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(LDMC) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(LDMC)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 0.7183 0.102608 7.9365 3.513e-09 ***
## mun 9 2.0019 0.222434 17.2048 < 2.2e-16 ***
## gen:mun 60 2.7765 0.046276 3.5793 1.933e-15 ***
## Residuals 539 6.9685 0.012929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm ((LDMC) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: (LDMC)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 0.11365 0.016235 7.5268 1.152e-08 ***
## mun 9 0.36282 0.040313 18.6893 < 2.2e-16 ***
## gen:mun 60 0.41566 0.006928 3.2117 6.157e-13 ***
## Residuals 539 1.16264 0.002157
## ---
## 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 0.414 0.00519 539 0.404 0.425
## CNCH13 0.429 0.00519 539 0.418 0.439
## FBO1 0.411 0.00519 539 0.400 0.421
## FCHI8 nonEst NA NA NA NA
## FEAR5 0.438 0.00519 539 0.428 0.449
## FGI4 0.432 0.00519 539 0.421 0.442
## FMA7 0.398 0.00519 539 0.388 0.408
## 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 -0.01433 0.00734 539 -1.951 0.3721
## CNCH12 - FBO1 0.00380 0.00734 539 0.518 0.9955
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -0.02413 0.00734 539 -3.285 0.0138
## CNCH12 - FGI4 -0.01719 0.00734 539 -2.340 0.1799
## CNCH12 - FMA7 0.01630 0.00734 539 2.220 0.2302
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 0.01813 0.00734 539 2.469 0.1353
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -0.00980 0.00734 539 -1.334 0.7660
## CNCH13 - FGI4 -0.00286 0.00734 539 -0.389 0.9988
## CNCH13 - FMA7 0.03063 0.00734 539 4.171 0.0005
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -0.02793 0.00734 539 -3.803 0.0022
## FBO1 - FGI4 -0.02099 0.00734 539 -2.858 0.0503
## FBO1 - FMA7 0.01250 0.00734 539 1.702 0.5308
## 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 0.00694 0.00734 539 0.945 0.9346
## FEAR5 - FMA7 0.04043 0.00734 539 5.505 <.0001
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 0.03349 0.00734 539 4.560 0.0001
## FGI4 - FSV1 nonEst NA NA NA NA
## FMA7 - FSV1 nonEst NA NA NA NA
##
## Results are averaged over the levels of: mun
## Note: contrasts are still on the ( scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## P value adjustment: tukey method for comparing a family of 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 0.421 0.00581 539 0.410 0.433
## Gig 0.416 0.00581 539 0.405 0.427
## HtC 0.400 0.00581 539 0.389 0.411
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
## RiN 0.411 0.00581 539 0.399 0.422
## SnV 0.370 0.00581 539 0.359 0.382
## Tam 0.441 0.00581 539 0.430 0.453
## ViG 0.422 0.00581 539 0.410 0.433
## Yac 0.428 0.00581 539 0.416 0.439
##
## 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 0.005220 0.00821 539 0.636 0.9984
## Chi - HtC 0.021158 0.00821 539 2.577 0.1666
## Chi - Jam nonEst NA NA NA NA
## Chi - PtR nonEst NA NA NA NA
## Chi - RiN 0.010603 0.00821 539 1.291 0.9019
## Chi - SnV 0.050704 0.00821 539 6.176 <.0001
## Chi - Tam -0.020214 0.00821 539 -2.462 0.2142
## Chi - ViG -0.000729 0.00821 539 -0.089 1.0000
## Chi - Yac -0.006414 0.00821 539 -0.781 0.9940
## Gig - HtC 0.015937 0.00821 539 1.941 0.5231
## Gig - Jam nonEst NA NA NA NA
## Gig - PtR nonEst NA NA NA NA
## Gig - RiN 0.005382 0.00821 539 0.656 0.9980
## Gig - SnV 0.045484 0.00821 539 5.540 <.0001
## Gig - Tam -0.025434 0.00821 539 -3.098 0.0426
## Gig - ViG -0.005949 0.00821 539 -0.725 0.9963
## Gig - Yac -0.011634 0.00821 539 -1.417 0.8493
## HtC - Jam nonEst NA NA NA NA
## HtC - PtR nonEst NA NA NA NA
## HtC - RiN -0.010555 0.00821 539 -1.286 0.9040
## HtC - SnV 0.029546 0.00821 539 3.599 0.0083
## HtC - Tam -0.041371 0.00821 539 -5.039 <.0001
## HtC - ViG -0.021886 0.00821 539 -2.666 0.1355
## HtC - Yac -0.027571 0.00821 539 -3.358 0.0189
## 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 0.040102 0.00821 539 4.884 <.0001
## RiN - Tam -0.030816 0.00821 539 -3.753 0.0048
## RiN - ViG -0.011331 0.00821 539 -1.380 0.8661
## RiN - Yac -0.017016 0.00821 539 -2.073 0.4341
## SnV - Tam -0.070918 0.00821 539 -8.638 <.0001
## SnV - ViG -0.051433 0.00821 539 -6.265 <.0001
## SnV - Yac -0.057118 0.00821 539 -6.957 <.0001
## Tam - ViG 0.019485 0.00821 539 2.373 0.2567
## Tam - Yac 0.013800 0.00821 539 1.681 0.6998
## ViG - Yac -0.005685 0.00821 539 -0.692 0.9972
##
## 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 0.401 0.0164 539 0.369 0.434
## CNCH13 0.422 0.0164 539 0.389 0.454
## FBO1 0.423 0.0164 539 0.391 0.456
## FCHI8 0.425 0.0164 539 0.393 0.457
## FEAR5 0.462 0.0164 539 0.429 0.494
## FGI4 0.449 0.0164 539 0.417 0.481
## FMA7 0.390 0.0164 539 0.358 0.422
## FSV1 0.397 0.0164 539 0.365 0.429
##
## mun = Gig:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.422 0.0164 539 0.390 0.454
## CNCH13 0.386 0.0164 539 0.354 0.418
## FBO1 0.398 0.0164 539 0.366 0.430
## FCHI8 0.450 0.0164 539 0.418 0.483
## FEAR5 0.407 0.0164 539 0.375 0.439
## FGI4 0.432 0.0164 539 0.400 0.464
## FMA7 0.417 0.0164 539 0.385 0.450
## FSV1 0.415 0.0164 539 0.383 0.447
##
## mun = HtC:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.405 0.0164 539 0.372 0.437
## CNCH13 0.383 0.0164 539 0.350 0.415
## FBO1 0.394 0.0164 539 0.361 0.426
## FCHI8 0.396 0.0164 539 0.364 0.428
## FEAR5 0.415 0.0164 539 0.383 0.448
## FGI4 0.434 0.0164 539 0.401 0.466
## FMA7 0.376 0.0164 539 0.344 0.408
## FSV1 0.398 0.0164 539 0.366 0.430
##
## mun = Jam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.447 0.0164 539 0.415 0.479
