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(GR) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(GR)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 2.0912 0.298737 21.4269 < 2e-16 ***
## mun 9 2.4208 0.268983 19.2928 < 2e-16 ***
## gen:mun 60 2.4055 0.040092 2.8756 1.1e-10 ***
## Residuals 539 7.5148 0.013942
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm ((GR) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: (GR)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 0.09616 0.0137369 14.5827 < 2.2e-16 ***
## mun 9 0.10382 0.0115359 12.2462 < 2.2e-16 ***
## gen:mun 60 0.13611 0.0022686 2.4082 1.153e-07 ***
## Residuals 539 0.50774 0.0009420
## ---
## 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 0.226 0.00343 539 0.220 0.233
## CNCH13 0.201 0.00343 539 0.194 0.208
## FBO1 0.226 0.00343 539 0.219 0.233
## FCHI8 nonEst NA NA NA NA
## FEAR5 0.208 0.00343 539 0.201 0.214
## FGI4 0.213 0.00343 539 0.206 0.220
## FMA7 0.239 0.00343 539 0.232 0.246
## FSV1 nonEst NA NA NA NA
##
## Results are averaged over the levels of: mun
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.02525 0.00485 539 5.203 <.0001
## CNCH12 - FBO1 0.00040 0.00485 539 0.082 1.0000
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 0.01864 0.00485 539 3.841 0.0019
## CNCH12 - FGI4 0.01330 0.00485 539 2.741 0.0691
## CNCH12 - FMA7 -0.01264 0.00485 539 -2.604 0.0980
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 -0.02485 0.00485 539 -5.121 <.0001
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -0.00661 0.00485 539 -1.363 0.7494
## CNCH13 - FGI4 -0.01195 0.00485 539 -2.462 0.1373
## CNCH13 - FMA7 -0.03789 0.00485 539 -7.807 <.0001
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 0.01824 0.00485 539 3.758 0.0026
## FBO1 - FGI4 0.01290 0.00485 539 2.658 0.0856
## FBO1 - FMA7 -0.01304 0.00485 539 -2.687 0.0796
## FBO1 - FSV1 nonEst NA NA NA NA
## FCHI8 - FEAR5 nonEst NA NA NA NA
## FCHI8 - FGI4 nonEst NA NA NA NA
## FCHI8 - FMA7 nonEst NA NA NA NA
## FCHI8 - FSV1 nonEst NA NA NA NA
## FEAR5 - FGI4 -0.00534 0.00485 539 -1.100 0.8814
## FEAR5 - FMA7 -0.03127 0.00485 539 -6.445 <.0001
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 -0.02594 0.00485 539 -5.345 <.0001
## FGI4 - FSV1 nonEst NA NA NA NA
## FMA7 - FSV1 nonEst NA NA NA NA
##
## Results are averaged over the levels of: mun
## Note: contrasts are still on the ( scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## P value adjustment: tukey method for comparing a family of 6 estimates
pwpp(g, type = "response")
## Warning: `aes_()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`
## ℹ The deprecated feature was likely used in the emmeans package.
## Please report the issue at <https://github.com/rvlenth/emmeans/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_segment()`).
#Municipios
m<-emmeans(modelo, pairwise ~ mun)
## NOTE: Results may be misleading due to involvement in interactions
m
## $emmeans
## mun emmean SE df lower.CL upper.CL
## Chi 0.230 0.00384 539 0.222 0.237
## Gig 0.210 0.00384 539 0.203 0.218
## HtC 0.216 0.00384 539 0.208 0.223
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
## RiN 0.202 0.00384 539 0.195 0.210
## SnV 0.219 0.00384 539 0.212 0.227
## Tam 0.199 0.00384 539 0.192 0.207
## ViG 0.208 0.00384 539 0.200 0.215
## Yac 0.239 0.00384 539 0.232 0.247
##
## 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 0.01941 0.00543 539 3.577 0.0090
## Chi - HtC 0.01386 0.00543 539 2.554 0.1753
## Chi - Jam nonEst NA NA NA NA
## Chi - PtR nonEst NA NA NA NA
## Chi - RiN 0.02753 0.00543 539 5.074 <.0001
## Chi - SnV 0.01064 0.00543 539 1.961 0.5093
## Chi - Tam 0.03048 0.00543 539 5.619 <.0001
## Chi - ViG 0.02177 0.00543 539 4.012 0.0018
## Chi - Yac -0.00972 0.00543 539 -1.791 0.6263
## Gig - HtC -0.00555 0.00543 539 -1.022 0.9710
## Gig - Jam nonEst NA NA NA NA
## Gig - PtR nonEst NA NA NA NA
## Gig - RiN 0.00813 0.00543 539 1.498 0.8087
## Gig - SnV -0.00877 0.00543 539 -1.616 0.7408
## Gig - Tam 0.01108 0.00543 539 2.042 0.4546
## Gig - ViG 0.00236 0.00543 539 0.435 0.9999
## Gig - Yac -0.02913 0.00543 539 -5.368 <.0001
## HtC - Jam nonEst NA NA NA NA
## HtC - PtR nonEst NA NA NA NA
## HtC - RiN 0.01367 0.00543 539 2.520 0.1892
## HtC - SnV -0.00322 0.00543 539 -0.593 0.9990
## HtC - Tam 0.01663 0.00543 539 3.064 0.0470
## HtC - ViG 0.00791 0.00543 539 1.457 0.8297
## HtC - Yac -0.02358 0.00543 539 -4.346 0.0004
## Jam - PtR nonEst NA NA NA NA
## Jam - RiN nonEst NA NA NA NA
## Jam - SnV nonEst NA NA NA NA
## Jam - Tam nonEst NA NA NA NA
## Jam - ViG nonEst NA NA NA NA
## Jam - Yac nonEst NA NA NA NA
## PtR - RiN nonEst NA NA NA NA
## PtR - SnV nonEst NA NA NA NA
## PtR - Tam nonEst NA NA NA NA
## PtR - ViG nonEst NA NA NA NA
## PtR - Yac nonEst NA NA NA NA
## RiN - SnV -0.01689 0.00543 539 -3.113 0.0407
## RiN - Tam 0.00295 0.00543 539 0.544 0.9994
## RiN - ViG -0.00577 0.00543 539 -1.063 0.9641
## RiN - Yac -0.03725 0.00543 539 -6.866 <.0001
## SnV - Tam 0.01984 0.00543 539 3.657 0.0068
## SnV - ViG 0.01112 0.00543 539 2.050 0.4488
## SnV - Yac -0.02036 0.00543 539 -3.752 0.0048
## Tam - ViG -0.00872 0.00543 539 -1.607 0.7461
## Tam - Yac -0.04020 0.00543 539 -7.410 <.0001
## ViG - Yac -0.03148 0.00543 539 -5.803 <.