setwd("G:/Mi unidad/Agrosavia/FeCa/Fenoma/Análisis/Carbono")
datos<-read.table("carbonf2.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(rgl)
## Warning: package 'rgl' was built under R version 4.4.3
library(tkrplot)
## Warning: package 'tkrplot' was built under R version 4.4.3
## Cargando paquete requerido: tcltk
library(GGEBiplots)
## Warning: package 'GGEBiplots' 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(total_alt_co2) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: log(total_alt_co2)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 5.767 0.8239 3.5647 0.001163 **
## mun 9 76.338 8.4820 36.7007 < 2.2e-16 ***
## gen:mun 60 18.987 0.3165 1.3693 0.052954 .
## Residuals 231 53.387 0.2311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Modelo bruto
modelo <- lm ((total_alt_co2) ~ gen * mun,
data = datos)
anova(modelo)
## Analysis of Variance Table
##
## Response: (total_alt_co2)
## Df Sum Sq Mean Sq F value Pr(>F)
## gen 7 243.1 34.72 2.4964 0.01723 *
## mun 9 6505.9 722.88 51.9710 < 2e-16 ***
## gen:mun 60 1017.1 16.95 1.2187 0.15344
## Residuals 231 3213.0 13.91
## ---
## 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 7.70 0.59 231 6.53 8.86
## CNCH13 9.06 0.59 231 7.90 10.22
## FBO1 7.83 0.59 231 6.67 8.99
## FCHI8 nonEst NA NA NA NA
## FEAR5 9.59 0.59 231 8.43 10.76
## FGI4 8.29 0.59 231 7.13 9.45
## FMA7 6.92 0.59 231 5.76 8.08
## 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 -1.365 0.834 231 -1.637 0.5750
## CNCH12 - FBO1 -0.133 0.834 231 -0.159 1.0000
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -1.898 0.834 231 -2.276 0.2080
## CNCH12 - FGI4 -0.595 0.834 231 -0.713 0.9802
## CNCH12 - FMA7 0.779 0.834 231 0.934 0.9374
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 1.232 0.834 231 1.477 0.6791
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -0.533 0.834 231 -0.640 0.9879
## CNCH13 - FGI4 0.770 0.834 231 0.923 0.9402
## CNCH13 - FMA7 2.144 0.834 231 2.571 0.1088
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -1.765 0.834 231 -2.117 0.2822
## FBO1 - FGI4 -0.462 0.834 231 -0.554 0.9938
## FBO1 - FMA7 0.912 0.834 231 1.093 0.8837
## 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 1.303 0.834 231 1.563 0.6238
## FEAR5 - FMA7 2.677 0.834 231 3.210 0.0187
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 1.374 0.834 231 1.647 0.5682
## 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
multcomp::cld(g, Letters = LETTERS)
## gen emmean SE df lower.CL upper.CL .group
## FMA7 6.92 0.59 231 5.76 8.08 A
## CNCH12 7.70 0.59 231 6.53 8.86 AB
## FBO1 7.83 0.59 231 6.67 8.99 AB
## FGI4 8.29 0.59 231 7.13 9.45 AB
## CNCH13 9.06 0.59 231 7.90 10.22 AB
## FEAR5 9.59 0.59 231 8.43 10.76 B
## FCHI8 nonEst NA NA NA NA
## 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
## 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
## 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.
pwpp(g, type = "response")
## Warning: `aes_()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`
## ℹ The deprecated feature was likely used in the emmeans package.
## Please report the issue at <https://github.com/rvlenth/emmeans/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Warning: Removed 26 rows containing missing values or values outside the scale range
## (`geom_segment()`).
#Municipios
m<-emmeans(modelo, pairwise ~ mun)
## NOTE: Results may be misleading due to involvement in interactions
m
## $emmeans
## mun emmean SE df lower.CL upper.CL
## Chi 3.53 0.659 231 2.23 4.83
## Gig 5.19 0.659 231 3.90 6.49
## HtC 5.98 0.659 231 4.68 7.28
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
## RiN 6.58 0.659 231 5.28 7.88
## SnV 3.35 0.659 231 2.05 4.65
## Tam 19.96 0.659 231 18.66 21.25
## ViG 9.21 0.659 231 7.91 10.51
## Yac 8.41 0.659 231 7.11 9.70
##
## Results are averaged over the levels of: gen
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## Chi - Gig -1.664 0.932 231 -1.784 0.6312
## Chi - HtC -2.449 0.932 231 -2.626 0.1519
## Chi - Jam nonEst NA NA NA NA
## Chi - PtR nonEst NA NA NA NA
## Chi - RiN -3.052 0.932 231 -3.273 0.0265
## Chi - SnV 0.178 0.932 231 0.191 1.0000
## Chi - Tam -16.426 0.932 231 -17.617 <.0001
## Chi - ViG -5.676 0.932 231 -6.088 <.0001
## Chi - Yac -4.875 0.932 231 -5.229 <.0001
## Gig - HtC -0.785 0.932 231 -0.842 0.9905
## Gig - Jam nonEst NA NA NA NA
## Gig - PtR nonEst NA NA NA NA
## Gig - RiN -1.388 0.932 231 -1.489 0.8129
## Gig - SnV 1.841 0.932 231 1.975 0.5010
## Gig - Tam -14.762 0.932 231 -15.832 <.0001
## Gig - ViG -4.013 0.932 231 -4.304 0.0006
## Gig - Yac -3.212 0.932 231 -3.444 0.0154
## HtC - Jam nonEst NA NA NA NA
## HtC - PtR nonEst NA NA NA NA
## HtC - RiN -0.603 0.932 231 -0.647 0.9981
## HtC - SnV 2.626 0.932 231 2.817 0.0959
## HtC - Tam -13.977 0.932 231 -14.991 <.0001
## HtC - ViG -3.228 0.932 231 -3.462 0.0146
## HtC - Yac -2.427 0.932 231 -2.603 0.1603
## 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 3.230 0.932 231 3.464 0.0145
## RiN - Tam -13.374 0.932 231 -14.344 <.0001
## RiN - ViG -2.624 0.932 231 -2.815 0.0963
## RiN - Yac -1.824 0.932 231 -1.956 0.5141
## SnV - Tam -16.603 0.932 231 -17.807 <.0001
## SnV - ViG -5.854 0.932 231 -6.279 <.0001
## SnV - Yac -5.053 0.932 231 -5.420 <.0001
## Tam - ViG 10.749 0.932 231 11.529 <.0001
## Tam - Yac 11.550 0.932 231 12.388 <.0001
## ViG - Yac 0.801 0.932 231 0.859 0.9892
##
## 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
multcomp::cld(m, Letters = LETTERS)