## CNCH13 0.541 0.0164 539 0.508 0.573
## FBO1 0.435 0.0164 539 0.403 0.467
## FCHI8 nonEst NA NA NA NA
## FEAR5 0.457 0.0164 539 0.425 0.489
## FGI4 0.453 0.0164 539 0.421 0.486
## FMA7 0.511 0.0164 539 0.479 0.543
## FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.369 0.0164 539 0.337 0.402
## CNCH13 0.438 0.0164 539 0.406 0.471
## FBO1 0.405 0.0164 539 0.373 0.438
## FCHI8 nonEst NA NA NA NA
## FEAR5 0.454 0.0164 539 0.421 0.486
## FGI4 0.396 0.0164 539 0.364 0.428
## FMA7 0.367 0.0164 539 0.335 0.400
## FSV1 0.393 0.0164 539 0.361 0.426
##
## mun = RiN:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.416 0.0164 539 0.383 0.448
## CNCH13 0.407 0.0164 539 0.374 0.439
## FBO1 0.400 0.0164 539 0.368 0.432
## FCHI8 0.348 0.0164 539 0.315 0.380
## FEAR5 0.476 0.0164 539 0.444 0.509
## FGI4 0.409 0.0164 539 0.377 0.442
## FMA7 0.415 0.0164 539 0.383 0.448
## FSV1 0.413 0.0164 539 0.381 0.446
##
## mun = SnV:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.343 0.0164 539 0.311 0.375
## CNCH13 0.391 0.0164 539 0.359 0.423
## FBO1 0.340 0.0164 539 0.308 0.372
## FCHI8 0.368 0.0164 539 0.335 0.400
## FEAR5 0.360 0.0164 539 0.327 0.392
## FGI4 0.400 0.0164 539 0.367 0.432
## FMA7 0.377 0.0164 539 0.345 0.409
## FSV1 0.386 0.0164 539 0.354 0.418
##
## mun = Tam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.448 0.0164 539 0.416 0.481
## CNCH13 0.441 0.0164 539 0.408 0.473
## FBO1 0.446 0.0164 539 0.413 0.478
## FCHI8 0.425 0.0164 539 0.393 0.458
## FEAR5 0.436 0.0164 539 0.403 0.468
## FGI4 0.456 0.0164 539 0.424 0.488
## FMA7 0.438 0.0164 539 0.406 0.470
## FSV1 0.441 0.0164 539 0.409 0.473
##
## mun = ViG:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.445 0.0164 539 0.413 0.478
## CNCH13 0.420 0.0164 539 0.388 0.452
## FBO1 0.417 0.0164 539 0.384 0.449
## FCHI8 0.414 0.0164 539 0.381 0.446
## FEAR5 0.448 0.0164 539 0.415 0.480
## FGI4 0.418 0.0164 539 0.386 0.450
## FMA7 0.397 0.0164 539 0.365 0.429
## FSV1 0.416 0.0164 539 0.384 0.449
##
## mun = Yac:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.447 0.0164 539 0.415 0.479
## CNCH13 0.459 0.0164 539 0.427 0.491
## FBO1 0.448 0.0164 539 0.416 0.480
## FCHI8 0.389 0.0164 539 0.357 0.422
## FEAR5 0.471 0.0164 539 0.439 0.503
## FGI4 0.468 0.0164 539 0.436 0.500
## FMA7 0.291 0.0164 539 0.259 0.323
## FSV1 0.447 0.0164 539 0.415 0.479
##
## 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 -0.020438 0.0232 539 -0.880 0.9877
## CNCH12 - FBO1 -0.022097 0.0232 539 -0.952 0.9807
## CNCH12 - FCHI8 -0.023937 0.0232 539 -1.031 0.9696
## CNCH12 - FEAR5 -0.060373 0.0232 539 -2.600 0.1582
## CNCH12 - FGI4 -0.047469 0.0232 539 -2.044 0.4530
## CNCH12 - FMA7 0.011513 0.0232 539 0.496 0.9997
## CNCH12 - FSV1 0.004241 0.0232 539 0.183 1.0000
## CNCH13 - FBO1 -0.001659 0.0232 539 -0.071 1.0000
## CNCH13 - FCHI8 -0.003499 0.0232 539 -0.151 1.0000
## CNCH13 - FEAR5 -0.039935 0.0232 539 -1.720 0.6744
## CNCH13 - FGI4 -0.027031 0.0232 539 -1.164 0.9417
## CNCH13 - FMA7 0.031951 0.0232 539 1.376 0.8680
## CNCH13 - FSV1 0.024679 0.0232 539 1.063 0.9641
## FBO1 - FCHI8 -0.001840 0.0232 539 -0.079 1.0000
## FBO1 - FEAR5 -0.038276 0.0232 539 -1.648 0.7205
## FBO1 - FGI4 -0.025372 0.0232 539 -1.093 0.9583
## FBO1 - FMA7 0.033611 0.0232 539 1.447 0.8346
## FBO1 - FSV1 0.026338 0.0232 539 1.134 0.9491
## FCHI8 - FEAR5 -0.036437 0.0232 539 -1.569 0.7687
## FCHI8 - FGI4 -0.023532 0.0232 539 -1.013 0.9724
## FCHI8 - FMA7 0.035450 0.0232 539 1.527 0.7929
## FCHI8 - FSV1 0.028177 0.0232 539 1.213 0.9279
## FEAR5 - FGI4 0.012905 0.0232 539 0.556 0.9993
## FEAR5 - FMA7 0.071887 0.0232 539 3.096 0.0429
## FEAR5 - FSV1 0.064614 0.0232 539 2.782 0.1016
## FGI4 - FMA7 0.058982 0.0232 539 2.540 0.1811
## FGI4 - FSV1 0.051709 0.0232 539 2.227 0.3373
## FMA7 - FSV1 -0.007273 0.0232 539 -0.313 1.0000
##
## mun = Gig:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.035879 0.0232 539 1.545 0.7825
## CNCH12 - FBO1 0.023687 0.0232 539 1.020 0.9714
## CNCH12 - FCHI8 -0.028645 0.0232 539 -1.234 0.9217
## CNCH12 - FEAR5 0.014982 0.0232 539 0.645 0.9982
## CNCH12 - FGI4 -0.010201 0.0232 539 -0.439 0.9999
## CNCH12 - FMA7 0.004389 0.0232 539 0.189 1.0000
## CNCH12 - FSV1 0.007009 0.0232 539 0.302 1.0000
## CNCH13 - FBO1 -0.012192 0.0232 539 -0.525 0.9995
## CNCH13 - FCHI8 -0.064524 0.0232 539 -2.779 0.1026
## CNCH13 - FEAR5 -0.020897 0.0232 539 -0.900 0.9860
## CNCH13 - FGI4 -0.046080 0.0232 539 -1.984 0.4935
## CNCH13 - FMA7 -0.031490 0.0232 539 -1.356 0.8765
## CNCH13 - FSV1 -0.028870 0.0232 539 -1.243 0.9186
## FBO1 - FCHI8 -0.052332 0.0232 539 -2.254 0.3216
## FBO1 - FEAR5 -0.008705 0.0232 539 -0.375 1.0000
## FBO1 - FGI4 -0.033888 0.0232 539 -1.459 0.8286
## FBO1 - FMA7 -0.019297 0.0232 539 -0.831 0.9913
## FBO1 - FSV1 -0.016677 0.0232 539 -0.718 0.9965
## FCHI8 - FEAR5 0.043627 0.0232 539 1.879 0.5662
## FCHI8 - FGI4 0.018444 0.0232 539 0.794 0.9934
## FCHI8 - FMA7 0.033035 0.0232 539 1.423 0.8467
## FCHI8 - FSV1 0.035654 0.0232 539 1.535 0.7880
## FEAR5 - FGI4 -0.025183 0.0232 539 -1.084 0.9599
## FEAR5 - FMA7 -0.010592 0.0232 539 -0.456 0.9998
## FEAR5 - FSV1 -0.007972 0.0232 539 -0.343 1.0000
## FGI4 - FMA7 0.014591 0.0232 539 0.628 0.9985
## FGI4 - FSV1 0.017210 0.0232 539 0.741 0.9957
## FMA7 - FSV1 0.002620 0.0232 539 0.113 1.0000
##
## mun = HtC:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.021933 0.0232 539 0.944 0.9815
## CNCH12 - FBO1 0.010971 0.0232 539 0.472 0.9998
## CNCH12 - FCHI8 0.008429 0.0232 539 0.363 1.0000
## CNCH12 - FEAR5 -0.010754 0.0232 539 -0.463 0.9998
## CNCH12 - FGI4 -0.028986 0.0232 539 -1.248 0.9169
## CNCH12 - FMA7 0.028530 0.0232 539 1.229 0.9232
## CNCH12 - FSV1 0.006449 0.0232 539 0.278 1.0000
## CNCH13 - FBO1 -0.010962 0.0232 539 -0.472 0.9998
## CNCH13 - FCHI8 -0.013504 0.0232 539 -0.582 0.9991
## CNCH13 - FEAR5 -0.032686 0.0232 539 -1.408 0.8537
## CNCH13 - FGI4 -0.050918 0.0232 539 -2.193 0.3578
## CNCH13 - FMA7 0.006598 0.0232 539 0.284 1.0000
## CNCH13 - FSV1 -0.015484 0.0232 539 -0.667 0.9978
## FBO1 - FCHI8 -0.002542 0.0232 539 -0.109 1.0000
## FBO1 - FEAR5 -0.021724 0.0232 539 -0.936 0.9825
## FBO1 - FGI4 -0.039956 0.0232 539 -1.721 0.6738
## FBO1 - FMA7 0.017560 0.0232 539 0.756 0.9951
## FBO1 - FSV1 -0.004521 0.0232 539 -0.195 1.0000
## FCHI8 - FEAR5 -0.019183 0.0232 539 -0.826 0.9916
## FCHI8 - FGI4 -0.037414 0.0232 539 -1.611 0.7435
## FCHI8 - FMA7 0.020101 0.0232 539 0.866 0.9889
## FCHI8 - FSV1 -0.001980 0.0232 539 -0.085 1.0000
## FEAR5 - FGI4 -0.018232 0.0232 539 -0.785 0.9938
## FEAR5 - FMA7 0.039284 0.0232 539 1.692 0.6928
## FEAR5 - FSV1 0.017203 0.0232 539 0.741 0.9957
## FGI4 - FMA7 0.057516 0.0232 539 2.477 0.2076
## FGI4 - FSV1 0.035435 0.0232 539 1.526 0.7933
## FMA7 - FSV1 -0.022081 0.0232 539 -0.951 0.9808
##
## mun = Jam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.093545 0.0232 539 -4.028 0.0009
## CNCH12 - FBO1 0.012097 0.0232 539 0.521 0.9953
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -0.009780 0.0232 539 -0.421 0.9983
## CNCH12 - FGI4 -0.006377 0.0232 539 -0.275 0.9998
## CNCH12 - FMA7 -0.063721 0.0232 539 -2.744 0.0685
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 0.105642 0.0232 539 4.549 0.0001
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 0.083765 0.0232 539 3.607 0.0045
## CNCH13 - FGI4 0.087168 0.0232 539 3.754 0.0026
## CNCH13 - FMA7 0.029824 0.0232 539 1.284 0.7936
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -0.021877 0.0232 539 -0.942 0.9354
## FBO1 - FGI4 -0.018474 0.0232 539 -0.796 0.9682
## FBO1 - FMA7 -0.075818 0.0232 539 -3.265 0.0147
## 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 0.003403 0.0232 539 0.147 1.0000
## FEAR5 - FMA7 -0.053941 0.0232 539 -2.323 0.1866
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 -0.057344 0.0232 539 -2.469 0.1351
## 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 -0.068952 0.0232 539 -2.969 0.0486
## CNCH12 - FBO1 -0.035870 0.0232 539 -1.545 0.7175
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -0.084109 0.0232 539 -3.622 0.0059
## CNCH12 - FGI4 -0.026702 0.0232 539 -1.150 0.9121
## CNCH12 - FMA7 0.001989 0.0232 539 0.086 1.0000
## CNCH12 - FSV1 -0.023955 0.0232 539 -1.032 0.9466
## CNCH13 - FBO1 0.033082 0.0232 539 1.425 0.7884
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -0.015158 0.0232 539 -0.653 0.9949
## CNCH13 - FGI4 0.042250 0.0232 539 1.819 0.5355
## CNCH13 - FMA7 0.070941 0.0232 539 3.055 0.0379
## CNCH13 - FSV1 0.044997 0.0232 539 1.938 0.4562
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -0.048239 0.0232 539 -2.077 0.3677
## FBO1 - FGI4 0.009168 0.0232 539 0.395 0.9997
## FBO1 - FMA7 0.037859 0.0232 539 1.630 0.6627
## FBO1 - FSV1 0.011915 0.0232 539 0.513 0.9987
## 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 0.057407 0.0232 539 2.472 0.1716
## FEAR5 - FMA7 0.086098 0.0232 539 3.708 0.0043
## FEAR5 - FSV1 0.060154 0.0232 539 2.590 0.1309
## FGI4 - FMA7 0.028691 0.0232 539 1.236 0.8800
## FGI4 - FSV1 0.002747 0.0232 539 0.118 1.0000
## FMA7 - FSV1 -0.025944 0.0232 539 -1.117 0.9228
##
## mun = RiN:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.008934 0.0232 539 0.385 0.9999
## CNCH12 - FBO1 0.015702 0.0232 539 0.676 0.9976
## CNCH12 - FCHI8 0.067912 0.0232 539 2.924 0.0698
## CNCH12 - FEAR5 -0.060787 0.0232 539 -2.618 0.1518
## CNCH12 - FGI4 0.006236 0.0232 539 0.269 1.0000
## CNCH12 - FMA7 0.000142 0.0232 539 0.006 1.0000
## CNCH12 - FSV1 0.002056 0.0232 539 0.089 1.0000
## CNCH13 - FBO1 0.006768 0.0232 539 0.291 1.0000
## CNCH13 - FCHI8 0.058978 0.0232 539 2.540 0.1811
## CNCH13 - FEAR5 -0.069721 0.0232 539 -3.002 0.0561
## CNCH13 - FGI4 -0.002698 0.0232 539 -0.116 1.0000
## CNCH13 - FMA7 -0.008792 0.0232 539 -0.379 0.9999
## CNCH13 - FSV1 -0.006877 0.0232 539 -0.296 1.0000
## FBO1 - FCHI8 0.052210 0.0232 539 2.248 0.3247
## FBO1 - FEAR5 -0.076489 0.0232 539 -3.294 0.0233
## FBO1 - FGI4 -0.009466 0.0232 539 -0.408 0.9999
## FBO1 - FMA7 -0.015560 0.0232 539 -0.670 0.9977
## FBO1 - FSV1 -0.013645 0.0232 539 -0.588 0.9990
## FCHI8 - FEAR5 -0.128699 0.0232 539 -5.542 <.0001
## FCHI8 - FGI4 -0.061676 0.0232 539 -2.656 0.1387
## FCHI8 - FMA7 -0.067770 0.0232 539 -2.918 0.0709
## FCHI8 - FSV1 -0.065856 0.0232 539 -2.836 0.0884
## FEAR5 - FGI4 0.067023 0.0232 539 2.886 0.0774
## FEAR5 - FMA7 0.060930 0.0232 539 2.624 0.1496
## FEAR5 - FSV1 0.062844 0.0232 539 2.706 0.1229
## FGI4 - FMA7 -0.006094 0.0232 539 -0.262 1.0000
## FGI4 - FSV1 -0.004180 0.0232 539 -0.180 1.0000
## FMA7 - FSV1 0.001914 0.0232 539 0.082 1.0000
##
## mun = SnV:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.047859 0.0232 539 -2.061 0.4418
## CNCH12 - FBO1 0.003162 0.0232 539 0.136 1.0000
## CNCH12 - FCHI8 -0.024601 0.0232 539 -1.059 0.9647
## CNCH12 - FEAR5 -0.016512 0.0232 539 -0.711 0.9967
## CNCH12 - FGI4 -0.056551 0.0232 539 -2.435 0.2265
## CNCH12 - FMA7 -0.034104 0.0232 539 -1.469 0.8239
## CNCH12 - FSV1 -0.042791 0.0232 539 -1.843 0.5911
## CNCH13 - FBO1 0.051021 0.0232 539 2.197 0.3551
## CNCH13 - FCHI8 0.023259 0.0232 539 1.002 