Efecto de diferentes niveles de radiación fotosintéticamente activa en el potencial fotosintético de Calycophyllum spruceanum, Lima – Perú, 2024
Autores: Ayala y Lucas; Asesor: Camel
2024-12-10
Se presenta a continuación la librería utilizada para el análisis estadístico:
library(MASS)
library(nnet)
library(colorspace)
library(MuMIn)
library(car)
library(lme4)
library(Matrix)
library(Rcpp)
library(lattice)
library(lmerTest)
library(nlme)
library(glmmML)
library(AICcmodavg)
library(bestglm)
library(mgcv)
library(pscl)
library(bbmle)
library(ggpmisc)
library(survival)
library(splines)
library(multcomp)
library(lsmeans)
library(Cairo)
library(effects)
library(fitdistrplus)
library(splines)
library(grid)
library(ggplot2)
library(gridExtra)
library(cowplot)
library(lmtest)
library(gridGraphics)
setwd("~/Rstudio")Base de datos optenida de PhotosynQ:
Colores asignados para cada tratamiento en los gráficos de dispersión:
color_values <- c("IA14h" = "#5E3C99", "IA18h" = "#95D840FF", "IB14h" = "#0571B0", "IB18h" = "#20A387FF", "Control" = "#FDE725") OE1: Evaluación del efecto de tres niveles de radiación fotosintética activa en el potencial fotosintético de Calycophyllum spruceanum en la etapa de juvenil.
Regresión lineal: Relación entre el LEF con ECSt-mAU, vH+, gH+ y PAR
LEF/ECSt_mAU
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000600 0.0004500 0.0005700 0.0006246 0.0007500 0.0017500## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.53 8.05 17.79 20.09 27.83 68.45
MCap<-glm(ECSt_mAU ~LEF, data=datos, family = gaussian,na.action = na.fail)
null = glm (ECSt_mAU~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.95547, p-value = 7.038e-14## R2m R2c
## [1,] 0.1606243 0.1606243## [1] 0.1608136
## attr(,"adj.r.squared")
## [1] -1.768864e-07##
## Call:
## glm(formula = ECSt_mAU ~ LEF, family = gaussian, data = datos,
## na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.708e-04 1.579e-05 29.81 <2e-16 ***
## LEF 7.658e-06 6.556e-07 11.68 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 5.419677e-08)
##
## Null deviance: 4.5983e-05 on 713 degrees of freedom
## Residual deviance: 3.8588e-05 on 712 degrees of freedom
## AIC: -9915.4
##
## Number of Fisher Scoring iterations: 2mid <- mean(LEF)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G1 <- ggplot(datos, aes(x = LEF, y = ECSt_mAU, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha=0.9, size = 4,color="gray10")+
geom_point(alpha=0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = LEF, y = ECSt_mAU),
method = "glm", color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_y_continuous(breaks = seq(0, 0.0021, by = 0.0003), limits = c(0, 0.0021)) +
scale_x_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60,70), limits = c(0, 70)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("LEF") + ylab("ECSt_mAU") +
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(legend.position = "") +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank())+
theme(legend.background = element_blank())+
labs(x = "LEF", y = "ECSt_mAU", color = "Tratamiento")+
annotate("text", x = 40, y = 0.00190, label = annotation_text,
size = 5, hjust = 0, color = "black")
G1
LEF/gH+
detach(datos)
datos <- read.csv2("Data_Capirona_VA_gH_vH_.csv", row.names=NULL)
attach(datos)
summary(gH)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.001 51.877 71.968 81.817 101.152 319.650## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.530 8.075 17.810 20.095 27.780 68.450
MCap<-glm(gH ~LEF, data=datos, family = gaussian,na.action = na.fail)
null = glm (gH~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.91101, p-value < 2.2e-16## R2m R2c
## [1,] 0.241045 0.241045## [1] 0.2413029
## attr(,"adj.r.squared")
## [1] 0.2413102##
## Call:
## glm(formula = gH ~ LEF, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 48.8235 2.6298 18.57 <2e-16 ***
## LEF 1.6418 0.1093 15.02 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1485.059)
##
## Null deviance: 1387783 on 710 degrees of freedom
## Residual deviance: 1052907 on 709 degrees of freedom
## AIC: 7214.3
##
## Number of Fisher Scoring iterations: 2mid <- mean(LEF)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G2 <- ggplot(datos, aes(x = LEF, y = gH, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha=0.9, size = 4,color="gray10")+
geom_point(alpha=0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = LEF, y = gH),
method = "glm", color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_y_continuous(breaks = seq(0, 320, by = 40), limits = c(0, 320)) +
scale_x_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60,70), limits = c(0, 70)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("LEF") + ylab("gH") +
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(legend.position = "") +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank())+
theme(legend.background = element_blank())+
labs(x = "LEF", y = "gH+", color = "Tratamiento")+
annotate("text", x = 38, y = 305, label = annotation_text,
size = 5, hjust = 0, color = "black")
G2
LEF/vH+
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00900 0.02900 0.04200 0.04714 0.06200 0.12600## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.530 8.075 17.810 20.095 27.780 68.450
MCap<-glm(vH ~LEF, data=datos, family = gaussian,na.action = na.fail)
null = glm (vH~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.96679, p-value = 1.284e-11## R2m R2c
## [1,] 0.784743 0.784743## [1] 0.7849809
## attr(,"adj.r.squared")
## [1] -0.007060174##
## Call:
## glm(formula = vH ~ LEF, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0163739 0.0007239 22.62 <2e-16 ***
## LEF 0.0015313 0.0000301 50.88 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0001125366)
##
## Null deviance: 0.371076 on 710 degrees of freedom
## Residual deviance: 0.079788 on 709 degrees of freedom
## AIC: -4442.8
##
## Number of Fisher Scoring iterations: 2mid <- mean(datos$LEF)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G3 <- ggplot(datos, aes(x = LEF, y = vH, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha=0.9, size = 4,color="gray10")+
geom_point(alpha=0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = LEF, y = vH),
method = "glm", color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_y_continuous(breaks = seq(0, 0.14, by = 0.02), limits = c(0, 0.14)) +
scale_x_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60,70), limits = c(0, 75)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("LEF") + ylab("vH") +
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(legend.position = "") +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank())+
theme(legend.background = element_blank())+
labs(x = "LEF", y = "vH+", color = "Tratamiento")+
annotate("text", x = 38, y = 0.01, label = annotation_text,
size = 5, hjust = 0, color = "black")
G3
LEF/gH+
detach(datos)
datos <- read.csv2("Data_Capirona.csv", row.names=NULL)
attach(datos)
summary(Light_Intensity)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 20.19 31.77 73.62 96.85 155.57 318.18## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.53 8.04 17.61 20.05 27.68 68.45
MCap<-glm(Light_Intensity ~LEF, data=datos, family = gaussian,na.action = na.fail)
null = glm (Light_Intensity~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.78953, p-value < 2.2e-16## R2m R2c
