setwd("G:/Mi unidad/Agrosavia/FeCa/Fenoma/Análisis/AguaT")
datos<-read.table("conductance.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(cond) ~ gen + mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(cond)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 0.2957 0.04225 0.5925 0.7595
## mun 9 4.5029 0.50032 7.0165 7.541e-07 ***
## Residuals 60 4.2784 0.07131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm (cond ~ gen + mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: cond
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 34.43 4.919 0.6519 0.7113
## mun 9 338.26 37.584 4.9807 5.249e-05 ***
## Residuals 60 452.76 7.546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##Contrastes a posteriori
#Genotipos
g<-emmeans(modelo, pairwise ~ gen)
g
## $emmeans
## gen emmean SE df lower.CL upper.CL
## CNCH12 10.84 0.869 60 9.11 12.6
## CNCH13 8.78 0.869 60 7.04 10.5
## FBO1 9.31 0.869 60 7.57 11.0
## FCHI8 9.00 0.986 60 7.02 11.0
## FEAR5 8.97 0.869 60 7.23 10.7
## FGI4 9.63 0.869 60 7.89 11.4
## FMA7 8.91 0.869 60 7.18 10.7
## FSV1 9.06 0.923 60 7.21 10.9
##
## Results are averaged over the levels of: mun
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 2.0676 1.23 60 1.683 0.6978