0.9741
## CNCH13 - FEAR5 0.031347 0.0232 539 1.350 0.8791
## CNCH13 - FGI4 -0.008691 0.0232 539 -0.374 1.0000
## CNCH13 - FMA7 0.013756 0.0232 539 0.592 0.9990
## CNCH13 - FSV1 0.005069 0.0232 539 0.218 1.0000
## FBO1 - FCHI8 -0.027762 0.0232 539 -1.196 0.9331
## FBO1 - FEAR5 -0.019674 0.0232 539 -0.847 0.9902
## FBO1 - FGI4 -0.059712 0.0232 539 -2.571 0.1688
## FBO1 - FMA7 -0.037265 0.0232 539 -1.605 0.7474
## FBO1 - FSV1 -0.045952 0.0232 539 -1.979 0.4972
## FCHI8 - FEAR5 0.008089 0.0232 539 0.348 1.0000
## FCHI8 - FGI4 -0.031950 0.0232 539 -1.376 0.8680
## FCHI8 - FMA7 -0.009503 0.0232 539 -0.409 0.9999
## FCHI8 - FSV1 -0.018190 0.0232 539 -0.783 0.9939
## FEAR5 - FGI4 -0.040038 0.0232 539 -1.724 0.6714
## FEAR5 - FMA7 -0.017592 0.0232 539 -0.758 0.9951
## FEAR5 - FSV1 -0.026278 0.0232 539 -1.132 0.9497
## FGI4 - FMA7 0.022447 0.0232 539 0.967 0.9789
## FGI4 - FSV1 0.013760 0.0232 539 0.593 0.9990
## FMA7 - FSV1 -0.008687 0.0232 539 -0.374 1.0000
##
## mun = Tam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.007649 0.0232 539 0.329 1.0000
## CNCH12 - FBO1 0.002589 0.0232 539 0.112 1.0000
## CNCH12 - FCHI8 0.022809 0.0232 539 0.982 0.9768
## CNCH12 - FEAR5 0.012568 0.0232 539 0.541 0.9994
## CNCH12 - FGI4 -0.007711 0.0232 539 -0.332 1.0000
## CNCH12 - FMA7 0.010101 0.0232 539 0.435 0.9999
## CNCH12 - FSV1 0.007477 0.0232 539 0.322 1.0000
## CNCH13 - FBO1 -0.005060 0.0232 539 -0.218 1.0000
## CNCH13 - FCHI8 0.015160 0.0232 539 0.653 0.9981
## CNCH13 - FEAR5 0.004918 0.0232 539 0.212 1.0000
## CNCH13 - FGI4 -0.015361 0.0232 539 -0.661 0.9979
## CNCH13 - FMA7 0.002452 0.0232 539 0.106 1.0000
## CNCH13 - FSV1 -0.000173 0.0232 539 -0.007 1.0000
## FBO1 - FCHI8 0.020220 0.0232 539 0.871 0.9885
## FBO1 - FEAR5 0.009978 0.0232 539 0.430 0.9999
## FBO1 - FGI4 -0.010301 0.0232 539 -0.444 0.9998
## FBO1 - FMA7 0.007512 0.0232 539 0.323 1.0000
## FBO1 - FSV1 0.004888 0.0232 539 0.210 1.0000
## FCHI8 - FEAR5 -0.010242 0.0232 539 -0.441 0.9999
## FCHI8 - FGI4 -0.030521 0.0232 539 -1.314 0.8933
## FCHI8 - FMA7 -0.012708 0.0232 539 -0.547 0.9994
## FCHI8 - FSV1 -0.015333 0.0232 539 -0.660 0.9979
## FEAR5 - FGI4 -0.020279 0.0232 539 -0.873 0.9883
## FEAR5 - FMA7 -0.002466 0.0232 539 -0.106 1.0000
## FEAR5 - FSV1 -0.005091 0.0232 539 -0.219 1.0000
## FGI4 - FMA7 0.017813 0.0232 539 0.767 0.9947
## FGI4 - FSV1 0.015188 0.0232 539 0.654 0.9980
## FMA7 - FSV1 -0.002624 0.0232 539 -0.113 1.0000
##
## mun = ViG:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.025105 0.0232 539 1.081 0.9606
## CNCH12 - FBO1 0.028793 0.0232 539 1.240 0.9196
## CNCH12 - FCHI8 0.031826 0.0232 539 1.371 0.8703
## CNCH12 - FEAR5 -0.002355 0.0232 539 -0.101 1.0000
## CNCH12 - FGI4 0.027203 0.0232 539 1.171 0.9397
## CNCH12 - FMA7 0.048271 0.0232 539 2.079 0.4301
## CNCH12 - FSV1 0.029016 0.0232 539 1.250 0.9165
## CNCH13 - FBO1 0.003688 0.0232 539 0.159 1.0000
## CNCH13 - FCHI8 0.006721 0.0232 539 0.289 1.0000
## CNCH13 - FEAR5 -0.027460 0.0232 539 -1.183 0.9367
## CNCH13 - FGI4 0.002098 0.0232 539 0.090 1.0000
## CNCH13 - FMA7 0.023166 0.0232 539 0.998 0.9747
## CNCH13 - FSV1 0.003911 0.0232 539 0.168 1.0000
## FBO1 - FCHI8 0.003033 0.0232 539 0.131 1.0000
## FBO1 - FEAR5 -0.031148 0.0232 539 -1.341 0.8826
## FBO1 - FGI4 -0.001590 0.0232 539 -0.068 1.0000
## FBO1 - FMA7 0.019478 0.0232 539 0.839 0.9908
## FBO1 - FSV1 0.000222 0.0232 539 0.010 1.0000
## FCHI8 - FEAR5 -0.034181 0.0232 539 -1.472 0.8222
## FCHI8 - FGI4 -0.004623 0.0232 539 -0.199 1.0000
## FCHI8 - FMA7 0.016445 0.0232 539 0.708 0.9968
## FCHI8 - FSV1 -0.002810 0.0232 539 -0.121 1.0000
## FEAR5 - FGI4 0.029558 0.0232 539 1.273 0.9086
## FEAR5 - FMA7 0.050626 0.0232 539 2.180 0.3655
## FEAR5 - FSV1 0.031371 0.0232 539 1.351 0.8787
## FGI4 - FMA7 0.021068 0.0232 539 0.907 0.9853
## FGI4 - FSV1 0.001812 0.0232 539 0.078 1.0000
## FMA7 - FSV1 -0.019255 0.0232 539 -0.829 0.9914
##
## mun = Yac:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.011993 0.0232 539 -0.516 0.9996
## CNCH12 - FBO1 -0.001019 0.0232 539 -0.044 1.0000
## CNCH12 - FCHI8 0.057563 0.0232 539 2.479 0.2067
## CNCH12 - FEAR5 -0.024129 0.0232 539 -1.039 0.9683
## CNCH12 - FGI4 -0.021300 0.0232 539 -0.917 0.9844
## CNCH12 - FMA7 0.155910 0.0232 539 6.714 <.0001
## CNCH12 - FSV1 0.000118 0.0232 539 0.005 1.0000
## CNCH13 - FBO1 0.010974 0.0232 539 0.473 0.9998
## CNCH13 - FCHI8 0.069556 0.0232 539 2.995 0.0573
## CNCH13 - FEAR5 -0.012136 0.0232 539 -0.523 0.9995
## CNCH13 - FGI4 -0.009307 0.0232 539 -0.401 0.9999
## CNCH13 - FMA7 0.167903 0.0232 539 7.230 <.0001
## CNCH13 - FSV1 0.012111 0.0232 539 0.522 0.9996
## FBO1 - FCHI8 0.058582 0.0232 539 2.523 0.1881
## FBO1 - FEAR5 -0.023110 0.0232 539 -0.995 0.9751
## FBO1 - FGI4 -0.020281 0.0232 539 -0.873 0.9883
## FBO1 - FMA7 0.156929 0.0232 539 6.758 <.0001
## FBO1 - FSV1 0.001137 0.0232 539 0.049 1.0000
## FCHI8 - FEAR5 -0.081692 0.0232 539 -3.518 0.0111
## FCHI8 - FGI4 -0.078863 0.0232 539 -3.396 0.0167
## FCHI8 - FMA7 0.098347 0.0232 539 4.235 0.0007
## FCHI8 - FSV1 -0.057445 0.0232 539 -2.474 0.2090
## FEAR5 - FGI4 0.002829 0.0232 539 0.122 1.0000
## FEAR5 - FMA7 0.180039 0.0232 539 7.753 <.0001
## FEAR5 - FSV1 0.024247 0.0232 539 1.044 0.9674
## FGI4 - FMA7 0.177210 0.0232 539 7.631 <.0001
## FGI4 - FSV1 0.021418 0.0232 539 0.922 0.9839
## FMA7 - FSV1 -0.155793 0.0232 539 -6.709 <.0001
##
## 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(LDMC) ~ 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 0.18971 0.027101 7 59.859 2.0962 0.05766 .