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 0.230 0.0109 539 0.209 0.251
## CNCH13 0.224 0.0109 539 0.202 0.245
## FBO1 0.243 0.0109 539 0.222 0.265
## FCHI8 0.230 0.0109 539 0.209 0.251
## FEAR5 0.206 0.0109 539 0.185 0.228
## FGI4 0.228 0.0109 539 0.207 0.249
## FMA7 0.263 0.0109 539 0.241 0.284
## FSV1 0.214 0.0109 539 0.193 0.235
##
## mun = Gig:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.216 0.0109 539 0.195 0.238
## CNCH13 0.202 0.0109 539 0.180 0.223
## FBO1 0.202 0.0109 539 0.181 0.223
## FCHI8 0.201 0.0109 539 0.180 0.222
## FEAR5 0.199 0.0109 539 0.178 0.221
## FGI4 0.203 0.0109 539 0.182 0.225
## FMA7 0.239 0.0109 539 0.217 0.260
## FSV1 0.220 0.0109 539 0.199 0.241
##
## mun = HtC:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.226 0.0109 539 0.204 0.247
## CNCH13 0.210 0.0109 539 0.189 0.231
## FBO1 0.235 0.0109 539 0.214 0.257
## FCHI8 0.206 0.0109 539 0.185 0.228
## FEAR5 0.202 0.0109 539 0.181 0.223
## FGI4 0.222 0.0109 539 0.200 0.243
## FMA7 0.231 0.0109 539 0.209 0.252
## FSV1 0.196 0.0109 539 0.174 0.217
##
## mun = Jam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.268 0.0109 539 0.246 0.289
## CNCH13 0.235 0.0109 539 0.213 0.256
## FBO1 0.248 0.0109 539 0.227 0.270
## FCHI8 nonEst NA NA NA NA
## FEAR5 0.233 0.0109 539 0.212 0.254
## FGI4 0.223 0.0109 539 0.202 0.244
## FMA7 0.219 0.0109 539 0.198 0.240
## FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.228 0.0109 539 0.207 0.250
## CNCH13 0.208 0.0109 539 0.187 0.230
## FBO1 0.214 0.0109 539 0.192 0.235
## FCHI8 nonEst NA NA NA NA
## FEAR5 0.193 0.0109 539 0.172 0.214
## FGI4 0.197 0.0109 539 0.175 0.218
## FMA7 0.249 0.0109 539 0.227 0.270
## FSV1 0.186 0.0109 539 0.165 0.208
##
## mun = RiN:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.214 0.0109 539 0.192 0.235
## CNCH13 0.189 0.0109 539 0.168 0.211
## FBO1 0.217 0.0109 539 0.196 0.239
## FCHI8 0.171 0.0109 539 0.150 0.193
## FEAR5 0.198 0.0109 539 0.176 0.219
## FGI4 0.228 0.0109 539 0.206 0.249
## FMA7 0.207 0.0109 539 0.186 0.229
## FSV1 0.193 0.0109 539 0.172 0.214
##
## mun = SnV:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.220 0.0109 539 0.198 0.241
## CNCH13 0.204 0.0109 539 0.183 0.226
## FBO1 0.240 0.0109 539 0.219 0.261
## FCHI8 0.215 0.0109 539 0.193 0.236
## FEAR5 0.214 0.0109 539 0.193 0.235
## FGI4 0.207 0.0109 539 0.186 0.229
## FMA7 0.258 0.0109 539 0.236 0.279
## FSV1 0.195 0.0109 539 0.173 0.216
##
## mun = Tam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.208 0.0109 539 0.187 0.229
## CNCH13 0.156 0.0109 539 0.135 0.178
## FBO1 0.207 0.0109 539 0.186 0.228
## FCHI8 0.270 0.0109 539 0.248 0.291
## FEAR5 0.191 0.0109 539 0.169 0.212
## FGI4 0.169 0.0109 539 0.148 0.190
## FMA7 0.219 0.0109 539 0.197 0.240
## FSV1 0.174 0.0109 539 0.153 0.196
##
## mun = ViG:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.215 0.0109 539 0.193 0.236
## CNCH13 0.189 0.0109 539 0.168 0.210
## FBO1 0.218 0.0109 539 0.197 0.239
## FCHI8 0.213 0.0109 539 0.192 0.235
## FEAR5 0.192 0.0109 539 0.170 0.213
## FGI4 0.210 0.0109 539 0.189 0.232
## FMA7 0.228 0.0109 539 0.207 0.250
## FSV1 0.198 0.0109 539 0.177 0.220
##
## mun = Yac:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.239 0.0109 539 0.218 0.260
## CNCH13 0.193 0.0109 539 0.172 0.215
## FBO1 0.234 0.0109 539 0.212 0.255
## FCHI8 0.235 0.0109 539 0.214 0.257
## FEAR5 0.249 0.0109 539 0.228 0.271
## FGI4 0.243 0.0109 539 0.222 0.265
## FMA7 0.278 0.0109 539 0.257 0.299
## FSV1 0.244 0.0109 539 0.222 0.265
##
## 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 0.006375 0.0153 539 0.415 0.9999
## CNCH12 - FBO1 -0.013125 0.0153 539 -0.855 0.9897
## CNCH12 - FCHI8 0.000125 0.0153 539 0.008 1.0000
## CNCH12 - FEAR5 0.023875 0.0153 539 1.556 0.7764
## CNCH12 - FGI4 0.002250 0.0153 539 0.147 1.0000
## CNCH12 - FMA7 -0.032375 0.0153 539 -2.110 0.4099
## CNCH12 - FSV1 0.016125 0.0153 539 1.051 0.9662
## CNCH13 - FBO1 -0.019500 0.0153 539 -1.271 0.9093
## CNCH13 - FCHI8 -0.006250 0.0153 539 -0.407 0.9999
## CNCH13 - FEAR5 0.017500 0.0153 539 1.140 0.9476
## CNCH13 - FGI4 -0.004125 0.0153 539 -0.269 1.0000
## CNCH13 - FMA7 -0.038750 0.0153 539 -2.525 0.1871
## CNCH13 - FSV1 0.009750 0.0153 539 0.635 0.9984
## FBO1 - FCHI8 0.013250 0.0153 539 0.863 0.9891
## FBO1 - FEAR5 0.037000 0.0153 539 2.411 0.2380
## FBO1 - FGI4 0.015375 0.0153 539 1.002 0.9741
## FBO1 - FMA7 -0.019250 0.0153 539 -1.254 0.9149
## FBO1 - FSV1 0.029250 0.0153 539 1.906 0.5473
## FCHI8 - FEAR5 0.023750 0.0153 539 1.548 0.7810
## FCHI8 - FGI4 0.002125 0.0153 539 0.138 1.0000
## FCHI8 - FMA7 -0.032500 0.0153 539 -2.118 0.4047
## FCHI8 - FSV1 0.016000 0.0153 539 1.043 0.9677
## FEAR5 - FGI4 -0.021625 0.0153 539 -1.409 0.8530
## FEAR5 - FMA7 -0.056250 0.0153 539 -3.665 0.0066
## FEAR5 - FSV1 -0.007750 0.0153 539 -0.505 0.9996
## FGI4 - FMA7 -0.034625 0.0153 539 -2.256 0.3201
## FGI4 - FSV1 0.013875 0.0153 539 0.904 0.9856
## FMA7 - FSV1 0.048500 0.0153 539 3.160 0.0353
##
## mun = Gig:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.014500 0.0153 539 0.945 0.9814
## CNCH12 - FBO1 0.014125 0.0153 539 0.920 0.9841
## CNCH12 - FCHI8 0.015250 0.0153 539 0.994 0.9753
## CNCH12 - FEAR5 0.016875 0.0153 539 1.100 0.9568
## CNCH12 - FGI4 0.012875 0.0153 539 0.839 0.9908
## CNCH12 - FMA7 -0.022500 0.0153 539 -1.466 0.8251
## CNCH12 - FSV1 -0.003625 0.0153 539 -0.236 1.0000
## CNCH13 - FBO1 -0.000375 0.0153 539 -0.024 1.0000
## CNCH13 - FCHI8 0.000750 0.0153 539 0.049 1.0000
## CNCH13 - FEAR5 0.002375 0.0153 539 0.155 1.0000
## CNCH13 - FGI4 -0.001625 0.0153 539 -0.106 1.0000
## CNCH13 - FMA7 -0.037000 0.0153 539 -2.411 0.2380
## CNCH13 - FSV1 -0.018125 0.0153 539 -1.181 0.9371
## FBO1 - FCHI8 0.001125 0.0153 539 0.073 1.0000
## FBO1 - FEAR5 0.002750 0.0153 539 0.179 1.0000
## FBO1 - FGI4 -0.001250 0.0153 539 -0.081 1.0000
## FBO1 - FMA7 -0.036625 0.0153 539 -2.387 0.2500
## FBO1 - FSV1 -0.017750 0.0153 539 -1.157 0.9436
## FCHI8 - FEAR5 0.001625 0.0153 539 0.106 1.0000
## FCHI8 - FGI4 -0.002375 0.0153 539 -0.155 1.0000
## FCHI8 - FMA7 -0.037750 0.0153 539 -2.460 0.2152
## FCHI8 - FSV1 -0.018875 0.0153 539 -1.230 0.9228
## FEAR5 - FGI4 -0.004000 0.0153 539 -0.261 1.0000
## FEAR5 - FMA7 -0.039375 0.0153 539 -2.566 0.1709
## FEAR5 - FSV1 -0.020500 0.0153 539 -1.336 0.8848
## FGI4 - FMA7 -0.035375 0.0153 539 -2.305 0.2926
## FGI4 - FSV1 -0.016500 0.0153 539 -1.075 0.9617
## FMA7 - FSV1 0.018875 0.0153 539 1.230 0.9228
##
## mun = HtC:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.015625 0.0153 539 1.018 0.9716
## CNCH12 - FBO1 -0.009750 0.0153 539 -0.635 0.9984
## CNCH12 - FCHI8 0.019375 0.0153 539 1.263 0.9121
## CNCH12 - FEAR5 0.023500 0.0153 539 1.531 0.7902
## CNCH12 - FGI4 0.004125 