setwd("G:/Mi unidad/Agrosavia/FeCa/Fenoma/Análisis/AguaT")
datos<-read.table("evd.csv", header=T, sep=';')
##Librerias
library(lme4)
## Cargando paquete requerido: Matrix
library(lmerTest) # p-values
## Warning: package 'lmerTest' was built under R version 4.4.3
##
## Adjuntando el paquete: 'lmerTest'
## The following object is masked from 'package:lme4':
##
## lmer
## The following object is masked from 'package:stats':
##
## step
library(emmeans) # post hoc
## Welcome to emmeans.
## Caution: You lose important information if you filter this package's results.
## See '? untidy'
library(dplyr)
##
## Adjuntando el paquete: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
##
## Adjuntando el paquete: 'tidyr'
## The following objects are masked from 'package:Matrix':
##
## expand, pack, unpack
library(agricolae)
## Warning: package 'agricolae' was built under R version 4.4.3
library(metan)
## Warning: package 'metan' was built under R version 4.4.3
## |=========================================================|
## | Multi-Environment Trial Analysis (metan) v1.19.0 |
## | Author: Tiago Olivoto |
## | Type 'citation('metan')' to know how to cite metan |
## | Type 'vignette('metan_start')' for a short tutorial |
## | Visit 'https://bit.ly/metanpkg' for a complete tutorial |
## |=========================================================|
##
## Adjuntando el paquete: 'metan'
## The following object is masked from 'package:tidyr':
##
## replace_na
## The following object is masked from 'package:dplyr':
##
## recode_factor
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.3
##Convertir a factor
datos$gen <- factor(datos$gen)
datos$municipio <- factor(datos$municipio)
datos$mun <- factor(datos$mun)
datos$reg <- factor(datos$reg)
#Modelo 0
modelo <- lm (log(VD) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(VD)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 0.25272 0.036103 20.8902 < 2.2e-16 ***
## mun 9 0.69833 0.077592 44.8974 < 2.2e-16 ***
## gen:mun 60 1.02242 0.017040 9.8601 < 2.2e-16 ***
## Residuals 154 0.26614 0.001728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm ((VD) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: (VD)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 461.79 65.970 19.9591 < 2.2e-16 ***
## mun 9 1289.65 143.294 43.3536 < 2.2e-16 ***
## gen:mun 60 1812.49 30.208 9.1394 < 2.2e-16 ***
## Residuals 154 509.01 3.305
## ---
## 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 43.1 0.332 154 42.4 43.7
## CNCH13 42.0 0.332 154 41.3 42.6
## FBO1 44.1 0.332 154 43.5 44.8
## FCHI8 nonEst NA NA NA NA
## FEAR5 43.4 0.332 154 42.8 44.1
## FGI4 45.2 0.332 154 44.5 45.8
## FMA7 44.5 0.332 154 43.8 45.2
## 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 1.083 0.469 154 2.306 0.1978
## CNCH12 - FBO1 -1.060 0.469 154 -2.259 0.2174
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -0.365 0.469 154 -0.777 0.9710
## CNCH12 - FGI4 -2.129 0.469 154 -4.536 0.0002
## CNCH12 - FMA7 -1.438 0.469 154 -3.063 0.0305
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 -2.143 0.469 154 -4.566 0.0001
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -1.448 0.469 154 -3.084 0.0287
## CNCH13 - FGI4 -3.212 0.469 154 -6.843 <.0001
## CNCH13 - FMA7 -2.521 0.469 154 -5.370 <.0001
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 0.696 0.469 154 1.482 0.6763
## FBO1 - FGI4 -1.069 0.469 154 -2.277 0.2098
## FBO1 - FMA7 -0.377 0.469 154 -0.804 0.9664
## FBO1 - FSV1 nonEst NA NA NA NA
## FCHI8 - FEAR5 nonEst NA NA NA NA
## FCHI8 - FGI4 nonEst NA NA NA NA
## FCHI8 - FMA7 nonEst NA NA NA NA
## FCHI8 - FSV1 nonEst NA NA NA NA
## FEAR5 - FGI4 -1.765 0.469 154 -3.759 0.0032
## FEAR5 - FMA7 -1.073 0.469 154 -2.286 0.2061
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 0.691 0.469 154 1.473 0.6819
## FGI4 - FSV1 nonEst NA NA NA NA
## FMA7 - FSV1 nonEst NA NA NA NA
##
## Results are averaged over the levels of: mun
## Note: contrasts are still on the ( scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## P value adjustment: tukey method for comparing a family of 6 estimates
pwpp(g, type = "response")
## Warning: `aes_()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`
## ℹ The deprecated feature was likely used in the emmeans package.
## Please report the issue at <https://github.com/rvlenth/emmeans/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_segment()`).
#Municipios
m<-emmeans(modelo, pairwise ~ mun)
## NOTE: Results may be misleading due to involvement in interactions
m
## $emmeans
## mun emmean SE df lower.CL upper.CL
## CH 43.8 0.371 154 43.0 44.5
## Gig 44.3 0.371 154 43.5 45.0
## Htc 41.3 0.371 154 40.6 42.0
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
## RiN 45.0 0.371 154 44.2 45.7
## SnV 42.3 0.371 154 41.5 43.0
## Tam 48.1 0.371 154 47.4 48.9
## ViG 39.4 0.371 154 38.6 40.1
## Yac 43.8 0.371 154 43.1 44.5
##
## Results are averaged over the levels of: gen
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CH - Gig -0.4810 0.525 154 -0.916 0.9841
## CH - Htc 2.4562 0.525 154 4.680 0.0002
## CH - Jam nonEst NA NA NA NA
## CH - PtR nonEst NA NA NA NA
## CH - RiN -1.1998 0.525 154 -2.286 0.3080
## CH - SnV 1.4903 0.525 154 2.840 0.0929
## CH - Tam -4.3750 0.525 154 -8.336 <.0001
## CH - ViG 4.3883 0.525 154 8.361 <.0001
## CH - Yac -0.0288 0.525 154 -0.055 1.0000
## Gig - Htc 2.9371 0.525 154 5.596 <.0001
## Gig - Jam nonEst NA NA NA NA
## Gig - PtR nonEst NA NA NA NA
## Gig - RiN -0.7189 0.525 154 -1.370 0.8695
## Gig - SnV 1.9712 0.525 154 3.756 0.0058
## Gig - Tam -3.8940 0.525 154 -7.420 <.0001
## Gig - ViG 4.8692 0.525 154 9.278 <.0001
## Gig - Yac 0.4521 0.525 154 0.861 0.9889
## Htc - Jam nonEst NA NA NA NA
## Htc - PtR nonEst NA NA NA NA
## Htc - RiN -3.6560 0.525 154 -6.966 <.0001
## Htc - SnV -0.9659 0.525 154 -1.840 0.5937
## Htc - Tam -6.8312 0.525 154 -13.016 <.0001
## Htc - ViG 1.9321 0.525 154 3.681 0.0076
## Htc - Yac -2.4850 0.525 154 -4.735 0.0001
## Jam - PtR nonEst NA NA NA NA
## Jam - RiN nonEst NA NA NA NA
## Jam - SnV nonEst NA NA NA NA
## Jam - Tam nonEst NA NA NA NA
## Jam - ViG nonEst NA NA NA NA
## Jam - Yac nonEst NA NA NA NA
## PtR - RiN nonEst NA NA NA NA
## PtR - SnV nonEst NA NA NA NA
## PtR - Tam nonEst NA NA NA NA
## PtR - ViG nonEst NA NA NA NA
## PtR - Yac nonEst NA NA NA NA
## RiN - SnV 2.6901 0.525 154 5.126 <.0001
## RiN - Tam -3.1752 0.525 154 -6.050 <.0001
## RiN - ViG 5.5881 0.525 154 10.648 <.0001
## RiN - Yac 1.1710 0.525 154 2.231 0.3392
## SnV - Tam -5.8653 0.525 154 -11.176 <.0001
## SnV - ViG 2.8980 0.525 154 5.522 <.0001
## SnV - Yac -1.5191 0.525 154 -2.895 0.0808
## Tam - ViG 8.7632 0.525 154 16.698 <.0001
## Tam - Yac 4.3462 0.525 154 8.281 <.0001
## ViG - Yac -4.4171 0.525 154 -8.416 <.0001
##
## Results are averaged over the levels of: gen
## Note: contrasts are still on the ( scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## P value adjustment: tukey method for comparing a family of 8 estimates
pwpp(m, type = "response")
## Warning: Removed 34 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 34 rows containing missing values or values outside the scale range