## ---
## 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(LDMC) ~ gen + (1 | mun) + (1 | mun:gen)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 390.51 -759.02
## (1 | mun) 10 383.91 -747.81 13.206 1 0.0002791 ***
## (1 | mun:gen) 10 359.89 -699.77 61.245 1 5.04e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(LDMC ~ 1 +
(1|gen) +
(1|mun) +
(1|gen:mun),
data = datos)
ranova(modelo_blup)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## LDMC ~ (1 | gen) + (1 | mun) + (1 | gen:mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 956.65 -1903.3
## (1 | gen) 4 955.49 -1903.0 2.323 1 0.1275
## (1 | mun) 4 947.76 -1887.5 17.795 1 2.460e-05 ***
## (1 | gen:mun) 4 931.74 -1855.5 49.836 1 1.671e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CNCH12 -0.0020943185
## CNCH13 0.0057151686
## FBO1 -0.0041661562
## FCHI8 -0.0060528858
## FEAR5 0.0110544124
## FGI4 0.0072723074
## FMA7 -0.0109794157
## FSV1 -0.0007491121
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 0.4160769
## CNCH13 0.4238864
## FBO1 0.4140051
## FCHI8 0.4121183
## FEAR5 0.4292256
## FGI4 0.4254435
## FMA7 0.4071918
## FSV1 0.4174221
#Blups Parcela
blups$mun
## (Intercept)
## Chi 0.002499302
## Gig -0.001915146
## HtC -0.015392451
## Jam 0.043975098
## PtR -0.012953012
## RiN -0.006466662
## SnV -0.040378021
## Tam 0.019592611
## ViG 0.003115396
## Yac 0.007922885
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## Chi 0.4206705
## Gig 0.4162561
## HtC 0.4027788
## Jam 0.4621463
## PtR 0.4052182
## RiN 0.4117046
## SnV 0.3777932
## Tam 0.4377638
## ViG 0.4212866
## Yac 0.4260941
#Blups interacción
blups$`gen:mun`
## (Intercept)
## CNCH12:Chi -0.0118893400
## CNCH12:Gig 0.0052543884
## CNCH12:HtC 0.0026547611
## CNCH12:Jam -0.0089200473
## CNCH12:PtR -0.0231914307
## CNCH12:RiN 0.0040882389
## CNCH12:SnV -0.0225011622
## CNCH12:Tam 0.0086787907
## CNCH12:ViG 0.0180000905
## CNCH12:Yac 0.0157893524
## CNCH13:Chi -0.0031950576
## CNCH13:Gig -0.0248233516
## CNCH13:HtC -0.0178215359
## CNCH13:Jam 0.0501053312
## CNCH13:PtR 0.0189025639
## CNCH13:RiN -0.0074388440
## CNCH13:SnV 0.0050715886
## CNCH13:Tam -0.0019640421
## CNCH13:ViG -0.0046603297
## CNCH13:Yac 0.0186695965
## FBO1:Chi 0.0047500930
## FBO1:Gig -0.0096264733
## FBO1:HtC -0.0034716361
## FBO1:Jam -0.0158219598
## FBO1:PtR 0.0029300247
## FBO1:RiN -0.0052954448
## FBO1:SnV -0.0232513748
## FBO1:Tam 0.0083225421
## FBO1:ViG -0.0003965893
## FBO1:Yac 0.0179173021
## FCHI8:Chi 0.0073154854
## FCHI8:Gig 0.0277007711
## FCHI8:HtC -0.0004228564
## FCHI8:RiN -0.0399410683
## FCHI8:SnV -0.0028391491
## FCHI8:Tam -0.0042993053
## FCHI8:ViG -0.0011854886
## FCHI8:Yac -0.0211152192
## FEAR5:Chi 0.0206228888
## FEAR5:Gig -0.0141121544
## FEAR5:HtC 0.0010058696
## FEAR5:Jam -0.0112394541
## FEAR5:PtR 0.0256620472
## FEAR5:RiN 0.0368855317
## FEAR5:SnV -0.0201855022
## FEAR5:Tam -0.0090258518
## FEAR5:ViG 0.0105690510
## FEAR5:Yac 0.0233489178
## FGI4:Chi 0.0143424580
## FGI4:Gig 0.0058290205
## FGI4:HtC 0.0161616365
## FGI4:Jam -0.0109783397
## FGI4:PtR -0.0112565694
## FGI4:RiN -0.0066536192
## FGI4:SnV 0.0099831287
## FGI4:Tam 0.0075391942
## FGI4:ViG -0.0071768859
## FGI4:Yac 0.0240050026
## FMA7:Chi -0.0136987849
## FMA7:Gig 0.0083495376
## FMA7:HtC -0.0108701345
## FMA7:Jam 0.0410661181
## FMA7:PtR -0.0184437144
## FMA7:RiN 0.0101073245
## FMA7:SnV 0.0070949799
## FMA7:Tam 0.0078414882
## FMA7:ViG -0.0091154625
## FMA7:Yac -0.0854316783
## FSV1:Chi -0.0157349965
## FSV1:Gig -0.0004971861
## FSV1:HtC -0.0027113547
## FSV1:PtR -0.0076256099
## FSV1:RiN 0.0017464340
## FSV1:SnV 0.0060322696
## FSV1:Tam 0.0026051869
## FSV1:ViG -0.0029022309
## FSV1:Yac 0.0147822297
fixef(modelo_blup)[1] + blups$`gen:mun`
## (Intercept)
## CNCH12:Chi 0.4062819
## CNCH12:Gig 0.4234256
## CNCH12:HtC 0.4208260
## CNCH12:Jam 0.4092512
## CNCH12:PtR 0.3949798
## CNCH12:RiN 0.4222595
## CNCH12:SnV 0.3956701
## CNCH12:Tam 0.4268500
## CNCH12:ViG 0.4361713
## CNCH12:Yac 0.4339606
## CNCH13:Chi 0.4149762
## CNCH13:Gig 0.3933479
## CNCH13:HtC 0.4003497
## CNCH13:Jam 0.4682766
## CNCH13:PtR 0.4370738
## CNCH13:RiN 0.4107324
## CNCH13:SnV 0.4232428
## CNCH13:Tam 0.4162072
## CNCH13:ViG 0.4135109
## CNCH13:Yac 0.4368408
## FBO1:Chi 0.4229213
## FBO1:Gig 0.4085448
## FBO1:HtC 0.4146996
## FBO1:Jam 0.4023493
## FBO1:PtR 0.4211012
## FBO1:RiN 0.4128758
## FBO1:SnV 0.3949198
## FBO1:Tam 0.4264938
## FBO1:ViG 0.4177746
## FBO1:Yac 0.4360885
## FCHI8:Chi 0.4254867
## FCHI8:Gig 0.4458720
## FCHI8:HtC 0.4177484
## FCHI8:RiN 0.3782302
## FCHI8:SnV 0.4153321
## FCHI8:Tam 0.4138719
## FCHI8:ViG 0.4169857
## FCHI8:Yac 0.3970560
## FEAR5:Chi 0.4387941
## FEAR5:Gig 0.4040591
## FEAR5:HtC 0.4191771
## FEAR5:Jam 0.4069318
## FEAR5:PtR 0.4438333
## FEAR5:RiN 0.4550568
## FEAR5:SnV 0.3979857
## FEAR5:Tam 0.4091454
## FEAR5:ViG 0.4287403
## FEAR5:Yac 0.4415201
## FGI4:Chi 0.4325137
## FGI4:Gig 0.4240002
## FGI4:HtC 0.4343329
## FGI4:Jam 0.4071929
## FGI4:PtR 0.4069147
## FGI4:RiN 0.4115176
## FGI4:SnV 0.4281544
## FGI4:Tam 0.4257104
## FGI4:ViG 0.4109943
## FGI4:Yac 0.4421762
## FMA7:Chi 0.4044724
## FMA7:Gig 0.4265208
## FMA7:HtC 0.4073011
## FMA7:Jam 0.4592373
## FMA7:PtR 0.3997275
## FMA7:RiN 0.4282785
## FMA7:SnV 0.4252662
## FMA7:Tam 0.4260127
## FMA7:ViG 0.4090558
## FMA7:Yac 0.3327395
## FSV1:Chi 0.4024362
## FSV1:Gig 0.4176740
## FSV1:HtC 0.4154599
## FSV1:PtR 0.4105456
## FSV1:RiN 0.4199177
## FSV1:SnV 0.4242035
## FSV1:Tam 0.4207764
## FSV1:ViG 0.4152690
## FSV1:Yac 0.4329535
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 -0.0020943185
## 2 CNCH13 0.0057151686
## 3 FBO1 -0.0041661562
## 4 FCHI8 -0.0060528858
## 5 FEAR5 0.0110544124
## 6 FGI4 0.0072723074
## 7 FMA7 -0.0109794157
## 8 FSV1 -0.0007491121
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 Chi 0.002499302
## 2 Gig -0.001915146
## 3 HtC -0.015392451
## 4 Jam 0.043975098
## 5 PtR -0.012953012
## 6 RiN -0.006466662
## 7 SnV -0.040378021
## 8 Tam 0.019592611
## 9 ViG 0.003115396
## 10 Yac 0.007922885
#Tabla blup_gen_mun
blup_gen_mun <- ranef(modelo_blup)$`gen:mun` %>%
tibble::rownames_to_column("gen:mun") %>%
rename(BLUP = `(Intercept)`)
blup_gen_mun