## [1,] 0.8663162 0.8663162## [1] 0.8664779
## attr(,"adj.r.squared")
## [1] 0.866488##
## Call:
## glm(formula = Light_Intensity ~ LEF, family = gaussian, data = datos,
## na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.16928 1.76112 -1.80 0.0723 .
## LEF 4.98861 0.07324 68.12 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 678.051)
##
## Null deviance: 3630909 on 716 degrees of freedom
## Residual deviance: 484806 on 715 degrees of freedom
## AIC: 6713
##
## Number of Fisher Scoring iterations: 2mid <- mean(datos$LEF)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G4 <- ggplot(datos, aes(x = LEF, y = Light_Intensity, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha=0.9, size = 4,color="gray10")+
geom_point(alpha=0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = LEF, y = Light_Intensity),
method = "glm", color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_y_continuous(breaks = seq(0, 320, by = 40), limits = c(0, 320)) +
scale_x_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60,70), limits = c(0, 75)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("LEF") + ylab("Light_Intensity") +
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(legend.position = "") +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank())+
theme(legend.background = element_blank())+
labs(x = "LEF", y = "PAR", color = "Tratamiento")+
annotate("text", x = 15, y = 20, label = annotation_text,
size = 5, hjust = 0, color = "black")
G4
GLMM: Variación de los parámetros fotosintéticos según el tratamiento aplicado
gH+
detach(datos)
datos <- read.csv2("Data_Capirona_VA_gH_vH_.csv", row.names=NULL)
attach(datos)
Height.Model<-glmer(gH~Expe+(1|Expe),family=Gamma(link="log"), data=datos, na.action = na.fail)
shapiro.test(resid(Height.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Height.Model)
## W = 0.90834, p-value < 2.2e-16
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: gH ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## 7166.5 7198.4 -3576.2 7152.5 704
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1052 -0.7227 -0.1986 0.5323 4.2788
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 6.132e-18 2.476e-09
## Residual 2.256e-01 4.750e-01
## Number of obs: 711, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 4.65785 0.03291 141.519 < 2e-16 ***
## ExpeIA14h -0.31604 0.05701 -5.544 2.96e-08 ***
## ExpeIA18h -0.23956 0.05685 -4.214 2.51e-05 ***
## ExpeIB14h -0.54999 0.05717 -9.621 < 2e-16 ***
## ExpeIB18h -0.58552 0.05750 -10.184 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.577
## ExpeIA18h -0.579 0.334
## ExpeIB14h -0.576 0.332 0.333
## ExpeIB18h -0.572 0.330 0.331 0.330
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.1935122 0.1935122
## lognormal 0.2101711 0.2101711
## trigamma 0.1762788 0.1762788##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = gH ~ Expe + (1 | Expe), data = datos, family = Gamma(link = "log"),
## na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 -0.31604 0.05701 -5.544 < 1e-04 ***
## IA18h - Control == 0 -0.23956 0.05685 -4.214 0.000231 ***
## IB14h - Control == 0 -0.54999 0.05717 -9.621 < 1e-04 ***
## IB18h - Control == 0 -0.58552 0.05750 -10.184 < 1e-04 ***
## IA18h - IA14h == 0 0.07648 0.06569 1.164 0.769710
## IB14h - IA14h == 0 -0.23395 0.06597 -3.547 0.003448 **
## IB18h - IA14h == 0 -0.26948 0.06625 -4.068 0.000432 ***
## IB14h - IA18h == 0 -0.31043 0.06583 -4.716 < 1e-04 ***
## IB18h - IA18h == 0 -0.34596 0.06611 -5.233 < 1e-04 ***
## IB18h - IB14h == 0 -0.03553 0.06639 -0.535 0.983508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "c" "b" "b" "a" "a"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 105.4 3.47 Inf 98.8 112.4
## IA14h 76.8 3.58 Inf 70.1 84.2
## IA18h 83.0 3.85 Inf 75.8 90.8
## IB14h 60.8 2.84 Inf 55.5 66.7
## IB18h 58.7 2.77 Inf 53.5 64.4
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scaleIndice <- c("gH","gH","gH","gH","gH")
Section <- c("Control","IA14h","IA18h","IB14h","IB18h")
Promedio <- c(105.4, 76.8, 83,60.8, 58.7)
SE <- c(3.47,3.58,3.85,2.84,2.77)
tgc <- data.frame(Indice,Section,Promedio,SE)
head(tgc)pd <- position_dodge(0)
Plot1 <- ggplot(tgc, aes(x = reorder(Section, Promedio), y = Promedio, group = Indice)) +
geom_errorbar(aes(ymin = Promedio - SE, ymax = Promedio + SE), width = 0.2, size = 1, position = pd) +
geom_line(aes(linetype = Indice), position = pd, size = 1) +
geom_point(aes(shape=Indice,fill=Indice),size = 5,color = "black", position = pd)+
geom_point(size = 4, color= "#95D840FF",position = pd) +
theme_classic() +
scale_y_continuous(breaks = c(50,60,70,80,90,100,110), limits = c(50,112)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 11, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 11, angle = 0)) +
guides(linetype = guide_legend("")) +
annotate("text", x = "Control", y = 111.5, label = "c",parse = TRUE,size=6)+
annotate("text", x = "IA14h", y = 83.5, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IA18h", y = 90, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IB14h", y = 66, label = "a",parse = TRUE,size=6)+
