setwd("G:/Mi unidad/Agrosavia/FeCa/Fenoma/Análisis/AguaS")
datos<-read.table("humecta.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(goteo) ~ gen + mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(goteo)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 2.28288 0.32613 16.9444 3.919e-12 ***
## mun 9 0.47085 0.05232 2.7182 0.01001 *
## Residuals 60 1.15481 0.01925
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm (goteo ~ gen + mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: goteo
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 7.0804 1.01148 21.207 4.496e-14 ***
## mun 9 1.2449 0.13832 2.900 0.006477 **
## Residuals 60 2.8618 0.04770
## ---
## 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 1.90 0.0691 60 1.76 2.04
## CNCH13 1.46 0.0691 60 1.32 1.60
## FBO1 2.07 0.0691 60 1.93 2.21
## FCHI8 1.59 0.0784 60 1.43 1.75
## FEAR5 1.42 0.0691 60 1.28 1.56
## FGI4 1.56 0.0691 60 1.42 1.70
## FMA7 2.24 0.0691 60 2.10 2.38
## FSV1 1.40 0.0734 60 1.26 1.55
##
## Results are averaged over the levels of: mun
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.4400 0.0977 60 4.505 0.0008
## CNCH12 - FBO1 -0.1700 0.0977 60 -1.741 0.6613
## CNCH12 - FCHI8 0.3085 0.1040 60 2.953 0.0798
## CNCH12 - FEAR5 0.4800 0.0977 60 4.915 0.0002
## CNCH12 - FGI4 0.3400 0.0977 60 3.481 0.0198
## CNCH12 - FMA7 -0.3400 0.0977 60 -3.481 0.0198
## CNCH12 - FSV1 0.4972 0.1010 60 4.934 0.0002
## CNCH13 - FBO1 -0.6100 0.0977 60 -6.246 <.0001
## CNCH13 - FCHI8 -0.1315 0.1040 60 -1.258 0.9102
## CNCH13 - FEAR5 0.0400 0.0977 60 0.410 0.9999
## CNCH13 - FGI4 -0.1000 0.0977 60 -1.024 0.9689
## CNCH13 - FMA7 -0.7800 0.0977 60 -7.986 <.0001
## CNCH13 - FSV1 0.0572 0.1010 60 0.568 0.9991
## FBO1 - FCHI8 0.4785 0.1040 60 4.580 0.0006
## FBO1 - FEAR5 0.6500 0.0977 60 6.655 <.0001
## FBO1 - FGI4 0.5100 0.0977 60 5.222 0.0001
## FBO1 - FMA7 -0.1700 0.0977 60 -1.741 0.6613
## FBO1 - FSV1 0.6672 0.1010 60 6.620 <.0001
## FCHI8 - FEAR5 0.1715 0.1040 60 1.641 0.7237
## FCHI8 - FGI4 0.0315 0.1040 60 0.301 1.0000
## FCHI8 - FMA7 -0.6485 0.1040 60 -6.208 <.0001
## FCHI8 - FSV1 0.1887 0.1070 60 1.771 0.6417
## FEAR5 - FGI4 -0.1400 0.0977 60 -1.433 0.8381
## FEAR5 - FMA7 -0.8200 0.0977 60 -8.396 <.0001
## FEAR5 - FSV1 0.0172 0.1010 60 0.171 1.0000
## FGI4 - FMA7 -0.6800 0.0977 60 -6.962 <.0001
## FGI4 - FSV1 0.1572 0.1010 60 1.560 0.7715
## FMA7 - FSV1 0.8372 0.1010 60 8.307 <.0001
##
## 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 1.69 0.0772 60 1.53 1.84
## Gig 1.69 0.0772 60 1.53 1.84
## HtC 1.49 0.0772 60 1.33 1.64
## Jam 1.63 0.0905 60 1.45 1.81
## PtR 1.81 0.0832 60 1.65 1.98
## RiN 1.55 0.0772 60 1.40 1.70
## SnV 1.82 0.0772 60 1.67 1.98
## Tam 1.74 0.0772 60 1.58 1.89
## ViG 1.94 0.0772 60 1.78 2.09
## Yac 1.70 0.0772 60 1.55 1.85
##
## Results are averaged over the levels of: gen
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Chi - Gig 0.0000 0.109 60 0.000 1.0000