## CNCH12 - FBO1 1.5363 1.23 60 1.251 0.9129
## CNCH12 - FCHI8 1.8472 1.31 60 1.406 0.8511
## CNCH12 - FEAR5 1.8783 1.23 60 1.529 0.7888
## CNCH12 - FGI4 1.2176 1.23 60 0.991 0.9740
## CNCH12 - FMA7 1.9301 1.23 60 1.571 0.7652
## CNCH12 - FSV1 1.7879 1.27 60 1.410 0.8489
## CNCH13 - FBO1 -0.5313 1.23 60 -0.432 0.9999
## CNCH13 - FCHI8 -0.2205 1.31 60 -0.168 1.0000
## CNCH13 - FEAR5 -0.1893 1.23 60 -0.154 1.0000
## CNCH13 - FGI4 -0.8500 1.23 60 -0.692 0.9969
## CNCH13 - FMA7 -0.1375 1.23 60 -0.112 1.0000
## CNCH13 - FSV1 -0.2797 1.27 60 -0.221 1.0000
## FBO1 - FCHI8 0.3109 1.31 60 0.237 1.0000
## FBO1 - FEAR5 0.3420 1.23 60 0.278 1.0000
## FBO1 - FGI4 -0.3187 1.23 60 -0.259 1.0000
## FBO1 - FMA7 0.3938 1.23 60 0.321 1.0000
## FBO1 - FSV1 0.2516 1.27 60 0.198 1.0000
## FCHI8 - FEAR5 0.0312 1.31 60 0.024 1.0000
## FCHI8 - FGI4 -0.6296 1.31 60 -0.479 0.9997
## FCHI8 - FMA7 0.0830 1.31 60 0.063 1.0000
## FCHI8 - FSV1 -0.0593 1.34 60 -0.044 1.0000
## FEAR5 - FGI4 -0.6608 1.23 60 -0.538 0.9994
## FEAR5 - FMA7 0.0518 1.23 60 0.042 1.0000
## FEAR5 - FSV1 -0.0905 1.27 60 -0.071 1.0000
## FGI4 - FMA7 0.7125 1.23 60 0.580 0.9990
## FGI4 - FSV1 0.5703 1.27 60 0.450 0.9998
## FMA7 - FSV1 -0.1422 1.27 60 -0.112 1.0000
##
## Results are averaged over the levels of: mun
## P value adjustment: tukey method for comparing a family of 8 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.
#Municipios
m<-emmeans(modelo, pairwise ~ mun)
m
## $emmeans
## mun emmean SE df lower.CL upper.CL
## Chi 7.89 0.971 60 5.95 9.84
## Gig 11.77 0.971 60 9.83 13.71
## HtC 6.27 0.971 60 4.33 8.21
## Jam 12.77 1.140 60 10.50 15.05
## PtR 5.71 1.050 60 3.61 7.80
## RiN 10.20 0.971 60 8.26 12.15
## SnV 8.27 0.971 60 6.33 10.21
## Tam 9.18 0.971 60 7.24 11.13
## ViG 10.48 0.971 60 8.54 12.43
## Yac 10.55 0.971 60 8.60 12.49
##
## Results are averaged over the levels of: gen
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Chi - Gig -3.878 1.37 60 -2.823 0.1521
## Chi - HtC 1.622 1.37 60 1.181 0.9726
## Chi - Jam -4.881 1.50 60 -3.263 0.0531
## Chi - PtR 2.184 1.43 60 1.530 0.8745
## Chi - RiN -2.310 1.37 60 -1.682 0.8009
## Chi - SnV -0.378 1.37 60 -0.275 1.0000
## Chi - Tam -1.292 1.37 60 -0.941 0.9943
## Chi - ViG -2.591 1.37 60 -1.886 0.6778
## Chi - Yac -2.655 1.37 60 -1.933 0.6472
## Gig - HtC 5.500 1.37 60 4.004 0.0062
## Gig - Jam -1.003 1.50 60 -0.670 0.9996
## Gig - PtR 6.062 1.43 60 4.247 0.0029
## Gig - RiN 1.568 1.37 60 1.142 0.9780
## Gig - SnV 3.500 1.37 60 2.548 0.2652
## Gig - Tam 2.586 1.37 60 1.883 0.6801
## Gig - ViG 1.287 1.37 60 0.937 0.9945
## Gig - Yac 1.223 1.37 60 0.890 0.9962
## HtC - Jam -6.502 1.50 60 -4.347 0.0021
## HtC - PtR 0.563 1.43 60 0.394 1.0000
## HtC - RiN -3.932 1.37 60 -2.863 0.1395
## HtC - SnV -2.000 1.37 60 -1.456 0.9036
## HtC - Tam -2.914 1.37 60 -2.121 0.5205
## HtC - ViG -4.213 1.37 60 -3.067 0.0868
## HtC - Yac -4.277 1.37 60 -3.114 0.0774
## Jam - PtR 7.065 1.53 60 4.603 0.0009
## Jam - RiN 2.571 1.50 60 1.718 0.7807
## Jam - SnV 4.503 1.50 60 3.010 0.0995
## Jam - Tam 3.589 1.50 60 2.399 0.3448
## Jam - ViG 2.290 1.50 60 1.531 0.8743
## Jam - Yac 2.226 1.50 60 1.488 0.8916
## PtR - RiN -4.495 1.43 60 -3.148 0.0711
## PtR - SnV -2.563 1.43 60 -1.795 0.7356
## PtR - Tam -3.476 1.43 60 -2.435 0.3245
## PtR - ViG -4.775 1.43 60 -3.345 0.0427
## PtR - Yac -4.839 1.43 60 -3.390 0.0378
## RiN - SnV 1.932 1.37 60 1.407 0.9204
## RiN - Tam 1.018 1.37 60 0.741 0.9991
## RiN - ViG -0.281 1.37 60 -0.204 1.0000
## RiN - Yac -0.345 1.37 60 -0.251 1.0000
## SnV - Tam -0.914 1.37 60 -0.665 0.9996
## SnV - ViG -2.213 1.37 60 -1.611 0.8375
## SnV - Yac -2.277 1.37 60 -1.658 0.8139
## Tam - ViG -1.299 1.37 60 -0.946 0.9941
## Tam - Yac -1.363 1.37 60 -0.992 0.9916
## ViG - Yac -0.064 1.37 60 -0.047 1.0000
##
## Results are averaged over the levels of: gen
## P value adjustment: tukey method for comparing a family of 10 estimates
pwpp(m, type = "response")
