tam 0.14889221
## FGI4:ViG -0.24608290
## FGI4:Yac 0.48002951
## FMA7:CH -0.34301082
## FMA7:Gig 0.35121878
## FMA7:Htc -0.03263779
## FMA7:Jam -0.02069102
## FMA7:PtR 0.01538530
## FMA7:RiN -0.42299041
## FMA7:SnV 0.19298368
## FMA7:Tam -0.28570461
## FMA7:ViG 0.64732253
## FMA7:Yac -0.27828668
## FSV1:CH -0.38063079
## FSV1:Gig -0.40099288
## FSV1:Htc 0.01405984
## FSV1:PtR 0.26775766
## FSV1:RiN 0.30728208
## FSV1:SnV -0.04609513
## FSV1:Tam 0.33194365
## FSV1:ViG -0.31267179
## FSV1:Yac 0.26919729
fixef(modelo_blup)[1] + blups$`gen:mun`
## (Intercept)
## CNCH12:CH 4.693417
## CNCH12:Gig 4.815491
## CNCH12:Htc 4.513844
## CNCH12:Jam 4.833550
## CNCH12:PtR 4.015007
## CNCH12:RiN 4.910355
## CNCH12:SnV 4.619012
## CNCH12:Tam 4.992534
## CNCH12:ViG 5.123038
## CNCH12:Yac 4.269400
## CNCH13:CH 5.240020
## CNCH13:Gig 4.868234
## CNCH13:Htc 4.822250
## CNCH13:Jam 4.891714
## CNCH13:PtR 4.619730
## CNCH13:RiN 4.198820
## CNCH13:SnV 4.604903
## CNCH13:Tam 4.831170
## CNCH13:ViG 4.343145
## CNCH13:Yac 4.708799
## FBO1:CH 4.558508
## FBO1:Gig 4.166245
## FBO1:Htc 5.126351
## FBO1:Jam 4.915760
## FBO1:PtR 4.608242
## FBO1:RiN 4.544778
## FBO1:SnV 4.651836
## FBO1:Tam 5.958874
## FBO1:ViG 4.184402
## FBO1:Yac 4.593721
## FCHI8:CH 4.802300
## FCHI8:Gig 4.555786
## FCHI8:Htc 4.896760
## FCHI8:RiN 4.451600
## FCHI8:SnV 4.660140
## FCHI8:Tam 4.630443
## FCHI8:ViG 4.060509
## FCHI8:Yac 4.833899
## FEAR5:CH 4.632081
## FEAR5:Gig 5.144124
## FEAR5:Htc 4.378128
## FEAR5:Jam 4.828827
## FEAR5:PtR 4.564673
## FEAR5:RiN 4.982723
## FEAR5:SnV 4.911509
## FEAR5:Tam 4.775511
## FEAR5:ViG 4.797607
## FEAR5:Yac 4.960297
## FGI4:CH 5.119912
## FGI4:Gig 4.995659
## FGI4:Htc 5.003578
## FGI4:Jam 3.777561
## FGI4:PtR 5.003419
## FGI4:RiN 5.390152
## FGI4:SnV 4.375484
## FGI4:Tam 4.876385
## FGI4:ViG 4.481410
## FGI4:Yac 5.207522
## FMA7:CH 4.384482
## FMA7:Gig 5.078712
## FMA7:Htc 4.694855
## FMA7:Jam 4.706802
## FMA7:PtR 4.742878
## FMA7:RiN 4.304503
## FMA7:SnV 4.920477
## FMA7:Tam 4.441788
## FMA7:ViG 5.374815
## FMA7:Yac 4.449206
## FSV1:CH 4.346862
## FSV1:Gig 4.326500
## FSV1:Htc 4.741553
## FSV1:PtR 4.995251
## FSV1:RiN 5.034775
## FSV1:SnV 4.681398
## FSV1:Tam 5.059437
## FSV1:ViG 4.414821
## FSV1:Yac 4.996690
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 -0.15059969
## 2 CNCH13 -0.04498257
## 3 FBO1 0.01040022
## 4 FCHI8 -0.28579204
## 5 FEAR5 0.21562790
## 6 FGI4 0.29430137
## 7 FMA7 -0.05429885
## 8 FSV1 0.01534367
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 CH -0.0058398543
## 2 Gig 0.0180328021
## 3 Htc 0.0492668873
## 4 Jam -0.0566239711
## 5 PtR -0.0748911125
## 6 RiN -0.0003083851
## 7 SnV -0.0544793519
## 8 Tam 0.2407264233
## 9 ViG -0.1433987584
## 10 Yac 0.0275153206
#Tabla blup_gen_mun
blup_gen_mun <- ranef(modelo_blup)$`gen:mun` %>%
