setwd("G:/Mi unidad/Agrosavia/FeCa/Fenoma/Análisis/MorfoA")
datos<-read.table("morfo.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(SLA) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(SLA)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 1.2898 0.18425 3.9893 0.0002881 ***
## mun 9 5.1666 0.57407 12.4294 < 2.2e-16 ***
## gen:mun 60 5.1424 0.08571 1.8557 0.0002049 ***
## Residuals 539 24.8944 0.04619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm ((SLA) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: (SLA)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 16495 2356.4 3.7304 0.0005894 ***
## mun 9 60328 6703.1 10.6115 3.272e-15 ***
## gen:mun 60 63407 1056.8 1.6730 0.0018042 **
## Residuals 539 340478 631.7
## ---
## 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 105 2.81 539 99.5 111
## CNCH13 102 2.81 539 96.9 108
## FBO1 106 2.81 539 100.4 111
## FCHI8 nonEst NA NA NA NA
## FEAR5 110 2.81 539 104.2 115
## FGI4 106 2.81 539 100.4 111
## FMA7 102 2.81 539 96.7 108
## 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 2.6420 3.97 539 0.665 0.9856
## CNCH12 - FBO1 -0.8650 3.97 539 -0.218 0.9999
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -4.6875 3.97 539 -1.180 0.8466
## CNCH12 - FGI4 -0.8441 3.97 539 -0.212 0.9999
## CNCH12 - FMA7 2.8153 3.97 539 0.708 0.9809
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 -3.5070 3.97 539 -0.883 0.9506
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -7.3295 3.97 539 -1.844 0.4381
## CNCH13 - FGI4 -3.4861 3.97 539 -0.877 0.9518
## CNCH13 - FMA7 0.1733 3.97 539 0.044 1.0000
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -3.8225 3.97 539 -0.962 0.9297
## FBO1 - FGI4 0.0209 3.97 539 0.005 1.0000
## FBO1 - FMA7 3.6803 3.97 539 0.926 0.9397
## 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 3.8434 3.97 539 0.967 0.9281
## FEAR5 - FMA7 7.5028 3.97 539 1.888 0.4106
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 3.6594 3.97 539 0.921 0.9411
## 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
## Chi 99.7 3.14 539 93.6 106
## Gig 94.1 3.14 539 88.0 100
## HtC 113.1 3.14 539 107.0 119
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
## RiN 121.0 3.14 539 114.8 127
## SnV 122.4 3.14 539 116.3 129
## Tam 101.0 3.14 539 94.8 107
## ViG 110.0 3.14 539 103.8 116
## Yac 96.9 3.14 539 90.7 103
##
## 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
## Chi - Gig 5.60 4.44 539 1.260 0.9129
## Chi - HtC -13.39 4.44 539 -3.014 0.0543
## Chi - Jam nonEst NA NA NA NA
## Chi - PtR nonEst NA NA NA NA
## Chi - RiN -21.29 4.44 539 -4.791 0.0001
## Chi - SnV -22.71 4.44 539 -5.111 <.0001
## Chi - Tam -1.25 4.44 539 -0.282 1.0000
## Chi - ViG -10.24 4.44 539 -2.305 0.2927
## Chi - Yac 2.88 4.44 539 0.648 0.9982
## Gig - HtC -18.99 4.44 539 -4.274 0.0006
## Gig - Jam nonEst NA NA NA NA
## Gig - PtR nonEst NA NA NA NA
## Gig - RiN -26.89 4.44 539 -6.051 <.0001
## Gig - SnV -28.31 4.44 539 -6.371 <.0001
## Gig - Tam -6.85 4.44 539 -1.543 0.7839
## Gig - ViG -15.84 4.44 539 -3.565 0.0094
## Gig - Yac -2.72 4.44 539 -0.612 0.9987
## HtC - Jam nonEst NA NA NA NA
## HtC - PtR nonEst NA NA NA NA
## HtC - RiN -7.90 4.44 539 -1.777 0.6358
## HtC - SnV -9.32 4.44 539 -2.097 0.4184
## HtC - Tam 12.14 4.44 539 2.732 0.1154
## HtC - ViG 3.15 4.44 539 0.709 0.9967
## HtC - Yac 16.27 4.44 539 3.662 0.0066
## 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 -1.42 4.44 539 -0.319 1.0000
## RiN - Tam 20.03 4.44 539 4.509 0.0002
## RiN - ViG 11.05 4.44 539 2.486 0.2035
## RiN - Yac 24.17 4.44 539 5.439 <.0001
## SnV - Tam 21.45 4.44 539 4.828 <.0001
## SnV - ViG 12.47 4.44 539 2.806 0.0957
## SnV - Yac 25.58 4.44 539 5.758 <.0001
## Tam - ViG -8.99 4.44 539 -2.023 0.4674
## Tam - Yac 4.13 4.44 539 0.930 0.9830
## ViG - Yac 13.12 4.44 539 2.953 0.0645
##
## 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
#Interacción
gm<-emmeans(modelo, pairwise ~ gen|mun)
gm
## $emmeans
## mun = Chi:
## gen emmean SE df lower.CL upper.CL
## CNCH12 102.6 8.89 539 85.2 120.1
## CNCH13 102.7 8.89 539 85.2 120.1
## FBO1 92.7 8.89 539 75.3 110.2
## FCHI8 104.6 8.89 539 87.2 122.1
## FEAR5 102.1 8.89 539 84.6 119.6
## FGI4 88.7 8.89 539 71.3 106.2
## FMA7 89.8 8.89 539 72.4 107.3
## FSV1 114.6 8.89 539 97.1 132.0
##
## mun = Gig:
## gen emmean SE df lower.CL upper.CL
## CNCH12 91.6 8.89 539 74.2 109.1
## CNCH13 62.7 8.89 539 45.2 80.1
## FBO1 104.6 8.89 539 87.2 122.1
## FCHI8 91.8 8.89 539 74.4 109.3
## FEAR5 114.9 8.89 539 97.5 132.4
## FGI4 102.2 8.89 539 84.7 119.6
## FMA7 83.9 8.89 539 66.4 101.3
## FSV1 101.3 8.89 539 83.9 118.8
##
## mun = HtC:
## gen emmean SE df lower.CL upper.CL
## CNCH12 104.4 8.89 539 86.9 121.8
## CNCH13 106.6 8.89 539 89.1 124.0
## FBO1 107.9 8.89 539 90.4 125.3
## FCHI8 122.3 8.89 539 104.8 139.7
## FEAR5 117.7 8.89 539 100.2 135.1
## FGI4 118.4 8.89 539 100.9 135.8
## FMA7 112.0 8.89 539 94.6 129.5
## FSV1 115.8 8.89 539 98.3 133.2
##
## mun = Jam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 95.7 8.89 539 78.2 113.1
## CNCH13 100.6 8.89 539 83.2 118.1
## FBO1 98.5 8.89 539 81.0 116.0
## FCHI8 nonEst NA NA NA NA
## FEAR5 100.1 8.89 539 82.7 