## (`geom_segment()`).
#Interacción
gm<-emmeans(modelo, pairwise ~ gen|mun)
gm
## $emmeans
## mun = CH:
## gen emmean SE df lower.CL upper.CL
## CNCH12 42.0 1.05 154 39.9 44.0
## CNCH13 44.6 1.05 154 42.6 46.7
## FBO1 45.1 1.05 154 43.0 47.2
## FCHI8 43.0 1.05 154 40.9 45.0
## FEAR5 42.6 1.05 154 40.5 44.7
## FGI4 48.0 1.05 154 45.9 50.0
## FMA7 41.8 1.05 154 39.7 43.9
## FSV1 43.1 1.05 154 41.1 45.2
##
## mun = Gig:
## gen emmean SE df lower.CL upper.CL
## CNCH12 46.9 1.05 154 44.8 48.9
## CNCH13 43.6 1.05 154 41.5 45.7
## FBO1 36.6 1.05 154 34.6 38.7
## FCHI8 39.9 1.05 154 37.8 42.0
## FEAR5 47.8 1.05 154 45.8 49.9
## FGI4 48.1 1.05 154 46.1 50.2
## FMA7 48.7 1.05 154 46.6 50.8
## FSV1 42.4 1.05 154 40.3 44.4
##
## mun = Htc:
## gen emmean SE df lower.CL upper.CL
## CNCH12 39.5 1.05 154 37.5 41.6
## CNCH13 41.7 1.05 154 39.6 43.8
## FBO1 48.0 1.05 154 45.9 50.0
## FCHI8 39.5 1.05 154 37.4 41.5
## FEAR5 40.3 1.05 154 38.3 42.4
## FGI4 42.5 1.05 154 40.5 44.6
## FMA7 40.5 1.05 154 38.4 42.6
## FSV1 38.4 1.05 154 36.4 40.5
##
## mun = Jam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 43.9 1.05 154 41.9 46.0
## CNCH13 39.7 1.05 154 37.7 41.8
## FBO1 44.2 1.05 154 42.2 46.3
## FCHI8 nonEst NA NA NA NA
## FEAR5 43.8 1.05 154 41.7 45.8
## FGI4 34.9 1.05 154 32.8 36.9
## FMA7 41.4 1.05 154 39.3 43.4
## FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## gen emmean SE df lower.CL upper.CL
## CNCH12 36.6 1.05 154 34.5 38.7
## CNCH13 40.7 1.05 154 38.7 42.8
## FBO1 43.2 1.05 154 41.2 45.3
## FCHI8 nonEst NA NA NA NA
## FEAR5 41.5 1.05 154 39.5 43.6
## FGI4 47.4 1.05 154 45.3 49.4
## FMA7 46.0 1.05 154 43.9 48.1
## FSV1 43.3 1.05 154 41.2 45.4
##
## mun = RiN:
## gen emmean SE df lower.CL upper.CL
## CNCH12 46.1 1.05 154 44.1 48.2
## CNCH13 42.7 1.05 154 40.6 44.8
## FBO1 46.1 1.05 154 44.0 48.2
## FCHI8 39.1 1.05 154 37.0 41.2
## FEAR5 45.6 1.05 154 43.6 47.7
## FGI4 50.6 1.05 154 48.5 52.6
## FMA7 43.4 1.05 154 41.3 45.5
## FSV1 46.1 1.05 154 44.0 48.2
##
## mun = SnV:
## gen emmean SE df lower.CL upper.CL
## CNCH12 42.4 1.05 154 40.3 44.5
## CNCH13 39.7 1.05 154 37.6 41.8
## FBO1 44.2 1.05 154 42.1 46.2
## FCHI8 38.3 1.05 154 36.2 40.4
## FEAR5 43.9 1.05 154 41.8 45.9
## FGI4 40.4 1.05 154 38.4 42.5
## FMA7 45.9 1.05 154 43.8 48.0
## FSV1 43.5 1.05 154 41.4 45.5
##
## mun = Tam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 49.9 1.05 154 47.8 52.0
## CNCH13 47.0 1.05 154 44.9 49.1
## FBO1 54.4 1.05 154 52.3 56.5
## FCHI8 47.6 1.05 154 45.6 49.7
## FEAR5 42.5 1.05 154 40.5 44.6
## FGI4 49.9 1.05 154 47.8 51.9
## FMA7 46.3 1.05 154 44.2 48.4
## FSV1 47.5 1.05 154 45.5 49.6
##
## mun = ViG:
## gen emmean SE df lower.CL upper.CL
## CNCH12 42.3 1.05 154 40.2 44.4
## CNCH13 36.9 1.05 154 34.8 39.0
## FBO1 37.0 1.05 154 35.0 39.1
## FCHI8 31.5 1.05 154 29.4 33.6
## FEAR5 41.4 1.05 154 39.4 43.5
## FGI4 41.8 1.05 154 39.7 43.8
## FMA7 47.6 1.05 154 45.5 49.6
## FSV1 36.5 1.05 154 34.4 38.6
##
## mun = Yac:
## gen emmean SE df lower.CL upper.CL
## CNCH12 40.9 1.05 154 38.9 43.0
## CNCH13 43.0 1.05 154 40.9 45.1
## FBO1 42.3 1.05 154 40.2 44.3
## FCHI8 42.6 1.05 154 40.6 44.7
## FEAR5 44.7 1.05 154 42.6 46.7
## FGI4 48.4 1.05 154 46.3 50.4
## FMA7 43.5 1.05 154 41.4 45.6
## FSV1 45.0 1.05 154 42.9 47.1
##
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## mun = CH:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -2.68433 1.48 154 -1.808 0.6154
## CNCH12 - FBO1 -3.15800 1.48 154 -2.127 0.4022
## CNCH12 - FCHI8 -0.99533 1.48 154 -0.671 0.9976
## CNCH12 - FEAR5 -0.64467 1.48 154 -0.434 0.9999
## CNCH12 - FGI4 -5.99433 1.48 154 -4.038 0.0021
## CNCH12 - FMA7 0.18567 1.48 154 0.125 1.0000
## CNCH12 - FSV1 -1.17433 1.48 154 -0.791 0.9934
## CNCH13 - FBO1 -0.47367 1.48 154 -0.319 1.0000
## CNCH13 - FCHI8 1.68900 1.48 154 1.138 0.9474
## CNCH13 - FEAR5 2.03967 1.48 154 1.374 0.8677
## CNCH13 - FGI4 -3.31000 1.48 154 -2.230 0.3400
## CNCH13 - FMA7 2.87000 1.48 154 1.933 0.5303
## CNCH13 - FSV1 1.51000 1.48 154 1.017 0.9712
## FBO1 - FCHI8 2.16267 1.48 154 1.457 0.8288
## FBO1 - FEAR5 2.51333 1.48 154 1.693 0.6917
## FBO1 - FGI4 -2.83633 1.48 154 -1.911 0.5457
## FBO1 - FMA7 3.34367 1.48 154 2.253 0.3269
## FBO1 - FSV1 1.98367 1.48 154 1.336 0.8835
## FCHI8 - FEAR5 0.35067 1.48 154 0.236 1.0000
## FCHI8 - FGI4 -4.99900 1.48 154 -3.368 0.0210
## FCHI8 - FMA7 1.18100 1.48 154 0.796 0.9931
## FCHI8 - FSV1 -0.17900 1.48 154 -0.121 1.0000
## FEAR5 - FGI4 -5.34967 1.48 154 -3.604 0.0098
## FEAR5 - FMA7 0.83033 1.48 154 0.559 0.9993
## FEAR5 - FSV1 -0.52967 1.48 154 -0.357 1.0000
## FGI4 - FMA7 6.18000 1.48 154 4.163 0.0013
## FGI4 - FSV1 4.82000 1.48 154 3.247 0.0303
## FMA7 - FSV1 -1.36000 1.48 154 -0.916 0.9841
##
## mun = Gig:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 3.27067 1.48 154 2.203 0.3556
## CNCH12 - FBO1 10.21933 1.48 154 6.884 <.0001
## CNCH12 - FCHI8 6.96800 1.48 154 4.694 0.0002
## CNCH12 - FEAR5 -0.98267 1.48 154 -0.662 0.9978
## CNCH12 - FGI4 -1.29500 1.48 154 -0.872 0.9881
## CNCH12 - FMA7 -1.86500 1.48 154 -1.256 0.9132
## CNCH12 - FSV1 4.49700 1.48 154 3.029 0.0564
## CNCH13 - FBO1 6.94867 1.48 154 4.681 0.0002
## CNCH13 - FCHI8 3.69733 1.48 154 2.491 0.2073
## CNCH13 - FEAR5 -4.25333 1.48 154 -2.865 0.0871
## CNCH13 - FGI4 -4.56567 1.48 154 -3.076 0.0496
## CNCH13 - FMA7 -5.13567 1.48 154 -3.460 0.0157
## CNCH13 - FSV1 1.22633 1.48 154 0.826 0.9914
## FBO1 - FCHI8 -3.25133 1.48 154 -2.190 0.3634
## FBO1 - FEAR5 -11.20200 1.48 154 -7.546 <.0001
## FBO1 - FGI4 -11.51433 1.48 154 -7.757 <.0001
## FBO1 - FMA7 -12.08433 1.48 154 -8.141 <.0001
## FBO1 - FSV1 -5.72233 1.48 154 -3.855 0.0041
## FCHI8 - FEAR5 -7.95067 1.48 154 -5.356 <.0001
## FCHI8 - FGI4 -8.26300 1.48 154 -5.566 <.0001
## FCHI8 - FMA7 -8.83300 1.48 154 -5.950 <.0001
## FCHI8 - FSV1 -2.47100 1.48 154 -1.665 0.7099
## FEAR5 - FGI4 -0.31233 1.48 154 -0.210 1.0000
## FEAR5 - FMA7 -0.88233 1.48 154 -0.594 0.9989
## FEAR5 - FSV1 5.47967 1.48 154 3.691 0.0073
## FGI4 - FMA7 -0.57000 1.48 154 -0.384 0.9999
## FGI4 - FSV1 5.79200 1.48 154 3.902 0.0035
## FMA7 - FSV1 6.36200 1.48 154 4.286 0.0008
##
## mun = Htc:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -2.16867 1.48 154 -1.461 0.8267
## CNCH12 - FBO1 -8.41267 1.48 154 -5.667 <.0001
## CNCH12 - FCHI8 0.07467 1.48 154 0.050 1.0000
## CNCH12 - FEAR5 -0.79933 1.48 154 -0.538 0.9994
## CNCH12 - FGI4 -2.99200 1.48 154 -2.016 0.4749
## CNCH12 - FMA7 -0.95100 1.48 154 -0.641 0.9982
## CNCH12 - FSV1 1.09700 1.48 154 0.739 0.9956
## CNCH13 - FBO1 -6.24400 1.48 154 -4.206 0.0011
## CNCH13 - FCHI8 2.24333 1.48 154 1.511 0.8004
## CNCH13 - FEAR5 1.36933 1.48 154 0.922 0.9835
## CNCH13 - FGI4 -0.82333 1.48 154 -0.555 0.9993
## CNCH13 - FMA7 1.21767 1.48 154 0.820 0.9917
## CNCH13 - FSV1 3.26567 1.48 154 2.200 0.3576
## FBO1 - FCHI8 8.48733 1.48 154 5.718 <.0001
## FBO1 - FEAR5 7.61333 1.48 154 5.129 <.0001
## FBO1 - FGI4 5.42067 1.48 154 3.652 0.0084
## FBO1 - FMA7 7.46167 1.48 154 5.027 <.0001
## FBO1 - FSV1 9.50967 1.48 154 6.406 <.0001
## FCHI8 - FEAR5 -0.87400 1.48 154 -0.589 0.9990
## FCHI8 - FGI4 -3.06667 1.48 154 -2.066 0.4417
## FCHI8 - FMA7 -1.02567 1.48 154 -0.691 0.9971
## FCHI8 - FSV1 1.02233 1.48 154 0.689 0.9972
## FEAR5 - FGI4 -2.19267 1.48 154 -1.477 0.8185
## FEAR5 - FMA7 -0.15167 1.48 154 -0.102 1.0000
## FEAR5 - FSV1 1.89633 1.48 154 1.277 0.9059
## FGI4 - FMA7 2.04100 1.48 154 1.375 0.8673
## FGI4 - FSV1 4.08900 1.48 154 2.755 0.1146
## FMA7 - FSV1 2.04800 1.48 154 1.380 0.8652
##
## mun = Jam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 4.20867 1.48 154 2.835 0.0573
## CNCH12 - FBO1 -0.28233 1.48 154 -0.190 1.0000
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 0.17667 1.48 154 0.119 1.0000
## CNCH12 - FGI4 9.07700 1.48 154 6.115 <.0001
## CNCH12 - FMA7 2.59267 1.48 154 1.747 0.5034
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 -4.49100 1.48 154 -3.025 0.0340
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -4.03200 1.48 154 -2.716 0.0778
## CNCH13 - FGI4 4.86833 1.48 154 3.280 0.0159
## CNCH13 - FMA7 -1.61600 1.48 154 -1.089 0.8853
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 0.45900 1.48 154 0.309 0.9996
## FBO1 - FGI4 9.35933 1.48 154 6.305 <.0001
## FBO1 - FMA7 2.87500 1.48 154 1.937 0.3839
## 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 8.90033 1.48 154 5.996 <.0001
## FEAR5 - FMA7 2.41600 1.48 154 1.628 0.5817
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 -6.48433 1.48 154 -4.368 0.0003
## 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 -4.11567 1.48 154 -2.773 0.0880
## CNCH12 - FBO1 -6.61567 1.48 154 -4.457 0.0003
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -4.92233 1.48 154 -3.316 0.0192
## CNCH12 - FGI4 -10.75900 1.48 154 -7.248 <.0001
## CNCH12 - FMA7 -9.36333 1.48 154 -6.308 <.0001
## CNCH12 - FSV1 -6.66733 1.48 154 -4.492 0.0003
## CNCH13 - FBO1 -2.50000 1.48 154 -1.684 0.6276
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -0.80667 1.48 154 -0.543 0.9981
## CNCH13 - FGI4 -6.64333 1.48 154 -4.475 0.0003
## CNCH13 - FMA7 -5.24767 1.48 154 -3.535 0.0095
## CNCH13 - FSV1 -2.55167 1.48 154 -1.719 0.6045
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 1.69333 1.48 154 1.141 0.9144
## FBO1 - FGI4 -4.14333 1.48 154 -2.791 0.0840
## FBO1 - FMA7 -2.74767 1.48 154 -1.851 0.5160
## FBO1 - FSV1 -0.05167 1.48 154 -0.035 1.0000
## FCHI8 - FEAR5 nonEst NA NA NA NA
## FCHI8 - FGI4 nonEst NA NA NA NA
## FCHI8 - FMA7 nonEst NA NA NA NA
## FCHI8 - FSV1 nonEst NA NA NA NA
## FEAR5 - FGI4 -5.83667 1.48 154 -3.932 0.0024
## FEAR5 - FMA7 -4.44100 1.48 154 -2.992 0.0494
## FEAR5 - FSV1 -1.74500 1.48 154 -1.176 0.9023
## FGI4 - FMA7 1.39567 1.48 154 0.940 0.9654
## FGI4 - FSV1 4.09167 1.48 154 2.756 0.0916
## FMA7 - FSV1 2.69600 1.48 154 1.816 0.5392
##
## mun = RiN:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 3.44233 1.48 154 2.319 0.2902
## CNCH12 - FBO1 0.02067 1.48 154 0.014 1.0000
## CNCH12 - FCHI8 7.06400 1.48 154 4.759 0.0001
## CNCH12 - FEAR5 0.49367 1.48 154 0.333 1.0000
## CNCH12 - FGI4 -4.42733 1.48 154 -2.983 0.0641
## CNCH12 - FMA7 2.75833 1.48 154 1.858 0.5816
## CNCH12 - FSV1 0.01900 1.48 154 0.013 1.0000
## CNCH13 - FBO1 -3.42167 1.48 154 -2.305 0.2977
## CNCH13 - FCHI8 3.62167 1.48 154 2.440 0.2300
## CNCH13 - FEAR5 -2.94867 1.48 154 -1.986 0.4944
## CNCH13 - FGI4 -7.86967 1.48 154 -5.302 <.0001
## CNCH13 - FMA7 -0.68400 1.48 154 -0.461 0.9998
## CNCH13 - FSV1 -3.42333 1.48 154 -2.306 0.2970
## FBO1 - FCHI8 7.04333 1.48 154 4.745 0.0001
## FBO1 - FEAR5 0.47300 1.48 154 0.319 1.0000
## FBO1 - FGI4 -4.44800 1.48 154 -2.996 0.0617
## FBO1 - FMA7 2.73767 1.48 154 1.844 0.5910
## FBO1 - FSV1 -0.00167 1.48 154 -0.001 1.0000
## FCHI8 - FEAR5 -6.57033 1.48 154 -4.426 0.0005
## FCHI8 - FGI4 -11.49133 1.48 154 -7.741 <.0001
## FCHI8 - FMA7 -4.30567 1.48 