## mun emmean SE df lower.CL upper.CL .group
## SnV 3.35 0.659 231 2.05 4.65 A
## Chi 3.53 0.659 231 2.23 4.83 A
## Gig 5.19 0.659 231 3.90 6.49 AB
## HtC 5.98 0.659 231 4.68 7.28 ABC
## RiN 6.58 0.659 231 5.28 7.88 BCD
## Yac 8.41 0.659 231 7.11 9.70 CD
## ViG 9.21 0.659 231 7.91 10.51 D
## Tam 19.96 0.659 231 18.66 21.25 E
## Jam nonEst NA NA NA NA
## PtR nonEst NA NA NA NA
##
## Results are averaged over the levels of: gen
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
## 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
## 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.
#Interacción
gm<-emmeans(modelo, pairwise ~ gen|mun)
gm
## $emmeans
## mun = Chi:
## gen emmean SE df lower.CL upper.CL
## CNCH12 3.09 1.86 231 -0.585 6.76
## CNCH13 4.70 1.86 231 1.027 8.38
## FBO1 4.35 1.86 231 0.672 8.02
## FCHI8 2.02 1.86 231 -1.657 5.69
## FEAR5 4.45 1.86 231 0.780 8.13
## FGI4 3.18 1.86 231 -0.497 6.85
## FMA7 3.30 1.86 231 -0.374 6.97
## FSV1 3.16 1.86 231 -0.516 6.83
##
## mun = Gig:
## gen emmean SE df lower.CL upper.CL
## CNCH12 2.82 1.86 231 -0.858 6.49
## CNCH13 5.88 1.86 231 2.210 9.56
## FBO1 5.60 1.86 231 1.924 9.27
## FCHI8 7.53 1.86 231 3.858 11.21
## FEAR5 6.39 1.86 231 2.715 10.06
## FGI4 4.72 1.86 231 1.046 8.39
## FMA7 4.81 1.86 231 1.138 8.49
## FSV1 3.80 1.86 231 0.127 7.48
##
## mun = HtC:
## gen emmean SE df lower.CL upper.CL
## CNCH12 5.94 1.86 231 2.269 9.62
## CNCH13 5.66 1.86 231 1.987 9.34
## FBO1 6.19 1.86 231 2.518 9.87
## FCHI8 5.14 1.86 231 1.464 8.81
## FEAR5 6.39 1.86 231 2.714 10.06
## FGI4 5.94 1.86 231 2.267 9.62
## FMA7 6.00 1.86 231 2.326 9.67
## FSV1 6.57 1.86 231 2.895 10.24
##
## mun = Jam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 10.59 1.86 231 6.914 14.26
## CNCH13 12.90 1.86 231 9.230 16.58
## FBO1 8.12 1.86 231 4.444 11.79
## FCHI8 nonEst NA NA NA NA
## FEAR5 12.41 1.86 231 8.740 16.09
## FGI4 9.89 1.86 231 6.215 13.56
## FMA7 7.81 1.86 231 4.135 11.48
## FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## gen emmean SE df lower.CL upper.CL
## CNCH12 8.48 1.86 231 4.802 12.15
## CNCH13 8.21 1.86 231 4.540 11.89
## FBO1 9.41 1.86 231 5.732 13.08
## FCHI8 nonEst NA NA NA NA
## FEAR5 10.82 1.86 231 7.149 14.50
## FGI4 6.79 1.86 231 3.121 10.47
## FMA7 6.25 1.86 231 2.580 9.93
## FSV1 7.31 1.86 231 3.635 10.98
##
## mun = RiN :
## gen emmean SE df lower.CL upper.CL
## CNCH12 7.41 1.86 231 3.735 11.08
## CNCH13 7.19 1.86 231 3.516 10.86
## FBO1 7.76 1.86 231 4.091 11.44
## FCHI8 5.30 1.86 231 1.629 8.98
## FEAR5 6.20 1.86 231 2.527 9.87
## FGI4 4.78 1.86 231 1.110 8.46
## FMA7 9.17 1.86 231 5.492 12.84
## FSV1 4.84 1.86 231 1.166 8.51
##
## mun = SnV:
## gen emmean SE df lower.CL upper.CL
## CNCH12 3.08 1.86 231 -0.595 6.75
## CNCH13 4.03 1.86 231 0.353 7.70
## FBO1 2.73 1.86 231 -0.942 6.41
## FCHI8 2.65 1.86 231 -1.027 6.32
## FEAR5 5.55 1.86 231 1.876 9.22
## FGI4 2.43 1.86 231 -1.242 6.11
## FMA7 3.90 1.86 231 0.222 7.57
## FSV1 2.46 1.86 231 -1.215 6.13
##
## mun = Tam:
## gen emmean SE df lower.CL upper.CL
## CNCH12 20.08 1.86 231 16.407 23.75
## CNCH13 23.68 1.86 231 20.010 27.36
## FBO1 20.98 1.86 231 17.302 24.65
## FCHI8 19.95 1.86 231 16.273 23.62
## FEAR5 16.66 1.86 231 12.990 20.34
## FGI4 24.03 1.86 231 20.357 27.71
## FMA7 13.53 1.86 231 9.858 17.21
## FSV1 20.73 1.86 231 17.059 24.41
##
## mun = ViG:
## gen emmean SE df lower.CL upper.CL
## CNCH12 6.10 1.86 231 2.424 9.77
## CNCH13 12.32 1.86 231 8.647 16.00
## FBO1 6.05 1.86 231 2.379 9.73
## FCHI8 5.78 1.86 231 2.102 9.45
## FEAR5 16.73 1.86 231 13.060 20.41
## FGI4 8.56 1.86 231 4.883 12.23
## FMA7 8.90 1.86 231 5.222 12.57
## FSV1 9.22 1.86 231 5.543 12.89
##
## mun = Yac:
## gen emmean SE df lower.CL upper.CL
## CNCH12 9.38 1.86 231 5.706 13.05
## CNCH13 6.02 1.86 231 2.350 9.70
## FBO1 7.11 1.86 231 3.431 10.78
## FCHI8 9.42 1.86 231 5.747 13.10
## FEAR5 10.33 1.86 231 6.653 14.00
## FGI4 12.58 1.86 231 8.909 16.26
## FMA7 5.51 1.86 231 1.835 9.18
## FSV1 6.90 1.86 231 3.223 10.57
##
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## mun = Chi:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -1.6122 2.64 231 -0.611 0.9987
## CNCH12 - FBO1 -1.2566 2.64 231 -0.476 0.9997
## CNCH12 - FCHI8 1.0724 2.64 231 0.407 0.9999
## CNCH12 - FEAR5 -1.3644 2.64 231 -0.517 0.9996
## CNCH12 - FGI4 -0.0880 2.64 231 -0.033 1.0000
## CNCH12 - FMA7 -0.2110 2.64 231 -0.080 1.0000
## CNCH12 - FSV1 -0.0683 2.64 231 -0.026 1.0000
## CNCH13 - FBO1 0.3556 2.64 231 0.135 1.0000
## CNCH13 - FCHI8 2.6845 2.64 231 1.018 0.9714
## CNCH13 - FEAR5 0.2477 2.64 231 0.094 1.0000
## CNCH13 - FGI4 1.5242 2.64 231 0.578 0.9991
## CNCH13 - FMA7 1.4011 2.64 231 0.531 0.9995
## CNCH13 - FSV1 1.5439 2.64 231 0.585 0.9990
## FBO1 - FCHI8 2.3290 2.64 231 0.883 0.9873
## FBO1 - FEAR5 -0.1079 2.64 231 -0.041 1.0000
## FBO1 - FGI4 1.1686 2.64 231 0.443 0.9998
## FBO1 - FMA7 1.0455 2.64 231 0.396 0.9999
## FBO1 - FSV1 1.1883 2.64 231 0.451 0.9998
## FCHI8 - FEAR5 -2.4368 2.64 231 -0.924 0.9835
## FCHI8 - FGI4 -1.1604 2.64 231 -0.440 0.9999
## FCHI8 - FMA7 -1.2834 2.64 231 -0.487 0.9997
## FCHI8 - FSV1 -1.1407 2.64 231 -0.433 0.9999
## FEAR5 - FGI4 1.2764 2.64 231 0.484 0.9997
## FEAR5 - FMA7 1.1534 2.64 231 0.437 0.9999
## FEAR5 - FSV1 1.2962 2.64 231 0.491 0.9997
## FGI4 - FMA7 -0.1231 2.64 231 -0.047 1.0000
## FGI4 - FSV1 0.0197 2.64 231 0.007 1.0000
## FMA7 - FSV1 0.1428 2.64 231 0.054 1.0000
##
## mun = Gig:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -3.0684 2.64 231 -1.164 0.9413
## CNCH12 - FBO1 -2.7817 2.64 231 -1.055 0.9652
## CNCH12 - FCHI8 -4.7163 2.64 231 -1.788 0.6286
## CNCH12 - FEAR5 -3.5731 2.64 231 -1.355 0.8764
## CNCH12 - FGI4 -1.9040 2.64 231 -0.722 0.9963
## CNCH12 - FMA7 -1.9960 2.64 231 -0.757 0.9950
## CNCH12 - FSV1 -0.9850 2.64 231 -0.373 1.0000
## CNCH13 - FBO1 0.2867 2.64 231 0.109 1.0000
## CNCH13 - FCHI8 -1.6479 2.64 231 -0.625 0.9985
## CNCH13 - FEAR5 -0.5047 2.64 231 -0.191 1.0000
## CNCH13 - FGI4 1.1643 2.64 231 0.442 0.9998
## CNCH13 - FMA7 1.0724 2.64 231 0.407 0.9999
## CNCH13 - FSV1 2.0834 2.64 231 0.790 0.9935
## FBO1 - FCHI8 -1.9346 2.64 231 -0.734 0.9959
## FBO1 - FEAR5 -0.7914 2.64 231 -0.300 1.0000
## FBO1 - FGI4 0.8777 2.64 231 0.333 1.0000
## FBO1 - FMA7 0.7857 2.64 231 0.298 1.0000
## FBO1 - FSV1 1.7968 2.64 231 0.681 0.9974
## FCHI8 - FEAR5 1.1432 2.64 231 0.433 0.9999
## FCHI8 - FGI4 