## gen:mun BLUP
## 1 CNCH12:Chi -0.0118893400
## 2 CNCH12:Gig 0.0052543884
## 3 CNCH12:HtC 0.0026547611
## 4 CNCH12:Jam -0.0089200473
## 5 CNCH12:PtR -0.0231914307
## 6 CNCH12:RiN 0.0040882389
## 7 CNCH12:SnV -0.0225011622
## 8 CNCH12:Tam 0.0086787907
## 9 CNCH12:ViG 0.0180000905
## 10 CNCH12:Yac 0.0157893524
## 11 CNCH13:Chi -0.0031950576
## 12 CNCH13:Gig -0.0248233516
## 13 CNCH13:HtC -0.0178215359
## 14 CNCH13:Jam 0.0501053312
## 15 CNCH13:PtR 0.0189025639
## 16 CNCH13:RiN -0.0074388440
## 17 CNCH13:SnV 0.0050715886
## 18 CNCH13:Tam -0.0019640421
## 19 CNCH13:ViG -0.0046603297
## 20 CNCH13:Yac 0.0186695965
## 21 FBO1:Chi 0.0047500930
## 22 FBO1:Gig -0.0096264733
## 23 FBO1:HtC -0.0034716361
## 24 FBO1:Jam -0.0158219598
## 25 FBO1:PtR 0.0029300247
## 26 FBO1:RiN -0.0052954448
## 27 FBO1:SnV -0.0232513748
## 28 FBO1:Tam 0.0083225421
## 29 FBO1:ViG -0.0003965893
## 30 FBO1:Yac 0.0179173021
## 31 FCHI8:Chi 0.0073154854
## 32 FCHI8:Gig 0.0277007711
## 33 FCHI8:HtC -0.0004228564
## 34 FCHI8:RiN -0.0399410683
## 35 FCHI8:SnV -0.0028391491
## 36 FCHI8:Tam -0.0042993053
## 37 FCHI8:ViG -0.0011854886
## 38 FCHI8:Yac -0.0211152192
## 39 FEAR5:Chi 0.0206228888
## 40 FEAR5:Gig -0.0141121544
## 41 FEAR5:HtC 0.0010058696
## 42 FEAR5:Jam -0.0112394541
## 43 FEAR5:PtR 0.0256620472
## 44 FEAR5:RiN 0.0368855317
## 45 FEAR5:SnV -0.0201855022
## 46 FEAR5:Tam -0.0090258518
## 47 FEAR5:ViG 0.0105690510
## 48 FEAR5:Yac 0.0233489178
## 49 FGI4:Chi 0.0143424580
## 50 FGI4:Gig 0.0058290205
## 51 FGI4:HtC 0.0161616365
## 52 FGI4:Jam -0.0109783397
## 53 FGI4:PtR -0.0112565694
## 54 FGI4:RiN -0.0066536192
## 55 FGI4:SnV 0.0099831287
## 56 FGI4:Tam 0.0075391942
## 57 FGI4:ViG -0.0071768859
## 58 FGI4:Yac 0.0240050026
## 59 FMA7:Chi -0.0136987849
## 60 FMA7:Gig 0.0083495376
## 61 FMA7:HtC -0.0108701345
## 62 FMA7:Jam 0.0410661181
## 63 FMA7:PtR -0.0184437144
## 64 FMA7:RiN 0.0101073245
## 65 FMA7:SnV 0.0070949799
## 66 FMA7:Tam 0.0078414882
## 67 FMA7:ViG -0.0091154625
## 68 FMA7:Yac -0.0854316783
## 69 FSV1:Chi -0.0157349965
## 70 FSV1:Gig -0.0004971861
## 71 FSV1:HtC -0.0027113547
## 72 FSV1:PtR -0.0076256099
## 73 FSV1:RiN 0.0017464340
## 74 FSV1:SnV 0.0060322696
## 75 FSV1:Tam 0.0026051869
## 76 FSV1:ViG -0.0029022309
## 77 FSV1:Yac 0.0147822297
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 0.4127019 0.4127019 0.4127019 0.4127019 0.4127019 0.4127019 0.4127019
## [8] 0.4127019 0.3657106 0.3657106 0.3657106 0.3657106 0.3657106 0.3657106
## [15] 0.3657106 0.3657106 0.4596445 0.4596445 0.4596445 0.4596445 0.4596445
## [22] 0.4596445 0.4596445 0.4596445 0.4022430 0.4022430 0.4022430 0.4022430
## [29] 0.4022430 0.4022430 0.4022430 0.4022430 0.4099809 0.4099809 0.4099809
## [36] 0.4099809 0.4099809 0.4099809 0.4099809 0.4099809 0.4123233 0.4123233
## [43] 0.4123233 0.4123233 0.4123233 0.4123233 0.4123233 0.4123233 0.4108325
## [50] 0.4108325 0.4108325 0.4108325 0.4108325 0.4108325 0.4108325 0.4108325
## [57] 0.4136985 0.4136985 0.4136985 0.4136985 0.4136985 0.4136985 0.4136985
## [64] 0.4136985 0.3830764 0.3830764 0.3830764 0.3830764 0.3830764 0.3830764
## [71] 0.3830764 0.3830764 0.3885800 0.3885800 0.3885800 0.3885800 0.3885800
## [78] 0.3885800 0.3885800 0.3885800 0.3950486 0.3950486 0.3950486 0.3950486
## [85] 0.3950486 0.3950486 0.3950486 0.3950486 0.3689012 0.3689012 0.3689012
## [92] 0.3689012 0.3689012 0.3689012 0.3689012 0.3689012 0.3686621 0.3686621
## [99] 0.3686621 0.3686621 0.3686621 0.3686621 0.3686621 0.3686621 0.3739088
## [106] 0.3739088 0.3739088 0.3739088 0.3739088 0.3739088 0.3739088 0.3739088
## [113] 0.3503757 0.3503757 0.3503757 0.3503757 0.3503757 0.3503757 0.3503757
## [120] 0.3503757 0.3531977 0.3531977 0.3531977 0.3531977 0.3531977 0.3531977
## [127] 0.3531977 0.3531977 0.4041864 0.4041864 0.4041864 0.4041864 0.4041864
## [134] 0.4041864 0.4041864 0.4041864 0.3959923 0.3959923 0.3959923 0.3959923
## [141] 0.3959923 0.3959923 0.3959923 0.3959923 0.4523478 0.4523478 0.4523478
## [148] 0.4523478 0.4523478 0.4523478 0.4523478 0.4523478 0.4219331 0.4219331
## [155] 0.4219331 0.4219331 0.4219331 0.4219331 0.4219331 0.4219331 0.4422853
## [162] 0.4422853 0.4422853 0.4422853 0.4422853 0.4422853 0.4422853 0.4422853
## [169] 0.4066869 0.4066869 0.4066869 0.4066869 0.4066869 0.4066869 0.4066869
## [176] 0.4066869 0.4231906 0.4231906 0.4231906 0.4231906 0.4231906 0.4231906
## [183] 0.4231906 0.4231906 0.4212545 0.4212545 0.4212545 0.4212545 0.4212545
## [190] 0.4212545 0.4212545 0.4212545 0.4401272 0.4401272 0.4401272 0.4401272
## [197] 0.4401272 0.4401272 0.4401272 0.4401272 0.4504789 0.4504789 0.4504789
## [204] 0.4504789 0.4504789 0.4504789 0.4504789 0.4504789 0.4573714 0.4573714
## [211] 0.4573714 0.4573714 0.4573714 0.4573714 0.4573714 0.4573714 0.3989260
## [218] 0.3989260 0.3989260 0.3989260 0.3989260 0.3989260 0.3989260 0.3989260
## [225] 0.4604974 0.4604974 0.4604974 0.4604974 0.4604974 0.4604974 0.4604974
## [232] 0.4604974 0.3296830 0.3296830 0.3296830 0.3296830 0.3296830 0.3296830
## [239] 0.3296830 0.3296830 0.4398453 0.4398453 0.4398453 0.4398453 0.4398453
## [246] 0.4398453 0.4398453 0.4398453 0.4397891 0.4397891 0.4397891 0.4397891
## [253] 0.4397891 0.4397891 0.4397891 0.4397891 0.3968435 0.3968435 0.3968435
## [260] 0.3968435 0.3968435 0.3968435 0.3968435 0.3968435 0.3757951 0.3757951
## [267] 0.3757951 0.3757951 0.3757951 0.3757951 0.3757951 0.3757951 0.4419347
## [274] 0.4419347 0.4419347 0.4419347 0.4419347 0.4419347 0.4419347 0.4419347
## [281] 0.4012340 0.4012340 0.4012340 0.4012340 0.4012340 0.4012340 0.4012340
## [288] 0.4012340 0.3799325 0.3799325 0.3799325 0.3799325 0.3799325 0.3799325
## [295] 0.3799325 0.3799325 0.4298359 0.4298359 0.4298359 0.4298359 0.4298359
## [302] 0.4298359 0.4298359 0.4298359 0.4039821 0.4039821 0.4039821 0.4039821
## [309] 0.4039821 0.4039821 0.4039821 0.4039821 0.4176353 0.4176353 0.4176353
## [316] 0.4176353 0.4176353 0.4176353 0.4176353 0.4176353 0.4011917 0.4011917
## [323] 0.4011917 0.4011917 0.4011917 0.4011917 0.4011917 0.4011917 0.4429101
## [330] 0.4429101 0.4429101 0.4429101 0.4429101 0.4429101 0.4429101 0.4429101
## [337] 0.4140482 0.4140482 0.4140482 0.4140482 0.4140482 0.4140482 0.4140482
## [344] 0.4140482 0.4213820 0.4213820 0.4213820 0.4213820 0.4213820 0.4213820
## [351] 0.4213820 0.4213820 