setwd("G:/Mi unidad/Agrosavia/FeCa/Fenoma/Análisis/Carbono")
carbon<-read.table("carbonf.csv", header=T, sep=';')
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'
carbon$gen <- factor(carbon$gen)
carbon$municipio <- factor(carbon$municipio)
carbon$reg <- factor(carbon$reg)
# Centrar estrés (muy importante para interpretar interceptos)
carbon$Estres_c <- scale(carbon$E, scale = FALSE)
### CO2 eq
##modelo_tasa estrés
modelo_tasa <- lmer(total_alt_co2 ~ Estres_c * gen +
(1 | municipio),
data = carbon)
anova(modelo_tasa, type = 3)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Estres_c 26.025 26.025 1 7.999 1.7857 0.21822
## gen 220.321 31.474 7 284.098 2.1596 0.03787 *
## Estres_c:gen 91.096 13.014 7 284.047 0.8929 0.51237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(modelo_tasa)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: total_alt_co2 ~ Estres_c * gen + (1 | municipio)
## Data: carbon
##
## REML criterion at convergence: 1654
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8155 -0.4777 -0.0568 0.4153 4.0252
##
## Random effects:
## Groups Name Variance Std.Dev.
## municipio (Intercept) 20.43 4.520
## Residual 14.57 3.818
## Number of obs: 308, groups: municipio, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.7306 1.5521 10.6100 4.981 0.000463 ***
## Estres_c 26.3451 27.0706 10.6100 0.973 0.352119
## genCNCH13 1.3937 0.8539 284.0123 1.632 0.103740
## genFBO1 0.1494 0.8539 284.0123 0.175 0.861241
## genFCHI8 -0.2085 0.9177 284.2789 -0.227 0.820428
## genFEAR5 1.8863 0.8539 284.0123 2.209 0.027961 *
## genFGI4 0.6066 0.8539 284.0123 0.710 0.478027
## genFMA7 -0.7766 0.8539 284.0123 -0.910 0.363828
## genFSV1 -0.2686 0.8811 284.1537 -0.305 0.760711
## Estres_c:genCNCH13 21.9487 14.8923 284.0123 1.474 0.141635
## Estres_c:genFBO1 12.5395 14.8923 284.0123 0.842 0.400491
## Estres_c:genFCHI8 6.1737 15.3868 284.1317 0.401 0.688552
## Estres_c:genFEAR5 -9.1885 14.8923 284.0123 -0.617 0.537734
## Estres_c:genFGI4 8.9492 14.8923 284.0123 0.601 0.548369
## Estres_c:genFMA7 1.5552 14.8923 284.0123 0.104 0.916902
## Estres_c:genFSV1 16.8806 15.0078 284.0480 1.125 0.261629
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
#A posteriori
emmeans(modelo_tasa, pairwise ~ gen)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
## gen emmean SE df lower.CL upper.CL
## CNCH12 7.73 1.55 10.6 4.30 11.2
## CNCH13 9.12 1.55 10.6 5.69 12.6
## FBO1 7.88 1.55 10.6 4.45 11.3
## FCHI8 7.52 1.59 11.6 4.05 11.0
## FEAR5 9.62 1.55 10.6 6.18 13.0
## FGI4 8.34 1.55 10.6 4.90 11.8
## FMA7 6.95 1.55 10.6 3.52 10.4
## FSV1 7.46 1.57 11.0 4.01 10.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -1.3937 0.854 284 -1.632 0.7304
## CNCH12 - FBO1 -0.1494 0.854 284 -0.175 1.0000
## CNCH12 - FCHI8 0.2085 0.918 284 0.227 1.0000
## CNCH12 - FEAR5 -1.8863 0.854 284 -2.209 0.3494
## CNCH12 - FGI4 -0.6066 0.854 284 -0.710 0.9967
## CNCH12 - FMA7 0.7766 0.854 284 0.910 0.9850
## CNCH12 - FSV1 0.2686 0.881 284 0.305 1.0000
## CNCH13 - FBO1 1.2443 0.854 284 1.457 0.8293
## CNCH13 - FCHI8 1.6022 0.918 284 1.746 0.6572
## CNCH13 - FEAR5 -0.4926 0.854 284 -0.577 0.9991
## CNCH13 - FGI4 0.7871 0.854 284 0.922 0.9838
## CNCH13 - FMA7 2.1703 0.854 284 2.542 0.1824
## CNCH13 - FSV1 1.6623 0.881 284 1.887 0.5613
## FBO1 - FCHI8 0.3579 0.918 284 0.390 0.9999
## FBO1 - FEAR5 -1.7370 0.854 284 -2.034 0.4607
## FBO1 - FGI4 -0.4572 0.854 284 -0.535 0.9995
## FBO1 - FMA7 0.9260 0.854 284 1.085 0.9596
## FBO1 - FSV1 0.4180 0.881 284 0.474 0.9998
## FCHI8 - FEAR5 -2.0948 0.918 284 -2.282 0.3070
## FCHI8 - FGI4 -0.8151 0.918 284 -0.888 0.9870
## FCHI8 - FMA7 0.5681 0.918 284 0.619 0.9986
## FCHI8 - FSV1 0.0601 0.936 284 0.064 1.0000
## FEAR5 - FGI4 1.2797 0.854 284 1.499 0.8077
## FEAR5 - FMA7 2.6630 0.854 284 3.119 0.0414
## FEAR5 - FSV1 2.1549 0.881 284 2.446 0.2237
## FGI4 - FMA7 1.3832 0.854 284 1.620 0.7379
## FGI4 - FSV1 0.8752 0.881 284 0.993 0.9751
## FMA7 - FSV1 -0.5080 0.881 284 -0.577 0.9991
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates
emtrends(modelo_tasa, pairwise ~ gen, var = "Estres_c")