annotate("text", x = "IB18h", y = 64, label = "a",parse = TRUE,size=6)+
labs(title = paste("", sep = "\n"), x = "Tratamiento", y = "gH+")+
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 1.5, fill = "NA")) +
theme(legend.position = "")
Plot1
vH+
Height.Model<-glmer(vH~Expe+(1|Expe),family=Gamma(link="log"), data=datos, na.action = na.fail)
shapiro.test(resid(Height.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Height.Model)
## W = 0.99661, p-value = 0.1356
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: vH ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## -4248 -4216 2131 -4262 704
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6635 -0.6877 -0.1052 0.5744 3.6833
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 0.00000 0.0000
## Residual 0.07878 0.2807
## Number of obs: 711, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) -2.64923 0.01842 -143.83 <2e-16 ***
## ExpeIA14h -0.57936 0.03190 -18.16 <2e-16 ***
## ExpeIA18h -0.39991 0.03182 -12.57 <2e-16 ***
## ExpeIB14h -0.97310 0.03199 -30.41 <2e-16 ***
## ExpeIB18h -0.96110 0.03218 -29.87 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.577
## ExpeIA18h -0.579 0.334
## ExpeIB14h -0.576 0.332 0.333
## ExpeIB18h -0.572 0.330 0.331 0.330
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.6675623 0.6675623
## lognormal 0.6759746 0.6759746
## trigamma 0.6587109 0.6587109##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = vH ~ Expe + (1 | Expe), data = datos, family = Gamma(link = "log"),
## na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 -0.57936 0.03190 -18.160 <1e-05 ***
## IA18h - Control == 0 -0.39991 0.03182 -12.570 <1e-05 ***
## IB14h - Control == 0 -0.97310 0.03199 -30.415 <1e-05 ***
## IB18h - Control == 0 -0.96110 0.03218 -29.868 <1e-05 ***
## IA18h - IA14h == 0 0.17945 0.03676 4.881 <1e-05 ***
## IB14h - IA14h == 0 -0.39374 0.03692 -10.666 <1e-05 ***
## IB18h - IA14h == 0 -0.38174 0.03708 -10.296 <1e-05 ***
## IB14h - IA18h == 0 -0.57320 0.03684 -15.559 <1e-05 ***
## IB18h - IA18h == 0 -0.56119 0.03700 -15.167 <1e-05 ***
## IB18h - IB14h == 0 0.01201 0.03715 0.323 0.998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "d" "c" "b" "a" "a"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 0.0707 0.001300 Inf 0.0682 0.0733
## IA14h 0.0396 0.001030 Inf 0.0376 0.0417
## IA18h 0.0474 0.001230 Inf 0.0451 0.0499
## IB14h 0.0267 0.000699 Inf 0.0254 0.0281
## IB18h 0.0270 0.000714 Inf 0.0257 0.0285
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scaleIndice <- c("vH","vH","vH","vH","vH")
Section <- c("Control","IA14h","IA18h","IB14h","IB18h")
Promedio <- c(0.0707,0.0396,0.0474,0.0267,0.0270)
SE <- c(0.001300,0.001030,0.001230,0.000699,0.000714)
tgc <- data.frame(Indice,Section,Promedio,SE)
head(tgc)pd <- position_dodge(0)
Plot2 <- ggplot(tgc, aes(x = reorder(Section, Promedio), y = Promedio, group = Indice)) +
geom_errorbar(aes(ymin = Promedio - SE, ymax = Promedio + SE), width = 0.2, size = 1, position = pd) +
geom_line(aes(linetype = Indice), position = pd, size = 1) +
geom_point(aes(shape=Indice,fill=Indice),size = 5,color = "black", position = pd)+
geom_point(size = 4, color= "#95D840FF",position = pd) +
theme_classic() +
scale_y_continuous(breaks = c(0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08), limits = c(0.01,0.08)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 11, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 11, angle = 0)) +
guides(linetype = guide_legend("")) +
annotate("text", x = "Control", y = 0.077, label = "d",parse = TRUE,size=6)+
annotate("text", x = "IA14h", y = 0.045, label = "c",parse = TRUE,size=6)+
annotate("text", x = "IA18h", y = 0.053, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IB14h", y = 0.032, label = "a",parse = TRUE,size=6)+
annotate("text", x = "IB18h", y = 0.032, label = "a",parse = TRUE,size=6)+
labs(title = paste("", sep = "\n"), x = "Tratamiento", y = "vH+")+
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 1.5, fill = "NA")) +
theme(legend.position = "")
Plot2
SPAD
detach(datos)
datos <- read.csv2("Data_Capirona.csv", row.names=NULL)
attach(datos)
Height.Model<-glmer(SPAD~Expe+(1|Expe),family=Gamma(link="log"), data=datos, na.action = na.fail)
shapiro.test(resid(Height.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Height.Model)
## W = 0.88983, p-value < 2.2e-16
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: SPAD ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## 4850.4 4882.4 -2418.2 4836.4 710
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9996 -0.4717 0.1058 0.6194 2.2675
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 0.0000 0.0000
## Residual 0.0381 0.1952
## Number of obs: 717, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 3.47066 0.01412 245.748 < 2e-16 ***
## ExpeIA14h -0.05538 0.02453 -2.258 0.02396 *
## ExpeIA18h 0.10800 0.02446 4.415 1.01e-05 ***
## ExpeIB14h -0.01779 0.02453 -0.725 0.46838
## ExpeIB18h 0.06906 0.02453 2.815 0.00488 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.576
## ExpeIA18h -0.577 0.332
## ExpeIB14h -0.576 0.331 0.332
## ExpeIB18h -0.576 0.331 0.332 0.331
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.07315578 0.07315578