# Modelo 1
modelo <- lmer(log(cond) ~ gen +
(1|mun),
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 0.2815 0.040214 7 59.936 0.5632 0.7827
ranova(modelo)
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## log(cond) ~ gen + (1 | mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 10 -24.843 69.686
## (1 | mun) 9 -35.831 89.663 21.977 1 2.759e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(cond ~ 1 +
(1|gen) +
(1|mun),
data = datos)
## boundary (singular) fit: see help('isSingular')
ranova(modelo_blup)
## boundary (singular) fit: see help('isSingular')
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## cond ~ (1 | gen) + (1 | mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 4 -192.85 393.7
## (1 | gen) 3 -192.85 391.7 0.000 1 1
## (1 | mun) 3 -200.65 407.3 15.597 1 7.837e-05 ***
## ---
## 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
## CNCH13 0
## FBO1 0
## FCHI8 0
## FEAR5 0
## FGI4 0
## FMA7 0
## FSV1 0
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 9.313144
## CNCH13 9.313144
## FBO1 9.313144
## FCHI8 9.313144
## FEAR5 9.313144
## FGI4 9.313144
## FMA7 9.313144
## FSV1 9.313144
#Blups Parcela
blups$mun
## (Intercept)
## Chi -1.1680593
## Gig 2.0201658
## HtC -2.5012992
## Jam 2.7589577
## PtR -2.8545089
## RiN 0.7311893
## SnV -0.8571742
## Tam -0.1059897
## ViG 0.9620463
## Yac 1.0146722
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## Chi 8.145085
## Gig 11.333310
## HtC 6.811845
## Jam 12.072102
## PtR 6.458636
## RiN 10.044334
## SnV 8.455970
## Tam 9.207155
## ViG 10.275191
## Yac 10.327817
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 0
## 2 CNCH13 0
## 3 FBO1 0
## 4 FCHI8 0
## 5 FEAR5 0
## 6 FGI4 0
## 7 FMA7 0
## 8 FSV1 0
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 Chi -1.1680593
## 2 Gig 2.0201658
## 3 HtC -2.5012992
## 4 Jam 2.7589577
## 5 PtR -2.8545089
## 6 RiN 0.7311893
## 7 SnV -0.8571742
## 8 Tam -0.1059897
## 9 ViG 0.9620463
## 10 Yac 1.0146722
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 10.044334 10.044334 10.044334 10.044334 10.044334 10.044334 10.044334
## [8] 10.044334 8.455970 8.455970 8.455970 8.455970 8.455970 8.455970
## [15] 8.455970 8.455970 8.145085 8.145085 8.145085 8.145085 8.145085
## [22] 8.145085 8.145085 8.145085 10.327817 10.327817 10.327817 10.327817
## [29] 10.327817 10.327817 10.327817 10.327817 6.458636 6.458636 6.458636
## [36] 6.458636 6.458636 6.458636 6.458636 10.275191 10.275191 10.275191
## [43] 10.275191 10.275191 10.275191 10.275191 10.275191 11.333310 11.333310
## [50] 11.333310 11.333310 11.333310 11.333310 11.333310 11.333310 9.207155
## [57] 9.207155 9.207155 9.207155 9.207155 9.207155 9.207155 9.207155
## [64] 6.811845 6.811845 6.811845 6.811845 6.811845 6.811845 6.811845
## [71] 6.811845 12.072102 12.072102 12.072102 12.072102 12.072102 12.072102
#Visualizar Blups gen
ggplot(blup_gen, aes(x=reorder(gen, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Genotipo") +
coord_flip()
#Visualizar Blups mun
ggplot(blup_mun, aes(x=reorder(mun, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Municipio") +
coord_flip()
##Componentes de varianza-heredabilidades
vc <- as.data.frame(VarCorr(modelo_blup))
vc