0.0153 539 0.269 1.0000
## CNCH12 - FMA7 -0.004875 0.0153 539 -0.318 1.0000
## CNCH12 - FSV1 0.030125 0.0153 539 1.963 0.5080
## CNCH13 - FBO1 -0.025375 0.0153 539 -1.654 0.7172
## CNCH13 - FCHI8 0.003750 0.0153 539 0.244 1.0000
## CNCH13 - FEAR5 0.007875 0.0153 539 0.513 0.9996
## CNCH13 - FGI4 -0.011500 0.0153 539 -0.749 0.9954
## CNCH13 - FMA7 -0.020500 0.0153 539 -1.336 0.8848
## CNCH13 - FSV1 0.014500 0.0153 539 0.945 0.9814
## FBO1 - FCHI8 0.029125 0.0153 539 1.898 0.5530
## FBO1 - FEAR5 0.033250 0.0153 539 2.167 0.3738
## FBO1 - FGI4 0.013875 0.0153 539 0.904 0.9856
## FBO1 - FMA7 0.004875 0.0153 539 0.318 1.0000
## FBO1 - FSV1 0.039875 0.0153 539 2.598 0.1587
## FCHI8 - FEAR5 0.004125 0.0153 539 0.269 1.0000
## FCHI8 - FGI4 -0.015250 0.0153 539 -0.994 0.9753
## FCHI8 - FMA7 -0.024250 0.0153 539 -1.580 0.7621
## FCHI8 - FSV1 0.010750 0.0153 539 0.701 0.9970
## FEAR5 - FGI4 -0.019375 0.0153 539 -1.263 0.9121
## FEAR5 - FMA7 -0.028375 0.0153 539 -1.849 0.5867
## FEAR5 - FSV1 0.006625 0.0153 539 0.432 0.9999
## FGI4 - FMA7 -0.009000 0.0153 539 -0.586 0.9990
## FGI4 - FSV1 0.026000 0.0153 539 1.694 0.6911
## FMA7 - FSV1 0.035000 0.0153 539 2.281 0.3062
##
## mun = Jam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.033000 0.0153 539 2.150 0.2632
## CNCH12 - FBO1 0.019125 0.0153 539 1.246 0.8137
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 0.034750 0.0153 539 2.264 0.2106
## CNCH12 - FGI4 0.044625 0.0153 539 2.908 0.0437
## CNCH12 - FMA7 0.048625 0.0153 539 3.169 0.0200
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 -0.013875 0.0153 539 -0.904 0.9454
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 0.001750 0.0153 539 0.114 1.0000
## CNCH13 - FGI4 0.011625 0.0153 539 0.758 0.9743
## CNCH13 - FMA7 0.015625 0.0153 539 1.018 0.9118
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 0.015625 0.0153 539 1.018 0.9118
## FBO1 - FGI4 0.025500 0.0153 539 1.662 0.5579
## FBO1 - FMA7 0.029500 0.0153 539 1.922 0.3895
## FBO1 - FSV1 nonEst NA NA NA NA
## FCHI8 - FEAR5 nonEst NA NA NA NA
## FCHI8 - FGI4 nonEst NA NA NA NA
## FCHI8 - FMA7 nonEst NA NA NA NA
## FCHI8 - FSV1 nonEst NA NA NA NA
## FEAR5 - FGI4 0.009875 0.0153 539 0.643 0.9876
## FEAR5 - FMA7 0.013875 0.0153 539 0.904 0.9454
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 0.004000 0.0153 539 0.261 0.9998
## FGI4 - FSV1 nonEst NA NA NA NA
## FMA7 - FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.020125 0.0153 539 1.311 0.8466
## CNCH12 - FBO1 0.014625 0.0153 539 0.953 0.9635
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 0.035250 0.0153 539 2.297 0.2473
## CNCH12 - FGI4 0.031625 0.0153 539 2.061 0.3778
## CNCH12 - FMA7 -0.020375 0.0153 539 -1.328 0.8388
## CNCH12 - FSV1 0.041875 0.0153 539 2.729 0.0932
## CNCH13 - FBO1 -0.005500 0.0153 539 -0.358 0.9998
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 0.015125 0.0153 539 0.986 0.9570
## CNCH13 - FGI4 0.011500 0.0153 539 0.749 0.9893
## CNCH13 - FMA7 -0.040500 0.0153 539 -2.639 0.1165
## CNCH13 - FSV1 0.021750 0.0153 539 1.417 0.7924
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 0.020625 0.0153 539 1.344 0.8308
## FBO1 - FGI4 0.017000 0.0153 539 1.108 0.9257
## FBO1 - FMA7 -0.035000 0.0153 539 -2.281 0.2553
## FBO1 - FSV1 0.027250 0.0153 539 1.776 0.5651
## FCHI8 - FEAR5 nonEst NA NA NA NA
## FCHI8 - FGI4 nonEst NA NA NA NA
## FCHI8 - FMA7 nonEst NA NA NA NA
## FCHI8 - FSV1 nonEst NA NA NA NA
## FEAR5 - FGI4 -0.003625 0.0153 539 -0.236 1.0000
## FEAR5 - FMA7 -0.055625 0.0153 539 -3.625 0.0058
## FEAR5 - FSV1 0.006625 0.0153 539 0.432 0.9995
## FGI4 - FMA7 -0.052000 0.0153 539 -3.388 0.0132
## FGI4 - FSV1 0.010250 0.0153 539 0.668 0.9942
## FMA7 - FSV1 0.062250 0.0153 539 4.056 0.0011
##
## mun = RiN:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.024375 0.0153 539 1.588 0.7573
## CNCH12 - FBO1 -0.003750 0.0153 539 -0.244 1.0000
## CNCH12 - FCHI8 0.042500 0.0153 539 2.769 0.1050
## CNCH12 - FEAR5 0.016250 0.0153 539 1.059 0.9648
## CNCH12 - FGI4 -0.013750 0.0153 539 -0.896 0.9864
## CNCH12 - FMA7 0.006250 0.0153 539 0.407 0.9999
## CNCH12 - FSV1 0.020625 0.0153 539 1.344 0.8815
## CNCH13 - FBO1 -0.028125 0.0153 539 -1.833 0.5979
## CNCH13 - FCHI8 0.018125 0.0153 539 1.181 0.9371
## CNCH13 - FEAR5 -0.008125 0.0153 539 -0.529 0.9995
## CNCH13 - FGI4 -0.038125 0.0153 539 -2.484 0.2043
## CNCH13 - FMA7 -0.018125 0.0153 539 -1.181 0.9371
## CNCH13 - FSV1 -0.003750 0.0153 539 -0.244 1.0000
## FBO1 - FCHI8 0.046250 0.0153 539 3.014 0.0544
## FBO1 - FEAR5 0.020000 0.0153 539 1.303 0.8975
## FBO1 - FGI4 -0.010000 0.0153 539 -0.652 0.9981
## FBO1 - FMA7 0.010000 0.0153 539 0.652 0.9981
## FBO1 - FSV1 0.024375 0.0153 539 1.588 0.7573
## FCHI8 - FEAR5 -0.026250 0.0153 539 -1.711 0.6804
## FCHI8 - FGI4 -0.056250 0.0153 539 -3.665 0.0066
## FCHI8 - FMA7 -0.036250 0.0153 539 -2.362 0.2623
## FCHI8 - FSV1 -0.021875 0.0153 539 -1.425 0.8453
## FEAR5 - FGI4 -0.030000 0.0153 539 -1.955 0.5136
## FEAR5 - FMA7 -0.010000 0.0153 539 -0.652 0.9981
## FEAR5 - FSV1 0.004375 0.0153 539 0.285 1.0000
## FGI4 - FMA7 0.020000 0.0153 539 1.303 0.8975
## FGI4 - FSV1 0.034375 0.0153 539 2.240 0.3295
## FMA7 - FSV1 0.014375 0.0153 539 0.937 0.9823
##
## mun = SnV:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.015375 0.0153 539 1.002 0.9741
## CNCH12 - FBO1 -0.020375 0.0153 539 -1.328 0.8881
## CNCH12 - FCHI8 0.005125 0.0153 539 0.334 1.0000
## CNCH12 - FEAR5 0.005750 0.0153 539 0.375 1.0000
## CNCH12 - FGI4 0.012250 0.0153 539 0.798 0.9932
## CNCH12 - FMA7 -0.037875 0.0153 539 -2.468 0.2115
## CNCH12 - FSV1 0.025125 0.0153 539 1.637 0.7274
## CNCH13 - FBO1 -0.035750 0.0153 539 -2.330 0.2794
## CNCH13 - FCHI8 -0.010250 0.0153 539 -0.668 0.9978
## CNCH13 - FEAR5 -0.009625 0.0153 539 -0.627 0.9985
## CNCH13 - FGI4 -0.003125 0.0153 539 -0.204 1.0000
## CNCH13 - FMA7 -0.053250 0.0153 539 -3.470 0.0130
## CNCH13 - FSV1 0.009750 0.0153 539 0.635 0.9984
## FBO1 - FCHI8 0.025500 0.0153 539 1.662 0.7120
## FBO1 - FEAR5 0.026125 0.0153 539 