2.8122 2.64 231 1.066 0.9630
## FCHI8 - FMA7 2.7203 2.64 231 1.032 0.9692
## FCHI8 - FSV1 3.7313 2.64 231 1.415 0.8497
## FEAR5 - FGI4 1.6691 2.64 231 0.633 0.9984
## FEAR5 - FMA7 1.5771 2.64 231 0.598 0.9989
## FEAR5 - FSV1 2.5881 2.64 231 0.981 0.9767
## FGI4 - FMA7 -0.0919 2.64 231 -0.035 1.0000
## FGI4 - FSV1 0.9191 2.64 231 0.349 1.0000
## FMA7 - FSV1 1.0110 2.64 231 0.383 0.9999
##
## mun = HtC:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.2826 2.64 231 0.107 1.0000
## CNCH12 - FBO1 -0.2490 2.64 231 -0.094 1.0000
## CNCH12 - FCHI8 0.8058 2.64 231 0.306 1.0000
## CNCH12 - FEAR5 -0.4449 2.64 231 -0.169 1.0000
## CNCH12 - FGI4 0.0022 2.64 231 0.001 1.0000
## CNCH12 - FMA7 -0.0561 2.64 231 -0.021 1.0000
## CNCH12 - FSV1 -0.6251 2.64 231 -0.237 1.0000
## CNCH13 - FBO1 -0.5316 2.64 231 -0.202 1.0000
## CNCH13 - FCHI8 0.5232 2.64 231 0.198 1.0000
## CNCH13 - FEAR5 -0.7275 2.64 231 -0.276 1.0000
## CNCH13 - FGI4 -0.2804 2.64 231 -0.106 1.0000
## CNCH13 - FMA7 -0.3387 2.64 231 -0.128 1.0000
## CNCH13 - FSV1 -0.9077 2.64 231 -0.344 1.0000
## FBO1 - FCHI8 1.0548 2.64 231 0.400 0.9999
## FBO1 - FEAR5 -0.1959 2.64 231 -0.074 1.0000
## FBO1 - FGI4 0.2512 2.64 231 0.095 1.0000
## FBO1 - FMA7 0.1929 2.64 231 0.073 1.0000
## FBO1 - FSV1 -0.3761 2.64 231 -0.143 1.0000
## FCHI8 - FEAR5 -1.2508 2.64 231 -0.474 0.9998
## FCHI8 - FGI4 -0.8036 2.64 231 -0.305 1.0000
## FCHI8 - FMA7 -0.8619 2.64 231 -0.327 1.0000
## FCHI8 - FSV1 -1.4309 2.64 231 -0.543 0.9994
## FEAR5 - FGI4 0.4471 2.64 231 0.170 1.0000
## FEAR5 - FMA7 0.3889 2.64 231 0.147 1.0000
## FEAR5 - FSV1 -0.1802 2.64 231 -0.068 1.0000
## FGI4 - FMA7 -0.0583 2.64 231 -0.022 1.0000
## FGI4 - FSV1 -0.6273 2.64 231 -0.238 1.0000
## FMA7 - FSV1 -0.5690 2.64 231 -0.216 1.0000
##
## mun = Jam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -2.3159 2.64 231 -0.878 0.9514
## CNCH12 - FBO1 2.4705 2.64 231 0.937 0.9366
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -1.8259 2.64 231 -0.692 0.9827
## CNCH12 - FGI4 0.6985 2.64 231 0.265 0.9998
## CNCH12 - FMA7 2.7793 2.64 231 1.054 0.8989
## CNCH12 - FSV1 nonEst NA NA NA NA
## CNCH13 - FBO1 4.7864 2.64 231 1.815 0.4581
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 0.4900 2.64 231 0.186 1.0000
## CNCH13 - FGI4 3.0145 2.64 231 1.143 0.8628
## CNCH13 - FMA7 5.0953 2.64 231 1.932 0.3853
## CNCH13 - FSV1 nonEst NA NA NA NA
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -4.2964 2.64 231 -1.629 0.5801
## FBO1 - FGI4 -1.7719 2.64 231 -0.672 0.9849
## FBO1 - FMA7 0.3089 2.64 231 0.117 1.0000
## 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 2.5244 2.64 231 0.957 0.9308
## FEAR5 - FMA7 4.6052 2.64 231 1.746 0.5027
## FEAR5 - FSV1 nonEst NA NA NA NA
## FGI4 - FMA7 2.0808 2.64 231 0.789 0.9692
## FGI4 - FSV1 nonEst NA NA NA NA
## FMA7 - FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.2620 2.64 231 0.099 1.0000
## CNCH12 - FBO1 -0.9295 2.64 231 -0.352 0.9998
## CNCH12 - FCHI8 nonEst NA NA NA NA
## CNCH12 - FEAR5 -2.3466 2.64 231 -0.890 0.9738
## CNCH12 - FGI4 1.6814 2.64 231 0.638 0.9955
## CNCH12 - FMA7 2.2219 2.64 231 0.843 0.9801
## CNCH12 - FSV1 1.1670 2.64 231 0.443 0.9994
## CNCH13 - FBO1 -1.1914 2.64 231 -0.452 0.9993
## CNCH13 - FCHI8 nonEst NA NA NA NA
## CNCH13 - FEAR5 -2.6085 2.64 231 -0.989 0.9560
## CNCH13 - FGI4 1.4195 2.64 231 0.538 0.9982
## CNCH13 - FMA7 1.9599 2.64 231 0.743 0.9897
## CNCH13 - FSV1 0.9051 2.64 231 0.343 0.9999
## FBO1 - FCHI8 nonEst NA NA NA NA
## FBO1 - FEAR5 -1.4171 2.64 231 -0.537 0.9983
## FBO1 - FGI4 2.6109 2.64 231 0.990 0.9558
## FBO1 - FMA7 3.1513 2.64 231 1.195 0.8955
## FBO1 - FSV1 2.0965 2.64 231 0.795 0.9853
## 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 4.0280 2.64 231 1.527 0.7281
## FEAR5 - FMA7 4.5684 2.64 231 1.732 0.5951
## FEAR5 - FSV1 3.5136 2.64 231 1.332 0.8361
## FGI4 - FMA7 0.5404 2.64 231 0.205 1.0000
## FGI4 - FSV1 -0.5144 2.64 231 -0.195 1.0000
## FMA7 - FSV1 -1.0548 2.64 231 -0.400 0.9997
##
## mun = RiN :
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.2198 2.64 231 0.083 1.0000
## CNCH12 - FBO1 -0.3554 2.64 231 -0.135 1.0000
## CNCH12 - FCHI8 2.1064 2.64 231 0.799 0.9931
## CNCH12 - FEAR5 1.2087 2.64 231 0.458 0.9998
## CNCH12 - FGI4 2.6258 2.64 231 0.996 0.9747
## CNCH12 - FMA7 -1.7569 2.64 231 -0.666 0.9978
## CNCH12 - FSV1 2.5699 2.64 231 0.974 0.9776
## CNCH13 - FBO1 -0.5751 2.64 231 -0.218 1.0000
## CNCH13 - FCHI8 1.8867 2.64 231 0.715 0.9965
## CNCH13 - FEAR5 0.9889 2.64 231 0.375 0.9999
## CNCH13 - FGI4 2.4060 2.64 231 0.912 0.9847
## CNCH13 - FMA7 -1.9767 2.64 231 -0.750 0.9953
## CNCH13 - FSV1 2.3501 2.64 231 0.891 0.9866
## FBO1 - FCHI8 2.4618 2.64 231 0.933 0.9825
## FBO1 - FEAR5 1.5640 2.64 231 0.593 0.9989
## FBO1 - FGI4 2.9812 2.64 231 1.130 0.9495
## FBO1 - FMA7 -1.4016 2.64 231 -0.531 0.9995
## FBO1 - FSV1 2.9252 2.64 231 1.109 0.9543
## FCHI8 - FEAR5 -0.8977 2.64 231 -0.340 1.0000
## FCHI8 - FGI4 0.5194 2.64 231 0.197 1.0000
## FCHI8 - FMA7 -3.8634 2.64 231 -1.465 0.8252
## FCHI8 - FSV1 0.4635 2.64 231 0.176 1.0000
## FEAR5 - FGI4 1.4171 2.64 231 0.537 0.9994
## FEAR5 - FMA7 -2.9656 2.64 231 -1.125 0.9509
## FEAR5 - FSV1 1.3612 2.64 231 0.516 0.9996
## FGI4 - FMA7 -4.3827 2.64 231 -1.662 0.7117
## FGI4 - FSV1 -0.0559 2.64 231 -0.021 1.0000
## FMA7 - FSV1 4.3268 2.64 231 1.641 0.7250
##
## mun = SnV:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.9477 2.64 231 -0.359 1.0000
## CNCH12 - FBO1 0.3474 2.64 231 0.132 1.0000
## CNCH12 - FCHI8 0.4322 2.64 231 0.164 1.0000
## CNCH12 - FEAR5 -2.4711 2.64 231 -0.937 0.9821
## CNCH12 - FGI4 0.6473 2.64 231 0.245 1.0000
## CNCH12 - FMA7 -0.8167 2.64 231 -0.310 1.0000
## CNCH12 - FSV1 0.6204 2.64 231 0.235 1.0000
## CNCH13 - FBO1 1.2951 2.64 231 0.491 0.9997
## CNCH13 - FCHI8 1.3800 2.64 231 0.523 0.9995
## CNCH13 - FEAR5 -1.5234 2.64 231 -0.578 0.9991
## CNCH13 - FGI4 1.5950 2.64 231 0.605 0.9988
## CNCH13 - FMA7 0.1310 2.64 231 0.050 1.0000
## CNCH13 - FSV1 1.5681 2.64 231 0.595 0.9989
## FBO1 - FCHI8 0.0849 2.64 231 0.032 1.0000
## FBO1 - FEAR5 -2.8185 2.64 231 -1.069 0.9626
## FBO1 - FGI4 0.2999 2.64 231 0.114 1.0000
## FBO1 - FMA7 -1.1641 2.64 231 -0.441 0.9998
## FBO1 - FSV1 0.2730 2.64 231 0.104 1.0000
## FCHI8 - FEAR5 -2.9034 2.64 231 -1.101 0.9561
## FCHI8 - FGI4 0.2150 2.64 231 0.082 1.0000
## FCHI8 - FMA7 -1.2490 2.64 231 -0.474 0.9998
## FCHI8 - FSV1 0.1882 2.64 231 0.071 