## Chi - HtC 0.2000 0.109 60 1.832 0.7129
## Chi - Jam 0.0570 0.119 60 0.479 1.0000
## Chi - PtR -0.1248 0.113 60 -1.099 0.9829
## Chi - RiN 0.1375 0.109 60 1.259 0.9587
## Chi - SnV -0.1375 0.109 60 -1.259 0.9587
## Chi - Tam -0.0500 0.109 60 -0.458 1.0000
## Chi - ViG -0.2500 0.109 60 -2.289 0.4108
## Chi - Yac -0.0125 0.109 60 -0.114 1.0000
## Gig - HtC 0.2000 0.109 60 1.832 0.7129
## Gig - Jam 0.0570 0.119 60 0.479 1.0000
## Gig - PtR -0.1248 0.113 60 -1.099 0.9829
## Gig - RiN 0.1375 0.109 60 1.259 0.9587
## Gig - SnV -0.1375 0.109 60 -1.259 0.9587
## Gig - Tam -0.0500 0.109 60 -0.458 1.0000
## Gig - ViG -0.2500 0.109 60 -2.289 0.4108
## Gig - Yac -0.0125 0.109 60 -0.114 1.0000
## HtC - Jam -0.1430 0.119 60 -1.203 0.9691
## HtC - PtR -0.3248 0.113 60 -2.861 0.1399
## HtC - RiN -0.0625 0.109 60 -0.572 0.9999
## HtC - SnV -0.3375 0.109 60 -3.091 0.0819
## HtC - Tam -0.2500 0.109 60 -2.289 0.4108
## HtC - ViG -0.4500 0.109 60 -4.121 0.0043
## HtC - Yac -0.2125 0.109 60 -1.946 0.6386
## Jam - PtR -0.1817 0.122 60 -1.489 0.8911
## Jam - RiN 0.0805 0.119 60 0.677 0.9996
## Jam - SnV -0.1945 0.119 60 -1.635 0.8255
## Jam - Tam -0.1070 0.119 60 -0.899 0.9959
## Jam - ViG -0.3070 0.119 60 -2.581 0.2493
## Jam - Yac -0.0695 0.119 60 -0.584 0.9999
## PtR - RiN 0.2623 0.113 60 2.311 0.3975
## PtR - SnV -0.0127 0.113 60 -0.112 1.0000
## PtR - Tam 0.0748 0.113 60 0.659 0.9996
## PtR - ViG -0.1252 0.113 60 -1.103 0.9825
## PtR - Yac 0.1123 0.113 60 0.989 0.9918
## RiN - SnV -0.2750 0.109 60 -2.518 0.2801
## RiN - Tam -0.1875 0.109 60 -1.717 0.7815
## RiN - ViG -0.3875 0.109 60 -3.549 0.0244
## RiN - Yac -0.1500 0.109 60 -1.374 0.9305
## SnV - Tam 0.0875 0.109 60 0.801 0.9983
## SnV - ViG -0.1125 0.109 60 -1.030 0.9891
## SnV - Yac 0.1250 0.109 60 1.145 0.9776
## Tam - ViG -0.2000 0.109 60 -1.832 0.7129
## Tam - Yac 0.0375 0.109 60 0.343 1.0000
## ViG - Yac 0.2375 0.109 60 2.175 0.4846
##
## 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(goteo) ~ 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 2.2835 0.32621 7 60.431 16.976 3.487e-12 ***
## ---
## 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(goteo) ~ gen + (1 | mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 10 24.918 -29.836
## (1 | mun) 9 22.360 -26.720 5.116 1 0.02371 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(goteo ~ 1 +
(1|gen) +
(1|mun),
data = datos)
ranova(modelo_blup)
## boundary (singular) fit: see help('isSingular')
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## goteo ~ (1 | gen) + (1 | mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 4 -9.709 27.418
## (1 | gen) 3 -37.204 80.408 54.990 1 1.211e-13 ***
## (1 | mun) 3 -12.628 31.256 5.838 1 0.01568 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CNCH12 0.1848321
## CNCH13 -0.2348607
## FBO1 0.3469861
## FCHI8 -0.1090417
## FEAR5 -0.2730145
## FGI4 -0.1394759
## FMA7 0.5091402
## FSV1 -0.2845656
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 1.891057
