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(AF) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(AF)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 2.412 0.34461 3.5716 0.0009109 ***
## mun 9 16.356 1.81736 18.8354 < 2.2e-16 ***
## gen:mun 60 11.887 0.19812 2.0533 1.606e-05 ***
## Residuals 539 52.006 0.09649
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm ((AF) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: (AF)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 60015 8574 2.2711 0.027597 *
## mun 9 541908 60212 15.9503 < 2.2e-16 ***
## gen:mun 60 378282 6305 1.6701 0.001863 **
## Residuals 539 2034714 3775
## ---
## 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 206 6.87 539 192 219
## CNCH13 194 6.87 539 181 208
## FBO1 214 6.87 539 201 228
## FCHI8 nonEst NA NA NA NA
## FEAR5 184 6.87 539 170 197
## FGI4 195 6.87 539 181 208
## FMA7 206 6.87 539 193 220
## 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 11.262 9.71 539 1.159 0.8559
## CNCH12 - FBO1 -8.812 9.71 539 -0.907 0.9447
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 21.817 9.71 539 2.246 0.2187
## CNCH12 - FGI4 10.774 9.71 539 1.109 0.8776
## CNCH12 - FMA7 -0.665 9.71 539 -0.068 1.0000
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 -20.074 9.71 539 -2.066 0.3066
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 10.555 9.71 539 1.086 0.8867
## CNCH13 - FGI4 -0.488 9.71 539 -0.050 1.0000
## CNCH13 - FMA7 -11.926 9.71 539 -1.228 0.8232
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 30.629 9.71 539 3.153 0.0210
## FBO1 - FGI4 19.586 9.71 539 2.016 0.3344
## FBO1 - FMA7 8.147 9.71 539 0.839 0.9602
## 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 -11.042 9.71 539 -1.137 0.8659
## FEAR5 - FMA7 -22.481 9.71 539 -2.314 0.1901
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 -11.439 9.71 539 -1.177 0.8475
## 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 219 7.68 539 203 234
## Gig 204 7.68 539 189 219
## HtC 248 7.68 539 232 263
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
## RiN 206 7.68 539 191 221
## SnV 186 7.68 539 171 202
## Tam 165 7.68 539 150 180
## ViG 226 7.68 539 211 241
## Yac 160 7.68 539 144 175
##
## 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 14.49 10.9 539 1.334 0.8855
## Chi - HtC -29.06 10.9 539 -2.676 0.1322
## Chi - Jam nonEst NA NA NA NA
## Chi - PtR nonEst NA NA NA NA
## Chi - RiN 12.65 10.9 539 1.165 0.9414
## Chi - SnV 32.08 10.9 539 2.954 0.0644
## Chi - Tam 53.17 10.9 539 4.895 <.0001
## Chi - ViG -7.18 10.9 539 -0.661 0.9979
## Chi - Yac 58.98 10.9 539 5.430 <.0001
## Gig - HtC -43.56 10.9 539 -4.010 0.0018
## Gig - Jam nonEst NA NA NA NA
## Gig - PtR nonEst NA NA NA NA
## Gig - RiN -1.84 10.9 539 -0.169 1.0000
## Gig - SnV 17.59 10.9 539 1.619 0.7385
## Gig - Tam 38.68 10.9 539 3.561 0.0095
## Gig - ViG -21.67 10.9 539 -1.995 0.4859
## Gig - Yac 44.49 10.9 539 4.096 0.0013
## HtC - Jam nonEst NA NA NA NA
## HtC - PtR nonEst NA NA NA NA
## HtC - RiN 41.72 10.9 539 3.841 0.0034
## HtC - SnV 61.14 10.9 539 5.630 <.0001
## HtC - Tam 82.24 10.9 539 7.571 <.0001
## HtC - ViG 21.88 10.9 539 2.015 0.4728
## HtC - Yac 88.04 10.9 539 8.106 <.0001
## 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 19.43 10.9 539 1.789 0.6281
## RiN - Tam 40.52 10.9 539 3.730 0.0052
## RiN - ViG -19.83 10.9 539 -1.826 0.6025
## RiN - Yac 46.33 10.9 539 4.265 0.0006
## SnV - Tam 21.09 10.9 539 1.942 0.5226
## SnV - ViG -39.26 10.9 539 -3.615 0.0079
## SnV - Yac 26.90 10.9 539 2.477 0.2077
## Tam - ViG -60.35 10.9 539 -5.557 <.0001
## Tam - Yac 5.81 10.9 539 0.535 0.9995
## ViG - Yac 66.16 10.9 539 6.091 <.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
#Interacción
gm<-emmeans(modelo, pairwise ~ gen|mun)
gm
## $emmeans
## mun = Chi:
## gen emmean SE df lower.CL upper.CL
## CNCH12 252.3 21.7 539 209.6 295
## CNCH13 231.1 21.7 539 188.4 274
## FBO1 233.0 21.7 539 190.3 276
## FCHI8 199.1 21.7 539 156.4 242
## FEAR5 177.3 21.7 539 134.7 220
## FGI4 212.5 21.7 539 169.9 255
## FMA7 228.6 21.7 539 185.9 271
## FSV1 214.2 21.7 539 171.5 257
##
## mun = Gig:
## gen emmean SE df lower.CL upper.CL
## CNCH12 220.3 21.7 539 177.6 263
## CNCH13 121.5 21.7 539 78.9 164
## FBO1 261.9 21.7 539 219.3 305
## FCHI8 169.6 21.7 539 126.9 212
## FEAR5 204.4 21.7 539 161.7 247
## FGI4 227.6 21.7 539 184.9 270
## FMA7 208.5 21.7 539 165.8 251
## FSV1 218.4 21.7 539 175.8 261
##
## mun = HtC:
## gen emmean SE df lower.CL upper.CL
## CNCH12 281.9 21.7 539 239.2 325
## CNCH13 236.3 21.7 539 193.6 279
## FBO1 241.3 21.7 539 198.7 284
## FCHI8 227.5 21.7 539 184.8 270
## FEAR5 235.3 21.7 539 192.6 278
## FGI4 312.0 21.7 539 269.3 355
## FMA7 203.5 21.7 539 160.9 246
## FSV1 242.8 21.7 539 200.1 285
##
## mun = Jam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 160.9 21.7 539 118.2 204
## CNCH13 156.9 21.7 539 114.2 200
## FBO1 153.7 21.7 539 111.0 196
## FCHI8 nonEst NA NA NA NA
## FEAR5 150.5 21.7 539 107.8 193
## FGI4 127.6 21.7 539 84.9 170
## FMA7 