## grp var1 var2 vcov sdcor
## 1 mun (Intercept) <NA> 4.182716 2.045169
## 2 gen (Intercept) <NA> 0.000000 0.000000
## 3 Residual <NA> <NA> 7.239472 2.690627
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## mun (Intercept) 2.0452
## gen (Intercept) 0.0000
## Residual 2.6906
varG <- vc$vcov[vc$grp=="gen"]
varGE <- vc$vcov[vc$grp=="gen:mun"]
varE <- vc$vcov[vc$grp=="Residual"]
e <- 10
r <- 4
# Heredabilidad genotipos y plasticidad
#H2 genotipos
H2g <- varG / (varG + varGE/e + varE/(r*e))
H2g
## numeric(0)
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## numeric(0)
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CNCH12 0
## CNCH13 0
## FBO1 0
## FCHI8 0
## FEAR5 0
## FGI4 0
## FMA7 0
## FSV1 0
##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
## CNCH12 0 9.313144
## CNCH13 0 9.313144
## FBO1 0 9.313144
## FCHI8 0 9.313144
## FEAR5 0 9.313144
## FGI4 0 9.313144
## FMA7 0 9.313144
## FSV1 0 9.313144
# 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")
### Estabilidad con Metan
modelo_metan <- gge(datos, mun, gen, cond)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $cond
## $coordgen
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 10.38908118 -0.7389941 -2.0074417 -2.792865 1.68282321 -0.2925090
## [2,] -4.37254986 4.9426069 -2.4974907 -3.544300 7.01544942 -0.7929734
## [3,] -0.02006140 2.6768724 -5.8671302 7.413993 -0.08252507 2.0626864
## [4,] -2.34149842 -4.7937613 -3.4896284 1.898260 -4.58287016 -4.4554781
## [5,] -3.00057154 -6.7526492 -0.5200041 -4.927063 1.09129918 0.6631124
## [6,] 0.35780775 -2.2257214 8.0578754 5.629876 4.50554037 -1.5454332
## [7,] -0.08798033 6.0058857 3.6443505 -2.495015 -5.71322482 -5.0782426
## [8,] -0.92422738 0.8857609 2.6794691 -1.182885 -3.91649212 9.4388374
## [,7] [,8]
## [1,] -1.219032 -4.220909
## [2,] -3.538237 -4.220909
## [3,] 4.888199 -4.220909
## [4,] -6.294036 -4.220909
## [5,] 6.628260 -4.220909
## [6,] -0.563524 -4.220909
## [7,] 3.270819 -4.220909
## [8,] -3.172449 -4.220909
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -1.7157433 1.80769688 -2.4319020 1.8226496 -0.8026857 1.5303568
## [2,] -2.9471917 0.06941256 -1.4462563 -1.4888889 0.7918939 -1.2338486
## [3,] 0.3068460 -1.18437870 -0.4726929 -1.5365984 -0.8682945 -0.1519053
## [4,] 0.2132527 -0.75594815 3.6458920 4.7319444 1.2473757 -0.2827476
## [5,] 4.9952439 -0.29399495 -2.3039564 0.7150383 -0.3027016 -0.3195777
## [6,] 6.4466424 5.23370255 4.0089425 -2.3134019 -0.4501665 0.2108247
## [7,] -2.2666267 1.55893234 -0.7884329 -1.1924491 3.9717271 0.4782881
## [8,] 0.4356654 1.15914238 -0.9456617 1.9679385 -0.6196985 -0.7687876
## [9,] 13.1957563 -2.80512647 -1.6112098 0.2788092 1.0916076 0.1160521
## [10,] -1.5420674 -7.52483231 2.2805230 -1.4941910 -0.1550086 0.5050909
## [,7] [,8]
## [1,] -0.02242043 -1.024598e-15
## [2,] 0.19974121 -1.414666e-15
## [3,] -0.37853845 -1.449546e-15
## [4,] 0.11433473 -8.617825e-16
## [5,] 0.56621974 7.107035e-16
## [6,] -0.01164920 -2.119487e-16
## [7,] -0.19589509 3.294356e-16
## [8,] -0.77156288 7.781680e-16
## [9,] -0.17740514 -5.060902e-16
## [10,] -0.07897742 2.571379e-16
##
## $eigenvalues
## [1] 1.612806e+01 1.004941e+01 7.225946e+00 6.615092e+00 4.609755e+00
## [6] 2.280866e+00 1.090366e+00 2.735192e-15
##
## $totalvar
## [1] 484.72
##
## $varexpl
## [1] 53.66 20.83 10.77 9.03 4.38 1.07 0.25 0.00
##
## $labelgen
## [1] "CNCH12" "CNCH13" "FBO1" "FCHI8" "FEAR5" "FGI4" "FMA7" "FSV1"
##
## $labelenv
## [1] "Chi" "Gig" "HtC" "Jam" "PtR" "RiN" "SnV" "Tam" "ViG" "Yac"
##
## $labelaxes
## [1] "PC1" "PC2" "PC3" "PC4" "PC5" "PC6" "PC7" "PC8"
##
## $ge_mat
## Chi Gig HtC Jam PtR RiN
## CNCH12 -1.7707766 -1.8560343 0.6992660 -1.3165785 4.4926077 5.0856576
## CNCH13 