1.702 0.6858
## FBO1 - FGI4 0.032625 0.0153 539 2.126 0.3995
## FBO1 - FMA7 -0.017500 0.0153 539 -1.140 0.9476
## FBO1 - FSV1 0.045500 0.0153 539 2.965 0.0624
## FCHI8 - FEAR5 0.000625 0.0153 539 0.041 1.0000
## FCHI8 - FGI4 0.007125 0.0153 539 0.464 0.9998
## FCHI8 - FMA7 -0.043000 0.0153 539 -2.802 0.0966
## FCHI8 - FSV1 0.020000 0.0153 539 1.303 0.8975
## FEAR5 - FGI4 0.006500 0.0153 539 0.424 0.9999
## FEAR5 - FMA7 -0.043625 0.0153 539 -2.843 0.0869
## FEAR5 - FSV1 0.019375 0.0153 539 1.263 0.9121
## FGI4 - FMA7 -0.050125 0.0153 539 -3.266 0.0254
## FGI4 - FSV1 0.012875 0.0153 539 0.839 0.9908
## FMA7 - FSV1 0.063000 0.0153 539 4.105 0.0012
##
## mun = Tam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.051500 0.0153 539 3.356 0.0191
## CNCH12 - FBO1 0.000875 0.0153 539 0.057 1.0000
## CNCH12 - FCHI8 -0.061500 0.0153 539 -4.008 0.0018
## CNCH12 - FEAR5 0.017250 0.0153 539 1.124 0.9514
## CNCH12 - FGI4 0.038875 0.0153 539 2.533 0.1838
## CNCH12 - FMA7 -0.010625 0.0153 539 -0.692 0.9972
## CNCH12 - FSV1 0.033750 0.0153 539 2.199 0.3538
## CNCH13 - FBO1 -0.050625 0.0153 539 -3.299 0.0229
## CNCH13 - FCHI8 -0.113000 0.0153 539 -7.363 <.0001
## CNCH13 - FEAR5 -0.034250 0.0153 539 -2.232 0.3343
## CNCH13 - FGI4 -0.012625 0.0153 539 -0.823 0.9918
## CNCH13 - FMA7 -0.062125 0.0153 539 -4.048 0.0015
## CNCH13 - FSV1 -0.017750 0.0153 539 -1.157 0.9436
## FBO1 - FCHI8 -0.062375 0.0153 539 -4.065 0.0014
## FBO1 - FEAR5 0.016375 0.0153 539 1.067 0.9633
## FBO1 - FGI4 0.038000 0.0153 539 2.476 0.2079
## FBO1 - FMA7 -0.011500 0.0153 539 -0.749 0.9954
## FBO1 - FSV1 0.032875 0.0153 539 2.142 0.3891
## FCHI8 - FEAR5 0.078750 0.0153 539 5.132 <.0001
## FCHI8 - FGI4 0.100375 0.0153 539 6.541 <.0001
## FCHI8 - FMA7 0.050875 0.0153 539 3.315 0.0217
## FCHI8 - FSV1 0.095250 0.0153 539 6.207 <.0001
## FEAR5 - FGI4 0.021625 0.0153 539 1.409 0.8530
## FEAR5 - FMA7 -0.027875 0.0153 539 -1.816 0.6091
## FEAR5 - FSV1 0.016500 0.0153 539 1.075 0.9617
## FGI4 - FMA7 -0.049500 0.0153 539 -3.226 0.0289
## FGI4 - FSV1 -0.005125 0.0153 539 -0.334 1.0000
## FMA7 - FSV1 0.044375 0.0153 539 2.892 0.0763
##
## mun = ViG:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.025750 0.0153 539 1.678 0.7016
## CNCH12 - FBO1 -0.003375 0.0153 539 -0.220 1.0000
## CNCH12 - FCHI8 0.001250 0.0153 539 0.081 1.0000
## CNCH12 - FEAR5 0.023000 0.0153 539 1.499 0.8081
## CNCH12 - FGI4 0.004250 0.0153 539 0.277 1.0000
## CNCH12 - FMA7 -0.013750 0.0153 539 -0.896 0.9864
## CNCH12 - FSV1 0.016250 0.0153 539 1.059 0.9648
## CNCH13 - FBO1 -0.029125 0.0153 539 -1.898 0.5530
## CNCH13 - FCHI8 -0.024500 0.0153 539 -1.597 0.7524
## CNCH13 - FEAR5 -0.002750 0.0153 539 -0.179 1.0000
## CNCH13 - FGI4 -0.021500 0.0153 539 -1.401 0.8567
## CNCH13 - FMA7 -0.039500 0.0153 539 -2.574 0.1678
## CNCH13 - FSV1 -0.009500 0.0153 539 -0.619 0.9986
## FBO1 - FCHI8 0.004625 0.0153 539 0.301 1.0000
## FBO1 - FEAR5 0.026375 0.0153 539 1.719 0.6751
## FBO1 - FGI4 0.007625 0.0153 539 0.497 0.9997
## FBO1 - FMA7 -0.010375 0.0153 539 -0.676 0.9976
## FBO1 - FSV1 0.019625 0.0153 539 1.279 0.9065
## FCHI8 - FEAR5 0.021750 0.0153 539 1.417 0.8492
## FCHI8 - FGI4 0.003000 0.0153 539 0.195 1.0000
## FCHI8 - FMA7 -0.015000 0.0153 539 -0.977 0.9775
## FCHI8 - FSV1 0.015000 0.0153 539 0.977 0.9775
## FEAR5 - FGI4 -0.018750 0.0153 539 -1.222 0.9253
## FEAR5 - FMA7 -0.036750 0.0153 539 -2.395 0.2460
## FEAR5 - FSV1 -0.006750 0.0153 539 -0.440 0.9999
## FGI4 - FMA7 -0.018000 0.0153 539 -1.173 0.9393
## FGI4 - FSV1 0.012000 0.0153 539 0.782 0.9940
## FMA7 - FSV1 0.030000 0.0153 539 1.955 0.5136
##
## mun = Yac:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.045875 0.0153 539 2.989 0.0583
## CNCH12 - FBO1 0.005625 0.0153 539 0.367 1.0000
## CNCH12 - FCHI8 0.003625 0.0153 539 0.236 1.0000
## CNCH12 - FEAR5 -0.010125 0.0153 539 -0.660 0.9979
## CNCH12 - FGI4 -0.004125 0.0153 539 -0.269 1.0000
## CNCH12 - FMA7 -0.038875 0.0153 539 -2.533 0.1838
## CNCH12 - FSV1 -0.004500 0.0153 539 -0.293 1.0000
## CNCH13 - FBO1 -0.040250 0.0153 539 -2.623 0.1500
## CNCH13 - FCHI8 -0.042250 0.0153 539 -2.753 0.1094
## CNCH13 - FEAR5 -0.056000 0.0153 539 -3.649 0.0070
## CNCH13 - FGI4 -0.050000 0.0153 539 -3.258 0.0261
## CNCH13 - FMA7 -0.084750 0.0153 539 -5.523 <.0001
## CNCH13 - FSV1 -0.050375 0.0153 539 -3.283 0.0241
## FBO1 - FCHI8 -0.002000 0.0153 539 -0.130 1.0000
## FBO1 - FEAR5 -0.015750 0.0153 539 -1.026 0.9704
## FBO1 - FGI4 -0.009750 0.0153 539 -0.635 0.9984
## FBO1 - FMA7 -0.044500 0.0153 539 -2.900 0.0746
## FBO1 - FSV1 -0.010125 0.0153 539 -0.660 0.9979
## FCHI8 - FEAR5 -0.013750 0.0153 539 -0.896 0.9864
## FCHI8 - FGI4 -0.007750 0.0153 539 -0.505 0.9996
## FCHI8 - FMA7 -0.042500 0.0153 539 -2.769 0.1050
## FCHI8 - FSV1 -0.008125 0.0153 539 -0.529 0.9995
## FEAR5 - FGI4 0.006000 0.0153 539 0.391 0.9999
## FEAR5 - FMA7 -0.028750 0.0153 539 -1.873 0.5698
## FEAR5 - FSV1 0.005625 0.0153 539 0.367 1.0000
## FGI4 - FMA7 -0.034750 0.0153 539 -2.264 0.3154
## FGI4 - FSV1 -0.000375 0.0153 539 -0.024 1.0000
## FMA7 - FSV1 0.034375 0.0153 539 2.240 0.3295
##
## 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(GR) ~ 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 0.69935 0.099907 7 60.084 7.1658 3.178e-06 ***
## ---
## 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(GR) ~ gen + (1 | mun) + (1 | mun:gen)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 373.84 -725.69
## (1 | mun) 10 363.22 -706.44 21.241 1 4.050e-06 ***
## (1 | mun:gen) 10 353.96 -687.93 39.760 1 2.872e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(GR ~ 1 +
(1|gen) +
(1|mun) +
(1|gen:mun),
data = datos)
ranova(modelo_blup)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## GR ~ (1 | gen) + (1 | mun) + (1 | gen:mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 1219.8 -2429.5