## $emtrends
## gen Estres_c.trend SE df lower.CL upper.CL
## CNCH12 26.3 27.1 10.6 -33.5 86.2
## CNCH13 48.3 27.1 10.6 -11.6 108.2
## FBO1 38.9 27.1 10.6 -21.0 98.7
## FCHI8 32.5 27.3 11.0 -27.7 92.7
## FEAR5 17.2 27.1 10.6 -42.7 77.0
## FGI4 35.3 27.1 10.6 -24.6 95.2
## FMA7 27.9 27.1 10.6 -32.0 87.8
## FSV1 43.2 27.1 10.7 -16.7 103.2
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -21.95 14.9 284 -1.474 0.8208
## CNCH12 - FBO1 -12.54 14.9 284 -0.842 0.9905
## CNCH12 - FCHI8 -6.17 15.4 284 -0.401 0.9999
## CNCH12 - FEAR5 9.19 14.9 284 0.617 0.9986
## CNCH12 - FGI4 -8.95 14.9 284 -0.601 0.9988
## CNCH12 - FMA7 -1.56 14.9 284 -0.104 1.0000
## CNCH12 - FSV1 -16.88 15.0 284 -1.125 0.9510
## CNCH13 - FBO1 9.41 14.9 284 0.632 0.9984
## CNCH13 - FCHI8 15.78 15.4 284 1.025 0.9703
## CNCH13 - FEAR5 31.14 14.9 284 2.091 0.4234
## CNCH13 - FGI4 13.00 14.9 284 0.873 0.9882
## CNCH13 - FMA7 20.39 14.9 284 1.369 0.8704
## CNCH13 - FSV1 5.07 15.0 284 0.338 1.0000
## FBO1 - FCHI8 6.37 15.4 284 0.414 0.9999
## FBO1 - FEAR5 21.73 14.9 284 1.459 0.8284
## FBO1 - FGI4 3.59 14.9 284 0.241 1.0000
## FBO1 - FMA7 10.98 14.9 284 0.738 0.9958
## FBO1 - FSV1 -4.34 15.0 284 -0.289 1.0000
## FCHI8 - FEAR5 15.36 15.4 284 0.998 0.9744
## FCHI8 - FGI4 -2.78 15.4 284 -0.180 1.0000
## FCHI8 - FMA7 4.62 15.4 284 0.300 1.0000
## FCHI8 - FSV1 -10.71 15.5 284 -0.692 0.9972
## FEAR5 - FGI4 -18.14 14.9 284 -1.218 0.9261
## FEAR5 - FMA7 -10.74 14.9 284 -0.721 0.9963
## FEAR5 - FSV1 -26.07 15.0 284 -1.737 0.6630
## FGI4 - FMA7 7.39 14.9 284 0.496 0.9997
## FGI4 - FSV1 -7.93 15.0 284 -0.528 0.9995
## FMA7 - FSV1 -15.33 15.0 284 -1.021 0.9710
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates
# Comparación genotipos en niveles de estrés
niveles_estres <- quantile(carbon$Estres_c, probs = c(0.1, 0.5, 0.9))
emmeans(modelo_tasa, pairwise ~ gen | Estres_c,
at = list(Estres_c = niveles_estres))
## $emmeans
## Estres_c = -0.09429:
## gen emmean SE df lower.CL upper.CL
## CNCH12 5.25 2.96 10.6 -1.29 11.79
## CNCH13 4.57 2.96 10.6 -1.97 11.11
## FBO1 4.21 2.96 10.6 -2.33 10.75
## FCHI8 4.46 3.04 11.8 -2.18 11.09
## FEAR5 8.00 2.96 10.6 1.46 14.54
## FGI4 5.01 2.96 10.6 -1.53 11.55
## FMA7 4.32 2.96 10.6 -2.22 10.86
## FSV1 3.39 2.98 11.0 -3.18 9.95
##
## Estres_c = 0.00571:
## gen emmean SE df lower.CL upper.CL
## CNCH12 7.88 1.56 10.6 4.42 11.34
## CNCH13 9.40 1.56 10.6 5.94 12.86
## FBO1 8.10 1.56 10.6 4.65 11.56
## FCHI8 7.71 1.59 11.5 4.22 11.20
## FEAR5 9.71 1.56 10.6 6.26 13.17
## FGI4 8.54 1.56 10.6 5.08 12.00
## FMA7 7.11 1.56 10.6 3.66 10.57
## FSV1 7.71 1.58 11.0 4.24 11.18
##
## Estres_c = 0.09061:
## gen emmean SE df lower.CL upper.CL
## CNCH12 10.12 2.93 10.6 3.63 16.60
## CNCH13 13.50 2.93 10.6 7.02 19.99
## FBO1 11.40 2.93 10.6 4.92 17.89
## FCHI8 10.47 2.93 10.6 3.98 16.95
## FEAR5 11.17 2.93 10.6 4.69 17.66
## FGI4 11.54 2.93 10.6 5.05 18.02
## FMA7 9.48 2.93 10.6 3.00 15.97
## FSV1 11.38 2.93 10.6 4.89 17.86
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Estres_c = -0.09429:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.67577 1.630 284 0.415 0.9999
## CNCH12 - FBO1 1.03292 1.630 284 0.635 0.9984
## CNCH12 - FCHI8 0.79060 1.770 284 0.447 0.9998
## CNCH12 - FEAR5 -2.75269 1.630 284 -1.692 0.6924
## CNCH12 - FGI4 0.23719 1.630 284 0.146 1.0000
## CNCH12 - FMA7 0.92328 1.630 284 0.568 0.9992
## CNCH12 - FSV1 1.86021 1.670 284 1.112 0.9539
## CNCH13 - FBO1 0.35715 1.630 284 0.220 1.0000
## CNCH13 - FCHI8 0.11483 1.770 284 0.065 1.0000
## CNCH13 - FEAR5 -3.42847 1.630 284 -2.108 0.4125
## CNCH13 - FGI4 -0.43859 1.630 284 -0.270 1.0000
## CNCH13 - FMA7 0.24750 1.630 284 0.152 1.0000
## CNCH13 - FSV1 1.18444 1.670 284 0.708 0.9967
## FBO1 - FCHI8 -0.24232 1.770 284 -0.137 1.0000
## FBO1 - FEAR5 -3.78562 1.630 284 -2.327 0.2825
## FBO1 - FGI4 -0.79574 1.630 284 -0.489 0.9997
## FBO1 - FMA7 -0.10965 1.630 284 -0.067 1.0000
## FBO1 - FSV1 0.82729 1.670 284 0.494 0.9997
## FCHI8 - FEAR5 -3.54329 1.770 284 -2.002 0.4826
## FCHI8 - FGI4 -0.55341 1.770 284 -0.313 1.0000
## FCHI8 - FMA7 0.13267 1.770 284 0.075 1.0000
## FCHI8 - FSV1 1.06961 1.800 284 0.594 0.9989
## FEAR5 - FGI4 2.98988 1.630 284 1.838 0.5946
## FEAR5 - FMA7 3.67597 1.630 284 2.260 0.3197
## FEAR5 - FSV1 