117.6
## FGI4 112.3 8.89 539 94.8 129.7
## FMA7 95.9 8.89 539 78.4 113.3
## FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## gen emmean SE df lower.CL upper.CL
## CNCH12 129.0 8.89 539 111.5 146.4
## CNCH13 107.4 8.89 539 90.0 124.9
## FBO1 104.0 8.89 539 86.5 121.4
## FCHI8 nonEst NA NA NA NA
## FEAR5 114.3 8.89 539 96.9 131.8
## FGI4 121.3 8.89 539 103.8 138.7
## FMA7 111.2 8.89 539 93.7 128.6
## FSV1 125.3 8.89 539 107.9 142.8
##
## mun = RiN:
## gen emmean SE df lower.CL upper.CL
## CNCH12 97.1 8.89 539 79.7 114.6
## CNCH13 119.5 8.89 539 102.0 136.9
## FBO1 139.9 8.89 539 122.4 157.3
## FCHI8 166.0 8.89 539 148.5 183.5
## FEAR5 106.2 8.89 539 88.7 123.6
## FGI4 115.1 8.89 539 97.7 132.6
## FMA7 108.7 8.89 539 91.3 126.2
## FSV1 115.6 8.89 539 98.2 133.1
##
## mun = SnV:
## gen emmean SE df lower.CL upper.CL
## CNCH12 132.3 8.89 539 114.8 149.7
## CNCH13 109.4 8.89 539 91.9 126.9
## FBO1 124.8 8.89 539 107.3 142.2
## FCHI8 149.8 8.89 539 132.4 167.3
## FEAR5 127.2 8.89 539 109.7 144.6
## FGI4 112.3 8.89 539 94.8 129.8
## FMA7 105.3 8.89 539 87.9 122.8
## FSV1 118.5 8.89 539 101.0 136.0
##
## mun = Tam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 86.6 8.89 539 69.1 104.0
## CNCH13 108.0 8.89 539 90.6 125.5
## FBO1 95.5 8.89 539 78.0 112.9
## FCHI8 98.6 8.89 539 81.1 116.0
## FEAR5 116.6 8.89 539 99.2 134.1
## FGI4 99.9 8.89 539 82.4 117.4
## FMA7 92.8 8.89 539 75.3 110.2
## FSV1 110.0 8.89 539 92.5 127.4
##
## mun = ViG:
## gen emmean SE df lower.CL upper.CL
## CNCH12 113.3 8.89 539 95.9 130.8
## CNCH13 110.3 8.89 539 92.8 127.8
## FBO1 103.5 8.89 539 86.0 120.9
## FCHI8 113.3 8.89 539 95.9 130.8
## FEAR5 109.0 8.89 539 91.5 126.5
## FGI4 109.2 8.89 539 91.7 126.6
## FMA7 104.5 8.89 539 87.0 122.0
## FSV1 116.6 8.89 539 99.2 134.1
##
## mun = Yac:
## gen emmean SE df lower.CL upper.CL
## CNCH12 98.1 8.89 539 80.6 115.5
## CNCH13 97.1 8.89 539 79.6 114.6
## FBO1 88.0 8.89 539 70.5 105.5
## FCHI8 109.0 8.89 539 91.6 126.5
## FEAR5 89.4 8.89 539 72.0 106.9
## FGI4 79.7 8.89 539 62.3 97.2
## FMA7 118.4 8.89 539 100.9 135.8
## FSV1 95.1 8.89 539 77.7 112.6
##
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## mun = Chi:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.0234 12.6 539 -0.002 1.0000
## CNCH12 - FBO1 9.9296 12.6 539 0.790 0.9936
## CNCH12 - FCHI8 -1.9706 12.6 539 -0.157 1.0000
## CNCH12 - FEAR5 0.5322 12.6 539 0.042 1.0000
## CNCH12 - FGI4 13.9072 12.6 539 1.107 0.9553
## CNCH12 - FMA7 12.7890 12.6 539 1.018 0.9717
## CNCH12 - FSV1 -11.9302 12.6 539 -0.949 0.9809
## CNCH13 - FBO1 9.9529 12.6 539 0.792 0.9935
## CNCH13 - FCHI8 -1.9472 12.6 539 -0.155 1.0000
## CNCH13 - FEAR5 0.5555 12.6 539 0.044 1.0000
## CNCH13 - FGI4 13.9306 12.6 539 1.109 0.9549
## CNCH13 - FMA7 12.8123 12.6 539 1.020 0.9714
## CNCH13 - FSV1 -11.9069 12.6 539 -0.947 0.9811
## FBO1 - FCHI8 -11.9001 12.6 539 -0.947 0.9812
## FBO1 - FEAR5 -9.3974 12.6 539 -0.748 0.9954
## FBO1 - FGI4 3.9777 12.6 539 0.317 1.0000
## FBO1 - FMA7 2.8594 12.6 539 0.228 1.0000
## FBO1 - FSV1 -21.8598 12.6 539 -1.740 0.6612
## FCHI8 - FEAR5 2.5027 12.6 539 0.199 1.0000
## FCHI8 - FGI4 15.8778 12.6 539 1.263 0.9118
## FCHI8 - FMA7 14.7595 12.6 539 1.174 0.9389
## FCHI8 - FSV1 -9.9597 12.6 539 -0.793 0.9935
## FEAR5 - FGI4 13.3751 12.6 539 1.064 0.9638
## FEAR5 - FMA7 12.2568 12.6 539 0.975 0.9777
## FEAR5 - FSV1 -12.4624 12.6 539 -0.992 0.9755
## FGI4 - FMA7 -1.1183 12.6 539 -0.089 1.0000
## FGI4 - FSV1 -25.8375 12.6 539 -2.056 0.4451
## FMA7 - FSV1 -24.7192 12.6 539 -1.967 0.5053
##
## mun = Gig:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 28.9790 12.6 539 2.306 0.2921
## CNCH12 - FBO1 -12.9655 12.6 539 -1.032 0.9695
## CNCH12 - FCHI8 -0.1648 12.6 539 -0.013 1.0000
## CNCH12 - FEAR5 -23.2670 12.6 539 -1.851 0.5850
## CNCH12 - FGI4 -10.5181 12.6 539 -0.837 0.9909
## CNCH12 - FMA7 7.7525 12.6 539 0.617 0.9987
## CNCH12 - FSV1 -9.7009 12.6 539 -0.772 0.9944
## CNCH13 - FBO1 -41.9445 12.6 539 -3.338 0.0202
## CNCH13 - FCHI8 -29.1438 12.6 539 -2.319 0.2850
## CNCH13 - FEAR5 -52.2460 12.6 539 -4.158 0.0010
## CNCH13 - FGI4 -39.4971 12.6 539 -3.143 0.0372
## CNCH13 - FMA7 -21.2265 12.6 539 -1.689 0.6944
## CNCH13 - FSV1 -38.6799 12.6 539 -3.078 0.0451
## FBO1 - FCHI8 12.8007 12.6 539 1.019 0.9716
## FBO1 - FEAR5 -10.3014 12.6 539 -0.820 0.9920
## FBO1 - FGI4 2.4474 12.6 539 0.195 1.0000
## FBO1 - FMA7 20.7180 12.6 539 1.649 0.7203
## FBO1 - FSV1 3.2646 12.6 539 0.260 1.0000
## FCHI8 - FEAR5 -23.1021 12.6 539 -1.838 0.5941
## FCHI8 - FGI4 -10.3533 12.6 539 -0.824 0.9917
## FCHI8 - FMA7 7.9173 12.6 539 0.630 0.9985
## FCHI8 - FSV1 -9.5361 12.6 539 -0.759 0.9950
## FEAR5 - FGI4 12.7488 12.6 539 1.014 0.9722
## FEAR5 - FMA7 31.0195 12.6 539 2.468 0.2114
## FEAR5 - FSV1 13.5660 12.6 539 1.080 0.9609
## FGI4 - FMA7 18.2706 12.6 539 1.454 0.8313
## FGI4 - FSV1 0.8172 12.6 539 0.065 1.0000
## FMA7 - FSV1 -17.4535 12.6 539 -1.389 0.8622
##
## mun = HtC:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -2.1947 12.6 539 -0.175 1.0000
## CNCH12 - FBO1 -3.4941 12.6 539 -0.278 1.0000
## CNCH12 - FCHI8 -17.8952 12.6 539 -1.424 0.8460
## CNCH12 - FEAR5 -13.2938 12.6 539 -1.058 