145.0 21.7 539 102.3 188
## FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## gen emmean SE df lower.CL upper.CL
## CNCH12 222.3 21.7 539 179.7 265
## CNCH13 199.4 21.7 539 156.7 242
## FBO1 199.0 21.7 539 156.3 242
## FCHI8 nonEst NA NA NA NA
## FEAR5 174.6 21.7 539 131.9 217
## FGI4 203.1 21.7 539 160.4 246
## FMA7 215.0 21.7 539 172.3 258
## FSV1 193.4 21.7 539 150.7 236
##
## mun = RiN:
## gen emmean SE df lower.CL upper.CL
## CNCH12 181.5 21.7 539 138.8 224
## CNCH13 202.0 21.7 539 159.4 245
## FBO1 226.4 21.7 539 183.8 269
## FCHI8 194.7 21.7 539 152.0 237
## FEAR5 204.8 21.7 539 162.1 247
## FGI4 259.3 21.7 539 216.6 302
## FMA7 191.8 21.7 539 149.1 234
## FSV1 186.4 21.7 539 143.8 229
##
## mun = SnV:
## gen emmean SE df lower.CL upper.CL
## CNCH12 174.2 21.7 539 131.5 217
## CNCH13 205.3 21.7 539 162.6 248
## FBO1 181.4 21.7 539 138.7 224
## FCHI8 204.6 21.7 539 162.0 247
## FEAR5 168.5 21.7 539 125.8 211
## FGI4 166.7 21.7 539 124.0 209
## FMA7 217.4 21.7 539 174.8 260
## FSV1 173.3 21.7 539 130.6 216
##
## mun = Tam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 158.2 21.7 539 115.5 201
## CNCH13 176.6 21.7 539 133.9 219
## FBO1 172.0 21.7 539 129.3 215
## FCHI8 162.3 21.7 539 119.7 205
## FEAR5 177.4 21.7 539 134.8 220
## FGI4 155.4 21.7 539 112.7 198
## FMA7 165.7 21.7 539 123.0 208
## FSV1 155.1 21.7 539 112.4 198
##
## mun = ViG:
## gen emmean SE df lower.CL upper.CL
## CNCH12 228.7 21.7 539 186.1 271
## CNCH13 218.5 21.7 539 175.9 261
## FBO1 288.6 21.7 539 246.0 331
## FCHI8 208.7 21.7 539 166.1 251
## FEAR5 221.2 21.7 539 178.6 264
## FGI4 199.0 21.7 539 156.3 242
## FMA7 262.7 21.7 539 220.0 305
## FSV1 178.0 21.7 539 135.3 221
##
## mun = Yac:
## gen emmean SE df lower.CL upper.CL
## CNCH12 174.8 21.7 539 132.2 218
## CNCH13 194.9 21.7 539 152.2 238
## FBO1 186.0 21.7 539 143.3 229
## FCHI8 135.0 21.7 539 92.3 178
## FEAR5 122.9 21.7 539 80.3 166
## FGI4 84.4 21.7 539 41.7 127
## FMA7 223.7 21.7 539 181.1 266
## FSV1 154.6 21.7 539 111.9 197
##
## 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 21.226 30.7 539 0.691 0.9972
## CNCH12 - FBO1 19.352 30.7 539 0.630 0.9985
## CNCH12 - FCHI8 53.199 30.7 539 1.732 0.6664
## CNCH12 - FEAR5 74.965 30.7 539 2.440 0.2242
## CNCH12 - FGI4 39.784 30.7 539 1.295 0.9006
## CNCH12 - FMA7 23.723 30.7 539 0.772 0.9944
## CNCH12 - FSV1 38.101 30.7 539 1.240 0.9195
## CNCH13 - FBO1 -1.874 30.7 539 -0.061 1.0000
## CNCH13 - FCHI8 31.973 30.7 539 1.041 0.9680
## CNCH13 - FEAR5 53.739 30.7 539 1.749 0.6547
## CNCH13 - FGI4 18.558 30.7 539 0.604 0.9988
## CNCH13 - FMA7 2.497 30.7 539 0.081 1.0000
## CNCH13 - FSV1 16.875 30.7 539 0.549 0.9994
## FBO1 - FCHI8 33.847 30.7 539 1.102 0.9563
## FBO1 - FEAR5 55.613 30.7 539 1.810 0.6133
## FBO1 - FGI4 20.433 30.7 539 0.665 0.9978
## FBO1 - FMA7 4.371 30.7 539 0.142 1.0000
## FBO1 - FSV1 18.749 30.7 539 0.610 0.9987
## FCHI8 - FEAR5 21.767 30.7 539 0.709 0.9967
## FCHI8 - FGI4 -13.414 30.7 539 -0.437 0.9999
## FCHI8 - FMA7 -29.476 30.7 539 -0.959 0.9797
## FCHI8 - FSV1 -15.098 30.7 539 -0.491 0.9997
## FEAR5 - FGI4 -35.181 30.7 539 -1.145 0.9464
## FEAR5 - FMA7 -51.242 30.7 539 -1.668 0.7080
## FEAR5 - FSV1 -36.864 30.7 539 -1.200 0.9318
## FGI4 - FMA7 -16.061 30.7 539 -0.523 0.9995
## FGI4 - FSV1 -1.683 30.7 539 -0.055 1.0000
## FMA7 - FSV1 14.378 30.7 539 0.468 0.9998
##
## mun = Gig:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 98.757 30.7 539 3.215 0.0299
## CNCH12 - FBO1 -41.653 30.7 539 -1.356 0.8766
## CNCH12 - FCHI8 50.715 30.7 539 1.651 0.7189
## CNCH12 - FEAR5 15.942 30.7 539 0.519 0.9996
## CNCH12 - FGI4 -7.263 30.7 539 -0.236 1.0000
## CNCH12 - FMA7 11.808 30.7 539 0.384 0.9999
## CNCH12 - FSV1 1.860 30.7 539 0.061 1.0000
## CNCH13 - FBO1 -140.410 30.7 539 -4.571 0.0002
## CNCH13 - FCHI8 -48.043 30.7 539 -1.564 0.7717
## CNCH13 - FEAR5 -82.815 30.7 539 -2.696 0.1260
## CNCH13 - FGI4 -106.021 30.7 539 -3.451 0.0139
## CNCH13 - FMA7 -86.949 30.7 539 -2.830 0.0898
## CNCH13 - FSV1 -96.897 30.7 539 -3.154 0.0360
## FBO1 - FCHI8 92.367 30.7 539 3.007 0.0555
## FBO1 - FEAR5 57.595 30.7 539 1.875 0.5689
## FBO1 - FGI4 34.389 30.7 539 1.119 0.9525
## FBO1 - FMA7 53.461 30.7 539 1.740 0.6608
## FBO1 - FSV1 43.513 30.7 539 1.416 0.8496
## FCHI8 - FEAR5 -34.773 30.7 539 -1.132 0.9496
## FCHI8 - FGI4 -57.978 30.7 539 -1.887 0.5603
## FCHI8 - FMA7 -38.907 30.7 539 -1.266 0.9108
## FCHI8 - FSV1 -48.855 30.7 539 -1.590 0.7561
## FEAR5 - FGI4 -23.205 30.7 539 -0.755 0.9951
## FEAR5 - FMA7 -4.134 30.7 539 -0.135 1.0000
## FEAR5 - FSV1 -14.082 30.7 539 -0.458 0.9998
## FGI4 - FMA7 19.071 30.7 539 0.621 0.9986
## FGI4 - FSV1 9.123 30.7 539 0.297 1.0000
## FMA7 - FSV1 -9.948 30.7 539 -0.324 1.0000
##
## mun = HtC:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 45.563 30.7 539 1.483 0.8163
## CNCH12 - FBO1 40.552 30.7 539 1.320 0.8911
## CNCH12 - FCHI8 54.365 30.7 539 1.770 0.6410
## CNCH12 - FEAR5 46.560 30.7 539 1.516 0.7989
## CNCH12 - FGI4 -30.114 30.7 539 -0.980 0.9771
## CNCH12 - FMA7 78.356 