## (1 | gen) 4 1212.1 -2416.2 15.396 1 8.715e-05 ***
## (1 | mun) 4 1212.3 -2416.6 14.997 1 0.0001077 ***
## (1 | gen:mun) 4 1206.6 -2405.3 26.274 1 2.963e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CNCH12 0.007773787
## CNCH13 -0.013251623
## FBO1 0.007440711
## FCHI8 0.001543602
## FEAR5 -0.007745464
## FGI4 -0.003300983
## FMA7 0.018296901
## FSV1 -0.010756932
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 0.2247630
## CNCH13 0.2037376
## FBO1 0.2244300
## FCHI8 0.2185328
## FEAR5 0.2092438
## FGI4 0.2136883
## FMA7 0.2352861
## FSV1 0.2062323
#Blups Parcela
blups$mun
## (Intercept)
## Chi 0.0103495174
## Gig -0.0054284164
## HtC -0.0009186205
## Jam 0.0146021326
## PtR -0.0047378460
## RiN -0.0120343146
## SnV 0.0016983315
## Tam -0.0144353045
## ViG -0.0073466676
## Yac 0.0182511880
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## Chi 0.2273388
## Gig 0.2115608
## HtC 0.2160706
## Jam 0.2315914
## PtR 0.2122514
## RiN 0.2049549
## SnV 0.2186876
## Tam 0.2025539
## ViG 0.2096426
## Yac 0.2352404
#Blups interacción
blups$`gen:mun`
## (Intercept)
## CNCH12:Chi -0.0029142266
## CNCH12:Gig -0.0018023414
## CNCH12:HtC 0.0010404000
## CNCH12:Jam 0.0165122382
## CNCH12:PtR 0.0048788020
## CNCH12:RiN 0.0005967371
## CNCH12:SnV -0.0039214521
## CNCH12:Tam -0.0013600913
## CNCH12:ViG -0.0016309946
## CNCH12:Yac -0.0022724722
## CNCH13:Chi 0.0056460161
## CNCH13:Gig 0.0020104594
## CNCH13:HtC 0.0041958627
## CNCH13:Jam 0.0095154791
## CNCH13:PtR 0.0054049123
## CNCH13:RiN -0.0013604299
## CNCH13:SnV -0.0006199143
## CNCH13:Tam -0.0191664104
## CNCH13:ViG -0.0043915748
## CNCH13:Yac -0.0167921008
## FBO1:Chi 0.0049493343
## FBO1:Gig -0.0098609702
## FBO1:HtC 0.0069319466
## FBO1:Jam 0.0055321068
## FBO1:PtR -0.0034719770
## FBO1:RiN 0.0029824805
## FBO1:SnV 0.0081782877
## FBO1:Tam -0.0016767379
## FBO1:ViG 0.0005356361
## FBO1:Yac -0.0053645463
## FCHI8:Chi 0.0006530362
## FCHI8:Gig -0.0070726242
## FCHI8:HtC -0.0066401226
## FCHI8:RiN -0.0205957354
## FCHI8:SnV -0.0032756920
## FCHI8:Tam 0.0382146924
## FCHI8:ViG 0.0012789302
## FCHI8:Yac -0.0007502612
## FEAR5:Chi -0.0077964953
## FEAR5:Gig -0.0025945063
## FEAR5:HtC -0.0036227560
## FEAR5:Jam 0.0052757012
## FEAR5:PtR -0.0066498853
## FEAR5:RiN 0.0001697601
## FEAR5:SnV 0.0017867265
## FEAR5:Tam -0.0023713689
## FEAR5:ViG -0.0060020003
## FEAR5:Yac 0.0127114775
## FGI4:Chi 0.0022420916
## FGI4:Gig -0.0028542164
## FGI4:HtC 0.0051011547
## FGI4:Jam -0.0030911789
## FGI4:PtR -0.0071287081
## FGI4:RiN 0.0151018640
## FGI4:SnV -0.0046081393
## FGI4:Tam -0.0176037803
## FGI4:ViG 0.0023567226
## FGI4:Yac 0.0066087620
## FMA7:Chi 0.0098538422
## FMA7:Gig 0.0051957596
## FMA7:HtC -0.0022597960
## FMA7:Jam -0.0180480366
## FMA7:PtR 0.0106352644
## FMA7:RiN -0.0092038023
## FMA7:SnV 0.0120602697
## FMA7:Tam -0.0013005591
## FMA7:ViG 0.0002544768
## FMA7:Yac 0.0142935502
## FSV1:Chi -0.0015085638
## FSV1:Gig 0.0111432571
## FSV1:HtC -0.0057341447
## FSV1:PtR -0.0087612740
## FSV1:RiN -0.0006269524
## FSV1:SnV -0.0077744941
## FSV1:Tam -0.0102527254
## FSV1:ViG -0.0002983693
## FSV1:Yac 0.0111843894
fixef(modelo_blup)[1] + blups$`gen:mun`
## (Intercept)
## CNCH12:Chi 0.2140750
## CNCH12:Gig 0.2151869
## CNCH12:HtC 0.2180296
## CNCH12:Jam 0.2335015
## CNCH12:PtR 0.2218680
## CNCH12:RiN 0.2175860
## CNCH12:SnV 0.2130678
## CNCH12:Tam 0.2156292
## CNCH12:ViG 0.2153582
## CNCH12:Yac 0.2147168
## CNCH13:Chi 0.2226353
## CNCH13:Gig 0.2189997
## CNCH13:HtC 0.2211851
## CNCH13:Jam 0.2265047
## CNCH13:PtR 0.2223942
## CNCH13:RiN 0.2156288
## CNCH13:SnV 0.2163693
## CNCH13:Tam 0.1978228
## CNCH13:ViG 0.2125977
## CNCH13:Yac 0.2001971
## FBO1:Chi 0.2219386
## FBO1:Gig 0.2071283
## FBO1:HtC 0.2239212
## FBO1:Jam 0.2225213
## FBO1:PtR 0.2135173
## FBO1:RiN 0.2199717
## FBO1:SnV 0.2251675
## FBO1:Tam 0.2153125
## FBO1:ViG 0.2175249
## FBO1:Yac 0.2116247
## FCHI8:Chi 0.2176423
## FCHI8:Gig 0.2099166
## FCHI8:HtC 0.2103491
## FCHI8:RiN 0.1963935
## FCHI8:SnV 0.2137136
## FCHI8:Tam 0.2552039
## FCHI8:ViG 0.2182682
## FCHI8:Yac 0.2162390
## FEAR5:Chi 0.2091927
## FEAR5:Gig 0.2143947
## FEAR5:HtC 0.2133665
## FEAR5:Jam 0.2222649
## FEAR5:PtR 0.2103394
## FEAR5:RiN 0.2171590
## FEAR5:SnV 0.2187760
## FEAR5:Tam 0.2146179
## FEAR5:ViG 0.2109872
## FEAR5:Yac 0.2297007
## FGI4:Chi 0.2192313
## FGI4:Gig 0.2141350
## FGI4:HtC 0.2220904
## FGI4:Jam 0.2138981
## FGI4:PtR 0.2098605
## FGI4:RiN 0.2320911
## FGI4:SnV 0.2123811
## FGI4:Tam 0.1993855
## FGI4:ViG 0.2193460
## FGI4:Yac 0.2235980
## FMA7:Chi 0.2268431
## FMA7:Gig 0.2221850
## FMA7:HtC 0.2147294
## FMA7:Jam 0.1989412
## FMA7:PtR 0.2276245
## FMA7:RiN 0.2077854
## FMA7:SnV 0.2290495
## FMA7:Tam 0.2156887
## FMA7:ViG 0.2172437
## FMA7:Yac 0.2312828
## FSV1:Chi 0.2154807
## FSV1:Gig 0.2281325
## FSV1:HtC 0.2112551
## FSV1:PtR 0.2082280
## FSV1:RiN 0.2163623
## FSV1:SnV 0.2092147
## FSV1:Tam 0.2067365
## FSV1:ViG 0.2166909
## FSV1:Yac 0.2281736
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 0.007773787
## 2 CNCH13 -0.013251623
## 3 FBO1 0.007440711
## 4 FCHI8 0.001543602
## 5 FEAR5 -0.007745464
## 6 FGI4 -0.003300983
## 7 FMA7 0.018296901
## 8 FSV1 -0.010756932
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 Chi 0.0103495174
## 2 Gig -0.0054284164
## 3 HtC -0.0009186205
## 4 Jam 0.0146021326
## 5 PtR -0.0047378460
## 6 RiN -0.0120343146
## 7 SnV 0.0016983315
## 8 Tam -0.0144353045
## 9 ViG -0.0073466676
## 10 Yac 0.0182511880
#Tabla blup_gen_mun
blup_gen_mun <- ranef(modelo_blup)$`gen:mun` %>%
tibble::rownames_to_column("gen:mun") %>%
rename(BLUP = `(Intercept)`)
blup_gen_mun