0.9650
## CNCH12 - FGI4 -14.0022 12.6 539 -1.114 0.9536
## CNCH12 - FMA7 -7.6498 12.6 539 -0.609 0.9988
## CNCH12 - FSV1 -11.4056 12.6 539 -0.908 0.9853
## CNCH13 - FBO1 -1.2994 12.6 539 -0.103 1.0000
## CNCH13 - FCHI8 -15.7005 12.6 539 -1.249 0.9165
## CNCH13 - FEAR5 -11.0991 12.6 539 -0.883 0.9875
## CNCH13 - FGI4 -11.8075 12.6 539 -0.940 0.9820
## CNCH13 - FMA7 -5.4551 12.6 539 -0.434 0.9999
## CNCH13 - FSV1 -9.2109 12.6 539 -0.733 0.9960
## FBO1 - FCHI8 -14.4010 12.6 539 -1.146 0.9462
## FBO1 - FEAR5 -9.7996 12.6 539 -0.780 0.9941
## FBO1 - FGI4 -10.5081 12.6 539 -0.836 0.9910
## FBO1 - FMA7 -4.1557 12.6 539 -0.331 1.0000
## FBO1 - FSV1 -7.9115 12.6 539 -0.630 0.9985
## FCHI8 - FEAR5 4.6014 12.6 539 0.366 1.0000
## FCHI8 - FGI4 3.8930 12.6 539 0.310 1.0000
## FCHI8 - FMA7 10.2453 12.6 539 0.815 0.9922
## FCHI8 - FSV1 6.4896 12.6 539 0.516 0.9996
## FEAR5 - FGI4 -0.7084 12.6 539 -0.056 1.0000
## FEAR5 - FMA7 5.6439 12.6 539 0.449 0.9998
## FEAR5 - FSV1 1.8882 12.6 539 0.150 1.0000
## FGI4 - FMA7 6.3524 12.6 539 0.505 0.9996
## FGI4 - FSV1 2.5966 12.6 539 0.207 1.0000
## FMA7 - FSV1 -3.7558 12.6 539 -0.299 1.0000
##
## mun = Jam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -4.9361 12.6 539 -0.393 0.9988
## CNCH12 - FBO1 -2.8218 12.6 539 -0.225 0.9999
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -4.4385 12.6 539 -0.353 0.9993
## CNCH12 - FGI4 -16.6139 12.6 539 -1.322 0.7728
## CNCH12 - FMA7 -0.1887 12.6 539 -0.015 1.0000
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 2.1143 12.6 539 0.168 1.0000
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 0.4975 12.6 539 0.040 1.0000
## CNCH13 - FGI4 -11.6778 12.6 539 -0.929 0.9389
## CNCH13 - FMA7 4.7474 12.6 539 0.378 0.9990
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -1.6168 12.6 539 -0.129 1.0000
## FBO1 - FGI4 -13.7921 12.6 539 -1.098 0.8823
## FBO1 - FMA7 2.6331 12.6 539 0.210 0.9999
## 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 -12.1753 12.6 539 -0.969 0.9276
## FEAR5 - FMA7 4.2498 12.6 539 0.338 0.9994
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 16.4252 12.6 539 1.307 0.7812
## 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 21.5625 12.6 539 1.716 0.6057
## CNCH12 - FBO1 24.9800 12.6 539 1.988 0.4236
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 14.6313 12.6 539 1.164 0.9072
## CNCH12 - FGI4 7.6975 12.6 539 0.613 0.9964
## CNCH12 - FMA7 17.8055 12.6 539 1.417 0.7927
## CNCH12 - FSV1 3.6646 12.6 539 0.292 0.9999
## CNCH13 - FBO1 3.4174 12.6 539 0.272 1.0000
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -6.9313 12.6 539 -0.552 0.9980
## CNCH13 - FGI4 -13.8650 12.6 539 -1.103 0.9270
## CNCH13 - FMA7 -3.7570 12.6 539 -0.299 0.9999
## CNCH13 - FSV1 -17.8980 12.6 539 -1.424 0.7886
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -10.3487 12.6 539 -0.824 0.9825
## FBO1 - FGI4 -17.2824 12.6 539 -1.375 0.8149
## FBO1 - FMA7 -7.1744 12.6 539 -0.571 0.9976
## FBO1 - FSV1 -21.3154 12.6 539 -1.696 0.6189
## 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 -6.9338 12.6 539 -0.552 0.9980
## FEAR5 - FMA7 3.1742 12.6 539 0.253 1.0000
## FEAR5 - FSV1 -10.9667 12.6 539 -0.873 0.9765
## FGI4 - FMA7 10.1080 12.6 539 0.804 0.9845
## FGI4 - FSV1 -4.0329 12.6 539 -0.321 0.9999
## FMA7 - FSV1 -14.1410 12.6 539 -1.125 0.9202
##
## mun = RiN:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -22.3622 12.6 539 -1.779 0.6343
## CNCH12 - FBO1 -42.7432 12.6 539 -3.401 0.0164
## CNCH12 - FCHI8 -68.8813 12.6 539 -5.481 <.0001
## CNCH12 - FEAR5 -9.0369 12.6 539 -0.719 0.9964
## CNCH12 - FGI4 -18.0238 12.6 539 -1.434 0.8410
## CNCH12 - FMA7 -11.6110 12.6 539 -0.924 0.9837
## CNCH12 - FSV1 -18.5267 12.6 539 -1.474 0.8210
## CNCH13 - FBO1 -20.3809 12.6 539 -1.622 0.7370
## CNCH13 - FCHI8 -46.5190 12.6 539 -3.702 0.0057
## CNCH13 - FEAR5 13.3254 12.6 539 1.060 0.9645
## CNCH13 - FGI4 4.3384 12.6 539 0.345 1.0000
## CNCH13 - FMA7 10.7513 12.6 539 0.856 0.9896
## CNCH13 - FSV1 3.8356 12.6 539 0.305 1.0000
## FBO1 - FCHI8 -26.1381 12.6 539 -2.080 0.4293
## FBO1 - FEAR5 33.7063 12.6 539 2.682 0.1303
## FBO1 - FGI4 24.7193 12.6 539 1.967 0.5053
## FBO1 - FMA7 31.1322 12.6 539 2.477 0.2074
## FBO1 - FSV1 24.2165 12.6 539 1.927 0.5328
## FCHI8 - FEAR5 59.8444 12.6 539 4.762 0.0001
## FCHI8 - FGI4 50.8574 12.6 539 4.047 0.0015
## FCHI8 - FMA7 57.2703 12.6 539 4.557 0.0002
## FCHI8 - FSV1 50.3546 12.6 539 4.007 0.0018
## FEAR5 - FGI4 -8.9870 12.6 539 -0.715 0.9965
## FEAR5 - FMA7 -2.5741 12.6 539 -0.205 1.0000
## FEAR5 - FSV1 -9.4898 12.6 539 -0.755 0.9952
## FGI4 - FMA7 6.4129 12.6 539 0.510 0.9996
## FGI4 - FSV1 -0.5028 12.6 539 -0.040 1.0000
## FMA7 - FSV1 -6.9157 12.6 539 -0.550 0.9994
##
## mun = SnV:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 22.8562 12.6 539 1.819 0.6075
## CNCH12 - FBO1 7.4929 12.6 539 0.596 0.9989
## CNCH12 - FCHI8 -17.5613 12.6 539 -1.397 0.8584
## CNCH12 - FEAR5 5.0873 12.6 539 0.405 0.9999
## CNCH12 - FGI4 19.9468 12.6 539 1.587 0.7579
## CNCH12 - FMA7 26.9323 12.6 539 2.143 0.3885
## CNCH12 - FSV1 13.7558 12.6 539 1.095 0.9578
## CNCH13 - FBO1 -15.3633 12.6 539 -1.223 0.9251
## CNCH13 - FCHI8 -40.4175 12.6 539 -3.216 