4.61290 1.670 284 2.757 0.1105
## FGI4 - FMA7 0.68609 1.630 284 0.422 0.9999
## FGI4 - FSV1 1.62302 1.670 284 0.970 0.9783
## FMA7 - FSV1 0.93693 1.670 284 0.560 0.9993
##
## Estres_c = 0.00571:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -1.51910 0.860 284 -1.766 0.6434
## CNCH12 - FBO1 -0.22102 0.860 284 -0.257 1.0000
## CNCH12 - FCHI8 0.17324 0.916 284 0.189 1.0000
## CNCH12 - FEAR5 -1.83385 0.860 284 -2.132 0.3967
## CNCH12 - FGI4 -0.65773 0.860 284 -0.765 0.9947
## CNCH12 - FMA7 0.76776 0.860 284 0.893 0.9866
## CNCH12 - FSV1 0.17215 0.885 284 0.195 1.0000
## CNCH13 - FBO1 1.29807 0.860 284 1.509 0.8020
## CNCH13 - FCHI8 1.69233 0.916 284 1.848 0.5880
## CNCH13 - FEAR5 -0.31475 0.860 284 -0.366 1.0000
## CNCH13 - FGI4 0.86137 0.860 284 1.002 0.9739
## CNCH13 - FMA7 2.28686 0.860 284 2.659 0.1398
## CNCH13 - FSV1 1.69125 0.885 284 1.912 0.5438
## FBO1 - FCHI8 0.39426 0.916 284 0.430 0.9999
## FBO1 - FEAR5 -1.61282 0.860 284 -1.875 0.5690
## FBO1 - FGI4 -0.43671 0.860 284 -0.508 0.9996
## FBO1 - FMA7 0.98878 0.860 284 1.150 0.9450
## FBO1 - FSV1 0.39318 0.885 284 0.444 0.9998
## FCHI8 - FEAR5 -2.00708 0.916 284 -2.191 0.3600
## FCHI8 - FGI4 -0.83097 0.916 284 -0.907 0.9852
## FCHI8 - FMA7 0.59452 0.916 284 0.649 0.9981
## FCHI8 - FSV1 -0.00108 0.932 284 -0.001 1.0000
## FEAR5 - FGI4 1.17612 0.860 284 1.368 0.8712
## FEAR5 - FMA7 2.60160 0.860 284 3.025 0.0542
## FEAR5 - FSV1 2.00600 0.885 284 2.268 0.3152
## FGI4 - FMA7 1.42549 0.860 284 1.657 0.7146
## FGI4 - FSV1 0.82988 0.885 284 0.938 0.9820
## FMA7 - FSV1 -0.59560 0.885 284 -0.673 0.9976
##
## Estres_c = 0.09061:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -3.38254 1.610 284 -2.097 0.4196
## CNCH12 - FBO1 -1.28563 1.610 284 -0.797 0.9932
## CNCH12 - FCHI8 -0.35091 1.610 284 -0.217 1.0000
## CNCH12 - FEAR5 -1.05375 1.610 284 -0.653 0.9980
## CNCH12 - FGI4 -1.41752 1.610 284 -0.879 0.9878
## CNCH12 - FMA7 0.63572 1.610 284 0.394 0.9999
## CNCH12 - FSV1 -1.26101 1.610 284 -0.781 0.9940
## CNCH13 - FBO1 2.09692 1.610 284 1.300 0.8984
## CNCH13 - FCHI8 3.03164 1.610 284 1.877 0.5676
## CNCH13 - FEAR5 2.32880 1.610 284 1.444 0.8361
## CNCH13 - FGI4 1.96503 1.610 284 1.218 0.9261
## CNCH13 - FMA7 4.01826 1.610 284 2.491 0.2036
## CNCH13 - FSV1 2.12154 1.610 284 1.314 0.8929
## FBO1 - FCHI8 0.93472 1.610 284 0.579 0.9991
## FBO1 - FEAR5 0.23188 1.610 284 0.144 1.0000
## FBO1 - FGI4 -0.13189 1.610 284 -0.082 1.0000
## FBO1 - FMA7 1.92135 1.610 284 1.191 0.9340
## FBO1 - FSV1 0.02462 1.610 284 0.015 1.0000
## FCHI8 - FEAR5 -0.70284 1.610 284 -0.435 0.9999
## FCHI8 - FGI4 -1.06661 1.610 284 -0.661 0.9979
## FCHI8 - FMA7 0.98663 1.610 284 0.611 0.9987
## FCHI8 - FSV1 -0.91010 1.620 284 -0.563 0.9992
## FEAR5 - FGI4 -0.36377 1.610 284 -0.225 1.0000
## FEAR5 - FMA7 1.68947 1.610 284 1.047 0.9666
## FEAR5 - FSV1 -0.20726 1.610 284 -0.128 1.0000
## FGI4 - FMA7 2.05323 1.610 284 1.273 0.9082
## FGI4 - FSV1 0.15651 1.610 284 0.097 1.0000
## FMA7 - FSV1 -1.89673 1.610 284 -1.175 0.9384
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates
# Visualización
library(ggplot2)
ggplot(carbon, aes(x = Estres_c, y = total_alt_co2,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (E)",
y = expression(t.CO[2][eq]/ha.año)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
### Carbono aéreo
##modelo_tasa estrés
modelo_tasa <- lmer(Mg.ab.diamalt ~ Estres_c * gen +
(1 | municipio),
data = carbon)
anova(modelo_tasa, type = 3)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Estres_c 9.510 9.5098 1 8.002 1.1986 0.3055
## gen 87.514 12.5020 7 284.078 1.5757 0.1423
## Estres_c:gen 61.603 8.8004 7 284.039 1.1092 0.3573
summary(modelo_tasa)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mg.ab.diamalt ~ Estres_c * gen + (1 | municipio)
## Data: carbon
##
## REML criterion at convergence: 1478.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9074 -0.3747 -0.0491 0.3375 4.0913
##
## Random effects:
## Groups Name Variance Std.Dev.
## municipio (Intercept) 14.700 3.834
## Residual 7.934 2.817
## Number of obs: 308, groups: municipio, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.88575 1.29197 9.95755 3.782 0.00362 **