154 -2.901 0.0795
## FCHI8 - FSV1 -7.04500 1.48 154 -4.746 0.0001
## FEAR5 - FGI4 -4.92100 1.48 154 -3.315 0.0247
## FEAR5 - FMA7 2.26467 1.48 154 1.526 0.7925
## FEAR5 - FSV1 -0.47467 1.48 154 -0.320 1.0000
## FGI4 - FMA7 7.18567 1.48 154 4.841 0.0001
## FGI4 - FSV1 4.44633 1.48 154 2.995 0.0619
## FMA7 - FSV1 -2.73933 1.48 154 -1.845 0.5903
##
## mun = SnV:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 2.69333 1.48 154 1.814 0.6113
## CNCH12 - FBO1 -1.77200 1.48 154 -1.194 0.9327
## CNCH12 - FCHI8 4.09933 1.48 154 2.762 0.1127
## CNCH12 - FEAR5 -1.46900 1.48 154 -0.990 0.9753
## CNCH12 - FGI4 1.95333 1.48 154 1.316 0.8916
## CNCH12 - FMA7 -3.51933 1.48 154 -2.371 0.2633
## CNCH12 - FSV1 -1.05933 1.48 154 -0.714 0.9965
## CNCH13 - FBO1 -4.46533 1.48 154 -3.008 0.0598
## CNCH13 - FCHI8 1.40600 1.48 154 0.947 0.9808
## CNCH13 - FEAR5 -4.16233 1.48 154 -2.804 0.1016
## CNCH13 - FGI4 -0.74000 1.48 154 -0.499 0.9997
## CNCH13 - FMA7 -6.21267 1.48 154 -4.185 0.0012
## CNCH13 - FSV1 -3.75267 1.48 154 -2.528 0.1917
## FBO1 - FCHI8 5.87133 1.48 154 3.955 0.0029
## FBO1 - FEAR5 0.30300 1.48 154 0.204 1.0000
## FBO1 - FGI4 3.72533 1.48 154 2.510 0.1993
## FBO1 - FMA7 -1.74733 1.48 154 -1.177 0.9373
## FBO1 - FSV1 0.71267 1.48 154 0.480 0.9997
## FCHI8 - FEAR5 -5.56833 1.48 154 -3.751 0.0059
## FCHI8 - FGI4 -2.14600 1.48 154 -1.446 0.8343
## FCHI8 - FMA7 -7.61867 1.48 154 -5.132 <.0001
## FCHI8 - FSV1 -5.15867 1.48 154 -3.475 0.0149
## FEAR5 - FGI4 3.42233 1.48 154 2.306 0.2974
## FEAR5 - FMA7 -2.05033 1.48 154 -1.381 0.8645
## FEAR5 - FSV1 0.40967 1.48 154 0.276 1.0000
## FGI4 - FMA7 -5.47267 1.48 154 -3.687 0.0074
## FGI4 - FSV1 -3.01267 1.48 154 -2.030 0.4656
## FMA7 - FSV1 2.46000 1.48 154 1.657 0.7145
##
## mun = Tam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 2.86400 1.48 154 1.929 0.5330
## CNCH12 - FBO1 -4.54433 1.48 154 -3.061 0.0517
## CNCH12 - FCHI8 2.23267 1.48 154 1.504 0.8042
## CNCH12 - FEAR5 7.33600 1.48 154 4.942 0.0001
## CNCH12 - FGI4 0.02067 1.48 154 0.014 1.0000
## CNCH12 - FMA7 3.59600 1.48 154 2.422 0.2381
## CNCH12 - FSV1 2.34433 1.48 154 1.579 0.7619
## CNCH13 - FBO1 -7.40833 1.48 154 -4.991 <.0001
## CNCH13 - FCHI8 -0.63133 1.48 154 -0.425 0.9999
## CNCH13 - FEAR5 4.47200 1.48 154 3.013 0.0591
## CNCH13 - FGI4 -2.84333 1.48 154 -1.915 0.5425
## CNCH13 - FMA7 0.73200 1.48 154 0.493 0.9997
## CNCH13 - FSV1 -0.51967 1.48 154 -0.350 1.0000
## FBO1 - FCHI8 6.77700 1.48 154 4.565 0.0003
## FBO1 - FEAR5 11.88033 1.48 154 8.003 <.0001
## FBO1 - FGI4 4.56500 1.48 154 3.075 0.0497
## FBO1 - FMA7 8.14033 1.48 154 5.484 <.0001
## FBO1 - FSV1 6.88867 1.48 154 4.641 0.0002
## FCHI8 - FEAR5 5.10333 1.48 154 3.438 0.0168
## FCHI8 - FGI4 -2.21200 1.48 154 -1.490 0.8117
## FCHI8 - FMA7 1.36333 1.48 154 0.918 0.9839
## FCHI8 - FSV1 0.11167 1.48 154 0.075 1.0000
## FEAR5 - FGI4 -7.31533 1.48 154 -4.928 0.0001
## FEAR5 - FMA7 -3.74000 1.48 154 -2.520 0.1952
## FEAR5 - FSV1 -4.99167 1.48 154 -3.363 0.0213
## FGI4 - FMA7 3.57533 1.48 154 2.409 0.2447
## FGI4 - FSV1 2.32367 1.48 154 1.565 0.7700
## FMA7 - FSV1 -1.25167 1.48 154 -0.843 0.9903
##
## mun = ViG:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 5.41067 1.48 154 3.645 0.0085
## CNCH12 - FBO1 5.28467 1.48 154 3.560 0.0113
## CNCH12 - FCHI8 10.80067 1.48 154 7.276 <.0001
## CNCH12 - FEAR5 0.89700 1.48 154 0.604 0.9988
## CNCH12 - FGI4 0.55033 1.48 154 0.371 1.0000
## CNCH12 - FMA7 -5.22900 1.48 154 -3.523 0.0128
## CNCH12 - FSV1 5.80900 1.48 154 3.913 0.0033
## CNCH13 - FBO1 -0.12600 1.48 154 -0.085 1.0000
## CNCH13 - FCHI8 5.39000 1.48 154 3.631 0.0090
## CNCH13 - FEAR5 -4.51367 1.48 154 -3.041 0.0547
## CNCH13 - FGI4 -4.86033 1.48 154 -3.274 0.0279
## CNCH13 - FMA7 -10.63967 1.48 154 -7.168 <.0001
## CNCH13 - FSV1 0.39833 1.48 154 0.268 1.0000
## FBO1 - FCHI8 5.51600 1.48 154 3.716 0.0067
## FBO1 - FEAR5 -4.38767 1.48 154 -2.956 0.0688
## FBO1 - FGI4 -4.73433 1.48 154 -3.189 0.0359
## FBO1 - FMA7 -10.51367 1.48 154 -7.083 <.0001
## FBO1 - FSV1 0.52433 1.48 154 0.353 1.0000
## FCHI8 - FEAR5 -9.90367 1.48 154 -6.672 <.0001
## FCHI8 - FGI4 -10.25033 1.48 154 -6.905 <.0001
## FCHI8 - FMA7 -16.02967 1.48 154 -10.799 <.0001
## FCHI8 - FSV1 -4.99167 1.48 154 -3.363 0.0213
## FEAR5 - FGI4 -0.34667 1.48 154 -0.234 1.0000
## FEAR5 - FMA7 -6.12600 1.48 154 -4.127 0.0015
## FEAR5 - FSV1 4.91200 1.48 154 3.309 0.0251
## FGI4 - FMA7 -5.77933 1.48 154 -3.893 0.0036
## FGI4 - FSV1 5.25867 1.48 154 3.543 0.0120
## FMA7 - FSV1 11.03800 1.48 154 7.436 <.0001
##
## mun = Yac:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -2.09400 1.48 154 -1.411 0.8512
## CNCH12 - FBO1 -1.34433 1.48 154 -0.906 0.9852
## CNCH12 - FCHI8 -1.70900 1.48 154 -1.151 0.9441
## CNCH12 - FEAR5 -3.73333 1.48 154 -2.515 0.1970
## CNCH12 - FGI4 -7.42767 1.48 154 -5.004 <.0001
## CNCH12 - FMA7 -2.58433 1.48 154 -1.741 0.6605
## CNCH12 - FSV1 -4.08600 1.48 154 -2.753 0.1151
## CNCH13 - FBO1 0.74967 1.48 154 0.505 0.9996
## CNCH13 - FCHI8 0.38500 1.48 154 0.259 1.0000
## CNCH13 - FEAR5 -1.63933 1.48 154 -1.104 0.9550
## CNCH13 - FGI4 -5.33367 1.48 154 -3.593 0.0102
## CNCH13 - FMA7 -0.49033 1.48 154 -0.330 1.0000
## CNCH13 - FSV1 -1.99200 1.48 154 -1.342 0.8812
## FBO1 - FCHI8 -0.36467 1.48 154 -0.246 1.0000
## FBO1 - FEAR5 -2.38900 1.48 154 -1.609 0.7440
## FBO1 - FGI4 -6.08333 1.48 154 -4.098 0.0017
## FBO1 - FMA7 -1.24000 1.48 154 -0.835 0.9908
## FBO1 - FSV1 -2.74167 1.48 154 -1.847 0.5892
## FCHI8 - FEAR5 -2.02433 1.48 154 -1.364 0.8721
## FCHI8 - FGI4 -5.71867 1.48 154 -3.852 0.0042
## FCHI8 - FMA7 -0.87533 1.48 154 -0.590 0.9990
## FCHI8 - FSV1 -2.37700 1.48 154 -1.601 0.7489
## FEAR5 - FGI4 -3.69433 1.48 154 -2.489 0.2082
## FEAR5 - FMA7 1.14900 1.48 154 0.774 0.9942
## FEAR5 - FSV1 -0.35267 1.48 154 -0.238 1.0000
## FGI4 - FMA7 4.84333 1.48 154 3.263 0.0289
## FGI4 - FSV1 3.34167 1.48 154 2.251 0.3277
## FMA7 - FSV1 -1.50167 1.48 154 -1.012 0.9721
##
## 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(VD) ~ 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 0.028132 0.0040188 7 60.209 2.3254 0.03616 *
## ---
## 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(VD) ~ gen + (1 | mun) + (1 | mun:gen)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 293.63 -565.26
## (1 | mun) 10 287.26 -554.52 12.739 1 0.0003582 ***
## (1 | mun:gen) 10 228.73 -437.46 129.795 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(VD ~ 1 +
(1|gen) +
(1|mun) +
(1|gen:mun),
data = datos)
ranova(modelo_blup)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## VD ~ (1 | gen) + (1 | mun) + (1 | gen:mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -560.83 1131.7
## (1 | gen) 4 -562.13 1132.3 2.593 1 0.1073230
## (1 | mun) 4 -567.16 1142.3 12.661 1 0.0003733 ***
## (1 | gen:mun) 4 -621.78 1251.6 121.899 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CNCH12 -0.05380921
## CNCH13 -0.69660287
## FBO1 0.57578462
## FCHI8 -1.74123837
## FEAR5 0.16277075
## FGI4 1.21040507
## FMA7 0.79988472
## FSV1 -0.25719469
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 43.09559
## CNCH13 42.45280
## FBO1 43.72519
## FCHI8 41.40816
## FEAR5 43.31217
## FGI4 44.35981
## FMA7 43.94929
## FSV1 42.89221
#Blups Parcela
blups$mun
## (Intercept)
## CH 0.4890770
## Gig 0.8678020
## Htc -1.4450025
## Jam -1.5909630
## PtR -0.5498514
## RiN 1.4338716
## SnV -0.6844356
## Tam 3.9341191
## ViG -2.9663987
## Yac 0.5117815
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## CH 43.63848
## Gig 44.01720
## Htc 41.70440
## Jam 41.55844
## PtR 42.59955
## RiN 44.58327
## SnV 42.46497
## Tam 47.08352
## ViG 40.18300
## Yac 43.66118
#Blups interacción
blups$`gen:mun`
## (Intercept)
## CNCH12:CH -1.44626992
## CNCH12:Gig 2.57600868
## CNCH12:Htc -1.87678101
## CNCH12:Jam 2.17790462
## CNCH12:PtR -5.28680359
## CNCH12:RiN 1.43723717
## CNCH12:SnV -0.01351144
## CNCH12:Tam 2.53798686
## CNCH12:ViG 1.95542452
## CNCH12:Yac -2.38948309
## CNCH13:CH 1.51977719
## CNCH13:Gig 0.23332828
## CNCH13:Htc 0.62956269
## CNCH13:Jam -1.00097837
## CNCH13:PtR -1.04476006
## CNCH13:RiN -1.05847959
## CNCH13:SnV -1.84151420
## CNCH13:Tam 0.55783921
## CNCH13:ViG -2.29501019
## CNCH13:Yac 0.05029723
## FBO1:CH 0.80773837
## FBO1:Gig -7.09552755
## FBO1:Htc 5.06162587
## FBO1:Jam 1.86833092
## FBO1:PtR 0.04962455
## FBO1:RiN 0.85754686
## FBO1:SnV 1.00491393
## FBO1:Tam 6.02787525
## FBO1:ViG -3.31698480
## FBO1:Yac -1.75231142
## FCHI8:CH 0.94534302
## FCHI8:Gig -2.13148280
## FCHI8:Htc -0.43904522
## FCHI8:RiN -3.35583581
## FCHI8:SnV -2.16366125
## FCHI8:Tam 2.05192181
## FCHI8:ViG -6.16878955
## FCHI8:Yac 0.63834511
## FEAR5:CH -1.06464177
## FEAR5:Gig 3.25895504
## FEAR5:Htc -1.35727155
## FEAR5:Jam 1.82733548
## FEAR5:PtR -1.09174693
## FEAR5:RiN 0.80407079
## FEAR5:SnV 1.10298843
## FEAR5:Tam -4.19494162
## FEAR5:ViG 0.96269696
## FEAR5:Yac 0.74561102
## FGI4:CH 2.77050811
## FGI4:Gig 2.60345335
## FGI4:Htc -0.33650501
## FGI4:Jam -7.04101878
## FGI4:PtR 3.17755077
## FGI4:RiN 4.25707549
## FGI4:SnV -2.88187140
## FGI4:Tam 1.39254894
## FGI4:ViG 0.33780254
## FGI4:Yac 3.10507431
## FMA7:CH -2.37283285
## FMA7:Gig 3.47756172
## FMA7:Htc -1.79003518
## FMA7:Jam -0.89443616
## FMA7:PtR 2.29931847
## FMA7:RiN -1.78279113
## FMA7:SnV 2.36283671
## FMA7:Tam -1.42879947
## FMA7:ViG 5.85589601
## FMA7:Yac -0.84666310
## FSV1:CH -0.21806946
## FSV1:Gig -1.25163692
## FSV1:Htc -2.67341510
## FSV1:PtR 0.83826342
## FSV1:RiN 1.60161203
## FSV1:SnV 1.11216952
## FSV1:Tam 0.62938757
## FSV1:ViG -3.04183494
## FSV1:Yac 1.43439244
fixef(modelo_blup)[1] + blups$`gen:mun`
## (Intercept)
## CNCH12:CH 41.70313
## CNCH12:Gig 45.72541
## CNCH12:Htc 41.27262
## CNCH12:Jam 45.32731
## CNCH12:PtR 37.86260
## CNCH12:RiN 44.58664
## CNCH12:SnV 43.13589
## CNCH12:Tam 45.68739
## CNCH12:ViG 45.10483
## CNCH12:Yac 40.75992
## CNCH13:CH 44.66918
## CNCH13:Gig 43.38273
## CNCH13:Htc 43.77896
## CNCH13:Jam 42.14842
## CNCH13:PtR 42.10464
## CNCH13:RiN 42.09092
## CNCH13:SnV 41.30789
## CNCH13:Tam 43.70724
## CNCH13:ViG 40.85439
## CNCH13:Yac 43.19970
## FBO1:CH 43.95714
## FBO1:Gig 36.05387
## FBO1:Htc 48.21103
## FBO1:Jam 45.01773
## FBO1:PtR 43.19903
## FBO1:RiN 44.00695
## FBO1:SnV 44.15432
## FBO1:Tam 49.17728
## FBO1:ViG 39.83242
## FBO1:Yac 41.39709
## FCHI8:CH 44.09474
## FCHI8:Gig 41.01792
## FCHI8:Htc 42.71036
## FCHI8:RiN 39.79357
## FCHI8:SnV 40.98574
## FCHI8:Tam 45.20132
## FCHI8:ViG 36.98061
## FCHI8:Yac 43.78775
## FEAR5:CH 42.08476
## FEAR5:Gig 46.40836
## FEAR5:Htc 41.79213
## FEAR5:Jam 44.97674
## FEAR5:PtR 42.05765
## FEAR5:RiN 43.95347
## FEAR5:SnV 44.25239
## FEAR5:Tam 38.95446
## FEAR5:ViG 44.11210
## FEAR5:Yac 43.89501
## FGI4:CH 45.91991
## FGI4:Gig 45.75285
## FGI4:Htc 42.81290
## FGI4:Jam 36.10838
## FGI4:PtR 46.32695
## FGI4:RiN 47.40648
## FGI4:SnV 40.26753
## FGI4:Tam 44.54195
## FGI4:ViG 43.48720
## FGI4:Yac 46.25448
## FMA7:CH 40.77657
## FMA7:Gig 46.62696
## FMA7:Htc 41.35937
## FMA7:Jam 42.25496
## FMA7:PtR 45.44872
## FMA7:RiN 41.36661
## FMA7:SnV 45.51224
## FMA7:Tam 41.72060
## FMA7:ViG 49.00530
## FMA7:Yac 42.30274
## FSV1:CH 42.93133
## FSV1:Gig 41.89776
## FSV1:Htc 40.47599
## FSV1:PtR 43.98766
## FSV1:RiN 44.75101
## FSV1:SnV 44.26157
## FSV1:Tam 43.77879
## FSV1:ViG 40.10757
## FSV1:Yac 44.58379
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 -0.05380921