1.0000
## FEAR5 - FGI4 3.1184 2.64 231 1.182 0.9363
## FEAR5 - FMA7 1.6544 2.64 231 0.627 0.9985
## FEAR5 - FSV1 3.0915 2.64 231 1.172 0.9390
## FGI4 - FMA7 -1.4640 2.64 231 -0.555 0.9993
## FGI4 - FSV1 -0.0269 2.64 231 -0.010 1.0000
## FMA7 - FSV1 1.4371 2.64 231 0.545 0.9994
##
## mun = Tam:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -3.6028 2.64 231 -1.366 0.8716
## CNCH12 - FBO1 -0.8956 2.64 231 -0.340 1.0000
## CNCH12 - FCHI8 0.1341 2.64 231 0.051 1.0000
## CNCH12 - FEAR5 3.4166 2.64 231 1.296 0.8998
## CNCH12 - FGI4 -3.9506 2.64 231 -1.498 0.8079
## CNCH12 - FMA7 6.5490 2.64 231 2.483 0.2078
## CNCH12 - FSV1 -0.6523 2.64 231 -0.247 1.0000
## CNCH13 - FBO1 2.7072 2.64 231 1.027 0.9700
## CNCH13 - FCHI8 3.7370 2.64 231 1.417 0.8487
## CNCH13 - FEAR5 7.0194 2.64 231 2.662 0.1399
## CNCH13 - FGI4 -0.3478 2.64 231 -0.132 1.0000
## CNCH13 - FMA7 10.1518 2.64 231 3.850 0.0038
## CNCH13 - FSV1 2.9506 2.64 231 1.119 0.9522
## FBO1 - FCHI8 1.0298 2.64 231 0.390 0.9999
## FBO1 - FEAR5 4.3123 2.64 231 1.635 0.7285
## FBO1 - FGI4 -3.0550 2.64 231 -1.158 0.9426
## FBO1 - FMA7 7.4447 2.64 231 2.823 0.0944
## FBO1 - FSV1 0.2434 2.64 231 0.092 1.0000
## FCHI8 - FEAR5 3.2825 2.64 231 1.245 0.9175
## FCHI8 - FGI4 -4.0847 2.64 231 -1.549 0.7799
## FCHI8 - FMA7 6.4149 2.64 231 2.432 0.2308
## FCHI8 - FSV1 -0.7864 2.64 231 -0.298 1.0000
## FEAR5 - FGI4 -7.3672 2.64 231 -2.794 0.1016
## FEAR5 - FMA7 3.1324 2.64 231 1.188 0.9348
## FEAR5 - FSV1 -4.0689 2.64 231 -1.543 0.7833
## FGI4 - FMA7 10.4996 2.64 231 3.981 0.0023
## FGI4 - FSV1 3.2983 2.64 231 1.251 0.9155
## FMA7 - FSV1 -7.2013 2.64 231 -2.731 0.1187
##
## mun = ViG:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -6.2229 2.64 231 -2.360 0.2665
## CNCH12 - FBO1 0.0454 2.64 231 0.017 1.0000
## CNCH12 - FCHI8 0.3218 2.64 231 0.122 1.0000
## CNCH12 - FEAR5 -10.6361 2.64 231 -4.033 0.0019
## CNCH12 - FGI4 -2.4589 2.64 231 -0.932 0.9826
## CNCH12 - FMA7 -2.7981 2.64 231 -1.061 0.9640
## CNCH12 - FSV1 -3.1189 2.64 231 -1.183 0.9362
## CNCH13 - FBO1 6.2684 2.64 231 2.377 0.2577
## CNCH13 - FCHI8 6.5447 2.64 231 2.482 0.2085
## CNCH13 - FEAR5 -4.4131 2.64 231 -1.673 0.7044
## CNCH13 - FGI4 3.7641 2.64 231 1.427 0.8438
## CNCH13 - FMA7 3.4248 2.64 231 1.299 0.8987
## CNCH13 - FSV1 3.1040 2.64 231 1.177 0.9377
## FBO1 - FCHI8 0.2763 2.64 231 0.105 1.0000
## FBO1 - FEAR5 -10.6815 2.64 231 -4.050 0.0018
## FBO1 - FGI4 -2.5043 2.64 231 -0.950 0.9807
## FBO1 - FMA7 -2.8435 2.64 231 -1.078 0.9608
## FBO1 - FSV1 -3.1644 2.64 231 -1.200 0.9313
## FCHI8 - FEAR5 -10.9578 2.64 231 -4.155 0.0012
## FCHI8 - FGI4 -2.7806 2.64 231 -1.054 0.9653
## FCHI8 - FMA7 -3.1199 2.64 231 -1.183 0.9361
## FCHI8 - FSV1 -3.4407 2.64 231 -1.305 0.8964
## FEAR5 - FGI4 8.1772 2.64 231 3.101 0.0444
## FEAR5 - FMA7 7.8380 2.64 231 2.972 0.0637
## FEAR5 - FSV1 7.5171 2.64 231 2.850 0.0879
## FGI4 - FMA7 -0.3392 2.64 231 -0.129 1.0000
## FGI4 - FSV1 -0.6601 2.64 231 -0.250 1.0000
## FMA7 - FSV1 -0.3208 2.64 231 -0.122 1.0000
##
## mun = Yac:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 3.3555 2.64 231 1.272 0.9082
## CNCH12 - FBO1 2.2745 2.64 231 0.862 0.9890
## CNCH12 - FCHI8 -0.0412 2.64 231 -0.016 1.0000
## CNCH12 - FEAR5 -0.9467 2.64 231 -0.359 1.0000
## CNCH12 - FGI4 -3.2028 2.64 231 -1.214 0.9270
## CNCH12 - FMA7 3.8714 2.64 231 1.468 0.8236
## CNCH12 - FSV1 2.4832 2.64 231 0.942 0.9816
## CNCH13 - FBO1 -1.0810 2.64 231 -0.410 0.9999
## CNCH13 - FCHI8 -3.3966 2.64 231 -1.288 0.9026
## CNCH13 - FEAR5 -4.3021 2.64 231 -1.631 0.7309
## CNCH13 - FGI4 -6.5583 2.64 231 -2.487 0.2063
## CNCH13 - FMA7 0.5159 2.64 231 0.196 1.0000
## CNCH13 - FSV1 -0.8722 2.64 231 -0.331 1.0000
## FBO1 - FCHI8 -2.3157 2.64 231 -0.878 0.9877
## FBO1 - FEAR5 -3.2212 2.64 231 -1.221 0.9249
## FBO1 - FGI4 -5.4773 2.64 231 -2.077 0.4330
## FBO1 - FMA7 1.5969 2.64 231 0.606 0.9988
## FBO1 - FSV1 0.2087 2.64 231 0.079 1.0000
## FCHI8 - FEAR5 -0.9055 2.64 231 -0.343 1.0000
## FCHI8 - FGI4 -3.1616 2.64 231 -1.199 0.9316
## FCHI8 - FMA7 3.9125 2.64 231 1.484 0.8155
## FCHI8 - FSV1 2.5244 2.64 231 0.957 0.9798
## FEAR5 - FGI4 -2.2561 2.64 231 -0.856 0.9895
## FEAR5 - FMA7 4.8180 2.64 231 1.827 0.6024
## FEAR5 - FSV1 3.4299 2.64 231 1.301 0.8979
## FGI4 - FMA7 7.0742 2.64 231 2.682 0.1333
## FGI4 - FSV1 5.6860 2.64 231 2.156 0.3825
## FMA7 - FSV1 -1.3881 2.64 231 -0.526 0.9995
##
## 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
multcomp::cld(gm, Letters = LETTERS)
## mun = Chi:
## gen emmean SE df lower.CL upper.CL .group
## FCHI8 2.02 1.86 231 -1.657 5.69 A
## CNCH12 3.09 1.86 231 -0.585 6.76 A
## FSV1 3.16 1.86 231 -0.516 6.83 A
## FGI4 3.18 1.86 231 -0.497 6.85 A
## FMA7 3.30 1.86 231 -0.374 6.97 A
## FBO1 4.35 1.86 231 0.672 8.02 A
## FEAR5 4.45 1.86 231 0.780 8.13 A
## CNCH13 4.70 1.86 231 1.027 8.38 A
##
## mun = Gig:
## gen emmean SE df lower.CL upper.CL .group
## CNCH12 2.82 1.86 231 -0.858 6.49 A
## FSV1 3.80 1.86 231 0.127 7.48 A
## FGI4 4.72 1.86 231 1.046 8.39 A
## FMA7 4.81 1.86 231 1.138 8.49 A
## FBO1 5.60 1.86 231 1.924 9.27 A
## CNCH13 5.88 1.86 231 2.210 9.56 A
## FEAR5 6.39 1.86 231 2.715 10.06 A
## FCHI8 7.53 1.86 231 3.858 11.21 A
##
## mun = HtC:
## gen emmean SE df lower.CL upper.CL .group
## FCHI8 5.14 1.86 231 1.464 8.81 A
## CNCH13 5.66 1.86 231 1.987 9.34 A
## FGI4 5.94 1.86 231 2.267 9.62 A
## CNCH12 5.94 1.86 231 2.269 9.62 A
## FMA7 6.00 1.86 231 2.326 9.67 A
## FBO1 6.19 1.86 231 2.518 9.87 A
## FEAR5 6.39 1.86 231 2.714 10.06 A
## FSV1 6.57 1.86 231 2.895 10.24 A
##
## mun = Jam:
## gen emmean SE df lower.CL upper.CL .group
## FMA7 7.81 1.86 231 4.135 11.48 A
## FBO1 8.12 1.86 231 4.444 11.79 A
## FGI4 9.89 1.86 231 6.215 13.56 A
## CNCH12 10.59 1.86 231 6.914 14.26 A
## FEAR5 12.41 1.86 231 8.740 16.09 A
## CNCH13 12.90 1.86 231 9.230 16.58 A
## FCHI8 nonEst NA NA NA NA
## FSV1 nonEst NA NA NA NA
##
## mun = PtR:
## gen emmean SE df lower.CL upper.CL .group
## FMA7 6.25 1.86 231 2.580 9.93 A
## FGI4 6.79 1.86 231 3.121 10.47 A
## FSV1 7.31 1.86 231 3.635 10.98 A
## CNCH13 8.21 1.86 231 4.540 11.89 A
## CNCH12 8.48 1.86 231 4.802 12.15 A
## FBO1 9.41 1.86 231 5.732 13.08 A
## FEAR5 10.82 1.86 231 7.149 14.50 A
## FCHI8 nonEst NA NA NA NA
##
## mun = RiN :
## gen emmean SE df lower.CL upper.CL .group
## FGI4 4.78 1.86 231 1.110 8.46 A
## FSV1 4.84 1.86 231 1.166 8.51 A
## FCHI8 5.30 1.86 231 1.629 8.98 A
## FEAR5 