30.7 539 2.551 0.1768
## CNCH12 - FSV1 39.127 30.7 539 1.274 0.9083
## CNCH13 - FBO1 -5.011 30.7 539 -0.163 1.0000
## CNCH13 - FCHI8 8.802 30.7 539 0.287 1.0000
## CNCH13 - FEAR5 0.998 30.7 539 0.032 1.0000
## CNCH13 - FGI4 -75.677 30.7 539 -2.463 0.2136
## CNCH13 - FMA7 32.794 30.7 539 1.067 0.9632
## CNCH13 - FSV1 -6.436 30.7 539 -0.210 1.0000
## FBO1 - FCHI8 13.813 30.7 539 0.450 0.9998
## FBO1 - FEAR5 6.009 30.7 539 0.196 1.0000
## FBO1 - FGI4 -70.665 30.7 539 -2.300 0.2953
## FBO1 - FMA7 37.805 30.7 539 1.231 0.9226
## FBO1 - FSV1 -1.425 30.7 539 -0.046 1.0000
## FCHI8 - FEAR5 -7.804 30.7 539 -0.254 1.0000
## FCHI8 - FGI4 -84.478 30.7 539 -2.750 0.1103
## FCHI8 - FMA7 23.992 30.7 539 0.781 0.9940
## FCHI8 - FSV1 -15.238 30.7 539 -0.496 0.9997
## FEAR5 - FGI4 -76.674 30.7 539 -2.496 0.1993
## FEAR5 - FMA7 31.796 30.7 539 1.035 0.9689
## FEAR5 - FSV1 -7.434 30.7 539 -0.242 1.0000
## FGI4 - FMA7 108.470 30.7 539 3.531 0.0106
## FGI4 - FSV1 69.240 30.7 539 2.254 0.3214
## FMA7 - FSV1 -39.230 30.7 539 -1.277 0.9071
##
## mun = Jam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 4.034 30.7 539 0.131 1.0000
## CNCH12 - FBO1 7.258 30.7 539 0.236 0.9999
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 10.450 30.7 539 0.340 0.9994
## CNCH12 - FGI4 33.346 30.7 539 1.085 0.8871
## CNCH12 - FMA7 15.955 30.7 539 0.519 0.9954
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 3.224 30.7 539 0.105 1.0000
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 6.416 30.7 539 0.209 0.9999
## CNCH13 - FGI4 29.312 30.7 539 0.954 0.9319
## CNCH13 - FMA7 11.921 30.7 539 0.388 0.9989
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 3.192 30.7 539 0.104 1.0000
## FBO1 - FGI4 26.088 30.7 539 0.849 0.9580
## FBO1 - FMA7 8.697 30.7 539 0.283 0.9998
## 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 22.897 30.7 539 0.745 0.9761
## FEAR5 - FMA7 5.505 30.7 539 0.179 1.0000
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 -17.391 30.7 539 -0.566 0.9931
## 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 22.921 30.7 539 0.746 0.9896
## CNCH12 - FBO1 23.337 30.7 539 0.760 0.9885
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 47.732 30.7 539 1.554 0.7119
## CNCH12 - FGI4 19.225 30.7 539 0.626 0.9960
## CNCH12 - FMA7 7.367 30.7 539 0.240 1.0000
## CNCH12 - FSV1 28.951 30.7 539 0.942 0.9655
## CNCH13 - FBO1 0.417 30.7 539 0.014 1.0000
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 24.811 30.7 539 0.808 0.9842
## CNCH13 - FGI4 -3.696 30.7 539 -0.120 1.0000
## CNCH13 - FMA7 -15.553 30.7 539 -0.506 0.9988
## CNCH13 - FSV1 6.030 30.7 539 0.196 1.0000
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 24.395 30.7 539 0.794 0.9855
## FBO1 - FGI4 -4.112 30.7 539 -0.134 1.0000
## FBO1 - FMA7 -15.970 30.7 539 -0.520 0.9986
## FBO1 - FSV1 5.613 30.7 539 0.183 1.0000
## 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 -28.507 30.7 539 -0.928 0.9680
## FEAR5 - FMA7 -40.364 30.7 539 -1.314 0.8454
## FEAR5 - FSV1 -18.781 30.7 539 -0.611 0.9965
## FGI4 - FMA7 -11.858 30.7 539 -0.386 0.9997
## FGI4 - FSV1 9.725 30.7 539 0.317 0.9999
## FMA7 - FSV1 21.583 30.7 539 0.703 0.9924
##
## mun = RiN:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -20.544 30.7 539 -0.669 0.9977
## CNCH12 - FBO1 -44.956 30.7 539 -1.463 0.8265
## CNCH12 - FCHI8 -13.201 30.7 539 -0.430 0.9999
## CNCH12 - FEAR5 -23.313 30.7 539 -0.759 0.9950
## CNCH12 - FGI4 -77.766 30.7 539 -2.531 0.1845
## CNCH12 - FMA7 -10.293 30.7 539 -0.335 1.0000
## CNCH12 - FSV1 -4.943 30.7 539 -0.161 1.0000
## CNCH13 - FBO1 -24.412 30.7 539 -0.795 0.9934
## CNCH13 - FCHI8 7.343 30.7 539 0.239 1.0000
## CNCH13 - FEAR5 -2.769 30.7 539 -0.090 1.0000
## CNCH13 - FGI4 -57.222 30.7 539 -1.863 0.5773
## CNCH13 - FMA7 10.250 30.7 539 0.334 1.0000
## CNCH13 - FSV1 15.600 30.7 539 0.508 0.9996
## FBO1 - FCHI8 31.755 30.7 539 1.034 0.9692
## FBO1 - FEAR5 21.643 30.7 539 0.705 0.9969
## FBO1 - FGI4 -32.810 30.7 539 -1.068 0.9631
## FBO1 - FMA7 34.663 30.7 539 1.128 0.9504
## FBO1 - FSV1 40.013 30.7 539 1.302 0.8978
## FCHI8 - FEAR5 -10.112 30.7 539 -0.329 1.0000
## FCHI8 - FGI4 -64.565 30.7 539 -2.102 0.4151
## FCHI8 - FMA7 2.908 30.7 539 0.095 1.0000
## FCHI8 - FSV1 8.258 30.7 539 0.269 1.0000
## FEAR5 - FGI4 -54.453 30.7 539 -1.773 0.6390
## FEAR5 - FMA7 13.020 30.7 539 0.424 0.9999
## FEAR5 - FSV1 18.370 30.7 539 0.598 0.9989
## FGI4 - FMA7 67.473 30.7 539 2.196 0.3555
## FGI4 - FSV1 72.823 30.7 539 2.371 0.2581
## FMA7 - FSV1 5.350 30.7 539 0.174 1.0000
##
## mun = SnV:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -31.114 30.7 539 -1.013 0.9725
## CNCH12 - FBO1 -7.171 30.7 539 -0.233 1.0000
## CNCH12 - FCHI8 -30.444 30.7 539 -0.991 0.9756
## CNCH12 - FEAR5 5.675 30.7 539 0.185 1.0000
## CNCH12 - FGI4 7.483 30.7 539 0.244 1.0000
## CNCH12 - FMA7 -43.257 30.7 539 -1.408 0.8535
## CNCH12 - FSV1 0.870 30.7 539 0.028 1.0000
## CNCH13 - FBO1 23.943 30.7 539 0.779 0.9941
## CNCH13 - FCHI8 0.670 30.7 539 0.022 1.0000
## CNCH13 - FEAR5 36.789 30.7 539 1.198 0.9325