## 2 CNCH13 -0.69660287
## 3 FBO1 0.57578462
## 4 FCHI8 -1.74123837
## 5 FEAR5 0.16277075
## 6 FGI4 1.21040507
## 7 FMA7 0.79988472
## 8 FSV1 -0.25719469
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 CH 0.4890770
## 2 Gig 0.8678020
## 3 Htc -1.4450025
## 4 Jam -1.5909630
## 5 PtR -0.5498514
## 6 RiN 1.4338716
## 7 SnV -0.6844356
## 8 Tam 3.9341191
## 9 ViG -2.9663987
## 10 Yac 0.5117815
#Tabla blup_gen_mun
blup_gen_mun <- ranef(modelo_blup)$`gen:mun` %>%
tibble::rownames_to_column("gen:mun") %>%
rename(BLUP = `(Intercept)`)
blup_gen_mun
## gen:mun BLUP
## 1 CNCH12:CH -1.44626992
## 2 CNCH12:Gig 2.57600868
## 3 CNCH12:Htc -1.87678101
## 4 CNCH12:Jam 2.17790462
## 5 CNCH12:PtR -5.28680359
## 6 CNCH12:RiN 1.43723717
## 7 CNCH12:SnV -0.01351144
## 8 CNCH12:Tam 2.53798686
## 9 CNCH12:ViG 1.95542452
## 10 CNCH12:Yac -2.38948309
## 11 CNCH13:CH 1.51977719
## 12 CNCH13:Gig 0.23332828
## 13 CNCH13:Htc 0.62956269
## 14 CNCH13:Jam -1.00097837
## 15 CNCH13:PtR -1.04476006
## 16 CNCH13:RiN -1.05847959
## 17 CNCH13:SnV -1.84151420
## 18 CNCH13:Tam 0.55783921
## 19 CNCH13:ViG -2.29501019
## 20 CNCH13:Yac 0.05029723
## 21 FBO1:CH 0.80773837
## 22 FBO1:Gig -7.09552755
## 23 FBO1:Htc 5.06162587
## 24 FBO1:Jam 1.86833092
## 25 FBO1:PtR 0.04962455
## 26 FBO1:RiN 0.85754686
## 27 FBO1:SnV 1.00491393
## 28 FBO1:Tam 6.02787525
## 29 FBO1:ViG -3.31698480
## 30 FBO1:Yac -1.75231142
## 31 FCHI8:CH 0.94534302
## 32 FCHI8:Gig -2.13148280
## 33 FCHI8:Htc -0.43904522
## 34 FCHI8:RiN -3.35583581
## 35 FCHI8:SnV -2.16366125
## 36 FCHI8:Tam 2.05192181
## 37 FCHI8:ViG -6.16878955
## 38 FCHI8:Yac 0.63834511
## 39 FEAR5:CH -1.06464177
## 40 FEAR5:Gig 3.25895504
## 41 FEAR5:Htc -1.35727155
## 42 FEAR5:Jam 1.82733548
## 43 FEAR5:PtR -1.09174693
## 44 FEAR5:RiN 0.80407079
## 45 FEAR5:SnV 1.10298843
## 46 FEAR5:Tam -4.19494162
## 47 FEAR5:ViG 0.96269696
## 48 FEAR5:Yac 0.74561102
## 49 FGI4:CH 2.77050811
## 50 FGI4:Gig 2.60345335
## 51 FGI4:Htc -0.33650501
## 52 FGI4:Jam -7.04101878
## 53 FGI4:PtR 3.17755077
## 54 FGI4:RiN 4.25707549
## 55 FGI4:SnV -2.88187140
## 56 FGI4:Tam 1.39254894
## 57 FGI4:ViG 0.33780254
## 58 FGI4:Yac 3.10507431
## 59 FMA7:CH -2.37283285
## 60 FMA7:Gig 3.47756172
## 61 FMA7:Htc -1.79003518
## 62 FMA7:Jam -0.89443616
## 63 FMA7:PtR 2.29931847
## 64 FMA7:RiN -1.78279113
## 65 FMA7:SnV 2.36283671
## 66 FMA7:Tam -1.42879947
## 67 FMA7:ViG 5.85589601
## 68 FMA7:Yac -0.84666310
## 69 FSV1:CH -0.21806946
## 70 FSV1:Gig -1.25163692
## 71 FSV1:Htc -2.67341510
## 72 FSV1:PtR 0.83826342
## 73 FSV1:RiN 1.60161203
## 74 FSV1:SnV 1.11216952
## 75 FSV1:Tam 0.62938757
## 76 FSV1:ViG -3.04183494
## 77 FSV1:Yac 1.43439244
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 42.13840 42.13840 42.13840 44.46165 44.46165 44.46165 45.02200 45.02200
## [9] 45.02200 42.84258 42.84258 42.84258 42.73661 42.73661 42.73661 47.61939
## [17] 47.61939 47.61939 42.06553 42.06553 42.06553 43.16321 43.16321 43.16321
## [25] 46.53940 46.53940 46.53940 43.55393 43.55393 43.55393 37.49746 37.49746
## [33] 37.49746 40.14448 40.14448 40.14448 47.43893 47.43893 47.43893 47.83106
## [41] 47.83106 47.83106 48.29465 48.29465 48.29465 42.50837 42.50837 42.50837
## [49] 39.77381 39.77381 39.77381 41.63736 41.63736 41.63736 47.34181 47.34181
## [57] 47.34181 39.52412 39.52412 39.52412 40.50990 40.50990 40.50990 42.57830
## [65] 42.57830 42.57830 40.71425 40.71425 40.71425 38.77379 38.77379 38.77379
## [73] 43.68253 43.68253 43.68253 39.86086 39.86086 39.86086 44.00255 44.00255
## [81] 44.00255 43.54854 43.54854 43.54854 35.72782 35.72782 35.72782 41.46389
## [89] 41.46389 41.46389 37.25894 37.25894 37.25894 40.85819 40.85819 40.85819
## [97] 43.22496 43.22496 43.22496 41.67057 41.67057 41.67057 46.98751 46.98751
## [105] 46.98751 45.69875 45.69875 45.69875 43.18062 43.18062 43.18062 42.82819
## [113] 42.82819 42.82819 45.96670 45.96670 45.96670 46.01660 46.01660 46.01660
## [121] 39.48620 39.48620 39.48620 45.55011 45.55011 45.55011 50.05075 50.05075
## [129] 50.05075 43.60037 43.60037 43.60037 45.92769 45.92769 45.92769 42.39764
## [137] 42.39764 42.39764 39.92685 39.92685 39.92685 44.04566 44.04566 44.04566
## [145] 38.56007 38.56007 38.56007 43.73072 43.73072 43.73072 40.79350 40.79350
## [153] 40.79350 45.62769 45.62769 45.62769 43.31994 43.31994 43.31994 49.56770
## [161] 49.56770 49.56770 46.94476 46.94476 46.94476 53.68718 53.68718 53.68718
## [169] 47.39420 47.39420 47.39420 43.05135 43.05135 43.05135 49.68647 49.68647
## [177] 49.68647 46.45461 46.45461 46.45461 47.45571 47.45571 47.45571 42.08462
## [185] 42.08462 42.08462 37.19139 37.19139 37.19139 37.44180 37.44180 37.44180
## [193] 32.27297 32.27297 32.27297 41.30847 41.30847 41.30847 41.73121 41.73121
## [201] 41.73121 46.83878 46.83878 46.83878 36.88397 36.88397 36.88397 41.21789
## [209] 41.21789 41.21789 43.01488 43.01488 43.01488 42.48466 42.48466 42.48466
## [217] 42.55829 42.55829 42.55829 44.56956 44.56956 44.56956 47.97666 47.97666
## [225] 47.97666 43.61440 43.61440 43.61440 44.83838 44.83838 44.83838
#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 VD varianza-heredabilidades