0.4371924 0.4371924 0.4371924 0.4371924 0.4371924
## [358] 0.4371924 0.4371924 0.4371924 0.4223415 0.4223415 0.4223415 0.4223415
## [365] 0.4223415 0.4223415 0.4223415 0.4223415 0.4167239 0.4167239 0.4167239
## [372] 0.4167239 0.4167239 0.4167239 0.4167239 0.4167239 0.4150098 0.4150098
## [379] 0.4150098 0.4150098 0.4150098 0.4150098 0.4150098 0.4150098 0.4136262
## [386] 0.4136262 0.4136262 0.4136262 0.4136262 0.4136262 0.4136262 0.4136262
## [393] 0.4131983 0.4131983 0.4131983 0.4131983 0.4131983 0.4131983 0.4131983
## [400] 0.4131983 0.4379040 0.4379040 0.4379040 0.4379040 0.4379040 0.4379040
## [407] 0.4379040 0.4379040 0.4293574 0.4293574 0.4293574 0.4293574 0.4293574
## [414] 0.4293574 0.4293574 0.4293574 0.4194161 0.4194161 0.4194161 0.4194161
## [421] 0.4194161 0.4194161 0.4194161 0.4194161 0.3971479 0.3971479 0.3971479
## [428] 0.3971479 0.3971479 0.3971479 0.3971479 0.3971479 0.4024634 0.4024634
## [435] 0.4024634 0.4024634 0.4024634 0.4024634 0.4024634 0.4024634 0.4396199
## [442] 0.4396199 0.4396199 0.4396199 0.4396199 0.4396199 0.4396199 0.4396199
## [449] 0.4346259 0.4346259 0.4346259 0.4346259 0.4346259 0.4346259 0.4346259
## [456] 0.4346259 0.4397924 0.4397924 0.4397924 0.4397924 0.4397924 0.4397924
## [463] 0.4397924 0.4397924 0.4274116 0.4274116 0.4274116 0.4274116 0.4274116
## [470] 0.4274116 0.4274116 0.4274116 0.4525753 0.4525753 0.4525753 0.4525753
## [477] 0.4525753 0.4525753 0.4525753 0.4525753 0.4443483 0.4443483 0.4443483
## [484] 0.4443483 0.4443483 0.4443483 0.4443483 0.4443483 0.4415150 0.4415150
## [491] 0.4415150 0.4415150 0.4415150 0.4415150 0.4415150 0.4415150 0.4419202
## [498] 0.4419202 0.4419202 0.4419202 0.4419202 0.4419202 0.4419202 0.4419202
## [505] 0.3993183 0.3993183 0.3993183 0.3993183 0.3993183 0.3993183 0.3993183
## [512] 0.3993183 0.3809292 0.3809292 0.3809292 0.3809292 0.3809292 0.3809292
## [519] 0.3809292 0.3809292 0.4148391 0.4148391 0.4148391 0.4148391 0.4148391
## [526] 0.4148391 0.4148391 0.4148391 0.3963030 0.3963030 0.3963030 0.3963030
## [533] 0.3963030 0.3963030 0.3963030 0.3963030 0.4262127 0.4262127 0.4262127
## [540] 0.4262127 0.4262127 0.4262127 0.4262127 0.4262127 0.4033392 0.4033392
## [547] 0.4033392 0.4033392 0.4033392 0.4033392 0.4033392 0.4033392 0.3906724
## [554] 0.3906724 0.3906724 0.3906724 0.3906724 0.3906724 0.3906724 0.3906724
## [561] 0.3951410 0.3951410 0.3951410 0.3951410 0.3951410 0.3951410 0.3951410
## [568] 0.3951410 0.5179668 0.5179668 0.5179668 0.5179668 0.5179668 0.5179668
## [575] 0.5179668 0.5179668 0.4584403 0.4584403 0.4584403 0.4584403 0.4584403
## [582] 0.4584403 0.4584403 0.4584403 0.4619613 0.4619613 0.4619613 0.4619613
## [589] 0.4619613 0.4619613 0.4619613 0.4619613 0.4922330 0.4922330 0.4922330
## [596] 0.4922330 0.4922330 0.4922330 0.4922330 0.4922330 0.4421582 0.4421582
## [603] 0.4421582 0.4421582 0.4421582 0.4421582 0.4421582 0.4421582 0.4511320
## [610] 0.4511320 0.4511320 0.4511320 0.4511320 0.4511320 0.4511320 0.4511320
#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> 0.0005958406 0.02440985
## 2 mun (Intercept) <NA> 0.0005926526 0.02434446
## 3 gen (Intercept) <NA> 0.0001036759 0.01018214
## 4 Residual <NA> <NA> 0.0021570243 0.04644378
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## gen:mun (Intercept) 0.024410
## mun (Intercept) 0.024344
## gen (Intercept) 0.010182
## Residual 0.046444
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] 0.4773609
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## [1] 2.743463
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CNCH12 -0.0020943185
## CNCH13 0.0057151686
## FBO1 -0.0041661562
## FCHI8 -0.0060528858
## FEAR5 0.0110544124
## FGI4 0.0072723074
## FMA7 -0.0109794157
## FSV1 -0.0007491121
##Predicho de carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## FEAR5 0.0110544124 0.4292256
## FGI4 0.0072723074 0.4254435
## CNCH13 0.0057151686 0.4238864
## FSV1 -0.0007491121 0.4174221
## CNCH12 -0.0020943185 0.4160769
## FBO1 -0.0041661562 0.4140051
## FCHI8 -0.0060528858 0.4121183
## FMA7 -0.0109794157 0.4071918
# Visualización ranking predichos
blup_gen$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(LDMC=mean(LDMC)) %>%
pivot_wider(names_from=mun,
values_from=LDMC)
## `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, LDMC)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $LDMC
## $coordgen
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.007633403 -0.0379849677 -0.023261263 -0.05594021 -0.02453716
## [2,] 0.012173090 0.0738437461 0.055898893 -0.02283561 0.01510535
## [3,] 0.013793833 -0.0283968963 -0.009401287 -0.03532577 0.04970024
## [4,] -0.029577203 -0.0439446496 0.040691507 0.03996681 0.04981469
## [5,] 0.050432410 0.0293102436 -0.063364843 0.03843226 0.02458948
## [6,] 0.026603148 -0.0209645878 0.023842847 0.05361317 -0.05940520
## [7,] -0.084948568 0.0286691403 -0.039246563 0.01036107 -0.01515659
## [8,] 0.003889887 -0.0005320286 0.014840708 -0.02827170 -0.04011081
## [,6] [,7] [,8]
## [1,] 0.05708520 -0.03539077 0.03833093
## [2,] 0.01296255 -0.02532560 0.03833093
## [3,] -0.07259237 -0.01456387 0.03833093
## [4,] 0.03751402 0.01835557 0.03833093
## [5,] 0.02096436 0.01860142 0.03833093
## [6,] -0.02906106 -0.03634835 0.03833093
## [7,] -0.01505769 -0.01193901 0.03833093
## [8,] -0.01181501 0.08661061 0.03833093
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.047177006 0.002549530 -0.0036533506 0.0422002469 0.014837457