## CNCH13 - FGI4 38.597 30.7 539 1.256 0.9142
## CNCH13 - FMA7 -12.143 30.7 539 -0.395 0.9999
## CNCH13 - FSV1 31.984 30.7 539 1.041 0.9679
## FBO1 - FCHI8 -23.273 30.7 539 -0.758 0.9951
## FBO1 - FEAR5 12.846 30.7 539 0.418 0.9999
## FBO1 - FGI4 14.654 30.7 539 0.477 0.9998
## FBO1 - FMA7 -36.086 30.7 539 -1.175 0.9389
## FBO1 - FSV1 8.041 30.7 539 0.262 1.0000
## FCHI8 - FEAR5 36.119 30.7 539 1.176 0.9386
## FCHI8 - FGI4 37.927 30.7 539 1.235 0.9213
## FCHI8 - FMA7 -12.813 30.7 539 -0.417 0.9999
## FCHI8 - FSV1 31.314 30.7 539 1.019 0.9715
## FEAR5 - FGI4 1.808 30.7 539 0.059 1.0000
## FEAR5 - FMA7 -48.932 30.7 539 -1.593 0.7546
## FEAR5 - FSV1 -4.805 30.7 539 -0.156 1.0000
## FGI4 - FMA7 -50.740 30.7 539 -1.652 0.7184
## FGI4 - FSV1 -6.613 30.7 539 -0.215 1.0000
## FMA7 - FSV1 44.127 30.7 539 1.436 0.8400
##
## mun = Tam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -18.390 30.7 539 -0.599 0.9989
## CNCH12 - FBO1 -13.771 30.7 539 -0.448 0.9998
## CNCH12 - FCHI8 -4.116 30.7 539 -0.134 1.0000
## CNCH12 - FEAR5 -19.225 30.7 539 -0.626 0.9985
## CNCH12 - FGI4 2.855 30.7 539 0.093 1.0000
## CNCH12 - FMA7 -7.471 30.7 539 -0.243 1.0000
## CNCH12 - FSV1 3.120 30.7 539 0.102 1.0000
## CNCH13 - FBO1 4.619 30.7 539 0.150 1.0000
## CNCH13 - FCHI8 14.273 30.7 539 0.465 0.9998
## CNCH13 - FEAR5 -0.835 30.7 539 -0.027 1.0000
## CNCH13 - FGI4 21.245 30.7 539 0.692 0.9972
## CNCH13 - FMA7 10.919 30.7 539 0.355 1.0000
## CNCH13 - FSV1 21.510 30.7 539 0.700 0.9970
## FBO1 - FCHI8 9.655 30.7 539 0.314 1.0000
## FBO1 - FEAR5 -5.454 30.7 539 -0.178 1.0000
## FBO1 - FGI4 16.626 30.7 539 0.541 0.9994
## FBO1 - FMA7 6.300 30.7 539 0.205 1.0000
## FBO1 - FSV1 16.891 30.7 539 0.550 0.9994
## FCHI8 - FEAR5 -15.108 30.7 539 -0.492 0.9997
## FCHI8 - FGI4 6.971 30.7 539 0.227 1.0000
## FCHI8 - FMA7 -3.355 30.7 539 -0.109 1.0000
## FCHI8 - FSV1 7.236 30.7 539 0.236 1.0000
## FEAR5 - FGI4 22.080 30.7 539 0.719 0.9964
## FEAR5 - FMA7 11.754 30.7 539 0.383 0.9999
## FEAR5 - FSV1 22.345 30.7 539 0.727 0.9962
## FGI4 - FMA7 -10.326 30.7 539 -0.336 1.0000
## FGI4 - FSV1 0.265 30.7 539 0.009 1.0000
## FMA7 - FSV1 10.591 30.7 539 0.345 1.0000
##
## mun = ViG:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 10.192 30.7 539 0.332 1.0000
## CNCH12 - FBO1 -59.886 30.7 539 -1.949 0.5174
## CNCH12 - FCHI8 20.010 30.7 539 0.651 0.9981
## CNCH12 - FEAR5 7.495 30.7 539 0.244 1.0000
## CNCH12 - FGI4 29.727 30.7 539 0.968 0.9787
## CNCH12 - FMA7 -33.941 30.7 539 -1.105 0.9557
## CNCH12 - FSV1 50.759 30.7 539 1.652 0.7180
## CNCH13 - FBO1 -70.078 30.7 539 -2.281 0.3059
## CNCH13 - FCHI8 9.818 30.7 539 0.320 1.0000
## CNCH13 - FEAR5 -2.697 30.7 539 -0.088 1.0000
## CNCH13 - FGI4 19.536 30.7 539 0.636 0.9984
## CNCH13 - FMA7 -44.133 30.7 539 -1.437 0.8399
## CNCH13 - FSV1 40.568 30.7 539 1.321 0.8909
## FBO1 - FCHI8 79.896 30.7 539 2.601 0.1578
## FBO1 - FEAR5 67.381 30.7 539 2.193 0.3574
## FBO1 - FGI4 89.614 30.7 539 2.917 0.0712
## FBO1 - FMA7 25.945 30.7 539 0.845 0.9904
## FBO1 - FSV1 110.646 30.7 539 3.602 0.0082
## FCHI8 - FEAR5 -12.515 30.7 539 -0.407 0.9999
## FCHI8 - FGI4 9.718 30.7 539 0.316 1.0000
## FCHI8 - FMA7 -53.951 30.7 539 -1.756 0.6501
## FCHI8 - FSV1 30.750 30.7 539 1.001 0.9742
## FEAR5 - FGI4 22.233 30.7 539 0.724 0.9963
## FEAR5 - FMA7 -41.436 30.7 539 -1.349 0.8795
## FEAR5 - FSV1 43.265 30.7 539 1.408 0.8533
## FGI4 - FMA7 -63.669 30.7 539 -2.073 0.4342
## FGI4 - FSV1 21.032 30.7 539 0.685 0.9974
## FMA7 - FSV1 84.701 30.7 539 2.757 0.1083
##
## mun = Yac:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -20.028 30.7 539 -0.652 0.9981
## CNCH12 - FBO1 -11.184 30.7 539 -0.364 1.0000
## CNCH12 - FCHI8 39.859 30.7 539 1.297 0.8997
## CNCH12 - FEAR5 51.884 30.7 539 1.689 0.6945
## CNCH12 - FGI4 90.463 30.7 539 2.945 0.0660
## CNCH12 - FMA7 -48.895 30.7 539 -1.592 0.7553
## CNCH12 - FSV1 20.266 30.7 539 0.660 0.9979
## CNCH13 - FBO1 8.844 30.7 539 0.288 1.0000
## CNCH13 - FCHI8 59.888 30.7 539 1.949 0.5174
## CNCH13 - FEAR5 71.913 30.7 539 2.341 0.2734
## CNCH13 - FGI4 110.492 30.7 539 3.597 0.0084
## CNCH13 - FMA7 -28.866 30.7 539 -0.940 0.9820
## CNCH13 - FSV1 40.295 30.7 539 1.312 0.8944
## FBO1 - FCHI8 51.043 30.7 539 1.662 0.7121
## FBO1 - FEAR5 63.068 30.7 539 2.053 0.4471
## FBO1 - FGI4 101.647 30.7 539 3.309 0.0222
## FBO1 - FMA7 -37.711 30.7 539 -1.228 0.9235
## FBO1 - FSV1 31.450 30.7 539 1.024 0.9708
## FCHI8 - FEAR5 12.025 30.7 539 0.391 0.9999
## FCHI8 - FGI4 50.604 30.7 539 1.647 0.7212
## FCHI8 - FMA7 -88.754 30.7 539 -2.889 0.0768
## FCHI8 - FSV1 -19.593 30.7 539 -0.638 0.9983
## FEAR5 - FGI4 38.579 30.7 539 1.256 0.9144
## FEAR5 - FMA7 -100.779 30.7 539 -3.281 0.0243
## FEAR5 - FSV1 -31.618 30.7 539 -1.029 0.9699
## FGI4 - FMA7 -139.358 30.7 539 -4.536 0.0002
## FGI4 - FSV1 -70.197 30.7 539 -2.285 0.3037
## FMA7 - FSV1 69.161 30.7 539 2.251 0.3229
##
## 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(AF) ~ 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 1.3289 0.18985 7 59.987 1.9676 0.07457 .