0.0297
## CNCH13 - FEAR5 -17.7689 12.6 539 -1.414 0.8507
## CNCH13 - FGI4 -2.9094 12.6 539 -0.232 1.0000
## CNCH13 - FMA7 4.0761 12.6 539 0.324 1.0000
## CNCH13 - FSV1 -9.1005 12.6 539 -0.724 0.9963
## FBO1 - FCHI8 -25.0542 12.6 539 -1.994 0.4871
## FBO1 - FEAR5 -2.4056 12.6 539 -0.191 1.0000
## FBO1 - FGI4 12.4539 12.6 539 0.991 0.9756
## FBO1 - FMA7 19.4394 12.6 539 1.547 0.7815
## FBO1 - FSV1 6.2628 12.6 539 0.498 0.9997
## FCHI8 - FEAR5 22.6486 12.6 539 1.802 0.6188
## FCHI8 - FGI4 37.5081 12.6 539 2.985 0.0590
## FCHI8 - FMA7 44.4936 12.6 539 3.541 0.0102
## FCHI8 - FSV1 31.3170 12.6 539 2.492 0.2010
## FEAR5 - FGI4 14.8595 12.6 539 1.182 0.9368
## FEAR5 - FMA7 21.8450 12.6 539 1.738 0.6620
## FEAR5 - FSV1 8.6684 12.6 539 0.690 0.9972
## FGI4 - FMA7 6.9855 12.6 539 0.556 0.9993
## FGI4 - FSV1 -6.1911 12.6 539 -0.493 0.9997
## FMA7 - FSV1 -13.1766 12.6 539 -1.049 0.9666
##
## mun = Tam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -21.4616 12.6 539 -1.708 0.6822
## CNCH12 - FBO1 -8.9310 12.6 539 -0.711 0.9967
## CNCH12 - FCHI8 -12.0370 12.6 539 -0.958 0.9799
## CNCH12 - FEAR5 -30.0524 12.6 539 -2.391 0.2476
## CNCH12 - FGI4 -13.3439 12.6 539 -1.062 0.9642
## CNCH12 - FMA7 -6.2087 12.6 539 -0.494 0.9997
## CNCH12 - FSV1 -23.4178 12.6 539 -1.863 0.5767
## CNCH13 - FBO1 12.5306 12.6 539 0.997 0.9748
## CNCH13 - FCHI8 9.4247 12.6 539 0.750 0.9954
## CNCH13 - FEAR5 -8.5908 12.6 539 -0.684 0.9974
## CNCH13 - FGI4 8.1177 12.6 539 0.646 0.9982
## CNCH13 - FMA7 15.2529 12.6 539 1.214 0.9278
## CNCH13 - FSV1 -1.9562 12.6 539 -0.156 1.0000
## FBO1 - FCHI8 -3.1059 12.6 539 -0.247 1.0000
## FBO1 - FEAR5 -21.1214 12.6 539 -1.681 0.6998
## FBO1 - FGI4 -4.4129 12.6 539 -0.351 1.0000
## FBO1 - FMA7 2.7223 12.6 539 0.217 1.0000
## FBO1 - FSV1 -14.4868 12.6 539 -1.153 0.9445
## FCHI8 - FEAR5 -18.0155 12.6 539 -1.434 0.8414
## FCHI8 - FGI4 -1.3070 12.6 539 -0.104 1.0000
## FCHI8 - FMA7 5.8282 12.6 539 0.464 0.9998
## FCHI8 - FSV1 -11.3809 12.6 539 -0.906 0.9855
## FEAR5 - FGI4 16.7085 12.6 539 1.330 0.8873
## FEAR5 - FMA7 23.8437 12.6 539 1.897 0.5533
## FEAR5 - FSV1 6.6346 12.6 539 0.528 0.9995
## FGI4 - FMA7 7.1352 12.6 539 0.568 0.9992
## FGI4 - FSV1 -10.0739 12.6 539 -0.802 0.9930
## FMA7 - FSV1 -17.2091 12.6 539 -1.369 0.8708
##
## mun = ViG:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 3.0456 12.6 539 0.242 1.0000
## CNCH12 - FBO1 9.8510 12.6 539 0.784 0.9939
## CNCH12 - FCHI8 0.0212 12.6 539 0.002 1.0000
## CNCH12 - FEAR5 4.3447 12.6 539 0.346 1.0000
## CNCH12 - FGI4 4.1662 12.6 539 0.332 1.0000
## CNCH12 - FMA7 8.8428 12.6 539 0.704 0.9969
## CNCH12 - FSV1 -3.3040 12.6 539 -0.263 1.0000
## CNCH13 - FBO1 6.8055 12.6 539 0.542 0.9994
## CNCH13 - FCHI8 -3.0244 12.6 539 -0.241 1.0000
## CNCH13 - FEAR5 1.2991 12.6 539 0.103 1.0000
## CNCH13 - FGI4 1.1206 12.6 539 0.089 1.0000
## CNCH13 - FMA7 5.7972 12.6 539 0.461 0.9998
## CNCH13 - FSV1 -6.3496 12.6 539 -0.505 0.9996
## FBO1 - FCHI8 -9.8299 12.6 539 -0.782 0.9940
## FBO1 - FEAR5 -5.5064 12.6 539 -0.438 0.9999
## FBO1 - FGI4 -5.6848 12.6 539 -0.452 0.9998
## FBO1 - FMA7 -1.0082 12.6 539 -0.080 1.0000
## FBO1 - FSV1 -13.1550 12.6 539 -1.047 0.9669
## FCHI8 - FEAR5 4.3235 12.6 539 0.344 1.0000
## FCHI8 - FGI4 4.1450 12.6 539 0.330 1.0000
## FCHI8 - FMA7 8.8216 12.6 539 0.702 0.9969
## FCHI8 - FSV1 -3.3252 12.6 539 -0.265 1.0000
## FEAR5 - FGI4 -0.1785 12.6 539 -0.014 1.0000
## FEAR5 - FMA7 4.4981 12.6 539 0.358 1.0000
## FEAR5 - FSV1 -7.6487 12.6 539 -0.609 0.9988
## FGI4 - FMA7 4.6766 12.6 539 0.372 1.0000
## FGI4 - FSV1 -7.4702 12.6 539 -0.594 0.9989
## FMA7 - FSV1 -12.1468 12.6 539 -0.967 0.9789
##
## mun = Yac:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.9548 12.6 539 0.076 1.0000
## CNCH12 - FBO1 10.0521 12.6 539 0.800 0.9931
## CNCH12 - FCHI8 -10.9951 12.6 539 -0.875 0.9882
## CNCH12 - FEAR5 8.6184 12.6 539 0.686 0.9973
## CNCH12 - FGI4 18.3434 12.6 539 1.460 0.8284
## CNCH12 - FMA7 -20.3106 12.6 539 -1.616 0.7404
## CNCH12 - FSV1 2.9401 12.6 539 0.234 1.0000
## CNCH13 - FBO1 9.0973 12.6 539 0.724 0.9963
## CNCH13 - FCHI8 -11.9499 12.6 539 -0.951 0.9807
## CNCH13 - FEAR5 7.6637 12.6 539 0.610 0.9987
## CNCH13 - FGI4 17.3886 12.6 539 1.384 0.8645
## CNCH13 - FMA7 -21.2654 12.6 539 -1.692 0.6924
## CNCH13 - FSV1 1.9853 12.6 539 0.158 1.0000
## FBO1 - FCHI8 -21.0472 12.6 539 -1.675 0.7036
## FBO1 - FEAR5 -1.4336 12.6 539 -0.114 1.0000
## FBO1 - FGI4 8.2913 12.6 539 0.660 0.9979
## FBO1 - FMA7 -30.3627 12.6 539 -2.416 0.2356
## FBO1 - FSV1 -7.1120 12.6 539 -0.566 0.9992
## FCHI8 - FEAR5 19.6136 12.6 539 1.561 0.7735
## FCHI8 - FGI4 29.3385 12.6 539 2.335 0.2767
## FCHI8 - FMA7 -9.3155 12.6 539 -0.741 0.9957
## FCHI8 - FSV1 13.9352 12.6 539 1.109 0.9548
## FEAR5 - FGI4 9.7249 12.6 539 0.774 0.9944
## FEAR5 - FMA7 -28.9290 12.6 539 -2.302 0.2943
## FEAR5 - FSV1 -5.6784 12.6 539 -0.452 0.9998
## FGI4 - FMA7 -38.6540 12.6 539 -3.076 0.0454
## FGI4 - FSV1 -15.4033 12.6 539 -1.226 0.9241
## FMA7 - FSV1 23.2507 12.6 539 1.850 0.5859
##
## 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(SLA) ~ 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 0.69954 0.099934 7 60.173 2.1637 0.05024 .