## lognormal 0.07443751 0.07443751
## trigamma 0.07187080 0.07187080##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = SPAD ~ Expe + (1 | Expe), data = datos, family = Gamma(link = "log"),
## na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 -0.05538 0.02453 -2.258 0.1572
## IA18h - Control == 0 0.10800 0.02446 4.415 <0.001 ***
## IB14h - Control == 0 -0.01779 0.02453 -0.725 0.9502
## IB18h - Control == 0 0.06906 0.02453 2.815 0.0387 *
## IA18h - IA14h == 0 0.16339 0.02831 5.772 <0.001 ***
## IB14h - IA14h == 0 0.03760 0.02836 1.325 0.6727
## IB18h - IA14h == 0 0.12444 0.02836 4.387 <0.001 ***
## IB14h - IA18h == 0 -0.12579 0.02831 -4.444 <0.001 ***
## IB18h - IA18h == 0 -0.03895 0.02831 -1.376 0.6404
## IB18h - IB14h == 0 0.08684 0.02836 3.062 0.0185 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "b" "b" "a" "b" "a"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 32.2 0.454 Inf 31.3 33.1
## IA14h 30.4 0.610 Inf 29.3 31.6
## IA18h 35.8 0.716 Inf 34.5 37.3
## IB14h 31.6 0.634 Inf 30.4 32.9
## IB18h 34.5 0.691 Inf 33.1 35.8
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scaleIndice <- c("SPAD","SPAD","SPAD","SPAD","SPAD")
Section <- c("Control","IA14h","IA18h","IB14h","IB18h")
Promedio <- c(32.2,30.4,35.8,31.6,34.5)
SE <- c(0.454,0.610,0.716,0.634,0.691)
tgc <- data.frame(Indice,Section,Promedio,SE)
head(tgc)pd <- position_dodge(0)
Plot3 <- ggplot(tgc, aes(x = reorder(Section, Promedio), y = Promedio, group = Indice)) +
geom_errorbar(aes(ymin = Promedio - SE, ymax = Promedio + SE), width = 0.2, size = 1, position = pd) +
geom_line(aes(linetype = Indice), position = pd, size = 1) +
geom_point(aes(shape=Indice,fill=Indice),size = 5,color = "black", position = pd)+
geom_point(size = 4, color= "#95D840FF",position = pd) +
theme_classic() +
scale_y_continuous(breaks = c(28,30,32,34,36,38), limits = c(28,38)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 11, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 11, angle = 0)) +
guides(linetype = guide_legend("")) +
annotate("text", x = "Control", y = 33.2, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IA14h", y = 31.6, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IA18h", y = 37.2, label = "a",parse = TRUE,size=6)+
annotate("text", x = "IB14h", y = 32.9, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IB18h", y = 35.8, label = "a",parse = TRUE,size=6)+
labs(title = paste("", sep = "\n"), x = "Tratamiento", y = "SPAD")+
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 1.5, fill = "NA")) +
theme(legend.position = "")
Plot3
LEF
Height.Model<-glmer(LEF~Expe+(1|Expe),family=Gamma(link="log"), data=datos, na.action = na.fail)
shapiro.test(resid(Height.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Height.Model)
## W = 0.98125, p-value = 5.993e-08
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: LEF ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## 3969.4 4001.4 -1977.7 3955.4 710
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9682 -0.6385 0.0244 0.5954 4.0728
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 0.00000 0.0000
## Residual 0.05182 0.2276
## Number of obs: 717, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 3.57009 0.01525 234.09 <2e-16 ***
## ExpeIA14h -0.80071 0.02649 -30.23 <2e-16 ***
## ExpeIA18h -0.64365 0.02642 -24.37 <2e-16 ***
## ExpeIB14h -1.58364 0.02649 -59.78 <2e-16 ***
## ExpeIB18h -1.60921 0.02649 -60.75 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.576
## ExpeIA18h -0.577 0.332
## ExpeIB14h -0.576 0.331 0.332
## ExpeIB18h -0.576 0.331 0.332 0.331
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.8921515 0.8921515
## lognormal 0.8945680 0.8945680
## trigamma 0.8896228 0.8896228##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = LEF ~ Expe + (1 | Expe), data = datos, family = Gamma(link = "log"),
## na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 -0.80071 0.02649 -30.228 <1e-05 ***
## IA18h - Control == 0 -0.64365 0.02642 -24.367 <1e-05 ***
## IB14h - Control == 0 -1.58364 0.02649 -59.785 <1e-05 ***
## IB18h - Control == 0 -1.60921 0.02649 -60.750 <1e-05 ***
## IA18h - IA14h == 0 0.15706 0.03057 5.138 <1e-05 ***
## IB14h - IA14h == 0 -0.78293 0.03063 -25.561 <1e-05 ***
## IB18h - IA14h == 0 -0.80850 0.03063 -26.396 <1e-05 ***
## IB14h - IA18h == 0 -0.93999 0.03057 -30.753 <1e-05 ***
## IB18h - IA18h == 0 -0.96556 0.03057 -31.590 <1e-05 ***
## IB18h - IB14h == 0 -0.02557 0.03063 -0.835 0.919
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "d" "c" "b" "a" "a"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 35.52 0.542 Inf 34.47 36.60
## IA14h 15.95 0.345 Inf 15.29 16.64
## IA18h 18.66 0.402 Inf 17.89 19.47
## IB14h 7.29 0.158 Inf 6.99 7.61
## IB18h 7.11 0.154 Inf 6.81 7.41
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scaleIndice <- c("LEF","LEF","LEF","LEF","LEF")