0.7777453 2.3408293 -0.4356118 -1.8406996 -2.0060122 -0.6212754
## FBO1 2.9960196 -0.3302184 -1.1632595 0.9663295 1.6807162 -2.2093865
## FCHI8 0.3601186 0.7873459 0.8988068 -0.4851731 -0.1375737 -4.8052824
## FEAR5 -1.2383448 1.4936840 0.9495594 -1.5759124 -1.0149919 -3.8363200
## FGI4 -1.6703031 -1.3303853 -1.1038147 5.3415160 -1.1129303 0.6357393
## FMA7 -0.4742124 0.1269122 -0.0448144 -0.7031829 -0.6015262 4.4151974
## FSV1 1.0197534 -1.2321334 0.1998683 -0.3862989 -1.3002898 1.3356700
## SnV Tam ViG Yac
## CNCH12 -1.089288967 0.01627761 12.0316095 -0.9362220
## CNCH13 4.354716476 -0.15050677 -5.0537315 -2.6852360
## FBO1 -0.024728281 1.50157119 0.2536893 -3.6772979
## FCHI8 -1.740416922 0.97000713 -1.3152476 2.3324058
## FEAR5 0.495250883 -2.06383146 -1.7670772 5.1311168
## FGI4 -0.006801001 -0.01122349 0.3677680 2.0711086
## FMA7 -1.348317009 0.29215581 -2.6788784 -2.9280187
## FSV1 -0.640415182 -0.55445001 -1.8381321 0.6921435
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 0.08376237
##
## $grand_mean
## [1] 9.307729
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 10.843381 8.775751 9.307072 8.994228 8.965042 9.625796 8.913260 9.037300
##
## $mean_env
## Chi Gig HtC Jam PtR RiN SnV Tam
## 7.892375 11.770375 6.270687 12.722727 5.733182 10.202527 8.270520 9.184224
## ViG Yac
## 10.483330 10.547342
##
## $scale_val
## Chi Gig HtC Jam PtR RiN SnV Tam
## 1.6228080 1.4730231 0.8453049 2.3283877 2.1170313 3.5980455 1.9143510 1.0603802
## ViG Yac
## 5.1534593 3.0872976
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración de variables
##Blup gen
blup_gen <- ranef(modelo_blup)$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 9.313144
## CNCH13 CNCH13 9.313144
## FBO1 FBO1 9.313144
## FCHI8 FCHI8 9.313144
## FEAR5 FEAR5 9.313144
## FGI4 FGI4 9.313144
## FMA7 FMA7 9.313144
## FSV1 FSV1 9.313144
##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(cond))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas de reacción joint regression env
ggplot(datos, aes(x = env, y = cond,
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 de reacción clima local
ggplot(datos, aes(x = E, y = cond,
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(cond ~ gen*env,
data=datos)
y=emtrends(mod_plas_lm, "gen", var = "env")
multcomp::cld(y, Letters = LETTERS)
## gen env.trend SE df lower.CL upper.CL .group
## FBO1 0.770 0.386 61 -0.00128 1.54 A
## CNCH13 0.829 0.386 61 0.05737 1.60 A
## CNCH12 0.855 0.386 61 0.08340 1.63 A
## FCHI8 0.886 0.565 61 -0.24399 2.02 A
## FMA7 0.977 0.386 61 0.20555 1.75 A
## FSV1 0.981 0.459 61 0.06226 1.90 A
## FEAR5 1.016 0.386 61 0.24434 1.79 A
## FGI4 1.601 0.386 61 0.82953 2.37 A
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 8 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.
# modelo blup factores aleatorios
modelo_plasticidad <- lmer(cond ~ 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(cond ~ gen*E,
data=datos)
z<-emtrends(mod_plas2_lm, "gen", var = "E")
multcomp::cld(z, Letters = LETTERS)
## gen E.trend SE df lower.CL upper.CL .group
## FEAR5 -17.514 19.6 61 -56.7 21.6 A
## FGI4 -13.897 19.6 61 -53.1 25.3 A
## FCHI8 -5.824 20.7 61 -47.2 35.6 A
## FSV1 -3.776 19.8 61 -43.5 35.9 A
## CNCH13 -0.111 19.6 61 -39.3 39.0 A
## FBO1 1.044 19.6 61 -38.1 40.2 A
## CNCH12 7.520 19.6 61 -31.6 46.7 A
## FMA7 8.840 19.6 61 -30.3 48.0 A
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 8 estimates
## significance level used: alpha = 0.05
## NOTE: If two or more means share the same grouping symbol,
## then we cannot show them to be different.
## But we also did not show them to be the same.