## ---
## 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(AF) ~ gen + (1 | mun) + (1 | mun:gen)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -204.16 430.33
## (1 | mun) 10 -219.07 458.15 29.824 1 4.731e-08 ***
## (1 | mun:gen) 10 -212.63 445.26 16.937 1 3.864e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(AF ~ 1 +
(1|gen) +
(1|mun) +
(1|gen:mun),
data = datos)
ranova(modelo_blup)
## boundary (singular) fit: see help('isSingular')
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## AF ~ (1 | gen) + (1 | mun) + (1 | gen:mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -3440.1 6890.1
## (1 | gen) 4 -3440.5 6889.0 0.8618 1 0.353228
## (1 | mun) 4 -3455.2 6918.3 30.2086 1 3.88e-08 ***
## (1 | gen:mun) 4 -3444.2 6896.4 8.2648 1 0.004042 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CNCH12 3.7341327
## CNCH13 -0.8007458
## FBO1 7.2825867
## FCHI8 -4.6894086
## FEAR5 -5.0509074
## FGI4 -0.6043802
## FMA7 4.0017798
## FSV1 -3.8730572
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 199.9794
## CNCH13 195.4445
## FBO1 203.5278
## FCHI8 191.5558
## FEAR5 191.1943
## FGI4 195.6409
## FMA7 200.2470
## FSV1 192.3722
#Blups Parcela
blups$mun
## (Intercept)
## Chi 20.017946
## Gig 6.990724
## HtC 46.144411
## Jam -42.257775
## PtR 3.584145
## RiN 8.644362
## SnV -8.819247
## Tam -27.777808
## ViG 26.473242
## Yac -32.999999
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## Chi 216.2632
## Gig 203.2360
## HtC 242.3896
## Jam 153.9875
## PtR 199.8294
## RiN 204.8896
## SnV 187.4260
## Tam 168.4674
## ViG 222.7185
## Yac 163.2452
#Blups interacción
blups$`gen:mun`
## (Intercept)
## CNCH12:Chi 13.0704942
## CNCH12:Gig 5.3894079
## CNCH12:HtC 14.4642520
## CNCH12:Jam 1.2934140
## CNCH12:PtR 7.5881807
## CNCH12:RiN -10.9783474
## CNCH12:SnV -6.8651339
## CNCH12:Tam -5.6562520
## CNCH12:ViG 0.9253204
## CNCH12:Yac 3.1757615
## CNCH13:Chi 6.3185225
## CNCH13:Gig -32.7258720
## CNCH13:HtC -2.1325554
## CNCH13:Jam 1.4958812
## CNCH13:PtR 0.1506089
## CNCH13:RiN -0.8335236
## CNCH13:SnV 7.5556868
## CNCH13:Tam 3.6172287
## CNCH13:ViG -1.3630813
## CNCH13:Yac 13.1121359
## FBO1:Chi 3.8068076
## FBO1:Gig 20.8034514
## FBO1:HtC -3.3752732
## FBO1:Jam -3.0781444
## FBO1:PtR -3.2877823
## FBO1:RiN 5.7718648
## FBO1:SnV -5.3996108
## FBO1:Tam -1.5210925
## FBO1:ViG 23.7152531
## FBO1:Yac 6.2645347
## FCHI8:Chi -5.0421020
## FCHI8:Gig -11.7184308
## FCHI8:HtC -4.1200081
## FCHI8:RiN -2.2307165
## FCHI8:SnV 8.8578936
## FCHI8:Tam -0.5836432
## FCHI8:ViG -3.7615251
## FCHI8:Yac -9.5408094
## FEAR5:Chi -13.7009718
## FEAR5:Gig 2.4941789
## FEAR5:HtC -0.8168152
## FEAR5:Jam 0.6199416
## FEAR5:PtR -8.1667491
## FEAR5:RiN 2.0060741
## FEAR5:SnV -5.6068891
## FEAR5:Tam 5.6743198
## FEAR5:ViG 1.4473244
## FEAR5:Yac -14.2589708
## FGI4:Chi -1.2682488
## FGI4:Gig 10.0826112
## FGI4:HtC 28.4010378
## FGI4:Jam -10.4409818
## FGI4:PtR 1.5662158
## FGI4:RiN 22.2348842
## FGI4:SnV -8.1369885
## FGI4:Tam -5.0562447
## FGI4:ViG -9.3451177
## FGI4:Yac -31.6638212
## FMA7:Chi 3.3656649
## FMA7:Gig 0.5044882
## FMA7:HtC -17.3411022
## FMA7:Jam -5.2690680
## FMA7:PtR 4.4996249
## FMA7:RiN -6.9228126
## FMA7:SnV 10.5250903
## FMA7:Tam -2.7424135
## FMA7:ViG 14.5471005
## FMA7:Yac 22.8465729
## FSV1:Chi 0.7350048
## FSV1:Gig 7.7143133
## FSV1:HtC 1.7138929
## FSV1:PtR -1.0457136
## FSV1:RiN -5.9014628
## FSV1:SnV -4.1396550
## FSV1:Tam -3.8411363
## FSV1:ViG -16.5308139
## FSV1:Yac -1.9451600
fixef(modelo_blup)[1] + blups$`gen:mun`
## (Intercept)
## CNCH12:Chi 209.3157
## CNCH12:Gig 201.6346
## CNCH12:HtC 210.7095
## CNCH12:Jam 197.5386
## CNCH12:PtR 203.8334
## CNCH12:RiN 185.2669
## CNCH12:SnV 189.3801
## CNCH12:Tam 190.5890
## CNCH12:ViG 197.1706
## CNCH12:Yac 199.4210
## CNCH13:Chi 202.5638
## CNCH13:Gig 163.5194
## CNCH13:HtC 194.1127
## CNCH13:Jam 197.7411
## CNCH13:PtR 196.3958
## CNCH13:RiN 195.4117
## CNCH13:SnV 203.8009
## CNCH13:Tam 199.8625
## CNCH13:ViG 194.8822
## CNCH13:Yac 209.3574
## FBO1:Chi 200.0520
## FBO1:Gig 217.0487
## FBO1:HtC 192.8700
## FBO1:Jam 193.1671
## FBO1:PtR 192.9575
## FBO1:RiN 202.0171
## FBO1:SnV 190.8456
## FBO1:Tam 194.7241
## FBO1:ViG 219.9605
## FBO1:Yac 202.5098
## FCHI8:Chi 191.2031
## FCHI8:Gig 184.5268
## FCHI8:HtC 192.1252
## FCHI8:RiN 194.0145
## FCHI8:SnV 205.1031
## FCHI8:Tam 195.6616
## FCHI8:ViG 192.4837
## FCHI8:Yac 186.7044
## FEAR5:Chi 182.5443
## FEAR5:Gig 198.7394
## FEAR5:HtC 195.4284
## FEAR5:Jam 196.8652
## FEAR5:PtR 188.0785
## FEAR5:RiN 198.2513
## FEAR5:SnV 190.6383
## FEAR5:Tam 201.9196
## FEAR5:ViG 197.6926
## FEAR5:Yac 181.9863
## FGI4:Chi 194.9770
## FGI4:Gig 206.3278
## FGI4:HtC 224.6463
## FGI4:Jam 185.8043
## FGI4:PtR 197.8114
## FGI4:RiN 218.4801
## FGI4:SnV 188.1082
## FGI4:Tam 191.1890
## FGI4:ViG 186.9001
## FGI4:Yac 164.5814
## FMA7:Chi 199.6109
## FMA7:Gig 196.7497
## FMA7:HtC 178.9041
## FMA7:Jam 190.9762
## FMA7:PtR 200.7449
## FMA7:RiN 189.3224
## FMA7:SnV 206.7703
## FMA7:Tam 193.5028
## FMA7:ViG 210.7923
## FMA7:Yac 219.0918
## FSV1:Chi 196.9802
## FSV1:Gig 203.9595
## FSV1:HtC 197.9591
## FSV1:PtR 195.1995
## FSV1:RiN 190.3438
## FSV1:SnV 192.1056
## FSV1:Tam 192.4041
## FSV1:ViG 179.7144
## FSV1:Yac 194.3001
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 3.7341327
## 2 CNCH13 -0.8007458
## 3 FBO1 7.2825867
## 4 FCHI8 -4.6894086
## 5 FEAR5 -5.0509074
## 6 FGI4 -0.6043802
## 7 FMA7 4.0017798
## 8 FSV1 -3.8730572
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 Chi 20.017946
## 2 Gig 6.990724
## 3 HtC 46.144411
## 4 Jam -42.257775
## 5 PtR 3.584145
## 6 RiN 8.644362
## 7 SnV -8.819247
## 8 Tam -27.777808
## 9 ViG 26.473242
## 10 Yac -32.999999
#Tabla blup_gen_mun