## Estres_c 17.63209 22.53328 9.95755 0.782 0.45213
## genCNCH13 0.78639 0.63002 284.01225 1.248 0.21298
## genFBO1 0.12652 0.63002 284.01225 0.201 0.84099
## genFCHI8 -0.02619 0.67715 284.21594 -0.039 0.96918
## genFEAR5 1.17832 0.63002 284.01225 1.870 0.06247 .
## genFGI4 0.57807 0.63002 284.01225 0.918 0.35964
## genFMA7 -0.52263 0.63002 284.01225 -0.830 0.40749
## genFSV1 -0.22794 0.65012 284.12030 -0.351 0.72614
## Estres_c:genCNCH13 19.75207 10.98814 284.01225 1.798 0.07331 .
## Estres_c:genFBO1 9.93819 10.98814 284.01225 0.904 0.36652
## Estres_c:genFCHI8 3.87471 11.35329 284.10334 0.341 0.73314
## Estres_c:genFEAR5 -6.39757 10.98814 284.01225 -0.582 0.56088
## Estres_c:genFGI4 3.98091 10.98814 284.01225 0.362 0.71740
## Estres_c:genFMA7 1.62342 10.98814 284.01225 0.148 0.88265
## Estres_c:genFSV1 13.01177 11.07342 284.03954 1.175 0.24096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
#A posteriori
emmeans(modelo_tasa, pairwise ~ gen)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
## gen emmean SE df lower.CL upper.CL
## CNCH12 4.89 1.29 9.94 2.00 7.77
## CNCH13 5.67 1.29 9.94 2.79 8.55
## FBO1 5.01 1.29 9.94 2.13 7.89
## FCHI8 4.86 1.32 10.68 1.95 7.77
## FEAR5 6.06 1.29 9.94 3.18 8.95
## FGI4 5.46 1.29 9.94 2.58 8.34
## FMA7 4.36 1.29 9.94 1.48 7.24
## FSV1 4.66 1.30 10.25 1.77 7.55
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.7864 0.630 284 -1.248 0.9166
## CNCH12 - FBO1 -0.1265 0.630 284 -0.201 1.0000
## CNCH12 - FCHI8 0.0262 0.677 284 0.039 1.0000
## CNCH12 - FEAR5 -1.1783 0.630 284 -1.870 0.5725
## CNCH12 - FGI4 -0.5781 0.630 284 -0.918 0.9842
## CNCH12 - FMA7 0.5226 0.630 284 0.830 0.9913
## CNCH12 - FSV1 0.2279 0.650 284 0.351 1.0000
## CNCH13 - FBO1 0.6599 0.630 284 1.047 0.9666
## CNCH13 - FCHI8 0.8126 0.677 284 1.200 0.9315
## CNCH13 - FEAR5 -0.3919 0.630 284 -0.622 0.9986
## CNCH13 - FGI4 0.2083 0.630 284 0.331 1.0000
## CNCH13 - FMA7 1.3090 0.630 284 2.078 0.4319
## CNCH13 - FSV1 1.0143 0.650 284 1.560 0.7736
## FBO1 - FCHI8 0.1527 0.677 284 0.225 1.0000
## FBO1 - FEAR5 -1.0518 0.630 284 -1.669 0.7070
## FBO1 - FGI4 -0.4516 0.630 284 -0.717 0.9965
## FBO1 - FMA7 0.6492 0.630 284 1.030 0.9695
## FBO1 - FSV1 0.3545 0.650 284 0.545 0.9994
## FCHI8 - FEAR5 -1.2045 0.677 284 -1.779 0.6351
## FCHI8 - FGI4 -0.6043 0.677 284 -0.892 0.9866
## FCHI8 - FMA7 0.4964 0.677 284 0.733 0.9959
## FCHI8 - FSV1 0.2017 0.691 284 0.292 1.0000
## FEAR5 - FGI4 0.6003 0.630 284 0.953 0.9804
## FEAR5 - FMA7 1.7010 0.630 284 2.700 0.1269
## FEAR5 - FSV1 1.4063 0.650 284 2.163 0.3775
## FGI4 - FMA7 1.1007 0.630 284 1.747 0.6563
## FGI4 - FSV1 0.8060 0.650 284 1.240 0.9193
## FMA7 - FSV1 -0.2947 0.650 284 -0.453 0.9998
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates
emtrends(modelo_tasa, pairwise ~ gen, var = "Estres_c")
## $emtrends
## gen Estres_c.trend SE df lower.CL upper.CL
## CNCH12 17.6 22.5 9.94 -32.6 67.9
## CNCH13 37.4 22.5 9.94 -12.9 87.6
## FBO1 27.6 22.5 9.94 -22.7 77.8
## FCHI8 21.5 22.7 10.26 -28.9 71.9
## FEAR5 11.2 22.5 9.94 -39.0 61.5
## FGI4 21.6 22.5 9.94 -28.6 71.9
## FMA7 19.3 22.5 9.94 -31.0 69.5
## FSV1 30.6 22.6 10.02 -19.6 80.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -19.752 11.0 284 -1.798 0.6223
## CNCH12 - FBO1 -9.938 11.0 284 -0.904 0.9855
## CNCH12 - FCHI8 -3.875 11.4 284 -0.341 1.0000
## CNCH12 - FEAR5 6.398 11.0 284 0.582 0.9991
## CNCH12 - FGI4 -3.981 11.0 284 -0.362 1.0000
## CNCH12 - FMA7 -1.623 11.0 284 -0.148 1.0000
## CNCH12 - FSV1 -13.012 11.1 284 -1.175 0.9384
## CNCH13 - FBO1 9.814 11.0 284 0.893 0.9865
## CNCH13 - FCHI8 15.877 11.4 284 1.398 0.8575
## CNCH13 - FEAR5 26.150 11.0 284 2.380 0.2553
## CNCH13 - FGI4 15.771 11.0 284 1.435 0.8401
## CNCH13 - FMA7 18.129 11.0 284 1.650 0.7194
## CNCH13 - FSV1 6.740 11.1 284 0.609 0.9987
## FBO1 - FCHI8 6.063 11.4 284 0.534 0.9995
## FBO1 - FEAR5 16.336 11.0 284 1.487 0.8141
## FBO1 - FGI4 5.957 11.0 284 0.542 0.9994
## FBO1 - FMA7 8.315 11.0 284 0.757 0.9950
## FBO1 - FSV1 -3.074 11.1 284 -0.278 1.0000
## FCHI8 - FEAR5 10.272 11.4 284 0.905 0.9855
## FCHI8 - FGI4 -0.106 11.4 284 -0.009 1.0000
## FCHI8 - FMA7 2.251 11.4 284 0.198 1.0000
## FCHI8 - FSV1 -9.137 11.4 284 -0.801 0.9930
## FEAR5 - FGI4 -10.378 11.0 284 -0.945 0.9813
## FEAR5 - FMA7 -8.021 11.0 284 -0.730 0.9960
## FEAR5 - FSV1 -19.409 11.1 284 -1.753 0.6525
## FGI4 - FMA7 2.357 11.0 284 0.215 1.0000
## FGI4 - FSV1 -9.031 11.1 284 -0.816 0.9922
## FMA7 - FSV1 -11.388 11.1 284 -1.028 0.9698
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates
# Comparación genotipos en niveles de estrés
niveles_estres <- quantile(carbon$Estres_c, probs = c(0.1, 0.5, 0.9))
emmeans(modelo_tasa, pairwise ~ gen | Estres_c,
at = list(Estres_c = niveles_estres))
## $emmeans
## Estres_c = -0.09429:
## gen emmean SE df lower.CL upper.CL
## CNCH12 3.22 2.46 9.94 -2.265 8.71
## CNCH13 2.15 2.46 9.94 -3.341 7.64
## FBO1 2.41 2.46 9.94 -3.076 7.90
## FCHI8 2.83 2.51 10.82 -2.714 8.38
## FEAR5 5.00 2.46 9.94 -0.484 10.49
## FGI4 3.43 2.46 9.94 -2.062 8.91
## FMA7 2.55 2.46 9.94 -2.941 8.04
## FSV1 1.77 2.48 10.22 -3.738 7.27
##
## Estres_c = 0.00571:
## gen emmean SE df lower.CL upper.CL
## CNCH12 4.99 1.30 9.94 2.085 7.89
## CNCH13 5.89 1.30 9.94 2.984 8.79
## FBO1 5.17 1.30 9.94 2.268 8.07
## FCHI8 4.98 1.32 10.58 2.059 7.91
## FEAR5 6.13 1.30 9.94 3.227 9.03
## FGI4 5.59 1.30 9.94 2.686 8.49
## FMA7 4.47 1.30 9.94 1.571 7.37
## FSV1 4.83 1.31 10.22 1.922 7.74
##
## Estres_c = 0.09061:
## gen emmean SE df lower.CL upper.CL
## CNCH12 6.48 2.44 9.94 1.040 11.93
## CNCH13 9.06 2.44 9.94 3.616 14.50
## FBO1 7.51 2.44 9.94 2.067 12.95
## FCHI8 6.81 2.44 9.95 1.365 12.25
## FEAR5 7.08 2.44 9.94 1.639 12.53
## FGI4 7.42 2.44 9.94 1.979 12.87
## FMA7 6.11 2.44 9.94 0.665 11.55
## FSV1 7.43 2.44 9.95 1.991 12.88
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Estres_c = -0.09429:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 1.07597 1.200 284 0.896 0.9862
## CNCH12 - FBO1 0.81053 1.200 284 0.675 0.9976
## CNCH12 - FCHI8 0.39153 1.310 284 0.300 1.0000
## CNCH12 - FEAR5 -1.78153 1.200 284 -1.484 0.8154
## CNCH12 - FGI4 -0.20272 1.200 284 -0.169 1.0000
## CNCH12 - FMA7 0.67570 1.200 284 0.563 0.9992
## CNCH12 - FSV1 1.45478 1.230 284 1.178 0.9376
## CNCH13 - FBO1 -0.26545 1.200 284 -0.221 1.0000
## CNCH13 - FCHI8 -0.68445 1.310 284 -0.524 0.9995
## CNCH13 - FEAR5 -2.85750 1.200 284 -2.381 0.2548
## CNCH13 - FGI4 -1.27869 1.200 284 -1.065 0.9634
## CNCH13 - FMA7 -0.40027 1.200 284 -0.333 1.0000
## CNCH13 - FSV1 0.37881 1.230 284 0.307 1.0000
## FBO1 - FCHI8 -0.41900 1.310 284 -0.321 1.0000
## FBO1 - FEAR5 -2.59205 1.200 284 -2.160 0.3796
## FBO1 - FGI4 -1.01324 1.200 284 -0.844 0.9903
## FBO1 - FMA7 -0.13483 1.200 284 -0.112 1.0000
## FBO1 - FSV1 0.64425 1.230 284 0.522 0.9995
## FCHI8 - FEAR5 -2.17305 1.310 284 -1.664 0.7106
## FCHI8 - FGI4 -0.59425 1.310 284 -0.455 0.9998
## FCHI8 - FMA7 0.28418 1.310 284 0.218 1.0000
## FCHI8 - FSV1 1.06325 1.330 284 0.800 0.9930
## FEAR5 - FGI4 1.57881 1.200 284 1.315 0.8925
## FEAR5 - FMA7 2.45723 1.200 284 2.047 0.4520
## FEAR5 - FSV1 3.23631 1.230 284 2.621 0.1527
## FGI4 - FMA7 0.87842 1.200 284 0.732 0.9960
## FGI4 - FSV1 1.65750 1.230 284 1.342 0.8818
## FMA7 - FSV1 0.77908 1.230 284 0.631 0.9984
##
## Estres_c = 0.00571:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.89923 0.635 284 -1.417 0.8488
## CNCH12 - FBO1 -0.18330 0.635 284 -0.289 1.0000
## CNCH12 - FCHI8 0.00405 0.676 284 0.006 1.0000
## CNCH12 - FEAR5 -1.14177 0.635 284 -1.799 0.6211
## CNCH12 - FGI4 -0.60081 0.635 284 -0.947 0.9811
## CNCH12 - FMA7 0.51336 0.635 284 0.809 0.9925
## CNCH12 - FSV1 0.15360 0.653 284 0.235 1.0000
## CNCH13 - FBO1 0.71594 0.635 284 1.128 0.9502
## CNCH13 - FCHI8 0.90329 0.676 284 1.337 0.8841
## CNCH13 - FEAR5 -0.24254 0.635 284 -0.382 0.9999
## CNCH13 - FGI4 0.29842 0.635 284 0.470 0.9998
## CNCH13 - FMA7 1.41259 0.635 284 2.226 0.3393
## CNCH13 - FSV1 1.05283 0.653 284 1.613 0.7421
## FBO1 - FCHI8 0.18735 0.676 284 0.277 1.0000
## FBO1 - FEAR5 -0.95848 0.635 284 -1.510 0.8014
## FBO1 - FGI4 -0.41752 0.635 284 -0.658 0.9979
## FBO1 - FMA7 0.69665 0.635 284 1.098 0.9569
## FBO1 - FSV1 0.33690 0.653 284 0.516 0.9996
## FCHI8 - FEAR5 -1.14583 0.676 284 -1.696 0.6902
## FCHI8 - FGI4 -0.60487 0.676 284 -0.895 0.9863
## FCHI8 - FMA7 0.50930 0.676 284 0.754 0.9952
## FCHI8 - FSV1 0.14955 0.688 284 0.217 1.0000
## FEAR5 - FGI4 0.54096 0.635 284 0.852 0.9898
## FEAR5 - FMA7 1.65513 0.635 284 2.608 0.1572
## FEAR5 - FSV1 1.29537 0.653 284 1.985 0.4941
## FGI4 - FMA7 1.11417 0.635 284 1.756 0.6504
## FGI4 - FSV1 0.75441 0.653 284 1.156 0.9435
## FMA7 - FSV1 -0.35976 0.653 284 -0.551 0.9993
##
## Estres_c = 