6.20 1.86 231 2.527 9.87 A
## CNCH13 7.19 1.86 231 3.516 10.86 A
## CNCH12 7.41 1.86 231 3.735 11.08 A
## FBO1 7.76 1.86 231 4.091 11.44 A
## FMA7 9.17 1.86 231 5.492 12.84 A
##
## mun = SnV:
## gen emmean SE df lower.CL upper.CL .group
## FGI4 2.43 1.86 231 -1.242 6.11 A
## FSV1 2.46 1.86 231 -1.215 6.13 A
## FCHI8 2.65 1.86 231 -1.027 6.32 A
## FBO1 2.73 1.86 231 -0.942 6.41 A
## CNCH12 3.08 1.86 231 -0.595 6.75 A
## FMA7 3.90 1.86 231 0.222 7.57 A
## CNCH13 4.03 1.86 231 0.353 7.70 A
## FEAR5 5.55 1.86 231 1.876 9.22 A
##
## mun = Tam:
## gen emmean SE df lower.CL upper.CL .group
## FMA7 13.53 1.86 231 9.858 17.21 A
## FEAR5 16.66 1.86 231 12.990 20.34 AB
## FCHI8 19.95 1.86 231 16.273 23.62 AB
## CNCH12 20.08 1.86 231 16.407 23.75 AB
## FSV1 20.73 1.86 231 17.059 24.41 AB
## FBO1 20.98 1.86 231 17.302 24.65 AB
## CNCH13 23.68 1.86 231 20.010 27.36 B
## FGI4 24.03 1.86 231 20.357 27.71 B
##
## mun = ViG:
## gen emmean SE df lower.CL upper.CL .group
## FCHI8 5.78 1.86 231 2.102 9.45 A
## FBO1 6.05 1.86 231 2.379 9.73 A
## CNCH12 6.10 1.86 231 2.424 9.77 A
## FGI4 8.56 1.86 231 4.883 12.23 A
## FMA7 8.90 1.86 231 5.222 12.57 AB
## FSV1 9.22 1.86 231 5.543 12.89 AB
## CNCH13 12.32 1.86 231 8.647 16.00 AB
## FEAR5 16.73 1.86 231 13.060 20.41 B
##
## mun = Yac:
## gen emmean SE df lower.CL upper.CL .group
## FMA7 5.51 1.86 231 1.835 9.18 A
## CNCH13 6.02 1.86 231 2.350 9.70 A
## FSV1 6.90 1.86 231 3.223 10.57 A
## FBO1 7.11 1.86 231 3.431 10.78 A
## CNCH12 9.38 1.86 231 5.706 13.05 A
## FCHI8 9.42 1.86 231 5.747 13.10 A
## FEAR5 10.33 1.86 231 6.653 14.00 A
## FGI4 12.58 1.86 231 8.909 16.26 A
##
## Results are given on the ( (not the response) scale.
## Confidence level used: 0.95
## 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
## 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 1
modelo <- lmer(log(total_alt_co2) ~ 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 3.4467 0.49239 7 60.046 2.1305 0.05376 .
## ---
## 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(total_alt_co2) ~ gen + (1 | mun) + (1 | mun:gen)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -246.08 514.15
## (1 | mun) 10 -282.23 584.47 72.311 1 <2e-16 ***
## (1 | mun:gen) 10 -247.32 514.65 2.495 1 0.1142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Modelo 2
modelo_blup <- lmer(total_alt_co2 ~ 1 +
(1|gen) +
(1|mun) +
(1|gen:mun),
data = datos)
ranova(modelo_blup)
## boundary (singular) fit: see help('isSingular')
## ANOVA-like table for random-effects: Single term deletions
##
## Model:
## total_alt_co2 ~ (1 | gen) + (1 | mun) + (1 | gen:mun)
## npar logLik AIC LRT Df Pr(>Chisq)
## <none> 5 -868.94 1747.9
## (1 | gen) 4 -869.68 1747.3 1.481 1 0.2236
## (1 | mun) 4 -918.36 1844.7 98.842 1 <2e-16 ***
## (1 | gen:mun) 4 -869.41 1746.8 0.951 1 0.3296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Extraer Blups
blups <- ranef(modelo_blup)
#Blups Gen
blups$gen
## (Intercept)
## CNCH12 -0.1694569
## CNCH13 0.4764206
## FBO1 -0.1065272
## FCHI8 -0.2339906
## FEAR5 0.7287737
## FGI4 0.1120320
## FMA7 -0.5378970
## FSV1 -0.2693545
#Valor predicho
fixef(modelo_blup)[1] + blups$gen
## (Intercept)
## CNCH12 7.884810
## CNCH13 8.530687
## FBO1 7.947739
## FCHI8 7.820276
## FEAR5 8.783040
## FGI4 8.166298
## FMA7 7.516369
## FSV1 7.784912
#Blups Parcela
blups$mun
## (Intercept)
## Chi -4.41892159
## Gig -2.79369920
## HtC -2.02702250
## Jam 2.08288392
## PtR 0.09230342
## RiN -1.43784455
## SnV -4.59246726
## Tam 11.62564759
## ViG 1.12575054
## Yac 0.34336964
fixef(modelo_blup)[1] + blups$mun
## (Intercept)
## Chi 3.635345
## Gig 5.260567
## HtC 6.027244
## Jam 10.137150
## PtR 8.146570
## RiN 6.616422
## SnV 3.461799
## Tam 19.679914
## ViG 9.180017
## Yac 8.397636
#Blups interacción
blups$`gen:mun`
## (Intercept)
## CNCH12:Chi -0.06694718
## CNCH12:Gig -0.40453386
## CNCH12:HtC 0.01525047
## CNCH12:Jam 0.11031706
## CNCH12:PtR 0.08875768
## CNCH12:RiN 0.17116812
## CNCH12:SnV -0.03790032
## CNCH12:Tam 0.10141905
## CNCH12:ViG -0.51786496
## CNCH12:Yac 0.20482212
## CNCH13:Chi 0.10487825
## CNCH13:Gig 0.02623882
## CNCH13:HtC -0.14985371
## CNCH13:Jam 0.40729129
## CNCH13:PtR -0.07267641
## CNCH13:RiN 0.01724079
## CNCH13:SnV 0.01577760
## CNCH13:Tam 0.62722609
## CNCH13:ViG 0.47385541
## CNCH13:Yac -0.50670156
## FBO1:Chi 0.14530635
## FBO1:Gig 0.07892530
## FBO1:HtC 0.04833654
## FBO1:Jam -0.34017507
## FBO1:PtR 0.24284593
## FBO1:RiN 0.22316960
## FBO1:SnV -0.11085945
## FBO1:Tam 0.24949254
## FBO1:ViG -0.53713433
## FBO1:Yac -0.21082317
## FCHI8:Chi -0.24616605
## FCHI8:Gig 0.44559858
## FCHI8:HtC -0.11656607
## FCHI8:RiN -0.19192088
## FCHI8:SnV -0.10328525
## FCHI8:Tam 0.08904147
## FCHI8:ViG -0.56360713
## FCHI8:Yac 0.22362169
## FEAR5:Chi 0.01595156
## FEAR5:Gig 0.07111673
## FEAR5:HtC -0.06535562
## FEAR5:Jam 0.27527591
## FEAR5:PtR 0.34630500
## FEAR5:RiN -0.20348355
## FEAR5:SnV 0.24179310
## FEAR5:Tam -0.66585670
## FEAR5:ViG 1.21373287
## FEAR5:Yac 0.21343719
## FGI4:Chi -0.10135450
## FGI4:Gig -0.11600859
## FGI4:HtC -0.03519500
## FGI4:Jam -0.06395385
## FGI4:PtR -0.26029386
## FGI4:RiN -0.34580894
## FGI4:SnV -0.20305570
## FGI4:Tam 0.75386493
## FGI4:ViG -0.13067887
## FGI4:Yac 0.72429922
## FMA7:Chi 0.03609846
## FMA7:Gig 0.01590914
## FMA7:HtC 0.09073559
## FMA7:Jam -0.31839218
## FMA7:PtR -0.24081988
## FMA7:RiN 0.54910817
## FMA7:SnV 0.17284611
## FMA7:Tam -0.99762355
## FMA7:ViG 0.04521536
## FMA7:Yac -0.41807241
## FSV1:Chi -0.03704516
## FSV1:Gig -0.21162176
## FSV1:HtC 0.14417172
## FSV1:PtR -0.10100030
## FSV1:RiN -0.26804599
## FSV1:SnV -0.13045700
## FSV1:Tam 0.23516918
## FSV1:ViG 0.05451130
## FSV1:Yac -0.21898349
fixef(modelo_blup)[1] + blups$`gen:mun`