tibble::rownames_to_column("gen:mun") %>%
rename(BLUP = `(Intercept)`)
blup_gen_mun
## gen:mun BLUP
## 1 CNCH12:CH -0.03407639
## 2 CNCH12:Gig 0.08799805
## 3 CNCH12:Htc -0.21364886
## 4 CNCH12:Jam 0.10605717
## 5 CNCH12:PtR -0.71248565
## 6 CNCH12:RiN 0.18286244
## 7 CNCH12:SnV -0.10848110
## 8 CNCH12:Tam 0.26504066
## 9 CNCH12:ViG 0.39554490
## 10 CNCH12:Yac -0.45809321
## 11 CNCH13:CH 0.51252713
## 12 CNCH13:Gig 0.14074131
## 13 CNCH13:Htc 0.09475743
## 14 CNCH13:Jam 0.16422085
## 15 CNCH13:PtR -0.10776322
## 16 CNCH13:RiN -0.52867313
## 17 CNCH13:SnV -0.12258965
## 18 CNCH13:Tam 0.10367745
## 19 CNCH13:ViG -0.38434816
## 20 CNCH13:Yac -0.01869350
## 21 FBO1:CH -0.16898446
## 22 FBO1:Gig -0.56124741
## 23 FBO1:Htc 0.39885839
## 24 FBO1:Jam 0.18826692
## 25 FBO1:PtR -0.11925100
## 26 FBO1:RiN -0.18271450
## 27 FBO1:SnV -0.07565738
## 28 FBO1:Tam 1.23138071
## 29 FBO1:ViG -0.54309061
## 30 FBO1:Yac -0.13377148
## 31 FCHI8:CH 0.07480687
## 32 FCHI8:Gig -0.17170708
## 33 FCHI8:Htc 0.16926661
## 34 FCHI8:RiN -0.27589245
## 35 FCHI8:SnV -0.06735306
## 36 FCHI8:Tam -0.09705012
## 37 FCHI8:ViG -0.66698426
## 38 FCHI8:Yac 0.10640624
## 39 FEAR5:CH -0.09541225
## 40 FEAR5:Gig 0.41663110
## 41 FEAR5:Htc -0.34936489
## 42 FEAR5:Jam 0.10133455
## 43 FEAR5:PtR -0.16282002
## 44 FEAR5:RiN 0.25523021
## 45 FEAR5:SnV 0.18401583
## 46 FEAR5:Tam 0.04801837
## 47 FEAR5:ViG 0.07011426
## 48 FEAR5:Yac 0.23280440
## 49 FGI4:CH 0.39241917
## 50 FGI4:Gig 0.26816574
## 51 FGI4:Htc 0.27608490
## 52 FGI4:Jam -0.94993143
## 53 FGI4:PtR 0.27592650
## 54 FGI4:RiN 0.66265877
## 55 FGI4:SnV -0.35200936
## 56 FGI4:Tam 0.14889221
## 57 FGI4:ViG -0.24608290
## 58 FGI4:Yac 0.48002951
## 59 FMA7:CH -0.34301082
## 60 FMA7:Gig 0.35121878
## 61 FMA7:Htc -0.03263779
## 62 FMA7:Jam -0.02069102
## 63 FMA7:PtR 0.01538530
## 64 FMA7:RiN -0.42299041
## 65 FMA7:SnV 0.19298368
## 66 FMA7:Tam -0.28570461
## 67 FMA7:ViG 0.64732253
## 68 FMA7:Yac -0.27828668
## 69 FSV1:CH -0.38063079
## 70 FSV1:Gig -0.40099288
## 71 FSV1:Htc 0.01405984
## 72 FSV1:PtR 0.26775766
## 73 FSV1:RiN 0.30728208
## 74 FSV1:SnV -0.04609513
## 75 FSV1:Tam 0.33194365
## 76 FSV1:ViG -0.31267179
## 77 FSV1:Yac 0.26919729
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 4.536977 4.536977 4.536977 5.189198 5.189198 5.189198 4.563069 4.563069
## [9] 4.563069 4.510668 4.510668 4.510668 4.841869 4.841869 4.841869 5.408374
## [17] 5.408374 5.408374 4.324343 4.324343 4.324343 4.356366 4.356366 4.356366
## [25] 4.682924 4.682924 4.682924 4.841284 4.841284 4.841284 4.194679 4.194679
## [33] 4.194679 4.288027 4.288027 4.288027 5.377785 5.377785 5.377785 5.307993
## [41] 5.307993 5.307993 5.042446 5.042446 5.042446 4.359876 4.359876 4.359876
## [49] 4.412511 4.412511 4.412511 4.826535 4.826535 4.826535 