Section <- c("Control","IA14h","IA18h","IB14h","IB18h")
Promedio <- c(35.52,15.95,18.66,7.29,7.11)
SE <- c(0.542,0.345,0.402,0.158,0.154)
tgc <- data.frame(Indice,Section,Promedio,SE)
head(tgc)pd <- position_dodge(0)
Plot4 <- ggplot(tgc, aes(x = reorder(Section, Promedio), y = Promedio, group = Indice)) +
geom_errorbar(aes(ymin = Promedio - SE, ymax = Promedio + SE), width = 0.2, size = 1, position = pd) +
geom_line(aes(linetype = Indice), position = pd, size = 1) +
geom_point(aes(shape=Indice,fill=Indice),size = 5,color = "black", position = pd)+
geom_point(size = 4, color= "#95D840FF",position = pd) +
theme_classic() +
scale_y_continuous(breaks = c(5,10,15,20,25,30,35,40), limits = c(5,40)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 11, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 11, angle = 0)) +
guides(linetype = guide_legend("")) +
annotate("text", x = "Control", y = 38.5, label = "d",parse = TRUE,size=6)+
annotate("text", x = "IA14h", y = 18.5, label = "c",parse = TRUE,size=6)+
annotate("text", x = "IA18h", y = 21.5, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IB14h", y = 10, label = "a",parse = TRUE,size=6)+
annotate("text", x = "IB18h", y = 10, label = "a",parse = TRUE,size=6)+
labs(title = paste("", sep = "\n"), x = "Tratamiento", y = "LEF")+
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 1.5, fill = "NA")) +
theme(legend.position = "")
Plot4
qL
Height.Model<-glmer(qL~Expe+(1|Expe),family=Gamma(link="log"), data=datos, na.action = na.fail)
shapiro.test(resid(Height.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Height.Model)
## W = 0.9409, p-value = 2.798e-16
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: qL ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## -1602.3 -1570.3 808.2 -1616.3 710
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3974 -0.5766 0.0449 0.5599 3.9339
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 0.0000 0.0000
## Residual 0.0162 0.1273
## Number of obs: 717, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) -0.81538 0.00858 -95.03 <2e-16 ***
## ExpeIA14h 0.33180 0.01490 22.27 <2e-16 ***
## ExpeIA18h 0.31069 0.01486 20.91 <2e-16 ***
## ExpeIB14h 0.50971 0.01490 34.20 <2e-16 ***
## ExpeIB18h 0.60363 0.01490 40.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.576
## ExpeIA18h -0.577 0.332
## ExpeIB14h -0.576 0.331 0.332
## ExpeIB18h -0.576 0.331 0.332 0.331
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.7656515 0.7656515
## lognormal 0.7670919 0.7670919
## trigamma 0.7641933 0.7641933##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = qL ~ Expe + (1 | Expe), data = datos, family = Gamma(link = "log"),
## na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 0.33180 0.01490 22.265 <1e-04 ***
## IA18h - Control == 0 0.31069 0.01486 20.907 <1e-04 ***
## IB14h - Control == 0 0.50971 0.01490 34.204 <1e-04 ***
## IB18h - Control == 0 0.60363 0.01490 40.506 <1e-04 ***
## IA18h - IA14h == 0 -0.02111 0.01720 -1.228 0.733
## IB14h - IA14h == 0 0.17791 0.01723 10.325 <1e-04 ***
## IB18h - IA14h == 0 0.27183 0.01723 15.775 <1e-04 ***
## IB14h - IA18h == 0 0.19902 0.01720 11.574 <1e-04 ***
## IB18h - IA18h == 0 0.29294 0.01720 17.036 <1e-04 ***
## IB18h - IB14h == 0 0.09392 0.01723 5.451 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "d" "c" "c" "b" "a"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 0.442 0.00380 Inf 0.435 0.450
## IA14h 0.617 0.00751 Inf 0.602 0.631
## IA18h 0.604 0.00732 Inf 0.590 0.618
## IB14h 0.737 0.00898 Inf 0.719 0.754
## IB18h 0.809 0.00986 Inf 0.790 0.829
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scaleIndice <- c("qL","qL","qL","qL","qL")
Section <- c("Control","IA14h","IA18h","IB14h","IB18h")
Promedio <- c(0.442,0.617,0.604,0.737,0.809)
SE <- c(0.00380,0.00751,0.00732,0.00898,0.00986)
tgc <- data.frame(Indice,Section,Promedio,SE)
head(tgc)pd <- position_dodge(0)
Plot5 <- ggplot(tgc, aes(x = reorder(Section, Promedio), y = Promedio, group = Indice)) +
geom_errorbar(aes(ymin = Promedio - SE, ymax = Promedio + SE), width = 0.2, size = 1, position = pd) +
geom_line(aes(linetype = Indice), position = pd, size = 1) +
geom_point(aes(shape=Indice,fill=Indice),size = 5,color = "black", position = pd)+
geom_point(size = 4, color= "#95D840FF",position = pd) +
theme_classic() +
scale_y_continuous(breaks = c(0.40,0.50,0.60,0.70,0.8,0.9), limits = c(0.40,0.9)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 11, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 11, angle = 0)) +
guides(linetype = guide_legend("")) +
annotate("text", x = "Control", y = 0.5, label = "d",parse = TRUE,size=6)+
annotate("text", x = "IA14h", y = 0.66, label = "c",parse = TRUE,size=6)+
annotate("text", x = "IA18h", y = 0.64, label = "c",parse = TRUE,size=6)+
annotate("text", x = "IB14h", y = 0.78, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IB18h", y = 0.85, label = "a",parse = TRUE,size=6)+
labs(title = paste("", sep = "\n"), x = "Tratamiento", y = "qL")+