blup_gen_mun <- ranef(modelo_blup)$`gen:mun` %>%
tibble::rownames_to_column("gen:mun") %>%
rename(BLUP = `(Intercept)`)
blup_gen_mun
## gen:mun BLUP
## 1 CNCH12:Chi 13.0704942
## 2 CNCH12:Gig 5.3894079
## 3 CNCH12:HtC 14.4642520
## 4 CNCH12:Jam 1.2934140
## 5 CNCH12:PtR 7.5881807
## 6 CNCH12:RiN -10.9783474
## 7 CNCH12:SnV -6.8651339
## 8 CNCH12:Tam -5.6562520
## 9 CNCH12:ViG 0.9253204
## 10 CNCH12:Yac 3.1757615
## 11 CNCH13:Chi 6.3185225
## 12 CNCH13:Gig -32.7258720
## 13 CNCH13:HtC -2.1325554
## 14 CNCH13:Jam 1.4958812
## 15 CNCH13:PtR 0.1506089
## 16 CNCH13:RiN -0.8335236
## 17 CNCH13:SnV 7.5556868
## 18 CNCH13:Tam 3.6172287
## 19 CNCH13:ViG -1.3630813
## 20 CNCH13:Yac 13.1121359
## 21 FBO1:Chi 3.8068076
## 22 FBO1:Gig 20.8034514
## 23 FBO1:HtC -3.3752732
## 24 FBO1:Jam -3.0781444
## 25 FBO1:PtR -3.2877823
## 26 FBO1:RiN 5.7718648
## 27 FBO1:SnV -5.3996108
## 28 FBO1:Tam -1.5210925
## 29 FBO1:ViG 23.7152531
## 30 FBO1:Yac 6.2645347
## 31 FCHI8:Chi -5.0421020
## 32 FCHI8:Gig -11.7184308
## 33 FCHI8:HtC -4.1200081
## 34 FCHI8:RiN -2.2307165
## 35 FCHI8:SnV 8.8578936
## 36 FCHI8:Tam -0.5836432
## 37 FCHI8:ViG -3.7615251
## 38 FCHI8:Yac -9.5408094
## 39 FEAR5:Chi -13.7009718
## 40 FEAR5:Gig 2.4941789
## 41 FEAR5:HtC -0.8168152
## 42 FEAR5:Jam 0.6199416
## 43 FEAR5:PtR -8.1667491
## 44 FEAR5:RiN 2.0060741
## 45 FEAR5:SnV -5.6068891
## 46 FEAR5:Tam 5.6743198
## 47 FEAR5:ViG 1.4473244
## 48 FEAR5:Yac -14.2589708
## 49 FGI4:Chi -1.2682488
## 50 FGI4:Gig 10.0826112
## 51 FGI4:HtC 28.4010378
## 52 FGI4:Jam -10.4409818
## 53 FGI4:PtR 1.5662158
## 54 FGI4:RiN 22.2348842
## 55 FGI4:SnV -8.1369885
## 56 FGI4:Tam -5.0562447
## 57 FGI4:ViG -9.3451177
## 58 FGI4:Yac -31.6638212
## 59 FMA7:Chi 3.3656649
## 60 FMA7:Gig 0.5044882
## 61 FMA7:HtC -17.3411022
## 62 FMA7:Jam -5.2690680
## 63 FMA7:PtR 4.4996249
## 64 FMA7:RiN -6.9228126
## 65 FMA7:SnV 10.5250903
## 66 FMA7:Tam -2.7424135
## 67 FMA7:ViG 14.5471005
## 68 FMA7:Yac 22.8465729
## 69 FSV1:Chi 0.7350048
## 70 FSV1:Gig 7.7143133
## 71 FSV1:HtC 1.7138929
## 72 FSV1:PtR -1.0457136
## 73 FSV1:RiN -5.9014628
## 74 FSV1:SnV -4.1396550
## 75 FSV1:Tam -3.8411363
## 76 FSV1:ViG -16.5308139
## 77 FSV1:Yac -1.9451600
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 195.1151 195.1151 195.1151 195.1151 195.1151 195.1151 195.1151 195.1151
## [9] 197.9695 197.9695 197.9695 197.9695 197.9695 197.9695 197.9695 197.9695
## [17] 201.8448 201.8448 201.8448 201.8448 201.8448 201.8448 201.8448 201.8448
## [25] 217.9440 217.9440 217.9440 217.9440 217.9440 217.9440 217.9440 217.9440
## [33] 203.2553 203.2553 203.2553 203.2553 203.2553 203.2553 203.2553 203.2553
## [41] 226.5201 226.5201 226.5201 226.5201 226.5201 226.5201 226.5201 226.5201
## [49] 201.9686 201.9686 201.9686 201.9686 201.9686 201.9686 201.9686 201.9686
## [57] 197.6454 197.6454 197.6454 197.6454 197.6454 197.6454 197.6454 197.6454
## [65] 179.4133 179.4133 179.4133 179.4133 179.4133 179.4133 179.4133 179.4133
## [73] 194.1809 194.1809 194.1809 194.1809 194.1809 194.1809 194.1809 194.1809
## [81] 178.6846 178.6846 178.6846 178.6846 178.6846 178.6846 178.6846 178.6846
## [89] 191.5945 191.5945 191.5945 191.5945 191.5945 191.5945 191.5945 191.5945
## [97] 176.7682 176.7682 176.7682 176.7682 176.7682 176.7682 176.7682 176.7682
## [105] 201.9529 201.9529 201.9529 201.9529 201.9529 201.9529 201.9529 201.9529
## [113] 189.3090 189.3090 189.3090 189.3090 189.3090 189.3090 189.3090 189.3090
## [121] 184.2950 184.2950 184.2950 184.2950 184.2950 184.2950 184.2950 184.2950
## [129] 213.1251 213.1251 213.1251 213.1251 213.1251 213.1251 213.1251 213.1251
## [137] 223.6306 223.6306 223.6306 223.6306 223.6306 223.6306 223.6306 223.6306
## [145] 197.5113 197.5113 197.5113 197.5113 197.5113 197.5113 197.5113 197.5113
## [153] 206.5317 206.5317 206.5317 206.5317 206.5317 206.5317 206.5317 206.5317
## [161] 214.3906 214.3906 214.3906 214.3906 214.3906 214.3906 214.3906 214.3906
## [169] 233.0678 233.0678 233.0678 233.0678 233.0678 233.0678 233.0678 233.0678
## [177] 221.7810 221.7810 221.7810 221.7810 221.7810 221.7810 221.7810 221.7810
## [185] 227.3526 227.3526 227.3526 227.3526 227.3526 227.3526 227.3526 227.3526
## [193] 157.4270 157.4270 157.4270 157.4270 157.4270 157.4270 157.4270 157.4270
## [201] 175.5566 175.5566 175.5566 175.5566 175.5566 175.5566 175.5566 175.5566
## [209] 130.9770 130.9770 130.9770 130.9770 130.9770 130.9770 130.9770 130.9770
## [217] 149.0150 149.0150 149.0150 149.0150 149.0150 149.0150 149.0150 149.0150
## [225] 143.9354 143.9354 143.9354 143.9354 143.9354 143.9354 143.9354 143.9354
## [233] 190.0936 190.0936 190.0936 190.0936 190.0936 190.0936 190.0936 190.0936
## [241] 176.7924 176.7924 176.7924 176.7924 176.7924 176.7924 176.7924 176.7924
## [249] 170.1551 170.1551 170.1551 170.1551 170.1551 170.1551 170.1551 170.1551
## [257] 194.9106 194.9106 194.9106 194.9106 194.9106 194.9106 194.9106 194.9106
## [265] 208.3308 208.3308 208.3308 208.3308 208.3308 208.3308 208.3308 208.3308
## [273] 186.6117 186.6117 186.6117 186.6117 186.6117 186.6117 186.6117 186.6117
## [281] 200.7912 200.7912 200.7912 200.7912 200.7912 200.7912 200.7912 200.7912
## [289] 211.1517 211.1517 211.1517 211.1517 211.1517 211.1517 211.1517 211.1517
## [297] 199.1792 199.1792 199.1792 199.1792 199.1792 199.1792 199.1792 199.1792
## [305] 203.8242 203.8242 203.8242 203.8242 203.8242 203.8242 203.8242 203.8242
## [313] 202.3146 202.3146 202.3146 202.3146 202.3146 202.3146 202.3146 202.3146
## [321] 241.2674 241.2674 241.2674 241.2674 241.2674 241.2674 241.2674 241.2674
## [329] 219.1149 219.1149 219.1149 219.1149 219.1149 219.1149 219.1149 219.1149
## [337] 214.2675 214.2675 214.2675 