## (Intercept)
## CNCH12:Chi 7.987319
## CNCH12:Gig 7.649733
## CNCH12:HtC 8.069517
## CNCH12:Jam 8.164583
## CNCH12:PtR 8.143024
## CNCH12:RiN 8.225435
## CNCH12:SnV 8.016366
## CNCH12:Tam 8.155685
## CNCH12:ViG 7.536401
## CNCH12:Yac 8.259089
## CNCH13:Chi 8.159145
## CNCH13:Gig 8.080505
## CNCH13:HtC 7.904413
## CNCH13:Jam 8.461558
## CNCH13:PtR 7.981590
## CNCH13:RiN 8.071507
## CNCH13:SnV 8.070044
## CNCH13:Tam 8.681493
## CNCH13:ViG 8.528122
## CNCH13:Yac 7.547565
## FBO1:Chi 8.199573
## FBO1:Gig 8.133192
## FBO1:HtC 8.102603
## FBO1:Jam 7.714091
## FBO1:PtR 8.297112
## FBO1:RiN 8.277436
## FBO1:SnV 7.943407
## FBO1:Tam 8.303759
## FBO1:ViG 7.517132
## FBO1:Yac 7.843443
## FCHI8:Chi 7.808100
## FCHI8:Gig 8.499865
## FCHI8:HtC 7.937700
## FCHI8:RiN 7.862346
## FCHI8:SnV 7.950981
## FCHI8:Tam 8.143308
## FCHI8:ViG 7.490659
## FCHI8:Yac 8.277888
## FEAR5:Chi 8.070218
## FEAR5:Gig 8.125383
## FEAR5:HtC 7.988911
## FEAR5:Jam 8.329542
## FEAR5:PtR 8.400571
## FEAR5:RiN 7.850783
## FEAR5:SnV 8.296060
## FEAR5:Tam 7.388410
## FEAR5:ViG 9.267999
## FEAR5:Yac 8.267704
## FGI4:Chi 7.952912
## FGI4:Gig 7.938258
## FGI4:HtC 8.019071
## FGI4:Jam 7.990313
## FGI4:PtR 7.793973
## FGI4:RiN 7.708457
## FGI4:SnV 7.851211
## FGI4:Tam 8.808131
## FGI4:ViG 7.923588
## FGI4:Yac 8.778566
## FMA7:Chi 8.090365
## FMA7:Gig 8.070176
## FMA7:HtC 8.145002
## FMA7:Jam 7.735874
## FMA7:PtR 7.813447
## FMA7:RiN 8.603375
## FMA7:SnV 8.227113
## FMA7:Tam 7.056643
## FMA7:ViG 8.099482
## FMA7:Yac 7.636194
## FSV1:Chi 8.017221
## FSV1:Gig 7.842645
## FSV1:HtC 8.198438
## FSV1:PtR 7.953266
## FSV1:RiN 7.786220
## FSV1:SnV 7.923809
## FSV1:Tam 8.289436
## FSV1:ViG 8.108778
## FSV1:Yac 7.835283
#Tabla blup_gen
blup_gen <- ranef(modelo_blup)$gen %>%
tibble::rownames_to_column("gen") %>%
rename(BLUP = `(Intercept)`)
blup_gen
## gen BLUP
## 1 CNCH12 -0.1694569
## 2 CNCH13 0.4764206
## 3 FBO1 -0.1065272
## 4 FCHI8 -0.2339906
## 5 FEAR5 0.7287737
## 6 FGI4 0.1120320
## 7 FMA7 -0.5378970
## 8 FSV1 -0.2693545
#Tabla blup_mun
blup_mun <- ranef(modelo_blup)$mun %>%
tibble::rownames_to_column("mun") %>%
rename(BLUP = `(Intercept)`)
blup_mun
## mun BLUP
## 1 Chi -4.41892159
## 2 Gig -2.79369920
## 3 HtC -2.02702250
## 4 Jam 2.08288392
## 5 PtR 0.09230342
## 6 RiN -1.43784455
## 7 SnV -4.59246726
## 8 Tam 11.62564759
## 9 ViG 1.12575054
## 10 Yac 0.34336964
#Tabla blup_gen_mun
blup_gen_mun <- ranef(modelo_blup)$`gen:mun` %>%
tibble::rownames_to_column("gen:mun") %>%
rename(BLUP = `(Intercept)`)
blup_gen_mun
## gen:mun BLUP
## 1 CNCH12:Chi -0.06694718
## 2 CNCH12:Gig -0.40453386
## 3 CNCH12:HtC 0.01525047
## 4 CNCH12:Jam 0.11031706
## 5 CNCH12:PtR 0.08875768
## 6 CNCH12:RiN 0.17116812
## 7 CNCH12:SnV -0.03790032
## 8 CNCH12:Tam 0.10141905
## 9 CNCH12:ViG -0.51786496
## 10 CNCH12:Yac 0.20482212
## 11 CNCH13:Chi 0.10487825
## 12 CNCH13:Gig 0.02623882
## 13 CNCH13:HtC -0.14985371
## 14 CNCH13:Jam 0.40729129
## 15 CNCH13:PtR -0.07267641
## 16 CNCH13:RiN 0.01724079
## 17 CNCH13:SnV 0.01577760
## 18 CNCH13:Tam 0.62722609
## 19 CNCH13:ViG 0.47385541
## 20 CNCH13:Yac -0.50670156
## 21 FBO1:Chi 0.14530635
## 22 FBO1:Gig 0.07892530
## 23 FBO1:HtC 0.04833654
## 24 FBO1:Jam -0.34017507
## 25 FBO1:PtR 0.24284593
## 26 FBO1:RiN 0.22316960
## 27 FBO1:SnV -0.11085945
## 28 FBO1:Tam 0.24949254
## 29 FBO1:ViG -0.53713433
## 30 FBO1:Yac -0.21082317
## 31 FCHI8:Chi -0.24616605
## 32 FCHI8:Gig 0.44559858
## 33 FCHI8:HtC -0.11656607
## 34 FCHI8:RiN -0.19192088
## 35 FCHI8:SnV -0.10328525
## 36 FCHI8:Tam 0.08904147
## 37 FCHI8:ViG -0.56360713
## 38 FCHI8:Yac 0.22362169
## 39 FEAR5:Chi 0.01595156
## 40 FEAR5:Gig 0.07111673
## 41 FEAR5:HtC -0.06535562
## 42 FEAR5:Jam 0.27527591
## 43 FEAR5:PtR 0.34630500
## 44 FEAR5:RiN -0.20348355
## 45 FEAR5:SnV 0.24179310
## 46 FEAR5:Tam -0.66585670
## 47 FEAR5:ViG 1.21373287
## 48 FEAR5:Yac 0.21343719
## 49 FGI4:Chi -0.10135450
## 50 FGI4:Gig -0.11600859
## 51 FGI4:HtC -0.03519500
## 52 FGI4:Jam -0.06395385
## 53 FGI4:PtR -0.26029386
## 54 FGI4:RiN -0.34580894
## 55 FGI4:SnV -0.20305570
## 56 FGI4:Tam 0.75386493
## 57 FGI4:ViG -0.13067887
## 58 FGI4:Yac 0.72429922
## 59 FMA7:Chi 0.03609846
## 60 FMA7:Gig 0.01590914
## 61 FMA7:HtC 0.09073559
## 62 FMA7:Jam -0.31839218
## 63 FMA7:PtR -0.24081988
## 64 FMA7:RiN 0.54910817
## 65 FMA7:SnV 0.17284611
## 66 FMA7:Tam -0.99762355
## 67 FMA7:ViG 0.04521536
## 68 FMA7:Yac -0.41807241
## 69 FSV1:Chi -0.03704516
## 70 FSV1:Gig -0.21162176
## 71 FSV1:HtC 0.14417172
## 72 FSV1:PtR -0.10100030
## 73 FSV1:RiN -0.26804599
## 74 FSV1:SnV -0.13045700
## 75 FSV1:Tam 0.23516918
## 76 FSV1:ViG 0.05451130
## 77 FSV1:Yac -0.21898349
#Predichos completos
datos$pred <- predict(modelo_blup)
datos$pred
## [1] 7.909298 7.909298 7.909298 7.909298 7.441667 7.441667 7.441667
## [8] 7.441667 9.339847 9.339847 9.339847 9.339847 8.387267 8.387267
## [15] 8.387267 8.387267 9.233967 9.233967 9.233967 9.233967 8.433001
## [22] 8.433001 8.433001 8.433001 8.367355 8.367355 8.367355 8.367355
## [29] 8.080286 8.080286 8.080286 8.080286 7.776215 7.776215 7.776215
## [36] 7.776215 7.367853 7.367853 7.367853 7.367853 9.221649 9.221649
## [43] 9.221649 9.221649 7.998308 7.998308 7.998308 7.998308 8.065871
## [50] 8.065871 8.065871 8.065871 8.550314 8.550314 8.550314 8.550314
## [57] 8.282889 8.282889 8.282889 8.282889 3.061988 3.061988 3.061988
## [64] 3.061988 3.096748 3.096748 3.096748 3.096748 4.432366 4.432366
## [71] 4.432366 4.432366 3.124523 3.124523 3.124523 3.124523 3.370775
## [78] 3.370775 3.370775 3.370775 3.254442 3.254442 3.254442 3.254442
## [85] 3.953997 3.953997 3.953997 3.953997 3.244413 3.244413 3.244413
## [92] 3.244413 3.328945 3.328945 3.328945 3.328945 3.133546 3.133546
## [99] 3.133546 3.133546 4.380070 4.380070 4.380070 4.380070 3.155188
## [106] 3.155188 3.155188 3.155188 3.646022 3.646022 3.646022 3.646022
## [113] 3.398941 3.398941 3.398941 3.398941 4.216644 4.216644 4.216644
## [120] 4.216644 3.674124 3.674124 3.674124 3.674124 9.280861 9.280861
## [127] 9.280861 9.280861 11.141200 11.141200 11.141200 11.141200 10.185229
## [134] 10.185229 10.185229 10.185229 10.078010 10.078010 10.078010 10.078010
## [141] 11.020862 11.020862 11.020862 11.020862 9.690448 9.690448 9.690448
## [148] 9.690448 6.079021 6.079021 6.079021 6.079021 6.627633 6.627633
## [155] 6.627633 6.627633 7.141712 7.141712 7.141712 7.141712 6.190510
## [162] 6.190510 6.190510 6.190510 6.382645 6.382645 6.382645 6.382645
## [169] 6.618133 6.618133 6.618133 6.618133 7.110083 7.110083 7.110083
## [176] 7.110083 6.733064 6.733064 6.733064 6.733064 8.965174 8.965174
## [183] 8.965174 8.965174 8.687335 8.687335 8.687335 8.687335 11.122524
## [190] 11.122524 11.122524 11.122524 8.382419 8.382419 8.382419 8.382419
## [197] 9.161370 9.161370 9.161370 9.161370 8.492695 8.492695 8.492695
## [204] 8.492695 10.130293 10.130293 10.130293 10.130293 8.536355 8.536355
## [211] 8.536355 8.536355 4.779591 4.779591 4.779591 4.779591 4.738579
## [218] 4.738579 4.738579 4.738579 6.060458 6.060458 6.060458 6.060458
## [225] 5.472175 5.472175 5.472175 5.472175 5.256591 5.256591 5.256591
## [232] 5.256591 4.686576 4.686576 4.686576 4.686576 5.763227 5.763227
## [239] 5.763227 5.763227 5.232965 5.232965 5.232965 5.232965 5.902061
## [246] 5.902061 5.902061 5.902061 5.580083 5.580083 5.580083 5.580083
## [253] 6.690662 6.690662 6.690662 6.690662 5.676687 5.676687 5.676687
## [260] 5.676687 6.104081 6.104081 6.104081 6.104081 5.873037 5.873037
## [267] 5.873037 5.873037 6.353811 6.353811 6.353811 6.353811 5.969053
## [274] 5.969053 5.969053 5.969053 19.645729 19.645729 19.645729 19.645729
## [281] 18.144393 18.144393 18.144393 18.144393 19.742831 19.742831 19.742831
## [288] 19.742831 19.534965 19.534965 19.534965 19.534965 20.545811 20.545811
## [295] 20.545811 20.545811 19.611876 19.611876 19.611876 19.611876 20.783561
## [302] 20.783561 20.783561 20.783561 19.822879 19.822879 19.822879 19.822879
#Visualizar Blups gen