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 1.5, fill = "NA")) +
theme(legend.position = "")
Plot5
Fv’/Fm’
Height.Model<-glmer(FvP_over_FmP~Expe+(1|Expe),family=Gamma(link="log"), data=datos, na.action = na.fail)
shapiro.test(resid(Height.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Height.Model)
## W = 0.90241, p-value < 2.2e-16
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: FvP_over_FmP ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## -1503.6 -1471.6 758.8 -1517.6 710
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2301 -0.5447 0.2355 0.7470 1.6242
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 0.00000 0.0000
## Residual 0.01645 0.1283
## Number of obs: 717, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) -0.472928 0.008738 -54.121 < 2e-16 ***
## ExpeIA14h -0.022044 0.015178 -1.452 0.1464
## ExpeIA18h 0.059229 0.015135 3.913 9.1e-05 ***
## ExpeIB14h -0.006342 0.015178 -0.418 0.6761
## ExpeIB18h -0.030558 0.015178 -2.013 0.0441 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.576
## ExpeIA18h -0.577 0.332
## ExpeIB14h -0.576 0.331 0.332
## ExpeIB18h -0.576 0.331 0.332 0.331
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.04805626 0.04805626
## lognormal 0.04843142 0.04843142
## trigamma 0.04768082 0.04768082##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = FvP_over_FmP ~ Expe + (1 | Expe), data = datos,
## family = Gamma(link = "log"), na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 -0.022044 0.015178 -1.452 0.590763
## IA18h - Control == 0 0.059229 0.015135 3.913 0.000833 ***
## IB14h - Control == 0 -0.006342 0.015178 -0.418 0.993541
## IB18h - Control == 0 -0.030558 0.015178 -2.013 0.257074
## IA18h - IA14h == 0 0.081273 0.017513 4.641 < 1e-04 ***
## IB14h - IA14h == 0 0.015702 0.017550 0.895 0.897862
## IB18h - IA14h == 0 -0.008514 0.017550 -0.485 0.988594
## IB14h - IA18h == 0 -0.065571 0.017513 -3.744 0.001632 **
## IB18h - IA18h == 0 -0.089787 0.017513 -5.127 < 1e-04 ***
## IB18h - IB14h == 0 -0.024216 0.017550 -1.380 0.637979
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "b" "b" "a" "b" "b"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 0.623 0.00545 Inf 0.613 0.634
## IA14h 0.610 0.00756 Inf 0.595 0.625
## IA18h 0.661 0.00817 Inf 0.645 0.677
## IB14h 0.619 0.00768 Inf 0.604 0.634
## IB18h 0.604 0.00750 Inf 0.590 0.619
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scaleIndice <- c("FvP_over_FmP","FvP_over_FmP","FvP_over_FmP","FvP_over_FmP","FvP_over_FmP")
Section <- c("Control","IA14h","IA18h","IB14h","IB18h")
Promedio <- c(0.623,0.610,0.661,0.619,0.604)
SE <- c(0.00545,0.00756,0.00817,0.00768,0.00750)
tgc <- data.frame(Indice,Section,Promedio,SE)
head(tgc)pd <- position_dodge(0)
Plot6 <- ggplot(tgc, aes(x = reorder(Section, Promedio), y = Promedio, group = Indice)) +
geom_errorbar(aes(ymin = Promedio - SE, ymax = Promedio + SE), width = 0.2, size = 1, position = pd) +
geom_line(aes(linetype = Indice), position = pd, size = 1) +
geom_point(aes(shape=Indice,fill=Indice),size = 5,color = "black", position = pd)+
geom_point(size = 4, color= "#95D840FF",position = pd) +
theme_classic() +
scale_y_continuous(breaks = c(0.59,0.60,0.61,0.62,0.63,0.64,0.65,0.66,0.67,0.68), limits = c(0.59,0.68)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 11, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 11, angle = 0)) +
guides(linetype = guide_legend("")) +
annotate("text", x = "Control", y = 0.635, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IA14h", y = 0.623, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IA18h", y = 0.673, label = "a",parse = TRUE,size=6)+
annotate("text", x = "IB14h", y = 0.632, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IB18h", y = 0.617, label = "b",parse = TRUE,size=6)+
labs(title = paste("", sep = "\n"), x = "Tratamiento", y = "Fv/Fm")+
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 1.5, fill = "NA")) +
theme(legend.position = "")
Plot6
OE2: Identificación de variaciones en el fotosistema II en la especie Calycophyllum spruceanum bajo diferentes niveles de radiación fotosintética activa.
GLMM: Variación del fotosistema II (Phi2, PhiNO, PhiNPQ) según el tratamiento aplicado
Phi2
Vulnerability.Model<-glmer(Phi2~Expe+(1|Expe),family = Gamma(link = "log"), data=datos, na.action = na.fail)
shapiro.test(resid(Vulnerability.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Vulnerability.Model)
## W = 0.88141, p-value < 2.2e-16
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: Phi2 ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## -1402.8 -1370.7 708.4 -1416.8 710
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8527 -0.4459 0.2627 0.6477 2.2255
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 0.00000 0.0000
## Residual 0.02817 0.1678
## Number of obs: 717, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) -0.85013 0.01188 -71.588 < 2e-16 ***
## ExpeIA14h 0.14168 0.02063 6.869 6.48e-12 ***
## ExpeIA18h 0.23701 0.02057 11.523 < 2e-16 ***
## ExpeIB14h 0.24275 0.02063 11.769 < 2e-16 ***
## ExpeIB18h 0.25707 0.02063 12.463 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.576
## ExpeIA18h -0.577 0.332
## ExpeIB14h -0.576 0.331 0.332
## ExpeIB18h -0.576 0.331 0.332 0.331
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.3011794 0.3011794