5.186018 5.186018
## [57] 5.186018 4.660234 4.660234 4.660234 4.643023 4.643023 4.643023 5.347146
## [65] 5.347146 5.347146 4.689823 4.689823 4.689823 4.806163 4.806163 4.806163
## [73] 4.626326 4.626326 4.626326 4.790107 4.790107 4.790107 4.869536 4.869536
## [81] 4.869536 4.987831 4.987831 4.987831 4.015239 4.015239 4.015239 4.595879
## [89] 4.595879 4.595879 3.789516 3.789516 3.789516 4.499856 4.499856 4.499856
## [97] 4.543751 4.543751 4.543751 4.705410 4.705410 4.705410 5.222830 5.222830
## [105] 5.222830 4.613688 4.613688 4.613688 4.935703 4.935703 4.935703 4.153529
## [113] 4.153529 4.153529 4.759447 4.759447 4.759447 4.554870 4.554870 4.554870
## [121] 4.165500 4.165500 4.165500 5.198043 5.198043 5.198043 5.684145 5.684145
## [129] 5.684145 4.249895 4.249895 4.249895 5.049810 5.049810 5.049810 4.413933
## [137] 4.413933 4.413933 4.505441 4.505441 4.505441 4.607756 4.607756 4.607756
## [145] 4.319868 4.319868 4.319868 5.072657 5.072657 5.072657 4.615306 4.615306
## [153] 4.615306 4.811698 4.811698 4.811698 4.642262 4.642262 4.642262 5.082660
## [161] 5.082660 5.082660 5.026914 5.026914 5.026914 6.210000 6.210000 6.210000
## [169] 4.585377 4.585377 4.585377 5.231866 5.231866 5.231866 5.411413 5.411413
## [177] 5.411413 4.628216 4.628216 4.628216 5.315507 5.315507 5.315507 4.829039
## [185] 4.829039 4.829039 4.154763 4.154763 4.154763 4.051404 4.051404 4.051404
## [193] 3.631318 3.631318 3.631318 4.869836 4.869836 4.869836 4.632313 4.632313
## [201] 4.632313 5.177118 5.177118 5.177118 4.286766 4.286766 4.286766 4.146315
## [209] 4.146315 4.146315 4.691332 4.691332 4.691332 4.631637 4.631637 4.631637
## [217] 4.575622 4.575622 4.575622 5.203441 5.203441 5.203441 5.529339 5.529339
## [225] 5.529339 4.422423 4.422423 4.422423 5.039549 5.039549 5.039549
#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 VLA varianza-heredabilidades
vc <- as.data.frame(VarCorr(modelo_blup))
vc
## grp var1 var2 vcov sdcor
## 1 gen:mun (Intercept) <NA> 0.15506661 0.3937850
## 2 mun (Intercept) <NA> 0.02137709 0.1462090
## 3 gen (Intercept) <NA> 0.04772909 0.2184699
## 4 Residual <NA> <NA> 0.04974188 0.2230289
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## gen:mun (Intercept) 0.39378
## mun (Intercept) 0.14621
## gen (Intercept) 0.21847
## Residual 0.22303
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] 0.7402235
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## [1] 2.404905
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CNCH12 -0.15059969
## CNCH13 -0.04498257
## FBO1 0.01040022
## FCHI8 -0.28579204
## FEAR5 0.21562790
## FGI4 0.29430137
## FMA7 -0.05429885
## FSV1 0.01534367
##Predicho VLA carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## FGI4 0.29430137 5.021794