## gen:mun BLUP
## 1 CNCH12:Chi -0.0029142266
## 2 CNCH12:Gig -0.0018023414
## 3 CNCH12:HtC 0.0010404000
## 4 CNCH12:Jam 0.0165122382
## 5 CNCH12:PtR 0.0048788020
## 6 CNCH12:RiN 0.0005967371
## 7 CNCH12:SnV -0.0039214521
## 8 CNCH12:Tam -0.0013600913
## 9 CNCH12:ViG -0.0016309946
## 10 CNCH12:Yac -0.0022724722
## 11 CNCH13:Chi 0.0056460161
## 12 CNCH13:Gig 0.0020104594
## 13 CNCH13:HtC 0.0041958627
## 14 CNCH13:Jam 0.0095154791
## 15 CNCH13:PtR 0.0054049123
## 16 CNCH13:RiN -0.0013604299
## 17 CNCH13:SnV -0.0006199143
## 18 CNCH13:Tam -0.0191664104
## 19 CNCH13:ViG -0.0043915748
## 20 CNCH13:Yac -0.0167921008
## 21 FBO1:Chi 0.0049493343
## 22 FBO1:Gig -0.0098609702
## 23 FBO1:HtC 0.0069319466
## 24 FBO1:Jam 0.0055321068
## 25 FBO1:PtR -0.0034719770
## 26 FBO1:RiN 0.0029824805
## 27 FBO1:SnV 0.0081782877
## 28 FBO1:Tam -0.0016767379
## 29 FBO1:ViG 0.0005356361
## 30 FBO1:Yac -0.0053645463
## 31 FCHI8:Chi 0.0006530362
## 32 FCHI8:Gig -0.0070726242
## 33 FCHI8:HtC -0.0066401226
## 34 FCHI8:RiN -0.0205957354
## 35 FCHI8:SnV -0.0032756920
## 36 FCHI8:Tam 0.0382146924
## 37 FCHI8:ViG 0.0012789302
## 38 FCHI8:Yac -0.0007502612
## 39 FEAR5:Chi -0.0077964953
## 40 FEAR5:Gig -0.0025945063
## 41 FEAR5:HtC -0.0036227560
## 42 FEAR5:Jam 0.0052757012
## 43 FEAR5:PtR -0.0066498853
## 44 FEAR5:RiN 0.0001697601
## 45 FEAR5:SnV 0.0017867265
## 46 FEAR5:Tam -0.0023713689
## 47 FEAR5:ViG -0.0060020003
## 48 FEAR5:Yac 0.0127114775
## 49 FGI4:Chi 0.0022420916
## 50 FGI4:Gig -0.0028542164
## 51 FGI4:HtC 0.0051011547
## 52 FGI4:Jam -0.0030911789
## 53 FGI4:PtR -0.0071287081
## 54 FGI4:RiN 0.0151018640
## 55 FGI4:SnV -0.0046081393
## 56 FGI4:Tam -0.0176037803
## 57 FGI4:ViG 0.0023567226
## 58 FGI4:Yac 0.0066087620
## 59 FMA7:Chi 0.0098538422
## 60 FMA7:Gig 0.0051957596
## 61 FMA7:HtC -0.0022597960
## 62 FMA7:Jam -0.0180480366
## 63 FMA7:PtR 0.0106352644
## 64 FMA7:RiN -0.0092038023
## 65 FMA7:SnV 0.0120602697
## 66 FMA7:Tam -0.0013005591
## 67 FMA7:ViG 0.0002544768
## 68 FMA7:Yac 0.0142935502
## 69 FSV1:Chi -0.0015085638
## 70 FSV1:Gig 0.0111432571
## 71 FSV1:HtC -0.0057341447
## 72 FSV1:PtR -0.0087612740
## 73 FSV1:RiN -0.0006269524
## 74 FSV1:SnV -0.0077744941
## 75 FSV1:Tam -0.0102527254
## 76 FSV1:ViG -0.0002983693
## 77 FSV1:Yac 0.0111843894
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 0.1935710 0.1935710 0.1935710 0.1935710 0.1935710 0.1935710 0.1935710
## [8] 0.1935710 0.1859028 0.1859028 0.1859028 0.1859028 0.1859028 0.1859028
## [15] 0.1859028 0.1859028 0.1973792 0.1973792 0.1973792 0.1973792 0.1973792
## [22] 0.1973792 0.1973792 0.1973792 0.2153781 0.2153781 0.2153781 0.2153781
## [29] 0.2153781 0.2153781 0.2153781 0.2153781 0.1903429 0.1903429 0.1903429
## [36] 0.1903429 0.1903429 0.1903429 0.1903429 0.1903429 0.2167558 0.2167558
## [43] 0.2167558 0.2167558 0.2167558 0.2167558 0.2167558 0.2167558 0.2140480
## [50] 0.2140480 0.2140480 0.2140480 0.2140480 0.2140480 0.2140480 0.2140480
## [57] 0.2133255 0.2133255 0.2133255 0.2133255 0.2133255 0.2133255 0.2133255
## [64] 0.2133255 0.2001561 0.2001561 0.2001561 0.2001561 0.2001561 0.2001561
## [71] 0.2001561 0.2001561 0.2048160 0.2048160 0.2048160 0.2048160 0.2048160
## [78] 0.2048160 0.2048160 0.2048160 0.2107785 0.2107785 0.2107785 0.2107785
## [85] 0.2107785 0.2107785 0.2107785 0.2107785 0.2169555 0.2169555 0.2169555
## [92] 0.2169555 0.2169555 0.2169555 0.2169555 0.2169555 0.2127288 0.2127288
## [99] 0.2127288 0.2127288 0.2127288 0.2127288 0.2127288 0.2127288 0.2490447
## [106] 0.2490447 0.2490447 0.2490447 0.2490447 0.2490447 0.2490447 0.2490447
## [113] 0.2343066 0.2343066 0.2343066 0.2343066 0.2343066 0.2343066 0.2343066
## [120] 0.2343066 0.2225399 0.2225399 0.2225399 0.2225399 0.2225399 0.2225399
## [127] 0.2225399 0.2225399 0.2150733 0.2150733 0.2150733 0.2150733 0.2150733
## [134] 0.2150733 0.2150733 0.2150733 0.2554895 0.2554895 0.2554895 0.2554895
## [141] 0.2554895 0.2554895 0.2554895 0.2554895 0.2117968 0.2117968 0.2117968
## [148] 0.2117968 0.2117968 0.2117968 0.2117968 0.2117968 0.2295354 0.2295354
## [155] 0.2295354 0.2295354 0.2295354 0.2295354 0.2295354 0.2295354 0.2262799
## [162] 0.2262799 0.2262799 0.2262799 0.2262799 0.2262799 0.2262799 0.2262799
## [169] 0.2321983 0.2321983 0.2321983 0.2321983 0.2321983 0.2321983 0.2321983
## [176] 0.2321983 0.2197332 0.2197332 0.2197332 0.2197332 0.2197332 0.2197332
## [183] 0.2197332 0.2197332 0.2397288 0.2397288 0.2397288 0.2397288 0.2397288
## [190] 0.2397288 0.2397288 0.2397288 0.2356679 0.2356679 0.2356679 0.2356679
## [197] 0.2356679 0.2356679 0.2356679 0.2356679 0.2051967 0.2051967 0.2051967
## [204] 0.2051967 0.2051967 0.2051967 0.2051967 0.2051967 0.2385482 0.2385482
## [211] 0.2385482 0.2385482 0.2385482 0.2385482 0.2385482 0.2385482 0.2360338
## [218] 0.2360338 0.2360338 0.2360338 0.2360338 0.2360338 0.2360338 0.2360338
## [225] 0.2402064 0.2402064 0.2402064 0.2402064 0.2402064 0.2402064 0.2402064
## [232] 0.2402064 0.2678309 0.2678309 0.2678309 0.2678309 0.2678309 0.2678309
## [239] 0.2678309 0.2678309 0.2373166 0.2373166 0.2373166 0.2373166 0.2373166
## [246] 0.2373166 0.2373166 0.2373166 0.2407417 0.2407417 0.2407417 0.2407417
## [253] 0.2407417 0.2407417 0.2407417 0.2407417 0.1927332 0.1927332 0.1927332
## [260] 0.1927332 0.1927332 0.1927332 0.1927332 0.1927332 0.2411836 0.2411836
## [267] 0.2411836 0.2411836 0.2411836 0.2411836 0.2411836 0.2411836 0.1978560
## [274] 0.1978560 0.1978560 0.1978560 0.1978560 0.1978560 0.1978560 0.1978560
## [281] 0.2018217 0.2018217 0.2018217 0.2018217 0.2018217 0.2018217 0.2018217
## [288] 0.2018217 0.2249040 0.2249040 0.2249040 0.2249040 0.2249040 0.2249040
## [295] 0.2249040 0.2249040 0.2044047 0.2044047 0.2044047 0.2044047 0.2044047
## [302] 0.2044047 0.2044047 0.2044047 0.2162201 0.2162201 0.2162201 0.2162201
## [309] 0.2162201 0.2162201 0.2162201 0.2162201 0.1985873 0.1985873 0.1985873
## [316] 0.1985873 0.1985873 0.1985873 0.1985873 0.1985873 0.2281940 0.2281940
## [323] 0.2281940 0.2281940 0.2281940 0.2281940 0.2281940 0.2281940 0.1958951
## [330] 0.1958951 0.1958951 0.1958951 0.1958951 0.1958951 0.1958951 0.1958951
## [337] 0.2124651 0.2124651 0.2124651 0.2124651 0.2124651 0.2124651 0.2124651
## [344] 0.2124651 0.2086983 0.2086983 0.2086983 0.2086983 0.2086983 0.2086983
## [351] 0.2086983 0.2086983 0.2157854 0.2157854 0.2157854 0.2157854 0.2157854
## [358] 0.2157854 0.2157854 0.2157854 0.1919994 0.1919994 0.1919994 0.1919994
## [365] 0.1919994 0.1919994 0.1919994 0.1919994 0.2176189 0.2176189 0.2176189
## [372] 0.2176189 0.2176189 0.2176189 0.2176189 0.2176189 0.2119472 0.2119472
## [379] 