## ---
## 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(SLA) ~ gen + (1 | mun) + (1 | mun:gen)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 24.944 -27.8878
## (1 | mun) 10 14.241 -8.4823 21.406 1 3.717e-06 ***
## (1 | mun:gen) 10 18.869 -17.7376 12.150 1 0.0004908 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(SLA ~ 1 +
(1|gen) +
(1|mun) +
(1|gen:mun),
data = datos)
ranova(modelo_blup)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## SLA ~ (1 | gen) + (1 | mun) + (1 | gen:mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -2889.6 5789.2
## (1 | gen) 4 -2890.7 5789.3 2.1337 1 0.144095
## (1 | mun) 4 -2899.4 5806.8 19.6464 1 9.318e-06 ***
## (1 | gen:mun) 4 -2893.8 5795.5 8.3325 1 0.003894 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CNCH12 -1.513479
## CNCH13 -3.017202
## FBO1 -1.021151
## FCHI8 6.208593
## FEAR5 1.154435
## FGI4 -1.033062
## FMA7 -3.115851
## FSV1 2.337717
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 106.2094
## CNCH13 104.7056
## FBO1 106.7017
## FCHI8 113.9314
## FEAR5 108.8773
## FGI4 106.6898
## FMA7 104.6070
## FSV1 110.0605
#Blups Parcela
blups$mun
## (Intercept)
## Chi -6.744487
## Gig -11.470445
## HtC 4.557904
## Jam -4.645195
## PtR 7.619417
## RiN 11.222594
## SnV 12.420251
## Tam -5.685539
## ViG 1.899439
## Yac -9.173938
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## Chi 100.97835
## Gig 96.25239
## HtC 112.28074
## Jam 103.07764
## PtR 115.34225
## RiN 118.94543
## SnV 120.14308
## Tam 102.03729
## ViG 109.62227
## Yac 98.54889
#Blups interacción
blups$`gen:mun`
## (Intercept)
## CNCH12:Chi 1.28729521
## CNCH12:Gig -1.25503076
## CNCH12:HtC -2.59225276
## CNCH12:Jam -2.39029696
## CNCH12:PtR 6.14625335
## CNCH12:RiN -8.24446824
## CNCH12:SnV 5.52960034
## CNCH12:Tam -5.67015639
## CNCH12:ViG 2.12514846
## CNCH12:Yac 0.41343774
## CNCH13:Chi 1.90716854
## CNCH13:Gig -12.40771091
## CNCH13:HtC -1.09099320
## CNCH13:Jam 0.22372593
## CNCH13:PtR -1.99595725
## CNCH13:RiN 1.44313005
## CNCH13:SnV -3.13774532
## CNCH13:Tam 3.65187360
## CNCH13:ViG 1.49927744
## CNCH13:Yac 0.63626605
## FBO1:Chi -2.94312540
## FBO1:Gig 3.80804790
## FBO1:HtC -1.37376194
## FBO1:Jam -1.44473087
## FBO1:PtR -4.19337623
## FBO1:RiN 8.90586904
## FBO1:SnV 2.28824427
## FBO1:Tam -2.24474332
## FBO1:ViG -2.07339974
## FBO1:Yac -3.86671589
## FCHI8:Chi -1.04733722
## FCHI8:Gig -4.32264624
## FCHI8:HtC 1.53718532
## FCHI8:RiN 16.58107810
## FCHI8:SnV 9.52349023
## FCHI8:Tam -3.91867099
## FCHI8:ViG -1.01797117
## FCHI8:Yac 1.74203228
## FEAR5:Chi -0.01167245
## FEAR5:Gig 7.10646791
## FEAR5:HtC 1.72096576
## FEAR5:Jam -1.67156850
## FEAR5:PtR -0.87578033
## FEAR5:RiN -5.65920741
## FEAR5:SnV 2.38162113
## FEAR5:Tam 5.44567394
## FEAR5:ViG -0.72137726
## FEAR5:Yac -4.16788745
## FGI4:Chi -4.55289398
## FGI4:Gig 2.81943625
## FGI4:HtC 2.89647037
## FGI4:Jam 4.15854258
## FGI4:PtR 2.82669287
## FGI4:RiN -1.12329373
## FGI4:SnV -2.76216643
## FGI4:Tam -0.44863386
## FGI4:ViG 0.23901306
## FGI4:Yac -7.22745930
## FMA7:Chi -3.25352714
## FMA7:Gig -3.75148037
## FMA7:HtC 1.16337839
## FMA7:Jam -1.66326571
## FMA7:PtR -0.43088151
## FMA7:RiN -2.88095367
## FMA7:SnV -4.75225891
## FMA7:Tam -2.49950884
## FMA7:ViG -0.81386543
## FMA7:Yac 9.30827801
## FSV1:Chi 4.56670888
## FSV1:Gig 1.11947323
## FSV1:HtC 0.47421746
## FSV1:PtR 3.09548041
## FSV1:RiN -2.28744691
## FSV1:SnV -1.61736164
## FSV1:Tam 2.27225979
## FSV1:ViG 1.90303266
## FSV1:Yac -2.34325492
fixef(modelo_blup)[1] + blups$`gen:mun`
## (Intercept)
## CNCH12:Chi 109.01013
## CNCH12:Gig 106.46780
## CNCH12:HtC 105.13058
## CNCH12:Jam 105.33254
## CNCH12:PtR 113.86909
## CNCH12:RiN 99.47836
## CNCH12:SnV 113.25243
## CNCH12:Tam 102.05268
## CNCH12:ViG 109.84798
## CNCH12:Yac 108.13627
## CNCH13:Chi 109.63000
## CNCH13:Gig 95.31512
## CNCH13:HtC 106.63184
## CNCH13:Jam 107.94656
## CNCH13:PtR 105.72687
## CNCH13:RiN 109.16596
## CNCH13:SnV 104.58509
## CNCH13:Tam 111.37471
## CNCH13:ViG 109.22211
## CNCH13:Yac 108.35910
## FBO1:Chi 104.77971
## FBO1:Gig 111.53088
## FBO1:HtC 106.34907
## FBO1:Jam 106.27810
## FBO1:PtR 103.52946
## FBO1:RiN 116.62870
## FBO1:SnV 110.01108
## FBO1:Tam 105.47809
## FBO1:ViG 105.64943
## FBO1:Yac 103.85612
## FCHI8:Chi 106.67549
## FCHI8:Gig 103.40019
## FCHI8:HtC 109.26002
## FCHI8:RiN 124.30391
## FCHI8:SnV 117.24632
## FCHI8:Tam 103.80416
## FCHI8:ViG 106.70486
## FCHI8:Yac 109.46486
## FEAR5:Chi 107.71116
## FEAR5:Gig 114.82930
## FEAR5:HtC 109.44380
## FEAR5:Jam 106.05126
## FEAR5:PtR 106.84705
## FEAR5:RiN 102.06362
## FEAR5:SnV 110.10445
## FEAR5:Tam 113.16851
## FEAR5:ViG 107.00145
## FEAR5:Yac 103.55494
## FGI4:Chi 103.16994
## FGI4:Gig 110.54227
## FGI4:HtC 110.61930
## FGI4:Jam 111.88137
## FGI4:PtR 110.54952
## FGI4:RiN 106.59954
## FGI4:SnV 104.96067
## FGI4:Tam 107.27420
## FGI4:ViG 107.96185
## FGI4:Yac 100.49537
## FMA7:Chi 104.46930
## FMA7:Gig 103.97135
## FMA7:HtC 108.88621
## FMA7:Jam 106.05957
## FMA7:PtR 107.29195
## FMA7:RiN 104.84188
## FMA7:SnV 102.97057
## FMA7:Tam 105.22332
## FMA7:ViG 106.90897
## FMA7:Yac 117.03111
## FSV1:Chi 112.28954
## FSV1:Gig 108.84231
## FSV1:HtC 108.19705
## FSV1:PtR 110.81831
## FSV1:RiN 105.43539
## FSV1:SnV 106.10547
## FSV1:Tam 109.99509
## FSV1:ViG 109.62586
## FSV1:Yac 105.37958
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 -1.513479
## 2 CNCH13 -3.017202
## 3 FBO1 -1.021151
## 4 FCHI8 6.208593
## 5 FEAR5 1.154435
## 6 FGI4 -1.033062
## 7 FMA7 -3.115851
## 8 FSV1 2.337717
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 Chi -6.744487
## 2 Gig -11.470445
## 3 HtC 4.557904
## 4 Jam -4.645195
## 5 PtR 7.619417
## 6 RiN 11.222594
## 7 SnV 12.420251
## 8 Tam -5.685539
## 9 ViG 1.899439
## 10 Yac -9.173938
#Tabla blup_gen_mun
blup_gen_mun <- ranef(modelo_blup)$`gen:mun` %>%
tibble::rownames_to_column("gen:mun") %>%
rename(BLUP = `(Intercept)`)
blup_gen_mun