## FEAR5 0.21562790 4.943121
## FSV1 0.01534367 4.742837
## FBO1 0.01040022 4.737893
## CNCH13 -0.04498257 4.682510
## FMA7 -0.05429885 4.673194
## CNCH12 -0.15059969 4.576893
## FCHI8 -0.28579204 4.441701
# Visualización ranking predichos
blup_gen$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(VLA=mean(VLA)) %>%
pivot_wider(names_from=mun,
values_from=VLA)
## `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, VLA)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $VLA
## $coordgen
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.4190117 -0.41179585 -0.3712998 -0.34481272 -1.018760481 -0.009339194
## [2,] 0.2048187 0.06320744 0.3537033 1.02910435 -0.005895151 0.082880323
## [3,] 0.1371718 0.85803048 -0.7625596 0.25351928 0.037863952 -0.358857346
## [4,] 0.6155347 0.29360642 0.8507853 -0.30101206 0.013650413 0.284840859
## [5,] -0.4655073 -0.45761191 -0.3476354 0.25644284 0.140103314 0.913770270
## [6,] -1.0224424 0.11877517 0.4871056 -0.11786917 -0.382413199 -0.493590835
## [7,] 0.2388936 -0.79039474 -0.0723995 -0.04305733 0.665041342 -0.742334136
## [8,] -0.1274807 0.32618299 -0.1376999 -0.73231520 0.550409810 0.322630059
## [,7] [,8]
## [1,] 0.25871395 -0.4937899
## [2,] 0.68541040 -0.4937899
## [3,] -0.41926738 -0.4937899
## [4,] -0.58817247 -0.4937899
## [5,] -0.48936177 -0.4937899
## [6,] -0.07916009 -0.4937899
## [7,] -0.15674643 -0.4937899
## [8,] 0.78858380 -0.4937899
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.6939938 0.10467484 0.44443091 0.628018013 -0.42674135 -0.01241384
## [2,] -0.8314382 -0.91546973 0.14847245 0.375218404 -0.08645752 0.02396141
## [3,] -0.5067774 0.53925039 0.06483958 0.147491306 0.08109450 -0.39879461
## [4,] 0.2414263 -0.10986803 -0.72716127 0.437670906 0.19056520 0.34659000
## [5,] -1.0275750 0.22121458 0.13633176 -0.008058167 0.62200504 -0.17288845
## [6,] -1.3965508 0.01697509 -0.20223401 -0.550105711 -0.38628163 0.21350053
## [7,] -0.3131730 -0.29317590 -0.29562627 0.096880765 0.32266731 0.15156062
## [8,] -0.5093475 0.97225288 -0.93502053 0.171545323 -0.19539269 -0.13093261
## [9,] -0.4624851 -1.23044305 -0.56385507 -0.099053745 -0.02515550 -0.33118722
## [10,] -1.1472845 0.26440566 0.28907651 -0.010691670 0.24698473 0.28445853
## [,7] [,8]
## [1,] 0.09960246 -5.494270e-16
## [2,] -0.12183958 4.986365e-16
## [3,] -0.08296950 4.452176e-16
## [4,] 0.09894891 4.492751e-16
## [5,] 0.12981687 -1.606847e-16
## [6,] 0.03345086 -2.046395e-18
## [7,] -0.14458312 -9.739972e-16
## [8,] -0.04849479 -8.060867e-17
## [9,] 0.06589865 -5.076592e-17
## [10,] -0.03703943 3.373998e-16
##
## $eigenvalues
## [1] 2.526254e+00 1.953524e+00 1.474846e+00 1.048773e+00 9.861567e-01
## [6] 7.655576e-01 2.972916e-01 1.431078e-15
##
## $totalvar
## [1] 15.12
##
## $varexpl
## [1] 42.21 25.24 14.39 7.27 6.43 3.88 0.58 