## lognormal 0.3041181 0.3041181
## trigamma 0.2982162 0.2982162##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = Phi2 ~ Expe + (1 | Expe), data = datos, family = Gamma(link = "log"),
## na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 0.141675 0.020626 6.869 < 1e-04 ***
## IA18h - Control == 0 0.237013 0.020569 11.523 < 1e-04 ***
## IB14h - Control == 0 0.242747 0.020626 11.769 < 1e-04 ***
## IB18h - Control == 0 0.257066 0.020626 12.463 < 1e-04 ***
## IA18h - IA14h == 0 0.095337 0.023801 4.006 0.000596 ***
## IB14h - IA14h == 0 0.101071 0.023850 4.238 0.000201 ***
## IB18h - IA14h == 0 0.115391 0.023850 4.838 < 1e-04 ***
## IB14h - IA18h == 0 0.005734 0.023801 0.241 0.999247
## IB18h - IA18h == 0 0.020054 0.023801 0.843 0.916363
## IB18h - IB14h == 0 0.014320 0.023850 0.600 0.974739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "c" "b" "a" "a" "a"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 0.427 0.00508 Inf 0.418 0.437
## IA14h 0.492 0.00830 Inf 0.476 0.509
## IA18h 0.542 0.00910 Inf 0.524 0.560
## IB14h 0.545 0.00919 Inf 0.527 0.563
## IB18h 0.553 0.00932 Inf 0.535 0.571
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scalePhiNO
Vulnerability.Model<-glmer(PhiNO~Expe+(1|Expe),family = Gamma(link = "log"), data=datos, na.action = na.fail)
shapiro.test(resid(Vulnerability.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Vulnerability.Model)
## W = 0.96828, p-value = 2.378e-11
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: PhiNO ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## -2760.4 -2728.4 1387.2 -2774.4 710
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7688 -0.6249 0.1256 0.6422 7.0852
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 0.00000 0.0000
## Residual 0.03984 0.1996
## Number of obs: 717, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) -1.60350 0.01319 -121.615 < 2e-16 ***
## ExpeIA14h -0.19913 0.02290 -8.695 < 2e-16 ***
## ExpeIA18h -0.07417 0.02284 -3.248 0.00116 **
## ExpeIB14h -0.27437 0.02290 -11.981 < 2e-16 ***
## ExpeIB18h -0.35074 0.02290 -15.316 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.576
## ExpeIA18h -0.577 0.332
## ExpeIB14h -0.576 0.331 0.332
## ExpeIB18h -0.576 0.331 0.332 0.331
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.3132633 0.3132633
## lognormal 0.3174947 0.3174947
## trigamma 0.3089803 0.3089803##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = PhiNO ~ Expe + (1 | Expe), data = datos, family = Gamma(link = "log"),
## na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 -0.19913 0.02290 -8.695 <0.001 ***
## IA18h - Control == 0 -0.07417 0.02284 -3.248 0.0102 *
## IB14h - Control == 0 -0.27437 0.02290 -11.981 <0.001 ***
## IB18h - Control == 0 -0.35074 0.02290 -15.316 <0.001 ***
## IA18h - IA14h == 0 0.12495 0.02643 4.729 <0.001 ***
## IB14h - IA14h == 0 -0.07524 0.02648 -2.841 0.0359 *
## IB18h - IA14h == 0 -0.15161 0.02648 -5.725 <0.001 ***
## IB14h - IA18h == 0 -0.20019 0.02643 -7.576 <0.001 ***
## IB18h - IA18h == 0 -0.27657 0.02643 -10.466 <0.001 ***
## IB18h - IB14h == 0 -0.07637 0.02648 -2.884 0.0315 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "e" "d" "c" "b" "a"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 0.201 0.00265 Inf 0.196 0.206
## IA14h 0.165 0.00309 Inf 0.159 0.171
## IA18h 0.187 0.00348 Inf 0.180 0.194
## IB14h 0.153 0.00286 Inf 0.147 0.159
## IB18h 0.142 0.00265 Inf 0.137 0.147
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scalePhiNPQ
Vulnerability.Model<-glmer(PhiNPQ~Expe+(1|Expe),family = Gamma(link = "log"), data=datos, na.action = na.fail)
shapiro.test(resid(Vulnerability.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Vulnerability.Model)
## W = 0.97098, p-value = 1.004e-10
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: PhiNPQ ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## -1274.3 -1242.2 644.1 -1288.3 710
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6399 -0.7301 -0.2740 0.5498 4.3248
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 0.0000 0.0000
## Residual 0.1094 0.3307
## Number of obs: 717, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) -0.99032 0.02018 -49.083 < 2e-16 ***
## ExpeIA14h -0.08054 0.03504 -2.298 0.0216 *
## ExpeIA18h -0.31320 0.03495 -8.962 < 2e-16 ***
## ExpeIB14h -0.20584 0.03504 -5.874 4.26e-09 ***
## ExpeIB18h -0.19481 0.03504 -5.559 2.71e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.576
## ExpeIA18h -0.577 0.332
## ExpeIB14h -0.576 0.331 0.332
## ExpeIB18h -0.576 0.331 0.332 0.331
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.1087462 0.1087462
## lognormal 0.1139245 0.1139245
## trigamma 0.1035172 0.1035172##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = PhiNPQ ~ Expe + (1 | Expe), data = datos, family = Gamma(link = "log"),