## gen:mun BLUP
## 1 CNCH12:Chi 1.28729521
## 2 CNCH12:Gig -1.25503076
## 3 CNCH12:HtC -2.59225276
## 4 CNCH12:Jam -2.39029696
## 5 CNCH12:PtR 6.14625335
## 6 CNCH12:RiN -8.24446824
## 7 CNCH12:SnV 5.52960034
## 8 CNCH12:Tam -5.67015639
## 9 CNCH12:ViG 2.12514846
## 10 CNCH12:Yac 0.41343774
## 11 CNCH13:Chi 1.90716854
## 12 CNCH13:Gig -12.40771091
## 13 CNCH13:HtC -1.09099320
## 14 CNCH13:Jam 0.22372593
## 15 CNCH13:PtR -1.99595725
## 16 CNCH13:RiN 1.44313005
## 17 CNCH13:SnV -3.13774532
## 18 CNCH13:Tam 3.65187360
## 19 CNCH13:ViG 1.49927744
## 20 CNCH13:Yac 0.63626605
## 21 FBO1:Chi -2.94312540
## 22 FBO1:Gig 3.80804790
## 23 FBO1:HtC -1.37376194
## 24 FBO1:Jam -1.44473087
## 25 FBO1:PtR -4.19337623
## 26 FBO1:RiN 8.90586904
## 27 FBO1:SnV 2.28824427
## 28 FBO1:Tam -2.24474332
## 29 FBO1:ViG -2.07339974
## 30 FBO1:Yac -3.86671589
## 31 FCHI8:Chi -1.04733722
## 32 FCHI8:Gig -4.32264624
## 33 FCHI8:HtC 1.53718532
## 34 FCHI8:RiN 16.58107810
## 35 FCHI8:SnV 9.52349023
## 36 FCHI8:Tam -3.91867099
## 37 FCHI8:ViG -1.01797117
## 38 FCHI8:Yac 1.74203228
## 39 FEAR5:Chi -0.01167245
## 40 FEAR5:Gig 7.10646791
## 41 FEAR5:HtC 1.72096576
## 42 FEAR5:Jam -1.67156850
## 43 FEAR5:PtR -0.87578033
## 44 FEAR5:RiN -5.65920741
## 45 FEAR5:SnV 2.38162113
## 46 FEAR5:Tam 5.44567394
## 47 FEAR5:ViG -0.72137726
## 48 FEAR5:Yac -4.16788745
## 49 FGI4:Chi -4.55289398
## 50 FGI4:Gig 2.81943625
## 51 FGI4:HtC 2.89647037
## 52 FGI4:Jam 4.15854258
## 53 FGI4:PtR 2.82669287
## 54 FGI4:RiN -1.12329373
## 55 FGI4:SnV -2.76216643
## 56 FGI4:Tam -0.44863386
## 57 FGI4:ViG 0.23901306
## 58 FGI4:Yac -7.22745930
## 59 FMA7:Chi -3.25352714
## 60 FMA7:Gig -3.75148037
## 61 FMA7:HtC 1.16337839
## 62 FMA7:Jam -1.66326571
## 63 FMA7:PtR -0.43088151
## 64 FMA7:RiN -2.88095367
## 65 FMA7:SnV -4.75225891
## 66 FMA7:Tam -2.49950884
## 67 FMA7:ViG -0.81386543
## 68 FMA7:Yac 9.30827801
## 69 FSV1:Chi 4.56670888
## 70 FSV1:Gig 1.11947323
## 71 FSV1:HtC 0.47421746
## 72 FSV1:PtR 3.09548041
## 73 FSV1:RiN -2.28744691
## 74 FSV1:SnV -1.61736164
## 75 FSV1:Tam 2.27225979
## 76 FSV1:ViG 1.90303266
## 77 FSV1:Yac -2.34325492
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 118.99570 118.99570 118.99570 118.99570 118.99570 118.99570 118.99570
## [8] 118.99570 141.73510 141.73510 141.73510 141.73510 141.73510 141.73510
## [15] 141.73510 141.73510 114.44065 114.44065 114.44065 114.44065 114.44065
## [22] 114.44065 114.44065 114.44065 126.83014 126.83014 126.83014 126.83014
## [29] 126.83014 126.83014 126.83014 126.83014 117.37135 117.37135 117.37135
## [36] 117.37135 117.37135 117.37135 117.37135 117.37135 116.78907 116.78907
## [43] 116.78907 116.78907 116.78907 116.78907 116.78907 116.78907 112.94862
## [50] 112.94862 112.94862 112.94862 112.94862 112.94862 112.94862 112.94862
## [57] 109.18748 109.18748 109.18748 109.18748 109.18748 109.18748 109.18748
## [64] 109.18748 120.86344 120.86344 120.86344 120.86344 120.86344 120.86344
## [71] 120.86344 120.86344 113.98814 113.98814 113.98814 113.98814 113.98814
## [78] 113.98814 113.98814 113.98814 116.34785 116.34785 116.34785 116.34785
## [85] 116.34785 116.34785 116.34785 116.34785 135.87517 135.87517 135.87517
## [92] 135.87517 135.87517 135.87517 135.87517 135.87517 123.67914 123.67914
## [99] 123.67914 123.67914 123.67914 123.67914 123.67914 123.67914 112.27497
## [106] 112.27497 112.27497 112.27497 112.27497 112.27497 112.27497 112.27497
## [113] 121.41018 121.41018 121.41018 121.41018 121.41018 121.41018 121.41018
## [120] 121.41018 124.15920 124.15920 124.15920 124.15920 124.15920 124.15920
## [127] 124.15920 124.15920 107.88277 107.88277 107.88277 107.88277 107.88277
## [134] 107.88277 107.88277 107.88277 94.60897 94.60897 94.60897 94.60897
## [141] 94.60897 94.60897 94.60897 94.60897 102.12111 102.12111 102.12111
## [148] 102.12111 102.12111 102.12111 102.12111 102.12111 106.13960 106.13960
## [155] 106.13960 106.13960 106.13960 106.13960 106.13960 106.13960 95.39239
## [162] 95.39239 95.39239 95.39239 95.39239 95.39239 95.39239 95.39239
## [169] 100.75216 100.75216 100.75216 100.75216 100.75216 100.75216 100.75216
## [176] 100.75216 99.86831 99.86831 99.86831 99.86831 99.86831 99.86831
## [183] 99.86831 99.86831 97.01407 97.01407 97.01407 97.01407 97.01407
## [190] 97.01407 97.01407 97.01407 98.54336 98.54336 98.54336 98.54336
## [197] 98.54336 98.54336 98.54336 98.54336 96.16796 96.16796 96.16796
## [204] 96.16796 96.16796 96.16796 96.16796 96.16796 90.28837 90.28837
## [211] 90.28837 90.28837 90.28837 90.28837 90.28837 90.28837 106.49952
## [218] 106.49952 106.49952 106.49952 106.49952 106.49952 106.49952 106.49952
## [225] 95.53544 95.53544 95.53544 95.53544 95.53544 95.53544 95.53544
## [232] 95.53544 104.74132 104.74132 104.74132 104.74132 104.74132 104.74132
## [239] 104.74132 104.74132 93.66103 93.66103 93.66103 93.66103 93.66103
## [246] 93.66103 93.66103 93.66103 97.44885 97.44885 97.44885 97.44885
## [253] 97.44885 97.44885 97.44885 97.44885 120.77545 120.77545 120.77545
## [260] 120.77545 120.77545 120.77545 120.77545 120.77545 111.79552 111.79552
## [267] 111.79552 111.79552 111.79552 111.79552 111.79552 111.79552 115.62090
## [274] 115.62090 115.62090 115.62090 115.62090 115.62090 115.62090 115.62090
## [281] 117.13588 117.13588 117.13588 117.13588 117.13588 117.13588 117.13588
## [288] 117.13588 119.97502 119.97502 119.97502 119.97502 119.97502 119.97502
## [295] 119.97502 119.97502 110.32909 110.32909 110.32909 110.32909 110.32909
## [302] 110.32909 110.32909 110.32909 110.12772 110.12772 110.12772 110.12772
## [309] 110.12772 110.12772 110.12772 110.12772 113.86302 113.86302 113.86302
## [316] 113.86302 113.86302 113.86302 113.86302 113.86302 105.69255 105.69255
## [323] 105.69255 105.69255 105.69255 105.69255 105.69255 105.69255 110.05533
## [330] 110.05533 110.05533 110.05533 110.05533 110.05533 110.05533 110.05533
## [337] 114.81289 114.81289 114.81289 114.81289 114.81289 114.81289 114.81289
## [344] 114.81289 108.82822 108.82822 108.82822 108.82822 108.82822 108.82822
## [351] 108.82822 108.82822 110.23394 110.23394 110.23394 110.23394 110.23394
## [358] 110.23394 110.23394 110.23394 108.10435 108.10435 108.10435 108.10435
## [365] 108.10435 108.10435 108.10435 108.10435 106.52772 106.52772 106.52772
## [372] 106.52772 106.52772 106.52772 106.52772 106.52772 99.70958 99.70958
## [379] 99.70958 99.70958 99.70958 99.70958 99.70958 99.70958 