ggplot(blup_gen, aes(x=reorder(gen, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Genotipo") +
coord_flip()
#Visualizar Blups mun
ggplot(blup_mun, aes(x=reorder(mun, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Municipio") +
coord_flip()
#Visualizar Blups gen:mun
ggplot(blup_gen_mun, aes(x=reorder(`gen:mun`, BLUP), y=BLUP)) +
geom_point(size=3) +
geom_hline(yintercept=0, linetype="dashed") +
labs(x = "Gen * Mun") +
coord_flip()
##Componentes de varianza-heredabilidades
vc <- as.data.frame(VarCorr(modelo_blup))
vc
## grp var1 var2 vcov sdcor
## 1 gen:mun (Intercept) <NA> 0.7520752 0.8672227
## 2 mun (Intercept) <NA> 22.2628612 4.7183537
## 3 gen (Intercept) <NA> 0.3798505 0.6163201
## 4 Residual <NA> <NA> 13.9092197 3.7295066
VarCorr(modelo_blup)
## Groups Name Std.Dev.
## gen:mun (Intercept) 0.86722
## mun (Intercept) 4.71835
## gen (Intercept) 0.61632
## Residual 3.72951
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] 0.4731639
#H2 plasticidad
H2ge <- varGE / (varG + varGE/e + varE/(r*e))
H2ge
## [1] 0.9368285
###ranking genotipos predichos
blup_gen <- ranef(modelo_blup)$gen
blup_gen
## (Intercept)
## CNCH12 -0.1694569
## CNCH13 0.4764206
## FBO1 -0.1065272
## FCHI8 -0.2339906
## FEAR5 0.7287737
## FGI4 0.1120320
## FMA7 -0.5378970
## FSV1 -0.2693545
##Predicho de carbono por genotipo
media <- fixef(modelo_blup)[1]
blup_gen$pred <- media + blup_gen[,1]
# Ranking predichos (publicar)
blup_gen[order(-blup_gen$pred),]
## (Intercept) pred
## FEAR5 0.7287737 8.783040
## CNCH13 0.4764206 8.530687
## FGI4 0.1120320 8.166298
## FBO1 -0.1065272 7.947739
## CNCH12 -0.1694569 7.884810
## FCHI8 -0.2339906 7.820276
## FSV1 -0.2693545 7.784912
## FMA7 -0.5378970 7.516369
# Visualización ranking predichos
blup_gen$gen <- rownames(blup_gen)
ggplot(blup_gen, aes(x=reorder(gen,pred), y=pred))+
geom_point(size=3)+
coord_flip()+
ylab("Carbono predicho (BLUP)")+
xlab("Genotipo")
### Análisis G×E (estabilidad)
## matriz genotipo × parcela.
mat <- datos %>%
group_by(gen,mun) %>%
summarise(total_alt_co2=mean(total_alt_co2)) %>%
pivot_wider(names_from=mun,
values_from=total_alt_co2)
## `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, total_alt_co2)
## Warning: Data imputation used to fill the GxE matrix
modelo_metan
## $total_alt_co2
## $coordgen
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 2.1067993 -0.4316305 -0.8991511 -2.869853 -5.3937292 4.5395296
## [2,] -1.8258440 3.0237550 6.1435565 0.925107 0.8593636 2.1883804
## [3,] 2.3649214 -1.0746937 1.9935227 3.497847 -3.7023381 -5.0810449
## [4,] 2.4741465 -0.2102677 -2.7112722 5.254716 3.2164134 3.3644028
## [5,] -6.8541186 0.9542592 -3.1009723 1.088511 -1.5743752 -0.6457962
## [6,] 2.1375879 4.0995615 -3.2472484 -2.600966 1.3785677 -2.9191886
## [7,] -1.0635388 -6.6535967 0.2381104 -1.727789 2.0017584 -0.5176899
## [8,] 0.6600463 0.2926128 1.5834543 -3.567573 3.2143394 -0.9285931
## [,7] [,8]
## [1,] 0.3965949 -3.012507
## [2,] -2.6311488 -3.012507
## [3,] 1.0223277 -3.012507
## [4,] 0.8562701 -3.012507
## [5,] 1.3929182 -3.012507
## [6,] -3.7972261 -3.012507
## [7,] -3.2875390 -3.012507
## [8,] 6.0478029 -3.012507
##
## $coordenv
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -1.4046269 0.47277276 1.28610684 0.105591744 -0.95074434 -0.91251731
## [2,] -1.0853811 0.41312523 -0.42473955 3.441728893 1.36942674 -0.16025363
## [3,] -0.4213545 -0.08534389 0.06754809 -0.641005782 -0.27445834 -0.64374200
## [4,] -2.7855938 3.16970526 0.76307512 0.002887331 -0.38759110 2.07036281
## [5,] -2.0268953 0.95710150 -0.22415785 1.641285344 -2.38217786 -0.25825735
## [6,] -0.5707601 -2.92975648 1.60705159 0.189851356 -1.72991164 0.06445634
## [7,] -2.7251879 -0.30293292 -0.00551218 0.301159567 -0.52012715 0.33234247
## [8,] 4.0450485 7.82340163 2.52598069 0.088594497 -0.09847215 -0.42944444
## [9,] -9.4860923 2.51836719 0.42848361 -0.806552936 0.79242786 -0.66015906
## [10,] 0.4325271 3.67988711 -5.09940174 -0.304624835 -0.83757110 -0.15240267
## [,7] [,8]
## [1,] -0.30397731 -2.662008e-16
## [2,] -0.47521054 1.582132e-16
## [3,] 0.53193629 6.862466e-16
## [4,] 0.08838995 1.267581e-16
## [5,] 1.27866955 -4.196268e-17
## [6,] -1.63052038 2.158538e-16
## [7,] -0.44080956 -1.337652e-16
## [8,] -0.34788407 2.890675e-17
## [9,] -0.18230864 1.813083e-18
## [10,] -0.77403515 3.222832e-17
##
## $eigenvalues
## [1] 1.137912e+01 1.005630e+01 6.133701e+00 3.979859e+00 3.644429e+00
## [6] 2.526645e+00 2.418738e+00 8.068586e-16
##
## $totalvar
## [1] 309.59
##
## $varexpl
## [1] 41.82 32.67 12.15 5.12 4.29 2.06 1.89 0.00
##
## $labelgen
## [1] "CNCH12" "CNCH13" "FBO1" "FCHI8" "FEAR5" "FGI4" "FMA7" "FSV1"
##
## $labelenv
## [1] "Chi" "Gig" "HtC" "Jam"
## [5] "PtR" "RiN............." "SnV" "Tam"
## [9] "ViG" "Yac"
##
## $labelaxes
## [1] "PC1" "PC2" "PC3" "PC4" "PC5" "PC6" "PC7" "PC8"
##
## $ge_mat
## Chi Gig HtC Jam PtR
## CNCH12 -0.4410066 -2.3780575 -0.03555883 0.4216642 0.35108775
## CNCH13 1.1711508 0.6903191 -0.31816336 2.7376100 0.08912237
## FBO1 0.8155550 0.4036597 0.21343421 -2.0487997 1.28055029
## FCHI8 -1.5133979 2.3382251 -0.84137777 -0.4480429 -0.40136661
## FEAR5 0.9234145 1.1950482 0.40937431 2.2475597 2.69766497
## FGI4 -0.3530111 -0.4740140 -0.03775573 -0.2768791 -1.33035428
## FMA7 -0.2299578 -0.3820886 0.02050027 -2.3576709 -1.87076914
## FSV1 -0.3727468 -1.3930920 0.58954690 -0.2754412 -0.81593536
## RiN............. SnV Tam ViG Yac
## CNCH12 0.8272707 -0.2735371 0.124812277 -3.10846229 0.9742296