0.09061:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -2.57618 1.190 284 -2.164 0.3767
## CNCH12 - FBO1 -1.02705 1.190 284 -0.863 0.9890
## CNCH12 - FCHI8 -0.32491 1.190 284 -0.273 1.0000
## CNCH12 - FEAR5 -0.59862 1.190 284 -0.503 0.9996
## CNCH12 - FGI4 -0.93879 1.190 284 -0.789 0.9936
## CNCH12 - FMA7 0.37553 1.190 284 0.315 1.0000
## CNCH12 - FSV1 -0.95110 1.190 284 -0.799 0.9931
## CNCH13 - FBO1 1.54914 1.190 284 1.301 0.8978
## CNCH13 - FCHI8 2.25128 1.190 284 1.889 0.5593
## CNCH13 - FEAR5 1.97757 1.190 284 1.661 0.7121
## CNCH13 - FGI4 1.63739 1.190 284 1.376 0.8677
## CNCH13 - FMA7 2.95171 1.190 284 2.480 0.2084
## CNCH13 - FSV1 1.62509 1.190 284 1.365 0.8725
## FBO1 - FCHI8 0.70214 1.190 284 0.589 0.9990
## FBO1 - FEAR5 0.42843 1.190 284 0.360 1.0000
## FBO1 - FGI4 0.08826 1.190 284 0.074 1.0000
## FBO1 - FMA7 1.40258 1.190 284 1.178 0.9376
## FBO1 - FSV1 0.07595 1.190 284 0.064 1.0000
## FCHI8 - FEAR5 -0.27371 1.190 284 -0.230 1.0000
## FCHI8 - FGI4 -0.61388 1.190 284 -0.515 0.9996
## FCHI8 - FMA7 0.70044 1.190 284 0.588 0.9990
## FCHI8 - FSV1 -0.62619 1.190 284 -0.525 0.9995
## FEAR5 - FGI4 -0.34017 1.190 284 -0.286 1.0000
## FEAR5 - FMA7 0.97415 1.190 284 0.818 0.9920
## FEAR5 - FSV1 -0.35248 1.190 284 -0.296 1.0000
## FGI4 - FMA7 1.31432 1.190 284 1.104 0.9556
## FGI4 - FSV1 -0.01231 1.190 284 -0.010 1.0000
## FMA7 - FSV1 -1.32663 1.190 284 -1.114 0.9534
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates
# Visualización
library(ggplot2)
ggplot(carbon, aes(x = Estres_c, y = Mg.ab.diamalt,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (E)",
y = expression(BA-t.C/ha)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
###Carbono subterráneo
##modelo_tasa estrés
modelo_tasa <- lmer(Mg.bg.diamalt ~ Estres_c * gen +
(1 | municipio),
data = carbon)
anova(modelo_tasa, type = 3)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Estres_c 0.4273 0.42729 1 8.002 1.1504 0.3147
## gen 4.4772 0.63960 7 284.076 1.7221 0.1036
## Estres_c:gen 2.8254 0.40362 7 284.038 1.0867 0.3718
summary(modelo_tasa)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Mg.bg.diamalt ~ Estres_c * gen + (1 | municipio)
## Data: carbon
##
## REML criterion at convergence: 584.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8426 -0.4042 -0.0459 0.3834 3.8407
##
## Random effects:
## Groups Name Variance Std.Dev.
## municipio (Intercept) 0.7013 0.8374
## Residual 0.3714 0.6094
## Number of obs: 308, groups: municipio, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.23112 0.28187 9.91930 4.368 0.00143 **
## Estres_c 3.71219 4.91616 9.91930 0.755 0.46774
## genCNCH13 0.18027 0.13631 284.01181 1.323 0.18706
## genFBO1 0.03055 0.13631 284.01181 0.224 0.82282
## genFCHI8 -0.01538 0.14651 284.21179 -0.105 0.91647
## genFEAR5 0.27161 0.13631 284.01181 1.993 0.04726 *
## genFGI4 0.11718 0.13631 284.01181 0.860 0.39071
## genFMA7 -0.11434 0.13631 284.01181 -0.839 0.40226
## genFSV1 -0.04926 0.14066 284.11789 -0.350 0.72645
## Estres_c:genCNCH13 4.18911 2.37738 284.01181 1.762 0.07913 .
## Estres_c:genFBO1 2.29129 2.37738 284.01181 0.964 0.33597
## Estres_c:genFCHI8 0.87831 2.45639 284.10123 0.358 0.72094
## Estres_c:genFEAR5 -1.34264 2.37738 284.01181 -0.565 0.57268
## Estres_c:genFGI4 0.94442 2.37738 284.01181 0.397 0.69148
## Estres_c:genFMA7 0.42813 2.37738 284.01181 0.180 0.85721
## Estres_c:genFSV1 2.88873 2.39583 284.03859 1.206 0.22892
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
#A posteriori
emmeans(modelo_tasa, pairwise ~ gen)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
## gen emmean SE df lower.CL upper.CL
## CNCH12 1.23 0.282 9.9 0.602 1.86
## CNCH13 1.41 0.282 9.9 0.783 2.04
## FBO1 1.26 0.282 9.9 0.633 1.89
## FCHI8 1.22 0.287 10.6 0.581 1.85
## FEAR5 1.50 0.282 9.9 0.874 2.13
## FGI4 1.35 0.282 9.9 0.719 1.98
## FMA7 1.12 0.282 9.9 0.488 1.75
## FSV1 1.18 0.284 10.2 0.551 1.81
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.1803 0.136 284 -1.323 0.8897