vc <- as.data.frame(VarCorr(modelo_blup))
vc
## grp var1 var2 vcov sdcor
## 1 gen:mun (Intercept) <NA> 9.050184 3.008352
## 2 mun (Intercept) <NA> 4.700997 2.168178
## 3 gen (Intercept) <NA> 1.483406 1.217952
## 4 Residual <NA> <NA> 3.305247 1.818034
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## gen:mun (Intercept) 3.0084
## mun (Intercept) 2.1682
## gen (Intercept) 1.2180
## Residual 1.8180
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] 0.6003127
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## [1] 3.662477
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CNCH12 -0.05380921
## CNCH13 -0.69660287
## FBO1 0.57578462
## FCHI8 -1.74123837
## FEAR5 0.16277075
## FGI4 1.21040507
## FMA7 0.79988472
## FSV1 -0.25719469
##Predicho VD carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## FGI4 1.21040507 44.35981
## FMA7 0.79988472 43.94929
## FBO1 0.57578462 43.72519
## FEAR5 0.16277075 43.31217
## CNCH12 -0.05380921 43.09559
## FSV1 -0.25719469 42.89221
## CNCH13 -0.69660287 42.45280
## FCHI8 -1.74123837 41.40816
# Visualización ranking predichos
blup_gen$gen <- rownames(blup_gen)
ggplot(blup_gen, aes(x=reorder(gen,pred), y=pred))+
geom_point(size=3)+
coord_flip()+
ylab("Fenotipo predicho (BLUP)")+
xlab("Genotipo")
### Análisis G×E (estabilidad)
## matriz genotipo × parcela.
mat <- datos %>%
group_by(gen,mun) %>%
summarise(VD=mean(VD)) %>%
pivot_wider(names_from=mun,
values_from=VD)
## `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, VD)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $VD
## $coordgen
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9820880 -3.20857299 3.4732564 -7.365742 -1.1746651 2.1906730
## [2,] -1.8135197 -1.08082985 -1.7202108 -0.417938 -1.6596102 -4.4298406
## [3,] -3.8022681 5.19813425 6.1566098 1.400573 0.2661794 -1.2477934
## [4,] -5.6491787 -2.74355429 -3.8464267 1.137472 -2.7667309 0.6328204
## [5,] 2.7158114 -3.14920411 0.3738819 1.341212 5.6348177 -4.9202324
## [6,] 3.3005464 6.19829698 -4.8198611 -2.877250 -0.5210918 -0.4888172
## [7,] 5.1266116 -1.25154687 1.7323780 4.996721 -4.9888617 1.7920241
## [8,] -0.8600909 0.03727686 -1.3496274 1.784952 5.2099627 6.4711660
## [,7] [,8]
## [1,] -0.41182802 3.462567
## [2,] 7.34553928 3.462567
## [3,] -0.97696176 3.462567
## [4,] -4.51011889 3.462567
## [5,] -2.95532974 3.462567
## [6,] -1.61027519 3.462567
## [7,] 0.09288942 3.462567
## [8,] 3.02608490 3.462567
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.2986614 4.71575125 -1.9973114 -1.1008053 0.1210285 -1.43330982
## [2,] 10.6726079 -2.86298060 -2.7632760 -2.2451370 -0.6008667 -0.85729496
## [3,] -1.8004916 5.75762601 3.8143213 0.6175846 -0.8850688 -3.15935015
## [4,] 0.1450889 -2.99942876 7.8885579 0.1409201 2.2378132 -0.90062030
## [5,] 4.5244495 6.40744121 -1.9220797 4.5683517 -0.1917838 0.56694223
## [6,] 4.6330651 5.66100033 0.6829879 -3.4555724 3.4537596 0.61298883
## [7,] 3.6736426 0.23180704 4.6909668 2.6559171 1.3825676 1.43099916
## [8,] -3.6638635 6.14998764 3.6537383 -2.7477429 -2.3954658 2.05174794
## [9,] 12.0968372 0.06766094 3.8322485 -0.1313357 -2.4162288 0.01936285
## [10,] 2.6369680 3.18940224 -3.8128055 0.6937229 1.8088769 -0.08161570
## [,7] [,8]
## [1,] 0.42090840 1.433419e-15
## [2,] -0.10607720 4.864556e-15
## [3,] -0.24133943 1.243481e-16
## [4,] 0.08792696 1.474487e-15
## [5,] 0.16250879 1.383193e-15
## [6,] 0.06227349 -1.716362e-15
## [7,] -0.07046580 2.419869e-15
## [8,] -0.11459262 2.317658e-15
## [9,] 0.05873763 -3.955823e-15
## [10,] -0.41136732 -8.838975e-16
##
## $eigenvalues
## [1] 1.842279e+01 1.391894e+01 1.253566e+01 7.386947e+00 5.906947e+00
## [6] 4.532503e+00 6.895283e-01 7.773314e-15
##
## $totalvar
## [1] 800.76
##
## $varexpl
## [1] 42.38 24.19 19.62 6.81 4.36 2.57 0.06 0.00
##
## $labelgen
## [1] "CNCH12" "CNCH13" "FBO1" "FCHI8" "FEAR5" "FGI4" "FMA7" "FSV1"
##
## $labelenv
## [1] "CH" "Gig" "Htc" "Jam" "PtR" "RiN" "SnV" "Tam" "ViG" "Yac"
##
## $labelaxes
## [1] "PC1" "PC2" "PC3" "PC4" "PC5" "PC6" "PC7" "PC8"
##
## $ge_mat
## CH Gig Htc Jam PtR RiN
## CNCH12 -1.8081667 2.601542 -1.7690000 3.2153105 -5.6200035 1.1713333
## CNCH13 0.8761667 -0.669125 0.3996667 -0.9933561 -1.5043368 -2.2710000
## FBO1 1.3498333 -7.617792 6.6436667 3.4976439 0.9956632 1.1506667
## FCHI8 -0.8128333 -4.366458 -1.8436667 -3.0561661 -3.1033090 -5.8926667
## FEAR5 -1.1635000 3.584208 -0.9696667 3.0386439 -0.6976701 0.6776667
## FGI4 4.1861667 3.896542 1.2230000 -5.8616895 5.1389965 5.5986667
## FMA7 -1.9938333 4.466542 -0.8180000 0.6226439 3.7433299 -1.5870000
## FSV1 -0.6338333 -1.895458 -2.8660000 -0.4630304 1.0473299 1.1523333
## SnV Tam ViG Yac
## CNCH12 0.1157917 1.7311667 2.940417 -2.8723333
## CNCH13 -2.5775417 -1.1328333 -2.470250 -0.7783333
## FBO1 1.8877917 6.2755000 -2.344250 -1.5280000
## FCHI8 -3.9835417 -0.5015000 -7.860250 -1.1633333
## FEAR5 1.5847917 -5.6048333 2.043417 0.8610000
## FGI4 -1.8375417 1.7105000 2.390083 4.5553333
## FMA7 3.6351250 -1.8648333 8.169417 -0.2880000
## FSV1 1.1751250 -0.6131667 -2.868583 1.2136667
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 0.1021073
##
## $grand_mean
## [1] 43.08816
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 43.05877 41.97607 44.11923 39.82979 43.42357 45.18817 44.49670 42.61300