## CNCH13 1.471364
## FBO1 2.053211
## FCHI8 1.597183
## FEAR5 1.433210
## FGI4 1.566749
## FMA7 2.215365
## FSV1 1.421659
#Blups Parcela
blups$mun
## (Intercept)
## Chi -0.01240165
## Gig -0.01240165
## HtC -0.14486525
## Jam -0.04275860
## PtR 0.06746099
## RiN -0.10347037
## SnV 0.07866708
## Tam 0.02071426
## ViG 0.15317786
## Yac -0.00412267
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## Chi 1.693823
## Gig 1.693823
## HtC 1.561359
## Jam 1.663466
## PtR 1.773686
## RiN 1.602754
## SnV 1.784892
## Tam 1.726939
## ViG 1.859402
## Yac 1.702102
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 0.1848321
## 2 CNCH13 -0.2348607
## 3 FBO1 0.3469861
## 4 FCHI8 -0.1090417
## 5 FEAR5 -0.2730145
## 6 FGI4 -0.1394759
## 7 FMA7 0.5091402
## 8 FSV1 -0.2845656
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 Chi -0.01240165
## 2 Gig -0.01240165
## 3 HtC -0.14486525
## 4 Jam -0.04275860
## 5 PtR 0.06746099
## 6 RiN -0.10347037
## 7 SnV 0.07866708
## 8 Tam 0.02071426
## 9 ViG 0.15317786
## 10 Yac -0.00412267
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 1.787586 1.367894 1.949740 1.493713 1.329740 1.463278 2.111894 1.318189
## [9] 1.969724 1.550031 2.131878 1.675850 1.511877 1.645416 2.294032 1.500326
## [17] 1.878655 1.458962 2.040809 1.584781 1.420808 1.554347 2.202963 1.409257
## [25] 1.878655 1.458962 2.040809 1.584781 1.420808 1.554347 2.202963 1.409257
## [33] 1.746191 1.326499 1.908345 1.452318 1.288345 1.421883 2.070500 1.276794
## [41] 1.848298 1.428605 2.010452 1.390451 1.523990 2.172606 1.958518 1.538825
## [49] 2.120672 1.500671 1.634210 2.282826 1.489120 1.911771 1.492078 2.073925
## [57] 1.617897 1.453924 1.587463 2.236079 1.442373 2.044235 1.624542 2.206389
## [65] 1.750361 1.586388 1.719927 2.368543 1.574837 1.886934 1.467241 2.049088
## [73] 1.593060 1.429087 1.562626 2.211242 1.417536
#Visualizar Blups gen
ggplot(blup_gen, aes(x=reorder(gen, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Genotipo") +
coord_flip()
#Visualizar Blups mun
ggplot(blup_mun, aes(x=reorder(mun, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Municipio") +
coord_flip()
##Componentes de varianza-heredabilidades
vc <- as.data.frame(VarCorr(modelo_blup))
vc
## grp var1 var2 vcov sdcor
## 1 mun (Intercept) <NA> 0.01167779 0.1080638
## 2 gen (Intercept) <NA> 0.09844025 0.3137519
## 3 Residual <NA> <NA> 0.04763125 0.2182458
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## mun (Intercept) 0.10806
## gen (Intercept) 0.31375
## Residual 0.21825
varG <- vc$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 0.1848321