## CNCH12 - FBO1 -0.0306 0.136 284 -0.224 1.0000
## CNCH12 - FCHI8 0.0154 0.147 284 0.105 1.0000
## CNCH12 - FEAR5 -0.2716 0.136 284 -1.993 0.4887
## CNCH12 - FGI4 -0.1172 0.136 284 -0.860 0.9892
## CNCH12 - FMA7 0.1143 0.136 284 0.839 0.9907
## CNCH12 - FSV1 0.0493 0.141 284 0.350 1.0000
## CNCH13 - FBO1 0.1497 0.136 284 1.098 0.9568
## CNCH13 - FCHI8 0.1956 0.147 284 1.335 0.8846
## CNCH13 - FEAR5 -0.0913 0.136 284 -0.670 0.9977
## CNCH13 - FGI4 0.0631 0.136 284 0.463 0.9998
## CNCH13 - FMA7 0.2946 0.136 284 2.161 0.3785
## CNCH13 - FSV1 0.2295 0.141 284 1.632 0.7307
## FBO1 - FCHI8 0.0459 0.147 284 0.313 1.0000
## FBO1 - FEAR5 -0.2411 0.136 284 -1.768 0.6419
## FBO1 - FGI4 -0.0866 0.136 284 -0.636 0.9983
## FBO1 - FMA7 0.1449 0.136 284 1.063 0.9638
## FBO1 - FSV1 0.0798 0.141 284 0.567 0.9992
## FCHI8 - FEAR5 -0.2870 0.147 284 -1.959 0.5118
## FCHI8 - FGI4 -0.1326 0.147 284 -0.905 0.9855
## FCHI8 - FMA7 0.0990 0.147 284 0.675 0.9976
## FCHI8 - FSV1 0.0339 0.149 284 0.227 1.0000
## FEAR5 - FGI4 0.1544 0.136 284 1.133 0.9491
## FEAR5 - FMA7 0.3860 0.136 284 2.831 0.0914
## FEAR5 - FSV1 0.3209 0.141 284 2.281 0.3077
## FGI4 - FMA7 0.2315 0.136 284 1.698 0.6883
## FGI4 - FSV1 0.1664 0.141 284 1.183 0.9362
## FMA7 - FSV1 -0.0651 0.141 284 -0.463 0.9998
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates
emtrends(modelo_tasa, pairwise ~ gen, var = "Estres_c")
## $emtrends
## gen Estres_c.trend SE df lower.CL upper.CL
## CNCH12 3.71 4.92 9.90 -7.26 14.7
## CNCH13 7.90 4.92 9.90 -3.07 18.9
## FBO1 6.00 4.92 9.90 -4.96 17.0
## FCHI8 4.59 4.95 10.22 -6.42 15.6
## FEAR5 2.37 4.92 9.90 -8.60 13.3
## FGI4 4.66 4.92 9.90 -6.31 15.6
## FMA7 4.14 4.92 9.90 -6.83 15.1
## FSV1 6.60 4.93 9.98 -4.38 17.6
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -4.1891 2.38 284 -1.762 0.6462
## CNCH12 - FBO1 -2.2913 2.38 284 -0.964 0.9790
## CNCH12 - FCHI8 -0.8783 2.46 284 -0.358 1.0000
## CNCH12 - FEAR5 1.3426 2.38 284 0.565 0.9992
## CNCH12 - FGI4 -0.9444 2.38 284 -0.397 0.9999
## CNCH12 - FMA7 -0.4281 2.38 284 -0.180 1.0000
## CNCH12 - FSV1 -2.8887 2.40 284 -1.206 0.9298
## CNCH13 - FBO1 1.8978 2.38 284 0.798 0.9931
## CNCH13 - FCHI8 3.3108 2.46 284 1.348 0.8795
## CNCH13 - FEAR5 5.5318 2.38 284 2.327 0.2827
## CNCH13 - FGI4 3.2447 2.38 284 1.365 0.8724
## CNCH13 - FMA7 3.7610 2.38 284 1.582 0.7608
## CNCH13 - FSV1 1.3004 2.40 284 0.543 0.9994
## FBO1 - FCHI8 1.4130 2.46 284 0.575 0.9991
## FBO1 - FEAR5 3.6339 2.38 284 1.529 0.7915
## FBO1 - FGI4 1.3469 2.38 284 0.567 0.9992
## FBO1 - FMA7 1.8632 2.38 284 0.784 0.9938
## FBO1 - FSV1 -0.5974 2.40 284 -0.249 1.0000
## FCHI8 - FEAR5 2.2210 2.46 284 0.904 0.9855
## FCHI8 - FGI4 -0.0661 2.46 284 -0.027 1.0000
## FCHI8 - FMA7 0.4502 2.46 284 0.183 1.0000
## FCHI8 - FSV1 -2.0104 2.47 284 -0.814 0.9922
## FEAR5 - FGI4 -2.2871 2.38 284 -0.962 0.9793
## FEAR5 - FMA7 -1.7708 2.38 284 -0.745 0.9955
## FEAR5 - FSV1 -4.2314 2.40 284 -1.766 0.6435
## FGI4 - FMA7 0.5163 2.38 284 0.217 1.0000
## FGI4 - FSV1 -1.9443 2.40 284 -0.812 0.9924
## FMA7 - FSV1 -2.4606 2.40 284 -1.027 0.9700
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates
# Comparación genotipos en niveles de estrés
niveles_estres <- quantile(carbon$Estres_c, probs = c(0.1, 0.5, 0.9))
emmeans(modelo_tasa, pairwise ~ gen | Estres_c,
at = list(Estres_c = niveles_estres))
## $emmeans
## Estres_c = -0.09429:
## gen emmean SE df lower.CL upper.CL
## CNCH12 0.881 0.537 9.90 -0.3169 2.08
## CNCH13 0.666 0.537 9.90 -0.5316 1.86
## FBO1 0.696 0.537 9.90 -0.5024 1.89
## FCHI8 0.783 0.548 10.77 -0.4274 1.99
## FEAR5 1.279 0.537 9.90 0.0813 2.48
## FGI4 0.909 0.537 9.90 -0.2888 2.11
## FMA7 0.726 0.537 9.90 -0.4717 1.92
## FSV1 0.559 0.541 10.17 -0.6423 1.76
##
## Estres_c = 0.00571:
## gen emmean SE df lower.CL upper.CL
## CNCH12 1.252 0.284 9.90 0.6189 1.89
## CNCH13 1.457 0.284 9.90 0.8231 2.09
## FBO1 1.296 0.284 9.90 0.6626 1.93
## FCHI8 1.242 0.288 10.53 0.6039 1.88
## FEAR5 1.516 0.284 9.90 0.8829 2.15
## FGI4 1.375 0.284 9.90 0.7415 2.01
## FMA7 1.140 0.284 9.90 0.5070 1.77
## FSV1 1.220 0.286 10.17 0.5842 1.85
##
## Estres_c = 0.09061:
## gen emmean SE df lower.CL upper.CL
## CNCH12 1.567 0.533 9.90 0.3793 2.76
## CNCH13 2.127 0.533 9.90 0.9392 3.32
## FBO1 1.806 0.533 9.90 0.6175 2.99
## FCHI8 1.632 0.533 9.91 0.4434 2.82
## FEAR5 1.717 0.533 9.90 0.5293 2.91
## FGI4 1.770 0.533 9.90 0.5821 2.96
## FMA7 1.492 0.533 9.90 0.3038 2.68
## FSV1 1.780 0.533 9.91 0.5918 2.97
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## Estres_c = -0.09429:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 0.21471 0.260 284 0.827 0.9915