##
## $mean_env
## CH Gig Htc Jam PtR RiN SnV Tam
## 43.77050 44.25146 41.31433 40.73236 42.23534 44.97033 42.28021 48.14550
## ViG Yac
## 39.38225 43.79933
##
## $scale_val
## CH Gig Htc Jam PtR RiN SnV Tam
## 2.062409 4.405918 2.979587 3.318638 3.514760 3.342962 2.579387 3.424850
## ViG Yac
## 4.882672 2.254917
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración VD variables
##Blup gen
blup_gen <- ranef(modelo_blup)$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 43.09559
## CNCH13 CNCH13 42.45280
## FBO1 FBO1 43.72519
## FCHI8 FCHI8 41.40816
## FEAR5 FEAR5 43.31217
## FGI4 FGI4 44.35981
## FMA7 FMA7 43.94929
## FSV1 FSV1 42.89221
##Plasticidad usando Fisher environments (joint regression)
#índice (creando valores VD x para definir env = promedio VD tasas en c/parcela)
indice_env <- datos %>%
group_by(mun) %>%
summarise(env = mean(VD))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas VD reacción joint regression env
ggplot(datos, aes(x = env, y = VD,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (local)",
y = expression(Fenotipo)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
#visualización Normas VD reacción clima local
ggplot(datos, aes(x = E, y = VD,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (Estrés)",
y = expression(Fenotipo)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
## plasticidad joint
# modelo factores fijos
mod_plas_lm <- lm(VD ~ gen*env,
data=datos)
emtrends(mod_plas_lm, "gen", var = "env")
## gen env.trend SE df lower.CL upper.CL
## CNCH12 1.016 0.247 215 0.530 1.502
## CNCH13 1.089 0.247 215 0.603 1.575
## FBO1 1.267 0.247 215 0.781 1.753
## FCHI8 1.548 0.258 215 1.040 2.056
## FEAR5 0.342 0.247 215 -0.144 0.828
## FGI4 1.531 0.247 215 1.045 2.017
## FMA7 0.170 0.247 215 -0.316 0.656
## FSV1 1.275 0.256 215 0.770 1.780
##
## Confidence level used: 0.95
# modelo blup factores aleatorios
modelo_plasticidad <- lmer(VD ~ env +
(env|gen) +
(1|mun),
data=datos)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+00
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(VD ~ gen*E,
data=datos)
emtrends(mod_plas2_lm, "gen", var = "E")
## gen E.trend SE df lower.CL upper.CL
## CNCH12 30.53 12.6 215 5.62 55.4
## CNCH13 11.99 12.6 215 -12.92 36.9
## FBO1 30.89 12.6 215 5.99 55.8
## FCHI8 4.74 13.4 215 -21.59 31.1
## FEAR5 -7.28 12.6 215 -32.18 17.6
## FGI4 7.41 12.6 215 -17.49 32.3
## FMA7 6.13 12.6 215 -18.77 31.0
## FSV1 -11.10 12.8 215 -36.33 14.1
##
## Confidence level used: 0.95
#Modelo factores aleatorios
modelo_plasticidad2 <- lmer(VD ~ E +
(E|gen) +
(1|mun),
data=datos)
pend2 <- ranef(modelo_plasticidad2)$gen
pend2$gen <- rownames(pend)
plasticidad2 <- pend2[,c("gen","E")]
colnames(plasticidad2)[2] <- "Pendiente2"
##Tabla selección MGIDI 1
tabla_sel <- blup_gen %>%
left_join(plasticidad, by="gen")
mgidi_mod <- mgidi(tabla_sel,
ideotype = c("h, h"))
##
## -------------------------------------------------------------------------------
## Principal Component Analysis
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## PC Eigenvalues `Variance (%)` `Cum. variance (%)`
## <chr> <dbl> <dbl> <dbl>
## 1 PC1 1.65 82.5 82.5
## 2 PC2 0.35 17.5 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C -0.91 0.83 0.17
## 2 Pendiente 0.91 0.83 0.17
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.8251927
## -------------------------------------------------------------------------------
## 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 4.31e+ 1 43.1 -0.0538 -1.25e- 1 increase 0
## 2 Pendiente FA1 -7.93e-15 0.000365 0.000365 4.60e+12 increase 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## CNCH12
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 CNCH12 0.0948
## 2 FSV1 0.289
## 3 FBO1 0.403
## 4 CNCH13 0.503
## 5 FGI4 0.703
## 6 FEAR5 0.855
## 7 FMA7 1.53
## 8 FCHI8 1.75
#SLAáfico Selección 1
plot(mgidi_mod)
##Tabla selección MGIDI 2 estrés
tabla_sel2 <- blup_gen %>%
left_join(plasticidad, by="gen") %>%
left_join(plasticidad2, by="gen")
mgidi_mod2<-mgidi(tabla_sel2,
ideotype = c("h, h, l"))
##
## -------------------------------------------------------------------------------
## Principal Component Analysis
## -------------------------------------------------------------------------------
## # A tibble: 3 × 4
## PC Eigenvalues `Variance (%)` `Cum. variance (%)`
## <chr> <dbl> <dbl> <dbl>
## 1 PC1 1.67 55.7 55.7
## 2 PC2 1.04 34.6 90.3
## 3 PC3 0.29 9.68 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 3 × 5
## VAR FA1 FA2 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 BLUP_C 0.89 -0.25 0.86 0.14
## 2 Pendiente -0.92 -0.15 0.87 0.13
## 3 Pendiente2 -0.04 0.99 0.98 0.02
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.9032499
## -------------------------------------------------------------------------------
## 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 4.31e+ 1 42.9 -0.257 -5.96e- 1 increase 0
## 2 Pendiente FA1 -7.93e-15 0.0215 0.0215 2.71e+14 increase 100
## 3 Pendiente2 FA2 2.90e-12 -9.98 -9.98 -3.45e+14 decrease 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FSV1
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod2$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FSV1 0.550
## 2 CNCH13 0.820
## 3 FEAR5 1.11
## 4 FGI4 1.13
## 5 FMA7 1.60
## 6 FCHI8 1.73
## 7 CNCH12 1.90
## 8 FBO1 2.16
#SLAáfico Selección 1
plot(mgidi_mod2)