89.38506
## [386] 89.38506 89.38506 89.38506 89.38506 89.38506 89.38506 89.38506
## [393] 104.51329 104.51329 104.51329 104.51329 104.51329 104.51329 104.51329
## [400] 104.51329 98.13833 98.13833 98.13833 98.13833 98.13833 98.13833
## [407] 98.13833 98.13833 98.03876 98.03876 98.03876 98.03876 98.03876
## [414] 98.03876 98.03876 98.03876 93.48388 93.48388 93.48388 93.48388
## [421] 93.48388 93.48388 93.48388 93.48388 80.82747 80.82747 80.82747
## [428] 80.82747 80.82747 80.82747 80.82747 80.82747 99.03928 99.03928
## [435] 99.03928 99.03928 99.03928 99.03928 99.03928 99.03928 106.64727
## [442] 106.64727 106.64727 106.64727 106.64727 106.64727 106.64727 106.64727
## [449] 96.42193 96.42193 96.42193 96.42193 96.42193 96.42193 96.42193
## [456] 96.42193 108.63740 108.63740 108.63740 108.63740 108.63740 108.63740
## [463] 108.63740 108.63740 104.32722 104.32722 104.32722 104.32722 104.32722
## [470] 104.32722 104.32722 104.32722 100.55560 100.55560 100.55560 100.55560
## [477] 100.55560 100.55560 100.55560 100.55560 94.85366 94.85366 94.85366
## [484] 94.85366 94.85366 94.85366 94.85366 94.85366 102.67196 102.67196
## [491] 102.67196 102.67196 102.67196 102.67196 102.67196 102.67196 98.77140
## [498] 98.77140 98.77140 98.77140 98.77140 98.77140 98.77140 98.77140
## [505] 115.09267 115.09267 115.09267 115.09267 115.09267 115.09267 115.09267
## [512] 115.09267 110.32826 110.32826 110.32826 110.32826 110.32826 110.32826
## [519] 110.32826 110.32826 115.15614 115.15614 115.15614 115.15614 115.15614
## [526] 115.15614 115.15614 115.15614 120.02651 120.02651 120.02651 120.02651
## [533] 120.02651 120.02651 120.02651 120.02651 114.14414 114.14414 114.14414
## [540] 114.14414 114.14414 114.14414 114.14414 114.14414 108.17500 108.17500
## [547] 108.17500 108.17500 108.17500 108.17500 108.17500 108.17500 108.17254
## [554] 108.17254 108.17254 108.17254 108.17254 108.17254 108.17254 108.17254
## [561] 109.88582 109.88582 109.88582 109.88582 109.88582 109.88582 109.88582
## [568] 109.88582 100.28416 100.28416 100.28416 100.28416 100.28416 100.28416
## [575] 100.28416 100.28416 106.20312 106.20312 106.20312 106.20312 106.20312
## [582] 106.20312 106.20312 106.20312 102.56050 102.56050 102.56050 102.56050
## [589] 102.56050 102.56050 102.56050 102.56050 98.29852 98.29852 98.29852
## [596] 98.29852 98.29852 98.29852 98.29852 98.29852 100.61176 100.61176
## [603] 100.61176 100.61176 100.61176 100.61176 100.61176 100.61176 99.17386
## [610] 99.17386 99.17386 99.17386 99.17386 99.17386 99.17386 99.17386
#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> 53.95108 7.345140
## 2 mun (Intercept) <NA> 89.90310 9.481725
## 3 gen (Intercept) <NA> 17.55818 4.190249
## 4 Residual <NA> <NA> 631.68465 25.133337
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## gen:mun (Intercept) 7.3451
## mun (Intercept) 9.4817
## gen (Intercept) 4.1902
## Residual 25.1333
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] 0.4531681
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## [1] 1.392451
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CNCH12 -1.513479
## CNCH13 -3.017202
## FBO1 -1.021151
## FCHI8 6.208593
## FEAR5 1.154435
## FGI4 -1.033062
## FMA7 -3.115851
## FSV1 2.337717
##Predicho de carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## FCHI8 6.208593 113.9314
## FSV1 2.337717 110.0605
## FEAR5 1.154435 108.8773
## FBO1 -1.021151 106.7017
## FGI4 -1.033062 106.6898
## CNCH12 -1.513479 106.2094
## CNCH13 -3.017202 104.7056
## FMA7 -3.115851 104.6070
# Visualización ranking predichos
blup_gen$gen <- rownames(blup_gen)
ggplot(blup_gen, aes(x=reorder(gen,pred), y=pred))+
geom_point(size=3)+
coord_flip()+
ylab("Carbono predicho (BLUP)")+
xlab("Genotipo")
### Análisis G×E (estabilidad)
## matriz genotipo × parcela.
mat <- datos %>%
group_by(gen,mun) %>%
summarise(SLA=mean(SLA)) %>%
pivot_wider(names_from=mun,
values_from=SLA)
## `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, SLA)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $SLA
## $coordgen
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 11.185789 -2.713475 -31.854498 -17.1356525 19.713162 -11.501313
## [2,] 9.288035 27.218483 8.717340 25.3651079 12.751097 -12.478962
## [3,] -8.458333 -3.015068 27.112710 -20.3684470 4.549973 -21.106817
## [4,] -40.712743 7.426796 -10.632004 0.7287585 -3.267395 6.360711
## [5,] 6.816421 -25.883120 2.394740 7.3378472 -19.992512 -13.177332
## [6,] 5.487183 -13.346694 14.406282 -1.2878203 21.525838 32.191422
## [7,] 14.947338 21.417553 -0.657748 -17.5715065 -26.982545 15.529318
## [8,] 1.446309 -11.104475 -9.486822 22.9317127 -8.297617 4.182973
## [,7] [,8]
## [1,] 0.7182751 -16.74271
## [2,] 9.8701681 -16.74271
## [3,] -16.2917958 -16.74271
## [4,] 9.2108350 -16.74271
## [5,] 24.7569867 -16.74271
## [6,] 6.7184132 -16.74271
## [7,] -1.2988032 -16.74271
## [8,] -33.6840792 -16.74271
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -5.2764183 -3.416449 -12.5932161 15.1320265 -1.9841215 -7.7616459
## [2,] -5.7241853 -38.918349 4.5923142 -10.2027490 -8.8928453 0.8699956
## [3,] -9.2796434 -6.581585 0.9275887 4.3117800 -5.9128428 9.6032818
## [4,] -13.9429567 -3.782198 1.0566791 7.2446239 3.8456373 11.2491519
## [5,] -11.6834064 -7.248980 -21.2033559 2.0262399 5.0037182 9.9897012
## [6,] -54.8474873 11.822238 15.1910082 0.2266227 -0.1087568 -0.5101408
## [7,] -30.2070155 -10.458559 -15.6357684 -5.6630536 2.7507781 -10.4694022
## [8,] 0.8125937 -9.215250 7.1631753 21.1693735 -8.4563767 -3.2063464
## [9,] -2.6179635 -2.351873 -8.2221506 6.9266604 2.6349365 0.9593097
## [10,] -4.8896066 21.571413 -14.1087767 -5.8152938 -17.7386731 2.5382224
## [,7] [,8]
## [1,] -6.5822511 3.084639e-15
## [2,] -3.5002059 8.580966e-17
## [3,] 3.3497405 5.048296e-15
## [4,] 0.8191464 9.845429e-15
## [5,] -0.8257148 -1.306367e-14
## [6,] -2.8614898 -3.622464e-15
## [7,] 6.2633579 3.821027e-15
## [8,] 4.5186727 -6.665036e-15
## [9,] -2.2198618 8.597011e-15
## [10,] -0.1759825 1.447755e-15
##
## $eigenvalues
## [1] 6.655644e+01 4.941057e+01 3.774194e+01 3.114990e+01 2.363260e+01
## [6] 2.251828e+01 1.187207e+01 2.123086e-14
##
## $totalvar