## [2,] -0.016134977 -0.036993546 0.0059352930 0.0269770476 -0.008476317
## [3,] 0.032679116 -0.018859198 -0.0039861343 0.0221879809 -0.019026530
## [4,] -0.036866668 0.080794850 0.0290010153 -0.0052566252 -0.006488421
## [5,] 0.054635803 0.045934543 0.0010856982 0.0181632559 0.031621604
## [6,] 0.042310488 0.043166840 -0.0658629281 0.0009498996 -0.021662049
## [7,] -0.005368925 0.025791317 0.0304813100 0.0259375835 -0.031317674
## [8,] 0.008726987 -0.002809725 -0.0006061879 -0.0056109918 -0.015630539
## [9,] 0.033401777 -0.003382817 -0.0162492343 -0.0067269066 0.001602536
## [10,] 0.155788911 -0.007589288 0.0314953363 -0.0198610108 -0.008669362
## [,6] [,7] [,8]
## [1,] -0.0030579584 -0.011366071 2.440223e-17
## [2,] 0.0172238334 0.005306316 3.603045e-17
## [3,] 0.0007818196 -0.004713070 -6.919282e-17
## [4,] 0.0117003037 -0.007379981 -4.130660e-19
## [5,] -0.0016886265 0.007487111 -1.358294e-17
## [6,] -0.0003835986 0.003591484 1.566951e-17
## [7,] -0.0050138893 0.007712086 2.780287e-18
## [8,] -0.0093528222 -0.011329350 2.508559e-17
## [9,] 0.0228787683 -0.003995721 -1.196042e-17
## [10,] 0.0014569360 0.001389683 1.252138e-17
##
## $eigenvalues
## [1] 1.875611e-01 1.139034e-01 8.619802e-02 6.710677e-02 5.889069e-02
## [6] 3.293469e-02 2.255448e-02 8.971267e-17
##
## $totalvar
## [1] 0.07
##
## $varexpl
## [1] 50.26 18.53 10.61 6.43 4.95 1.55 0.73 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 -0.019820014 0.005887364 0.004571525 -0.024375008 -0.032341566
## CNCH13 0.000618067 -0.029991580 -0.017361158 0.069169849 0.036610167
## FBO1 0.002277209 -0.017799180 -0.006399009 -0.036471991 0.003528456
## FCHI8 0.004116756 0.034532550 -0.003857334 -0.013926337 -0.011208120
## FEAR5 0.040553284 -0.009094139 0.015325213 -0.014595255 0.051767676
## FGI4 0.027648746 0.016088652 0.033557118 -0.017998087 -0.005639350
## FMA7 -0.031333360 0.001498023 -0.023958713 0.039345653 -0.034330524
## FSV1 -0.024060689 -0.001121690 -0.001877643 -0.001148824 -0.008386739
## RiN SnV Tam ViG Yac
## CNCH12 0.005024351 -0.027406984 0.0069353125 0.023482432 0.01939373
## CNCH13 -0.003909446 0.020452394 -0.0007140968 -0.001622728 0.03138682
## FBO1 -0.010677447 -0.030568518 0.0043460175 -0.005310897 0.02041278
## FCHI8 -0.062887536 -0.002806142 -0.0158741608 -0.008343517 -0.03816936
## FEAR5 0.065811709 -0.010894717 -0.0056322519 0.025837451 0.04352286
## FGI4 -0.001211755 0.029143629 0.0146466548 -0.003720930 0.04069373
## FMA7 0.004882078 0.006696820 -0.0031659809 -0.024788469 -0.13651664
## FSV1 0.002968046 0.015383519 -0.0005414944 -0.005533341 0.01927608
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 9.223711
##
## $grand_mean
## [1] 0.4181935
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 0.4143286 0.4286573 0.4105272 0.4063512 0.4384537 0.4315143 0.3980264 0.4176892
##
## $mean_env
## Chi Gig HtC Jam PtR RiN SnV Tam
## 0.4211268 0.4159065 0.3999690 0.4714705 0.4017795 0.4105241 0.3704225 0.4413403
## ViG Yac
## 0.4218553 0.4275404
##
## $scale_val
## Chi Gig HtC Jam PtR RiN
## 0.025028745 0.019958937 0.018191925 0.035835160 0.030437219 0.034794645
## SnV Tam ViG Yac
## 0.021913134 0.009071531 0.016811488 0.060702329
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración de variables
##Blup gen
blup_gen <- ranef(modelo_blup)$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 0.4160769
## CNCH13 CNCH13 0.4238864
## FBO1 FBO1 0.4140051
## FCHI8 FCHI8 0.4121183
## FEAR5 FEAR5 0.4292256
## FGI4 FGI4 0.4254435
## FMA7 FMA7 0.4071918
## FSV1 FSV1 0.4174221
##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(LDMC))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas de reacción joint regression env
ggplot(datos, aes(x = env, y = LDMC,
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 = LDMC,
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(LDMC ~ gen*env,
data=datos)
emtrends(mod_plas_lm, "gen", var = "env")
## gen env.trend SE df lower.CL upper.CL
## CNCH12 1.085 0.221 600 0.651 1.52
## CNCH13 1.431 0.221 600 0.998 1.86
## FBO1 0.971 0.221 600 0.538 1.40
## FCHI8 0.821 0.321 600 0.191 1.45
## FEAR5 0.770 0.221 600 0.336 1.20
## FGI4 0.649 0.221 600 0.216 1.08
## FMA7 1.183 0.221 600 0.749 1.62
## FSV1 0.837 0.316 600 0.217 1.46
##
## Confidence level used: 0.95
# modelo blup factores aleatorios
modelo_plasticidad <- lmer(LDMC ~ 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(LDMC ~ gen*E,
data=datos)
emtrends(mod_plas2_lm, "gen", var = "E")
## gen E.trend SE df lower.CL upper.CL
## CNCH12 0.14640 0.109 600 -0.0672 0.3600
## CNCH13 -0.28670 0.109 600 -0.5003 -0.0731
## FBO1 -0.00613 0.109 600 -0.2198 0.2075
## FCHI8 0.17029 0.115 600 -0.0556 0.3962
## FEAR5 -0.14964 0.109 600 -0.3633 0.0640
## FGI4 0.00476 0.109 600 -0.2089 0.2184
## FMA7 0.36469 0.109 600 0.1511 0.5783
## FSV1 0.02915 0.110 600 -0.1873 0.2456
##
## Confidence level used: 0.95
#Modelo factores aleatorios
modelo_plasticidad2 <- lmer(LDMC ~ E +
(E|gen) +
(1|mun),
data=datos)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00441722 (tol = 0.002, component 1)
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 1.88 94.2 94.2
## 2 PC2 0.12 5.81 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C -0.97 0.94 0.06
## 2 Pendiente 0.97 0.94 0.06
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.9418538
## -------------------------------------------------------------------------------
## Selection differential
## -------------------------------------------------------------------------------
## # A tibble: 2 × 8
## VAR Factor Xo Xs SD SDperc sense goal
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 BLUP_C FA1 4.18e- 1 0.417 -0.000749 -1.79e- 1 increase 0
## 2 Pendiente FA1 -2.63e-13 -0.0124 -0.0124 -4.70e+12 increase 0
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FSV1
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FSV1 0.00259
## 2 CNCH13 0.203
## 3 CNCH12 0.366
## 4 FBO1 0.494
## 5 FCHI8 0.681
## 6 FGI4 1.20
## 7 FMA7 1.53
## 8 FEAR5 1.53
#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 2.77 92.2 92.2
## 2 PC2 0.22 7.31 99.5
## 3 PC3 0.01 0.48 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 3 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C 0.99 0.98 0.02
## 2 Pendiente -0.93 0.86 0.14
## 3 Pendiente2 0.96 0.92 0.08
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.9221151
## -------------------------------------------------------------------------------
## 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 4.18e- 1 0.424 0.00572 1.37e 0 increase 100
## 2 Pendiente FA1 -2.63e-13 0.0311 0.0311 1.18e13 increase 100
## 3 Pendiente2 FA1 -8.86e-14 -0.184 -0.184 -2.07e14 decrease 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## CNCH13
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod2$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 CNCH13 0.0566
## 2 FSV1 0.464
## 3 FGI4 0.549
## 4 CNCH12 0.881
## 5 FBO1 0.922
## 6 FEAR5 0.995
## 7 FCHI8 1.22
## 8 FMA7 2.08
#Gráfico Selección 1
plot(mgidi_mod2)