## CNCH13 0.6075204 0.6742043 3.727625379 3.11446727 -2.3812271
## FBO1 1.1826367 -0.6209013 1.020450102 -3.15389605 -1.3002691
## FCHI8 -1.2791352 -0.7057704 -0.009328154 -3.43023228 1.0154174
## FEAR5 -0.3813949 2.1975812 -3.291819364 7.52760523 1.9209080
## FGI4 -1.7985196 -0.9208182 4.075404659 -0.64958850 4.1770569
## FMA7 2.5842193 0.5431793 -6.424218592 -0.31035630 -2.8971286
## FSV1 -1.7425973 -0.8939377 0.777073694 0.01046292 -1.5089870
##
## $centering
## [1] "environment"
##
## $scaling
## [1] "none"
##
## $svp
## [1] "environment"
##
## $d
## [1] 0.1173619
##
## $grand_mean
## [1] 8.049886
##
## $mean_gen
## CNCH12 CNCH13 FBO1 FCHI8 FEAR5 FGI4 FMA7 FSV1
## 7.696131 9.061149 7.829128 7.522386 9.594481 8.291039 6.917457 7.487321
##
## $mean_env
## Chi Gig HtC Jam
## 3.530409 5.194226 5.979109 10.166410
## PtR RiN............. SnV Tam
## 8.125164 6.582278 3.352743 19.955986
## ViG Yac
## 9.206750 8.405790
##
## $scale_val
## Chi Gig HtC Jam
## 0.9016426 1.4881178 0.4428740 1.8060730
## PtR RiN............. SnV Tam
## 1.4709024 1.5667639 1.0812983 3.4696974
## ViG Yac
## 3.7466029 2.4245537
##
## attr(,"class")
## [1] "gge"
##
## attr(,"class")
## [1] "gge"
#Grafica metan
plot(modelo_metan)
## Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the metan package.
## Please report the issue at <https://github.com/nepem-ufsc/metan/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
### Selección ideotípica
##Integración de variables
##Blup gen
blup_gen <- ranef(modelo_blup)$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 7.884810
## CNCH13 CNCH13 8.530687
## FBO1 FBO1 7.947739
## FCHI8 FCHI8 7.820276
## FEAR5 FEAR5 8.783040
## FGI4 FGI4 8.166298
## FMA7 FMA7 7.516369
## FSV1 FSV1 7.784912
##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(total_alt_co2))
datos <- left_join(datos, indice_env, by="mun")
#visualización Normas de reacción joint regression env
ggplot(datos, aes(x = env, y = total_alt_co2,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (local)",
y = expression(t.CO[2][eq]/ha.año)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
#visualización Normas de reacción clima local
ggplot(datos, aes(x = E, y = total_alt_co2,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (Estrés)",
y = expression(t.CO[2][eq]/ha.año)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
## plasticidad joint
# modelo factores fijos
mod_plas_lm <- lm(total_alt_co2 ~ gen*env,
data=datos)
emtrends(mod_plas_lm, "gen", var = "env")
## gen env.trend SE df lower.CL upper.CL
## CNCH12 1.039 0.128 292 0.787 1.291
## CNCH13 1.205 0.128 292 0.953 1.457
## FBO1 0.997 0.128 292 0.744 1.249
## FCHI8 1.011 0.130 292 0.755 1.267
## FEAR5 0.790 0.128 292 0.538 1.042
## FGI4 1.283 0.128 292 1.031 1.535
## FMA7 0.573 0.128 292 0.321 0.825
## FSV1 1.093 0.130 292 0.837 1.348
##
## Confidence level used: 0.95
# modelo blup factores aleatorios
modelo_plasticidad <- lmer(total_alt_co2 ~ 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(total_alt_co2 ~ gen*E,
data=datos)
emtrends(mod_plas2_lm, "gen", var = "E")
## gen E.trend SE df lower.CL upper.CL
## CNCH12 26.3 15.6 292 -4.43 57.1
## CNCH13 48.3 15.6 292 17.52 79.1
## FBO1 38.9 15.6 292 8.11 69.7
## FCHI8 39.0 16.5 292 6.44 71.5
## FEAR5 17.2 15.6 292 -13.62 47.9
## FGI4 35.3 15.6 292 4.52 66.1
## FMA7 27.9 15.6 292 -2.88 58.7
## FSV1 46.0 15.8 292 14.80 77.2
##
## Confidence level used: 0.95
#Modelo factores aleatorios
modelo_plasticidad2 <- lmer(total_alt_co2 ~ E +
(E|gen) +
(1|mun),
data=datos)
## boundary (singular) fit: see help('isSingular')
## Warning: Model failed to converge with 1 negative eigenvalue: -1.3e+00
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 1.38 68.8 68.8
## 2 PC2 0.62 31.2 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 2 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C 0.83 0.69 0.31
## 2 Pendiente 0.83 0.69 0.31
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.6881142
## -------------------------------------------------------------------------------
## 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 8.05e+ 0 8.53 0.476 5.92e 0 increase 100
## 2 Pendiente FA1 1.18e-14 0.159 0.159 1.34e15 increase 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## CNCH13
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 CNCH13 0.493
## 2 FGI4 0.888
## 3 FEAR5 1.14
## 4 CNCH12 1.97
## 5 FBO1 1.97
## 6 FSV1 1.98
## 7 FCHI8 2.14
## 8 FMA7 3.76
#Gráfico Selección 1
plot(mgidi_mod)
##Tabla selección MGIDI 2 estrés
tabla_sel2 <- blup_gen %>%
left_join(plasticidad, by="gen") %>%
left_join(plasticidad2, by="gen")
mgidi_mod2<-mgidi(tabla_sel2,
ideotype = c("h, h, l"))
##
## -------------------------------------------------------------------------------
## Principal Component Analysis
## -------------------------------------------------------------------------------
## # A tibble: 3 × 4
## PC Eigenvalues `Variance (%)` `Cum. variance (%)`
## <chr> <dbl> <dbl> <dbl>
## 1 PC1 2.2 73.3 73.3
## 2 PC2 0.8 26.7 99.9
## 3 PC3 0 0.06 100
## -------------------------------------------------------------------------------
## Factor Analysis - factorial loadings after rotation-
## -------------------------------------------------------------------------------
## # A tibble: 3 × 4
## VAR FA1 Communality Uniquenesses
## <chr> <dbl> <dbl> <dbl>
## 1 BLUP_C 0.98 0.95 0.05
## 2 Pendiente 0.56 0.32 0.68
## 3 Pendiente2 0.96 0.93 0.07
## -------------------------------------------------------------------------------
## Comunalit Mean: 0.7327794
## -------------------------------------------------------------------------------
## 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 8.05e+ 0 8.78 0.729 9.05e 0 increase 100
## 2 Pendiente FA1 1.18e-14 -0.103 -0.103 -8.72e14 increase 0
## 3 Pendiente2 FA1 -1.56e-15 -2.12 -2.12 -1.36e17 decrease 100
## ------------------------------------------------------------------------------
## Selected genotypes
## -------------------------------------------------------------------------------
## FEAR5
## -------------------------------------------------------------------------------
#ranking MGIDI 1
mgidi_mod2$MGIDI
## # A tibble: 8 × 2
## Genotype MGIDI
## <chr> <dbl>
## 1 FEAR5 0.485
## 2 CNCH13 0.699
## 3 FGI4 1.36
## 4 FBO1 2.15
## 5 CNCH12 2.20
## 6 FCHI8 2.40
## 7 FSV1 2.42
## 8 FMA7 3.51
#Gráfico Selección 1
plot(mgidi_mod2)