## CNCH13 -0.2348607
## FBO1 0.3469861
## FCHI8 -0.1090417
## FEAR5 -0.2730145
## FGI4 -0.1394759
## FMA7 0.5091402
## FSV1 -0.2845656
##Predicho de carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## FMA7 0.5091402 2.215365
## FBO1 0.3469861 2.053211
## CNCH12 0.1848321 1.891057
## FCHI8 -0.1090417 1.597183
## FGI4 -0.1394759 1.566749
## CNCH13 -0.2348607 1.471364
## FEAR5 -0.2730145 1.433210
## FSV1 -0.2845656 1.421659
# 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, goteo)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $goteo
## $coordgen
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.1600946 -0.139968429 -0.26436698 -0.3480480 -0.34740840 -0.41131295
## [2,] -0.1685562 0.468050894 0.08717483 0.2611966 0.14561381 -0.40775963
## [3,] 0.3677286 0.274203612 -0.38705936 0.1623613 -0.02678087 0.37940894
## [4,] -0.1703866 -0.510973144 -0.12805152 0.3513171 0.13547495 0.02478650
## [5,] -0.2690118 -0.001775904 -0.20482893 -0.3550078 0.44608955 0.07374474
## [6,] -0.1209513 0.085420755 0.37625561 -0.3191126 -0.11376313 0.31643899
## [7,] 0.4775974 -0.165915404 0.41369086 0.0453296 0.20313649 -0.09711778
## [8,] -0.2765146 -0.009042380 0.10718549 0.2019638 -0.44236240 0.12181119
## [,7] [,8]
## [1,] 0.0866149 0.2761009
## [2,] 0.1511442 0.2761009
## [3,] 0.0486352 0.2761009
## [4,] 0.2910463 0.2761009
## [5,] -0.2980465 0.2761009
## [6,] 0.3939785 0.2761009
## [7,] -0.2326015 0.2761009
## [8,] -0.4407711 0.2761009
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.9910139 0.100032482 0.07859718 0.03747377 -0.03888360 0.085453539
## [2,] 0.3936742 -0.869527184 0.29889434 -0.09911056 0.13555680 0.012304573
## [3,] 0.4582379 -0.913333991 -0.24242485 0.01759385 -0.15759050 -0.022661772
## [4,] 0.9484455 -0.058707163 0.01590355 0.10125521 -0.07223763 0.046765267
## [5,] 0.7285261 0.059065890 -0.49152908 -0.10884331 0.10640195 0.024601143
## [6,] 0.8984904 0.200593849 0.05456680 0.04020143 -0.04560041 -0.046603715
## [7,] 0.9627424 0.001173823 -0.06502177 0.08586776 0.09830779 -0.059758362
## [8,] 0.9679910 -0.003306643 0.03553041 0.16210257 0.11216080 -0.003926765
## [9,] 1.1477861 0.280718794 0.06648557 -0.19377924 -0.02212265 -0.007934306
## [10,] 0.9491722 0.183301375 0.17004882 -0.08028852 -0.09321000 -0.030667962
## [,7] [,8]
## [1,] -0.015459091 6.340096e-18
## [2,] -0.002303847 -2.463758e-17
## [3,] -0.012702111 2.227592e-17
## [4,] 0.049569175 -4.369481e-17
## [5,] -0.006025623 -1.011005e-17
## [6,] -0.060266400 -7.898814e-17
## [7,] 0.057351144 -1.232763e-17
## [8,] -0.039966981 6.650929e-17
## [9,] -0.003291780 1.219777e-17
## [10,] 0.021939363 4.896221e-17
##
## $eigenvalues
## [1] 2.768912e+00 1.326594e+00 6.617731e-01 3.360052e-01 3.078366e-01
## [6] 1.323682e-01 1.091319e-01 1.285128e-16
##
## $totalvar
## [1] 10.1
##
## $varexpl
## [1] 75.91 17.42 4.34 1.12 0.94 0.17 0.12 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 SnV Tam
## CNCH12 0.1125 0.1125 0.4125 0.1674492 0.29271064 