## [1] 10472.45
##
## $varexpl
## [1] 42.30 23.31 13.60 9.27 5.33 4.84 1.35 0.00
##
## $labelgen
## [1] "CNCH12" "CNCH13" "FBO1" "FCHI8" "FEAR5" "FGI4" "FMA7" "FSV1"
##
## $labelenv
## [1] "Chi" "Gig" "HtC" "Jam" "PtR" "RiN" "SnV" "Tam" "ViG" "Yac"
##
## $labelaxes
## [1] "PC1" "PC2" "PC3" "PC4" "PC5" "PC6" "PC7" "PC8"
##
## $ge_mat
## Chi Gig HtC Jam PtR RiN
## CNCH12 2.904220 -2.485600 -8.741940 -7.527806 10.829449 -23.898130
## CNCH13 2.927591 -31.464608 -6.547222 -2.591730 -10.733090 -1.535885
## FBO1 -7.025342 10.479921 -5.247788 -4.706020 -14.150510 18.845064
## FCHI8 4.874787 -2.320780 9.153246 12.673146 14.535247 44.983137
## FEAR5 2.372050 20.781358 4.551842 -3.089263 -3.801831 -14.861274
## FGI4 -11.003014 8.032506 5.260270 9.086063 3.131933 -5.874281
## FMA7 -9.884736 -10.238123 -1.092094 -7.339095 -6.976083 -12.287169
## FSV1 14.834444 7.215326 2.663686 3.494705 7.164885 -5.371462
## SnV Tam ViG Yac
## CNCH12 9.813774 -14.431564 3.3709363 1.2003795
## CNCH13 -13.042466 7.030083 0.3253428 0.2456067
## FBO1 2.320830 -5.500529 -6.4801064 -8.8517031
## FCHI8 27.375029 -2.394605 3.3497476 12.1955221
## FEAR5 4.726455 15.620853 -0.9737339 -7.4180714
## FGI4 -10.133065 -1.087619 -0.7952526 -17.1430038
## FMA7 -17.118565 -8.222860 -5.4718703 21.5109698
## FSV1 -3.941992 8.986242 6.6749366 -1.7396997
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 0.02111685
##
## $grand_mean
## [1] 107.9603
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 105.0637 102.4217 105.9287 120.4028 109.7511 105.9078 102.2483 111.9584
##
## $mean_env
## Chi Gig HtC Jam PtR RiN SnV Tam
## 99.73197 94.13267 113.12303 103.20333 118.14098 121.01935 122.43833 100.98661
## ViG Yac
## 109.97328 96.85356
##
## $scale_val
## Chi Gig HtC Jam PtR RiN SnV Tam
## 8.738960 15.877570 6.406318 7.599958 10.476169 21.997696 14.416248 9.908467
## ViG Yac
## 4.484232 12.207980
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración de variables
##Blup gen
blup_gen <- ranef(modelo_blup)$gen
media <- fixef(modelo_blup)[1]
blup_gen$BLUP_C <- media + blup_gen[,1]
blup_gen$gen <- rownames(blup_gen)
blup_gen <- blup_gen[,c("gen","BLUP_C")]
blup_gen
## gen BLUP_C
## CNCH12 CNCH12 106.2094
## CNCH13 CNCH13 104.7056
## FBO1 FBO1 106.7017
## FCHI8 FCHI8 113.9314
## FEAR5 FEAR5 108.8773
## FGI4 FGI4 106.6898
## FMA7 FMA7 104.6070
## FSV1 FSV1 110.0605
##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(SLA))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas de reacción joint regression env
ggplot(datos, aes(x = env, y = SLA,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (local)",
y = expression(t.CO[2][eq]/ha.año)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
#visualización Normas de reacción clima local
ggplot(datos, aes(x = E, y = SLA,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (Estrés)",
y = expression(t.CO[2][eq]/ha.año)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
## plasticidad joint
# modelo factores fijos
mod_plas_lm <- lm(SLA ~ gen*env,
data=datos)
emtrends(mod_plas_lm, "gen", var = "env")
## gen env.trend SE df lower.CL upper.CL
## CNCH12 1.019 0.291 600 0.4474 1.59
## CNCH13 1.023 0.291 600 0.4512 1.59
## FBO1 1.207 0.291 600 0.6351 1.78
## FCHI8 2.166 0.312 600 1.5534 2.78
## FEAR5 0.544 0.291 600 -0.0275 1.12
## FGI4 0.923 0.291 600 0.3516 1.50
## FMA7 0.569 0.291 600 -0.0025 1.14
## FSV1 0.667 0.300 600 0.0785 1.26
##
## Confidence level used: 0.95
# modelo blup factores aleatorios
modelo_plasticidad <- lmer(SLA ~ env +
(env|gen) +
(1|mun),
data=datos)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 2.67419 (tol = 0.002, component 1)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
## - Rescale variables?
pend <- ranef(modelo_plasticidad)$gen
pend$gen <- rownames(pend)
plasticidad <- pend[,c("gen","env")]
colnames(plasticidad)[2] <- "Pendiente"
#plasticidad Estrés
# modelo factores fijos
mod_plas2_lm <- lm(SLA ~ gen*E,
data=datos)
emtrends(mod_plas2_lm, "gen", var = "E")
## gen E.trend SE df lower.CL upper.CL
## CNCH12 -100.76 53.7 600 -206.3 4.74
## CNCH13 -8.95 53.7 600 -114.5 96.55
## FBO1 32.45 53.7 600 -73.1 137.95
## FCHI8 -75.00 56.8 600 -186.6 36.55
## FEAR5 91.91 53.7 600 -13.6 197.41
## FGI4 77.18 53.7 600 -28.3 182.69
## FMA7 -59.62 53.7 600 -165.1 45.89
## FSV1 14.42 54.4 600 -92.5 121.33
##
## Confidence level used: 0.95
#Modelo factores aleatorios
modelo_plasticidad2 <- lmer(SLA ~ 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 1.84 91.9 91.9
## 2 PC2 0.16 8.07 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C 0.96 0.92 0.08
## 2 Pendiente 0.96 0.92 0.08
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.9192712
## -------------------------------------------------------------------------------
## 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 1.08e+ 2 114. 6.21 5.76e 0 increase 100
## 2 Pendiente FA1 3.02e-12 1.01 1.01 3.33e13 increase 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FCHI8
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FCHI8 0.0000000001
## 2 FSV1 1.94
## 3 FEAR5 2.32
## 4 FBO1 2.37
## 5 FGI4 2.57
## 6 CNCH12 2.61
## 7 CNCH13 2.96
## 8 FMA7 3.29
#Gráfico 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 1.95 65.0 65.0
## 2 PC2 0.93 31.1 96.0
## 3 PC3 0.12 3.96 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 3 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C -0.9 0.81 0.19
## 2 Pendiente -0.97 0.93 0.07
## 3 Pendiente2 -0.46 0.21 0.79
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.6497537
## -------------------------------------------------------------------------------
## 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 1.08e+ 2 114. 6.21 5.76e 0 increase 100
## 2 Pendiente FA1 3.02e-12 1.01 1.01 3.33e13 increase 100
## 3 Pendiente2 FA1 -7.11e-12 -39.2 -39.2 -5.51e14 decrease 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FCHI8
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod2$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FCHI8 0.0547
## 2 FSV1 2.18
## 3 CNCH12 2.40
## 4 FBO1 2.61
## 5 FEAR5 2.77
## 6 FGI4 2.94
## 7 CNCH13 3.02
## 8 FMA7 3.17
#Gráfico Selección 1
plot(mgidi_mod2)