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 -0.1824583 -0.07129167 -0.43654167 0.06709365 -0.83627090 0.0521250
## CNCH13 0.5282083 0.09270833 0.01045833 0.23709365 -0.06127090 -0.6298750
## FBO1 -0.1707917 -0.62895833 0.40245833 0.31909365 -0.01860423 -0.1915417
## FCHI8 -0.1971250 -0.49395833 -0.14787500 -0.42310483 -0.40543705 -0.5908750
## FEAR5 0.1158750 0.65870833 -0.22054167 0.42809365 0.13839577 0.4984583
## FGI4 0.7345417 0.57304167 0.55045833 -0.65690635 0.70272910 1.0281250
## FMA7 -0.4281250 0.31637500 -0.13987500 0.02309365 0.06572910 -0.5222083
## FSV1 -0.4001250 -0.44662500 -0.01854167 0.00554290 0.41472910 0.3557917
## SnV Tam ViG Yac
## CNCH12 -0.21600000 -0.09883333 0.4311667 -0.68529167
## CNCH13 -0.12600000 -0.17183333 -0.3265000 -0.09329167
## FBO1 -0.01866667 1.13183333 -0.4468333 -0.16529167
## FCHI8 -0.30566667 -0.63483333 -0.8801667 -0.19562500
## FEAR5 0.47400000 0.02716667 0.4371667 0.44570833
## FGI4 -0.04066667 0.21750000 0.1658333 0.79804167
## FMA7 0.21400000 -0.61216667 0.8061667 -0.38995833
## FSV1 0.01900000 0.14116667 -0.1868333 0.28570833
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 0.7159997
##
## $grand_mean
## [1] 4.720364
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 4.522733 4.666333 4.741633 4.292897 5.020667 5.127633 4.653667 4.737345
##
## $mean_env
## CH Gig Htc Jam PtR RiN SnV Tam
## 4.715792 4.763625 4.826208 4.570573 4.549604 4.726875 4.618333 5.209833
## ViG Yac
## 4.440167 4.782625
##
## $scale_val
## CH Gig Htc Jam PtR RiN SnV Tam
## 0.4270652 0.4949894 0.3264680 0.3697311 0.4716431 0.5964265 0.2475016 0.5562727
## ViG Yac
## 0.5568133 0.4800508
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración VLA variables
##Blup gen
blup_gen <- ranef(modelo_blup)$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 4.576893
## CNCH13 CNCH13 4.682510
## FBO1 FBO1 4.737893
## FCHI8 FCHI8 4.441701
## FEAR5 FEAR5 4.943121
## FGI4 FGI4 5.021794
## FMA7 FMA7 4.673194
## FSV1 FSV1 4.742837
##Plasticidad usando Fisher environments (joint regression)
#índice (creando valores VLA x para definir env = promedio VLA tasas en c/parcela)
indice_env <- datos %>%
group_by(mun) %>%
summarise(env = mean(VLA))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas VLA reacción joint regression env
ggplot(datos, aes(x = env, y = VLA,
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 VLA reacción clima local
ggplot(datos, aes(x = E, y = VLA,
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(VLA ~ gen*env,
data=datos)
emtrends(mod_plas_lm, "gen", var = "env")
## gen env.trend SE df lower.CL upper.CL
## CNCH12 0.697 0.367 215 -0.0268 1.421
## CNCH13 1.059 0.367 215 0.3356 1.783
## FBO1 2.854 0.367 215 2.1307 3.578
## FCHI8 1.159 0.384 215 0.4033 1.915
## FEAR5 0.459 0.367 215 -0.2648 1.183
## FGI4 1.373 0.367 215 0.6492 2.097
## FMA7 -0.546 0.367 215 -1.2696 0.178
## FSV1 1.219 0.372 215 0.4856 1.953
##
## Confidence level used: 0.95
# modelo blup factores aleatorios
modelo_plasticidad <- lmer(VLA ~ env +