214.2675 214.2675 214.2675 214.2675 214.2675
## [345] 212.7690 212.7690 212.7690 212.7690 212.7690 212.7690 212.7690 212.7690
## [353] 227.3779 227.3779 227.3779 227.3779 227.3779 227.3779 227.3779 227.3779
## [361] 220.5546 220.5546 220.5546 220.5546 220.5546 220.5546 220.5546 220.5546
## [369] 253.7163 253.7163 253.7163 253.7163 253.7163 253.7163 253.7163 253.7163
## [377] 207.0772 207.0772 207.0772 207.0772 207.0772 207.0772 207.0772 207.0772
## [385] 207.7422 207.7422 207.7422 207.7422 207.7422 207.7422 207.7422 207.7422
## [393] 200.6792 200.6792 200.6792 200.6792 200.6792 200.6792 200.6792 200.6792
## [401] 186.8281 186.8281 186.8281 186.8281 186.8281 186.8281 186.8281 186.8281
## [409] 212.7142 212.7142 212.7142 212.7142 212.7142 212.7142 212.7142 212.7142
## [417] 212.3595 212.3595 212.3595 212.3595 212.3595 212.3595 212.3595 212.3595
## [425] 169.7093 169.7093 169.7093 169.7093 169.7093 169.7093 169.7093 169.7093
## [433] 231.3220 231.3220 231.3220 231.3220 231.3220 231.3220 231.3220 231.3220
## [441] 160.7532 160.7532 160.7532 160.7532 160.7532 160.7532 160.7532 160.7532
## [449] 169.7268 169.7268 169.7268 169.7268 169.7268 169.7268 169.7268 169.7268
## [457] 169.0908 169.0908 169.0908 169.0908 169.0908 169.0908 169.0908 169.0908
## [465] 163.1944 163.1944 163.1944 163.1944 163.1944 163.1944 163.1944 163.1944
## [473] 162.8068 162.8068 162.8068 162.8068 162.8068 162.8068 162.8068 162.8068
## [481] 166.5453 166.5453 166.5453 166.5453 166.5453 166.5453 166.5453 166.5453
## [489] 171.2839 171.2839 171.2839 171.2839 171.2839 171.2839 171.2839 171.2839
## [497] 174.2289 174.2289 174.2289 174.2289 174.2289 174.2289 174.2289 174.2289
## [505] 240.2305 240.2305 240.2305 240.2305 240.2305 240.2305 240.2305 240.2305
## [513] 229.0503 229.0503 229.0503 229.0503 229.0503 229.0503 229.0503 229.0503
## [521] 236.5219 236.5219 236.5219 236.5219 236.5219 236.5219 236.5219 236.5219
## [529] 233.5802 233.5802 233.5802 233.5802 233.5802 233.5802 233.5802 233.5802
## [537] 270.1863 270.1863 270.1863 270.1863 270.1863 270.1863 270.1863 270.1863
## [545] 260.5880 260.5880 260.5880 260.5880 260.5880 260.5880 260.5880 260.5880
## [553] 239.4563 239.4563 239.4563 239.4563 239.4563 239.4563 239.4563 239.4563
## [561] 246.2970 246.2970 246.2970 246.2970 246.2970 246.2970 246.2970 246.2970
## [569] 154.6826 154.6826 154.6826 154.6826 154.6826 154.6826 154.6826 154.6826
## [577] 142.9421 142.9421 142.9421 142.9421 142.9421 142.9421 142.9421 142.9421
## [585] 149.5565 149.5565 149.5565 149.5565 149.5565 149.5565 149.5565 149.5565
## [593] 152.7202 152.7202 152.7202 152.7202 152.7202 152.7202 152.7202 152.7202
## [601] 158.1919 158.1919 158.1919 158.1919 158.1919 158.1919 158.1919 158.1919
## [609] 159.0150 159.0150 159.0150 159.0150 159.0150 159.0150 159.0150 159.0150
#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> 320.55686 17.904102
## 2 mun (Intercept) <NA> 880.81523 29.678532
## 3 gen (Intercept) <NA> 53.42066 7.308944
## 4 Residual <NA> <NA> 3774.98116 61.440875
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## gen:mun (Intercept) 17.9041
## mun (Intercept) 29.6785
## gen (Intercept) 7.3089
## Residual 61.4409
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] 0.2970275
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## [1] 1.782348
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CNCH12 3.7341327
## CNCH13 -0.8007458
## FBO1 7.2825867
## FCHI8 -4.6894086
## FEAR5 -5.0509074
## FGI4 -0.6043802
## FMA7 4.0017798
## FSV1 -3.8730572
##Predicho de carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## FBO1 7.2825867 203.5278
## FMA7 4.0017798 200.2470
## CNCH12 3.7341327 199.9794
## FGI4 -0.6043802 195.6409
## CNCH13 -0.8007458 195.4445
## FSV1 -3.8730572 192.3722
## FCHI8 -4.6894086 191.5558
## FEAR5 -5.0509074 191.1943
# Visualización ranking predichos
blup_gen$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(AF=mean(AF)) %>%
pivot_wider(names_from=mun,
values_from=AF)
## `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, AF)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $AF
## $coordgen
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 3.842801 -24.24356 -61.904437 -30.347259 40.90384 43.604590 7.203667
## [2,] 32.189195 52.17033 -35.534989 45.391111 18.88365 -38.694917 -6.866215
## [3,] 18.592543 -66.54768 24.884544 24.452856 13.28392 -20.067243 -49.105943
## [4,] -4.297389 41.39262 25.971483 1.364778 -22.40608 60.846593 -48.677225
## [5,] -21.057036 14.52309 57.219786 -4.864143 57.42259 -2.379354 41.556124
## [6,] -71.496051 -15.46876 -20.755009 38.342765 -35.84744 -4.291816 20.655394
## [7,] 55.779188 -14.36381 12.819250 -2.334822 -49.63331 11.925273 53.748591
## [8,] -13.553251 12.53776 -2.700628 -72.005287 -22.60717 -50.943125 -18.514393
## [,8]
## [1,] -35.821
## [2,] -35.821
## [3,] -35.821
## [4,] -35.821
## [5,] -35.821
## [6,] -35.821
## [7,] -35.821
## [8,] -35.821
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 27.65262 -25.976288 -47.118604 1.2710202 -3.016592 -0.1982050