## CNCH12 - FBO1 0.18549 0.260 284 0.714 0.9965
## CNCH12 - FCHI8 0.09819 0.283 284 0.347 1.0000
## CNCH12 - FEAR5 -0.39821 0.260 284 -1.533 0.7887
## CNCH12 - FGI4 -0.02813 0.260 284 -0.108 1.0000
## CNCH12 - FMA7 0.15471 0.260 284 0.596 0.9989
## CNCH12 - FSV1 0.32163 0.267 284 1.204 0.9303
## CNCH13 - FBO1 -0.02922 0.260 284 -0.113 1.0000
## CNCH13 - FCHI8 -0.11652 0.283 284 -0.412 0.9999
## CNCH13 - FEAR5 -0.61292 0.260 284 -2.360 0.2652
## CNCH13 - FGI4 -0.24284 0.260 284 -0.935 0.9824
## CNCH13 - FMA7 -0.06000 0.260 284 -0.231 1.0000
## CNCH13 - FSV1 0.10692 0.267 284 0.400 0.9999
## FBO1 - FCHI8 -0.08730 0.283 284 -0.309 1.0000
## FBO1 - FEAR5 -0.58370 0.260 284 -2.248 0.3267
## FBO1 - FGI4 -0.21362 0.260 284 -0.823 0.9917
## FBO1 - FMA7 -0.03078 0.260 284 -0.119 1.0000
## FBO1 - FSV1 0.13614 0.267 284 0.510 0.9996
## FCHI8 - FEAR5 -0.49640 0.283 284 -1.757 0.6499
## FCHI8 - FGI4 -0.12632 0.283 284 -0.447 0.9998
## FCHI8 - FMA7 0.05652 0.283 284 0.200 1.0000
## FCHI8 - FSV1 0.22344 0.288 284 0.777 0.9942
## FEAR5 - FGI4 0.37008 0.260 284 1.425 0.8450
## FEAR5 - FMA7 0.55292 0.260 284 2.129 0.3987
## FEAR5 - FSV1 0.71984 0.267 284 2.694 0.1285
## FGI4 - FMA7 0.18284 0.260 284 0.704 0.9968
## FGI4 - FSV1 0.34976 0.267 284 1.309 0.8949
## FMA7 - FSV1 0.16692 0.267 284 0.625 0.9985
##
## Estres_c = 0.00571:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.20420 0.137 284 -1.487 0.8138
## CNCH12 - FBO1 -0.04364 0.137 284 -0.318 1.0000
## CNCH12 - FCHI8 0.01036 0.146 284 0.071 1.0000
## CNCH12 - FEAR5 -0.26394 0.137 284 -1.922 0.5366
## CNCH12 - FGI4 -0.12257 0.137 284 -0.893 0.9865
## CNCH12 - FMA7 0.11190 0.137 284 0.815 0.9922
## CNCH12 - FSV1 0.03276 0.141 284 0.232 1.0000
## CNCH13 - FBO1 0.16056 0.137 284 1.169 0.9399
## CNCH13 - FCHI8 0.21456 0.146 284 1.467 0.8241
## CNCH13 - FEAR5 -0.05974 0.137 284 -0.435 0.9999
## CNCH13 - FGI4 0.08163 0.137 284 0.595 0.9989
## CNCH13 - FMA7 0.31610 0.137 284 2.302 0.2959
## CNCH13 - FSV1 0.23696 0.141 284 1.678 0.7015
## FBO1 - FCHI8 0.05400 0.146 284 0.369 1.0000
## FBO1 - FEAR5 -0.22030 0.137 284 -1.605 0.7473
## FBO1 - FGI4 -0.07893 0.137 284 -0.575 0.9991
## FBO1 - FMA7 0.15554 0.137 284 1.133 0.9491
## FBO1 - FSV1 0.07640 0.141 284 0.541 0.9994
## FCHI8 - FEAR5 -0.27431 0.146 284 -1.876 0.5685
## FCHI8 - FGI4 -0.13293 0.146 284 -0.909 0.9850
## FCHI8 - FMA7 0.10154 0.146 284 0.694 0.9971
## FCHI8 - FSV1 0.02239 0.149 284 0.150 1.0000
## FEAR5 - FGI4 0.14137 0.137 284 1.030 0.9696
## FEAR5 - FMA7 0.37584 0.137 284 2.738 0.1158
## FEAR5 - FSV1 0.29670 0.141 284 2.101 0.4167
## FGI4 - FMA7 0.23447 0.137 284 1.708 0.6822
## FGI4 - FSV1 0.15533 0.141 284 1.100 0.9565
## FMA7 - FSV1 -0.07914 0.141 284 -0.560 0.9993
##
## Estres_c = 0.09061:
## contrast estimate SE df t.ratio p.value
## CNCH12 - CNCH13 -0.55986 0.258 284 -2.174 0.3708
## CNCH12 - FBO1 -0.23817 0.258 284 -0.925 0.9835
## CNCH12 - FCHI8 -0.06421 0.258 284 -0.249 1.0000
## CNCH12 - FEAR5 -0.14995 0.258 284 -0.582 0.9991
## CNCH12 - FGI4 -0.20275 0.258 284 -0.787 0.9937
## CNCH12 - FMA7 0.07555 0.258 284 0.293 1.0000
## CNCH12 - FSV1 -0.21250 0.258 284 -0.825 0.9916
## CNCH13 - FBO1 0.32169 0.258 284 1.249 0.9163
## CNCH13 - FCHI8 0.49565 0.258 284 1.923 0.5364
## CNCH13 - FEAR5 0.40990 0.258 284 1.592 0.7551
## CNCH13 - FGI4 0.35710 0.258 284 1.387 0.8628
## CNCH13 - FMA7 0.63541 0.258 284 2.467 0.2139
## CNCH13 - FSV1 0.34736 0.258 284 1.348 0.8794
## FBO1 - FCHI8 0.17397 0.258 284 0.675 0.9976
## FBO1 - FEAR5 0.08822 0.258 284 0.343 1.0000
## FBO1 - FGI4 0.03542 0.258 284 0.138 1.0000
## FBO1 - FMA7 0.31372 0.258 284 1.218 0.9261
## FBO1 - FSV1 0.02567 0.258 284 0.100 1.0000
## FCHI8 - FEAR5 -0.08575 0.258 284 -0.333 1.0000
## FCHI8 - FGI4 -0.13855 0.258 284 -0.537 0.9994
## FCHI8 - FMA7 0.13976 0.258 284 0.542 0.9994
## FCHI8 - FSV1 -0.14829 0.258 284 -0.575 0.9991
## FEAR5 - FGI4 -0.05280 0.258 284 -0.205 1.0000
## FEAR5 - FMA7 0.22550 0.258 284 0.876 0.9880
## FEAR5 - FSV1 -0.06254 0.258 284 -0.243 1.0000
## FGI4 - FMA7 0.27830 0.258 284 1.081 0.9604
## FGI4 - FSV1 -0.00974 0.258 284 -0.038 1.0000
## FMA7 - FSV1 -0.28805 0.258 284 -1.118 0.9525
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates
# Visualización
library(ggplot2)
ggplot(carbon, aes(x = Estres_c, y = Mg.bg.diamalt,
color = gen)) +
geom_smooth(method = "lm", se = FALSE) +
labs(x = "Ambiente (E)",
y = expression(BS-t.C/ha)) +
theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