0.2119472 0.2119472 0.2119472 0.2119472 0.2119472 0.2119472 0.2350535
## [386] 0.2350535 0.2350535 0.2350535 0.2350535 0.2350535 0.2350535 0.2350535
## [393] 0.2012209 0.2012209 0.2012209 0.2012209 0.2012209 0.2012209 0.2012209
## [400] 0.2012209 0.2060318 0.2060318 0.2060318 0.2060318 0.2060318 0.2060318
## [407] 0.2060318 0.2060318 0.2054056 0.2054056 0.2054056 0.2054056 0.2054056
## [414] 0.2054056 0.2054056 0.2054056 0.2175323 0.2175323 0.2175323 0.2175323
## [421] 0.2175323 0.2175323 0.2175323 0.2175323 0.2003197 0.2003197 0.2003197
## [428] 0.2003197 0.2003197 0.2003197 0.2003197 0.2003197 0.2091406 0.2091406
## [435] 0.2091406 0.2091406 0.2091406 0.2091406 0.2091406 0.2091406 0.1815443
## [442] 0.1815443 0.1815443 0.1815443 0.1815443 0.1815443 0.1815443 0.1815443
## [449] 0.2195503 0.2195503 0.2195503 0.2195503 0.2195503 0.2195503 0.2195503
## [456] 0.2195503 0.1924371 0.1924371 0.1924371 0.1924371 0.1924371 0.1924371
## [463] 0.1924371 0.1924371 0.2423122 0.2423122 0.2423122 0.2423122 0.2423122
## [470] 0.2423122 0.2423122 0.2423122 0.1816492 0.1816492 0.1816492 0.1816492
## [477] 0.1816492 0.1816492 0.1816492 0.1816492 0.2089676 0.2089676 0.2089676
## [484] 0.2089676 0.2089676 0.2089676 0.2089676 0.2089676 0.1701359 0.1701359
## [491] 0.1701359 0.1701359 0.1701359 0.1701359 0.1701359 0.1701359 0.2083179
## [498] 0.2083179 0.2083179 0.2083179 0.2083179 0.2083179 0.2083179 0.2083179
## [505] 0.1995795 0.1995795 0.1995795 0.1995795 0.1995795 0.1995795 0.1995795
## [512] 0.1995795 0.2321077 0.2321077 0.2321077 0.2321077 0.2321077 0.2321077
## [519] 0.2321077 0.2321077 0.2047024 0.2047024 0.2047024 0.2047024 0.2047024
## [526] 0.2047024 0.2047024 0.2047024 0.2109741 0.2109741 0.2109741 0.2109741
## [533] 0.2109741 0.2109741 0.2109741 0.2109741 0.2178708 0.2178708 0.2178708
## [540] 0.2178708 0.2178708 0.2178708 0.2178708 0.2178708 0.2248848 0.2248848
## [547] 0.2248848 0.2248848 0.2248848 0.2248848 0.2248848 0.2248848 0.2070149
## [554] 0.2070149 0.2070149 0.2070149 0.2070149 0.2070149 0.2070149 0.2070149
## [561] 0.2304433 0.2304433 0.2304433 0.2304433 0.2304433 0.2304433 0.2304433
## [568] 0.2304433 0.2278552 0.2278552 0.2278552 0.2278552 0.2278552 0.2278552
## [575] 0.2278552 0.2278552 0.2251992 0.2251992 0.2251992 0.2251992 0.2251992
## [582] 0.2251992 0.2251992 0.2251992 0.2291216 0.2291216 0.2291216 0.2291216
## [589] 0.2291216 0.2291216 0.2291216 0.2291216 0.2318402 0.2318402 0.2318402
## [596] 0.2318402 0.2318402 0.2318402 0.2318402 0.2318402 0.2445642 0.2445642
## [603] 0.2445642 0.2445642 0.2445642 0.2445642 0.2445642 0.2445642 0.2558774
## [610] 0.2558774 0.2558774 0.2558774 0.2558774 0.2558774 0.2558774 0.2558774
#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> 0.0001655084 0.01286501
## 2 mun (Intercept) <NA> 0.0001539710 0.01240850
## 3 gen (Intercept) <NA> 0.0001409755 0.01187331
## 4 Residual <NA> <NA> 0.0009420051 0.03069210
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## gen:mun (Intercept) 0.012865
## mun (Intercept) 0.012409
## gen (Intercept) 0.011873
## Residual 0.030692
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] 0.7785413
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## [1] 0.9140248
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CNCH12 0.007773787
## CNCH13 -0.013251623
## FBO1 0.007440711
## FCHI8 0.001543602
## FEAR5 -0.007745464
## FGI4 -0.003300983
## FMA7 0.018296901
## FSV1 -0.010756932
##Predicho de carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## FMA7 0.018296901 0.2352861
## CNCH12 0.007773787 0.2247630
## FBO1 0.007440711 0.2244300
## FCHI8 0.001543602 0.2185328
## FGI4 -0.003300983 0.2136883
## FEAR5 -0.007745464 0.2092438
## FSV1 -0.010756932 0.2062323
## CNCH13 -0.013251623 0.2037376
# Visualización ranking predichos
blup_gen$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(GR=mean(GR)) %>%
pivot_wider(names_from=mun,
values_from=GR)
## `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, GR)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $GR
## $coordgen
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.01026163 -0.003841048 0.0277233050 -0.021008733 -0.048427585
## [2,] -0.03188598 -0.001407567 0.0289091500 0.043631449 0.002631111
## [3,] 0.01226632 -0.010745713 0.0279273520 -0.017231092 0.028234156
## [4,] 0.02170155 0.053840815 -0.0009048515 0.004480703 0.014601073
## [5,] -0.01472987 0.009663826 -0.0199496412 -0.019709760 -0.005745227
## [6,] -0.01501613 -0.022897324 -0.0083380004 -0.023512818 0.028035471
## [7,] 0.04122251 -0.028133113 -0.0218861380 0.028780386 -0.003173510
## [8,] -0.02382003 0.003520124 -0.0334811760 0.004569865 -0.016155490
## [,6] [,7] [,8]
## [1,] -0.012283749 -0.008359541 0.02359179
## [2,] 0.005995792 -0.009746542 0.02359179
## [3,] 0.012993116 0.039840148 0.02359179
## [4,] -0.014398501 -0.009209370 0.02359179
## [5,] 0.050739519 -0.013844806 0.02359179
## [6,] -0.028505069 -0.030419295 0.02359179
## [7,] 0.006815633 -0.006434387 0.02359179
## [8,] -0.021356742 0.038173794 0.02359179
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.036951620 -0.019265939 7.624159e-03 0.013138207 0.008749405