## [2,] -29.64543 -100.880328 19.931538 -29.7665826 -7.135605 -1.3775705
## [3,] -69.08925 -24.041313 -49.250884 12.3122987 8.286152 3.0523886
## [4,] 18.47051 -4.723666 -7.378429 0.7284077 22.770101 -0.9640662
## [5,] 13.32964 -18.242914 -30.487107 -0.6458405 -9.218286 6.4746140
## [6,] -40.49981 -22.935404 5.626554 44.7921228 -13.643354 -13.9377057
## [7,] 40.30048 17.837686 2.477157 13.1422083 -21.558818 10.9591644
## [8,] 10.74467 3.852220 10.458187 11.6595068 13.265113 -5.1611738
## [9,] 56.61172 -60.386445 20.658547 34.0197312 11.346583 10.9668600
## [10,] 114.35592 -16.382732 -18.539058 -7.3327695 -2.815227 -12.8065062
## [,7] [,8]
## [1,] -8.1042795 -5.131608e-15
## [2,] -0.1119708 1.453844e-15
## [3,] 1.4852356 7.388455e-15
## [4,] 0.7045520 3.298304e-16
## [5,] 8.7703341 -6.362188e-15
## [6,] 0.4497968 -1.317650e-15
## [7,] -0.9574346 5.573248e-15
## [8,] 1.9382675 -9.245456e-16
## [9,] -0.5741982 -4.384962e-16
## [10,] 2.2907623 4.643113e-15
##
## $eigenvalues
## [1] 1.630879e+02 1.286805e+02 8.333441e+01 6.757450e+01 4.117874e+01
## [6] 2.606498e+01 1.248063e+01 1.338004e-14
##
## $totalvar
## [1] 57198.11
##
## $varexpl
## [1] 46.50 28.95 12.14 7.98 2.96 1.19 0.27 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 33.793767 16.270812 34.301093 14.948825 23.38027021 -24.376903
## CNCH13 12.567742 -82.486577 -11.261763 10.914453 0.45941396 -3.833367
## FBO1 14.442014 57.923404 -6.250479 7.690610 0.04290458 20.578752
## FCHI8 -19.404843 -34.443997 -20.063489 -10.547864 -14.12884144 -11.175947
## FEAR5 -41.171417 0.328649 -12.259362 4.498933 -24.35159229 -1.063929
## FGI4 -5.990697 23.534087 64.414878 -18.397589 4.15521708 53.389033
## FMA7 10.070685 4.462741 -44.055379 -1.006249 16.01288271 -14.083810
## FSV1 -4.307250 14.410882 -4.825499 -8.101119 -5.57025479 -19.433829
## SnV Tam ViG Yac
## CNCH12 -12.244971 -7.1245463 3.044465 15.295819
## CNCH13 18.869002 11.2649938 -7.147435 35.324059
## FBO1 -5.073685 6.6461888 62.930553 26.479845
## FCHI8 18.199447 -3.0083938 -16.965147 -24.563599
## FEAR5 -17.919579 12.1000444 -4.450160 -36.588583
## FGI4 -19.727560 -9.9798171 -26.682985 -75.167549
## FMA7 31.012013 0.3462272 36.985703 64.190506
## FSV1 -13.114667 -10.2446970 -47.714993 -4.970496
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 0.009870002
##
## $grand_mean
## [1] 195.7896
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 205.5185 194.2567 214.3306 182.1794 183.7019 194.7443 206.1832 185.4024
##
## $mean_env
## Chi Gig HtC Jam PtR RiN SnV Tam
## 218.5143 204.0221 247.5789 145.9701 198.9415 205.8617 186.4342 165.3436
## ViG Yac
## 225.6956 159.5342
##
## $scale_val
## Chi Gig HtC Jam PtR RiN SnV Tam
## 23.102355 42.074484 33.824620 11.577983 15.325889 25.592586 19.664936 9.117981
## ViG Yac
## 35.223518 44.593907
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración de variables
##Blup gen
blup_gen <- ranef(modelo_blup)$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 199.9794
## CNCH13 CNCH13 195.4445
## FBO1 FBO1 203.5278
## FCHI8 FCHI8 191.5558
## FEAR5 FEAR5 191.1943
## FGI4 FGI4 195.6409
## FMA7 FMA7 200.2470
## FSV1 FSV1 192.3722
##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(AF))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas de reacción joint regression env
ggplot(datos, aes(x = env, y = AF,
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 = AF,
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(AF ~ gen*env,
data=datos)
emtrends(mod_plas_lm, "gen", var = "env")
## gen env.trend SE df lower.CL upper.CL
## CNCH12 1.206 0.235 600 0.744 1.67
## CNCH13 0.614 0.235 600 0.152 1.08
## FBO1 1.129 0.235 600 0.667 1.59
## FCHI8 0.854 0.278 600 0.309 1.40
## FEAR5 0.918 0.235 600 0.456 1.38
## FGI4 1.838 0.235 600 1.376 2.30
## FMA7 0.606 0.235 600 0.144 1.07
## FSV1 0.887 0.278 600 0.342 1.43
##
## Confidence level used: 0.95
# modelo blup factores aleatorios
modelo_plasticidad <- lmer(AF ~ env +
(env|gen) +
(1|mun),
data=datos)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 1.38588 (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(AF ~ gen*E,
data=datos)
emtrends(mod_plas2_lm, "gen", var = "E")
## gen E.trend SE df lower.CL upper.CL
## CNCH12 181.8 132 600 -78.2 442
## CNCH13 -38.5 132 600 -298.6 222
## FBO1 233.9 132 600 -26.2 494
## FCHI8 161.6 140 600 -113.4 437
## FEAR5 413.8 132 600 153.7 674
## FGI4 626.7 132 600 366.6 887
## FMA7 -132.2 132 600 -392.3 128
## FSV1 156.0 134 600 -107.5 420
##
## Confidence level used: 0.95
#Modelo factores aleatorios
modelo_plasticidad2 <- lmer(AF ~ 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 1.17 58.5 58.5
## 2 PC2 0.83 41.5 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C 0.76 0.59 0.41
## 2 Pendiente 0.76 0.59 0.41
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.585083
## -------------------------------------------------------------------------------
## 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 1.96e+ 2 204. 7.28 3.71e 0 increase 100
## 2 Pendiente FA1 -7.91e-13 0.113 0.113 1.42e13 increase 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FBO1
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FBO1 1.11
## 2 FGI4 1.13
## 3 CNCH12 1.51
## 4 FMA7 2.44
## 5 CNCH13 3.14
## 6 FSV1 3.19
## 7 FEAR5 3.28
## 8 FCHI8 3.37
#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 1.82 60.6 60.6
## 2 PC2 1.13 37.7 98.3
## 3 PC3 0.05 1.66 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 3 × 5
## VAR FA1 FA2 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BLUP_C 0.03 1 0.99 0.01
## 2 Pendiente -0.97 0.21 0.98 0.02
## 3 Pendiente2 0.92 0.37 0.98 0.02
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.9833512
## -------------------------------------------------------------------------------
## 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 -7.91e-13 0.113 0.113 1.42e13 increase 100
## 2 Pendiente2 FA1 -3.58e-11 -10.2 -10.2 -2.85e13 decrease 100
## 3 BLUP_C FA2 1.96e+ 2 204. 7.28 3.71e 0 increase 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FBO1
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod2$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FBO1 0.826
## 2 CNCH12 1.41
## 3 FMA7 2.25
## 4 FGI4 2.69
## 5 CNCH13 2.90
## 6 FSV1 3.19
## 7 FCHI8 3.38
## 8 FEAR5 3.52
#Gráfico Selección 1
plot(mgidi_mod2)