0.15 0.175 0.0625
## CNCH13 -0.1875 -0.5875 -0.6875 -0.2325508 -0.20728936 -0.05 -0.125 -0.1375
## FBO1 0.5125 -0.2875 0.0125 0.4674492 0.59271064 0.45 0.475 0.4625
## FCHI8 -0.2875 0.4125 0.5125 -0.1181522 -0.14897448 -0.35 -0.125 -0.1375
## FEAR5 -0.3875 -0.0875 -0.1875 -0.4325508 -0.00728936 -0.35 -0.325 -0.3375
## FGI4 -0.0875 0.0125 -0.2875 -0.1325508 -0.30728936 -0.15 -0.225 -0.2375
## FMA7 0.6125 0.6125 0.3125 0.5674492 0.19271064 0.55 0.575 0.6625
## FSV1 -0.2875 -0.1875 -0.0875 -0.2865432 -0.40728936 -0.25 -0.425 -0.3375
## ViG Yac
## CNCH12 0.2625 0.2
## CNCH13 -0.1375 -0.1
## FBO1 0.5625 0.4
## FCHI8 -0.5375 -0.4
## FEAR5 -0.3375 -0.4
## FGI4 -0.0375 0.0
## FMA7 0.6625 0.6
## FSV1 -0.4375 -0.3
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 1.280523
##
## $grand_mean
## [1] 1.705234
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 1.900000 1.460000 2.070000 1.587271 1.420000 1.560000 2.240000 1.404601
##
## $mean_env
## Chi Gig HtC Jam PtR RiN SnV Tam
## 1.687500 1.687500 1.487500 1.632551 1.807289 1.550000 1.825000 1.737500
## ViG Yac
## 1.937500 1.700000
##
## $scale_val
## Chi Gig HtC Jam PtR RiN SnV Tam
## 0.3796145 0.3833592 0.4015595 0.3631929 0.3379877 0.3505098 0.3693624 0.3739270
## ViG Yac
## 0.4533605 0.3741657
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración de variables
##Blup gen
blup_gen <- ranef(modelo_blup)$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 1.891057
## CNCH13 CNCH13 1.471364
## FBO1 FBO1 2.053211
## FCHI8 FCHI8 1.597183
## FEAR5 FEAR5 1.433210
## FGI4 FGI4 1.566749
## FMA7 FMA7 2.215365
## FSV1 FSV1 1.421659
##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(goteo))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas de reacción joint regression env
ggplot(datos, aes(x = env, y = goteo,
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 = goteo,
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(goteo ~ 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
## FCHI8 -0.450 0.509 61 -1.4674 0.568 A
## FSV1 0.231 0.485 61 -0.7388 1.200 AB
## CNCH12 0.886 0.485 61 -0.0826 1.855 AB
## FEAR5 1.017 0.485 61 0.0482 1.986 AB
## FGI4 1.116 0.485 61 0.1471 2.085 AB
## FMA7 1.242 0.485 61 0.2728 2.211 AB
## CNCH13 1.746 0.485 61 0.7769 2.715 AB
## FBO1 2.030 0.485 61 1.0610 2.999 B
##
## 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(goteo ~ 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(goteo ~ 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
## FBO1 -2.6401 1.31 61 -5.2511 -0.0291 A
## CNCH13 -2.4119 1.31 61 -5.0229 0.1991 A
## FGI4 -1.1190 1.31 61 -3.7301 1.4920 A
## FMA7 -0.5074 1.31 61 -3.1184 2.1036 A
## CNCH12 -0.4157 1.31 61 -3.0268 2.1953 A
## FEAR5 -0.0352 1.31 61 -2.6462 2.5759 A
## FSV1 0.1592 1.32 61 -2.4866 2.8050 A
## FCHI8 2.6921 1.38 61 -0.0687 5.4528 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.