(env|gen) +
(1|mun),
data=datos)
## boundary (singular) fit: see help('isSingular')
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(VLA ~ gen*E,
data=datos)
emtrends(mod_plas2_lm, "gen", var = "E")
## gen E.trend SE df lower.CL upper.CL
## CNCH12 4.1432 1.42 215 1.351 6.94
## CNCH13 0.7616 1.42 215 -2.031 3.55
## FBO1 5.4334 1.42 215 2.641 8.23
## FCHI8 0.0185 1.50 215 -2.934 2.97
## FEAR5 -0.0637 1.42 215 -2.856 2.73
## FGI4 1.3657 1.42 215 -1.426 4.16
## FMA7 1.8143 1.42 215 -0.978 4.61
## FSV1 0.5466 1.44 215 -2.283 3.38
##
## Confidence level used: 0.95
#Modelo factores aleatorios
modelo_plasticidad2 <- lmer(VLA ~ 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 1.1 54.9 54.9
## 2 PC2 0.9 45.1 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C 0.74 0.55 0.45
## 2 Pendiente 0.74 0.55 0.45
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.548679
## -------------------------------------------------------------------------------
## 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 4.73e+ 0 4.74 0.0104 2.20e- 1 increase 100
## 2 Pendiente FA1 1.09e-11 1.55 1.55 1.43e+13 increase 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FBO1
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FBO1 1.03
## 2 FGI4 1.03
## 3 FEAR5 1.96
## 4 FSV1 2.16
## 5 CNCH13 2.50
## 6 CNCH12 3.14
## 7 FCHI8 3.33
## 8 FMA7 3.66
#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 1.48 49.4 49.4
## 2 PC2 1.08 36 85.4
## 3 PC3 0.44 14.6 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 3 × 5
## VAR FA1 FA2 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BLUP_C -0.01 0.96 0.93 0.07
## 2 Pendiente 0.88 0.22 0.83 0.17
## 3 Pendiente2 -0.82 0.37 0.8 0.2
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.8540534
## -------------------------------------------------------------------------------
## Selection differential
## -------------------------------------------------------------------------------
## # A tibble: 3 × 8
## VAR Factor Xo Xs SD SDperc sense goal
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Pendiente FA1 1.09e-11 0.308 0.308 2.83e12 increase 100
## 2 Pendiente2 FA1 4.15e-13 -0.412 -0.412 -9.93e13 decrease 100
## 3 BLUP_C FA2 4.73e+ 0 5.02 0.294 6.23e 0 increase 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FGI4
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod2$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FGI4 0.840
## 2 FEAR5 1.89
## 3 FSV1 2.09
## 4 CNCH13 2.45
## 5 FBO1 2.56
## 6 CNCH12 3.32
## 7 FMA7 3.37
## 8 FCHI8 3.49
#SLAáfico Selección 1
plot(mgidi_mod2)