## [2,] 0.018600903 -0.017045231 -1.453165e-02 0.012616309 -0.015789535
## [3,] 0.022838797 -0.022506179 1.863177e-02 -0.005601145 0.006621782
## [4,] 0.006490071 0.007207738 3.336060e-02 -0.016879027 -0.018332678
## [5,] 0.044363585 -0.014673131 1.418723e-02 0.017061290 -0.011427385
## [6,] 0.002509301 -0.039906511 7.124408e-03 -0.023913799 0.003792854
## [7,] 0.045818759 -0.021015222 5.369693e-03 0.004971279 0.006523497
## [8,] 0.075617793 0.055431540 -4.773848e-05 -0.008253945 0.003874172
## [9,] 0.032847351 -0.009535922 1.324612e-03 -0.004683841 0.003614218
## [10,] 0.038954718 -0.017153244 -4.073112e-02 -0.016156124 -0.007059176
## [,6] [,7] [,8]
## [1,] -0.0067157281 0.0024174353 2.815588e-17
## [2,] -0.0069563771 0.0050640400 3.891516e-19
## [3,] -0.0012922325 -0.0001702132 1.842745e-17
## [4,] 0.0025227722 0.0033027838 6.524451e-18
## [5,] 0.0008494113 -0.0078613338 -7.175962e-18
## [6,] -0.0045567879 -0.0024283193 -3.766340e-18
## [7,] 0.0164625444 0.0038697472 -1.513337e-17
## [8,] -0.0026911801 -0.0005192360 3.542684e-18
## [9,] -0.0104550688 0.0020360562 -3.790385e-17
## [10,] 0.0040321809 -0.0013129079 1.251444e-17
##
## $eigenvalues
## [1] 1.210925e-01 8.323154e-02 6.058893e-02 4.343749e-02 3.116452e-02
## [6] 2.295293e-02 1.145587e-02 5.545815e-17
##
## $totalvar
## [1] 0.03
##
## $varexpl
## [1] 48.88 23.09 12.24 6.29 3.24 1.76 0.44 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
## CNCH12 0.00040625 0.0059375 0.009765625 0.0321844849 0.017311676
## CNCH13 -0.00596875 -0.0085625 -0.005859375 -0.0008155151 -0.002813324
## FBO1 0.01353125 -0.0081875 0.019515625 0.0130594849 0.002686676
## FCHI8 0.00028125 -0.0093125 -0.009609375 0.0013290039 0.001943270
## FEAR5 -0.02346875 -0.0109375 -0.013734375 -0.0025655151 -0.017938324
## FGI4 -0.00184375 -0.0069375 0.005640625 -0.0124405151 -0.014313324
## FMA7 0.03278125 0.0284375 0.014640625 -0.0164405151 0.037686676
## FSV1 -0.01571875 0.0095625 -0.020359375 -0.0143109134 -0.024563324
## RiN SnV Tam ViG Yac
## CNCH12 0.0115625 0.000671875 0.008765625 0.006671875 -0.0003125
## CNCH13 -0.0128125 -0.014703125 -0.042734375 -0.019078125 -0.0461875
## FBO1 0.0153125 0.021046875 0.007890625 0.010046875 -0.0059375
## FCHI8 -0.0309375 -0.004453125 0.070265625 0.005421875 -0.0039375
## FEAR5 -0.0046875 -0.005078125 -0.008484375 -0.016328125 0.0098125
## FGI4 0.0253125 -0.011578125 -0.030109375 0.002421875 0.0038125
## FMA7 0.0053125 0.038546875 0.019390625 0.020421875 0.0385625
## FSV1 -0.0090625 -0.024453125 -0.024984375 -0.009578125 0.0041875
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 14.98629
##
## $grand_mean
## [1] 0.2170285
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 0.2263250 0.2010750 0.2259250 0.2191276 0.2076875 0.2130250 0.2389625 0.2041005
##
## $mean_env
## Chi Gig HtC Jam PtR RiN SnV Tam
## 0.2297187 0.2103125 0.2158594 0.2354405 0.2110633 0.2021875 0.2190781 0.1992344
## ViG Yac
## 0.2079531 0.2394375
##
## $scale_val
## Chi Gig HtC Jam PtR RiN SnV
## 0.01729920 0.01378518 0.01440338 0.01623604 0.02024458 0.01797816 0.02043489
## Tam ViG Yac
## 0.03561973 0.01373261 0.02329795
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración de variables
##Blup gen
blup_gen <- ranef(modelo_blup)$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 0.2247630
## CNCH13 CNCH13 0.2037376
## FBO1 FBO1 0.2244300
## FCHI8 FCHI8 0.2185328
## FEAR5 FEAR5 0.2092438
## FGI4 FGI4 0.2136883
## FMA7 FMA7 0.2352861
## FSV1 FSV1 0.2062323
##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(GR))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas de reacción joint regression env
ggplot(datos, aes(x = env, y = GR,
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 = GR,
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(GR ~ gen*env,
data=datos)
emtrends(mod_plas_lm, "gen", var = "env")
## gen env.trend SE df lower.CL upper.CL
## CNCH12 1.028 0.272 600 0.494 1.563
## CNCH13 1.018 0.272 600 0.484 1.552
## FBO1 0.913 0.272 600 0.379 1.448
## FCHI8 0.366 0.318 600 -0.259 0.991
## FEAR5 1.223 0.272 600 0.689 1.758
## FGI4 1.006 0.272 600 0.472 1.541
## FMA7 0.934 0.272 600 0.399 1.468
## FSV1 1.287 0.316 600 0.667 1.908
##
## Confidence level used: 0.95
# modelo blup factores aleatorios
modelo_plasticidad <- lmer(GR ~ env +
(env|gen) +
(1|mun),
data=datos)
## boundary (singular) fit: see help('isSingular')
pend <- ranef(modelo_plasticidad)$gen
pend$gen <- rownames(pend)
plasticidad <- pend[,c("gen","env")]
colnames(plasticidad)[2] <- "Pendiente"
#plasticidad Estrés
# modelo factores fijos
mod_plas2_lm <- lm(GR ~ gen*E,
data=datos)
emtrends(mod_plas2_lm, "gen", var = "E")
## gen E.trend SE df lower.CL upper.CL
## CNCH12 -0.1461 0.0659 600 -0.275 -0.01678
## CNCH13 -0.1595 0.0659 600 -0.289 -0.03015
## FBO1 -0.1251 0.0659 600 -0.254 0.00423
## FCHI8 0.0231 0.0696 600 -0.114 0.15985
## FEAR5 -0.2103 0.0659 600 -0.340 -0.08098
## FGI4 -0.1932 0.0659 600 -0.323 -0.06386
## FMA7 -0.2532 0.0659 600 -0.382 -0.12382
## FSV1 -0.1860 0.0667 600 -0.317 -0.05494
##
## Confidence level used: 0.95
#Modelo factores aleatorios
modelo_plasticidad2 <- lmer(GR ~ 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 2 100. 100.
## 2 PC2 0 0.03 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C -1 1 0
## 2 Pendiente 1 1 0
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.9996821
## -------------------------------------------------------------------------------
## 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 2.17e- 1 0.219 0.00154 7.11e- 1 increase 100
## 2 Pendiente FA1 -1.16e-13 -0.0197 -0.0197 -1.70e+13 increase 0
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FCHI8
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FCHI8 0.0447
## 2 FBO1 0.457
## 3 CNCH12 0.482
## 4 FGI4 0.541
## 5 FEAR5 0.963
## 6 FSV1 1.22
## 7 FMA7 1.46
## 8 CNCH13 1.46
#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 2.01 67.0 67.0
## 2 PC2 0.99 33.0 100.
## 3 PC3 0 0.01 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 3 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C 1 1 0
## 2 Pendiente -1 0.99 0.01
## 3 Pendiente2 0.15 0.02 0.98
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.670433
## -------------------------------------------------------------------------------
## 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 2.17e- 1 0.219 0.00154 7.11e- 1 increase 100
## 2 Pendiente FA1 -1.16e-13 -0.0197 -0.0197 -1.70e+13 increase 0
## 3 Pendiente2 FA1 -5.12e-15 0.0695 0.0695 1.36e+15 decrease 0
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FCHI8
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod2$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FCHI8 0.305
## 2 FBO1 0.328
## 3 CNCH12 0.372
## 4 FGI4 0.603
## 5 FEAR5 1.01
## 6 FSV1 1.27
## 7 FMA7 1.45
## 8 CNCH13 1.55
#Gráfico Selección 1
plot(mgidi_mod2)