## na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 -0.08054 0.03504 -2.298 0.1438
## IA18h - Control == 0 -0.31320 0.03495 -8.962 <0.001 ***
## IB14h - Control == 0 -0.20584 0.03504 -5.874 <0.001 ***
## IB18h - Control == 0 -0.19481 0.03504 -5.559 <0.001 ***
## IA18h - IA14h == 0 -0.23266 0.04044 -5.754 <0.001 ***
## IB14h - IA14h == 0 -0.12530 0.04052 -3.092 0.0169 *
## IB18h - IA14h == 0 -0.11427 0.04052 -2.820 0.0382 *
## IB14h - IA18h == 0 0.10736 0.04044 2.655 0.0601 .
## IB18h - IA18h == 0 0.11839 0.04044 2.928 0.0279 *
## IB18h - IB14h == 0 0.01103 0.04052 0.272 0.9988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "c" "c" "b" "ab" "a"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 0.371 0.00749 Inf 0.357 0.386
## IA14h 0.343 0.00982 Inf 0.324 0.363
## IA18h 0.272 0.00775 Inf 0.257 0.287
## IB14h 0.302 0.00866 Inf 0.286 0.320
## IB18h 0.306 0.00876 Inf 0.289 0.323
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale(Phi2/PhiNO/PhiNPQ) / Tratamiento
Indice <- c("Phi2","Phi2","Phi2","Phi2","Phi2","PhiNO","PhiNO","PhiNO","PhiNO","PhiNO","PhiNPQ","PhiNPQ","PhiNPQ","PhiNPQ","PhiNPQ")
Section <- c("Control","IA14h","IA18h","IB14h","IB18h","Control","IA14h","IA18h","IB14h","IB18h","Control","IA14h","IA18h","IB14h","IB18h")
Promedio <- c(0.427,0.492,0.542,0.545,0.553,0.201,0.165,0.187,0.153,0.142,0.371,0.343,0.272,0.302,0.306)
SE<- c(0.00508,0.00830,0.00910,0.00919,0.00932,0.00265,0.00309,0.00348,0.00286,0.00265,0.00749,0.00982,0.00775,0.00866,0.00876)
GA1<- data.frame(Indice,Section,Promedio,SE)
pd <- position_dodge(0)
IA<- ggplot(GA1,aes(x = Section, y = Promedio, group = Indice)) +
geom_errorbar(aes(ymin = Promedio-SE, ymax = Promedio+SE),size=1, width = 0.15) +
scale_shape_manual(values=c(21,22,23))+
scale_fill_manual(values=c("#3366CC", "#95D840FF","#FDE725"))+
scale_linetype_manual(values = c("solid","dashed", "dotted"))+
geom_line(aes(linetype=Indice),size = 1)+
geom_point(aes(shape=Indice,fill=Indice),size = 4.5)+
theme_classic()+ scale_y_continuous(breaks =c(0.1,0.2,0.3,0.4,0.5,0.6,0.7),limits = c(0.1,0.7))+
theme(axis.text.x = element_text(face="bold",color="black", size=13, angle=0),
axis.text.y = element_text(face="bold", color="black",size=13, angle=0))+
guides(linetype = guide_legend("")) +
labs(title = paste("", sep = "\n"), x = "Tratamiento", y = "Phi2/PhiNO/PhiNPQ")+
theme(axis.text=element_text(size=15), axis.title=element_text(size=15,face="bold"))+
theme(panel.border = element_rect(linetype = "solid",size=1.5,fill = "NA"))+
theme(legend.position="bottom")
IA
Regresión lineal: Relación entre el SPAD con Phi2, PhiNPQ y PhiNO
SPAD/Phi2
detach(datos)
datos <- read.csv2("Data_Capirona_VA_PhiNO_.csv", row.names=NULL)
attach(datos)
summary(Phi2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1510 0.4405 0.5200 0.4976 0.5747 0.6460## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.858 29.393 33.591 32.876 36.893 51.584
MCap<-glm(Phi2 ~SPAD, data=datos, family = gaussian,na.action = na.fail)
null = glm (Phi2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.94556, p-value = 1.553e-15## R2m R2c
## [1,] 0.1874721 0.1874721## [1] 0.187686
## attr(,"adj.r.squared")
## [1] -0.03566333##
## Call:
## glm(formula = Phi2 ~ SPAD, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2844528 0.0169398 16.79 <2e-16 ***
## SPAD 0.0064848 0.0005056 12.83 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.007615616)
##
## Null deviance: 6.6752 on 713 degrees of freedom
## Residual deviance: 5.4223 on 712 degrees of freedom
## AIC: -1452.3
##
## Number of Fisher Scoring iterations: 2mid <- mean(SPAD)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
F1 <- ggplot(datos, aes(x = SPAD, y = Phi2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = SPAD, y = Phi2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6, 0.8), limits = c(0, 0.8)) +
scale_x_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60), limits = c(0, 60)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("SPAD") + ylab(Phi2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(x = "SPAD", y = "Phi2", color = "Tratamiento") +
annotate("text", x = 30, y = 0.07, label = annotation_text,
size = 5, hjust = 0, color = "black")
F1
SPAD/PhiNPQ
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1650 0.2372 0.2925 0.3282 0.3872 0.7340## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.858 29.393 33.591 32.876 36.893 51.584
MCap<-glm(PhiNPQ ~SPAD, data=datos, family = gaussian,na.action = na.fail)
null = glm (PhiNPQ~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.92764, p-value < 2.2e-16## R2m R2c
## [1,] 0.2241499 0.2241499## [1] 0.2243941
## attr(,"adj.r.squared")
## [1] -0.06647513##
## Call:
## glm(formula = PhiNPQ ~ SPAD, family = gaussian, data = datos,
## na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.607108 0.019803 30.66 <2e-16 ***
## SPAD -0.008483 0.000591 -14.35 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0104075)
##
## Null deviance: 9.5540 on 713 degrees of freedom
## Residual deviance: 7.4101 on 712 degrees of freedom
## AIC: -1229.3
##
## Number of Fisher Scoring iterations: 2mid <- mean(SPAD)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
F2 <- ggplot(datos, aes(x = SPAD, y = PhiNPQ, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = SPAD, y = PhiNPQ),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_y_continuous(breaks = c(0, 0.2, 0.4, 0.6, 0.8), limits = c(0, 0.8)) +
scale_x_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60), limits = c(0, 60)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("SPAD") + ylab(PhiNPQ) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(x = "SPAD", y = "PhiNPQ", color = "Tratamiento") +
annotate("text", x = 30, y = 0.06, label = annotation_text,
size = 5, hjust = 0, color = "black")
F2
SPAD/PhiNO
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0780 0.1450 0.1710 0.1741 0.1998 0.3040## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.858 29.393 33.591 32.876 36.893 51.584
MCap<-glm(PhiNO ~SPAD, data=datos, family = gaussian,na.action = na.fail)
null = glm (PhiNO~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.98767, p-value = 1.02e-05## R2m R2c
## [1,] 0.09677935 0.09677935## [1] 0.0969021
## attr(,"adj.r.squared")
## [1] -0.002943957##
## Call:
## glm(formula = PhiNO ~ SPAD, family = gaussian, data = datos,
## na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1083205 0.0076753 14.113 <2e-16 ***
## SPAD 0.0020023 0.0002291 8.741 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.001563433)
##
## Null deviance: 1.2326 on 713 degrees of freedom
## Residual deviance: 1.1132 on 712 degrees of freedom
## AIC: -2582.8
##
## Number of Fisher Scoring iterations: 2mid <- mean(SPAD)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
F3 <- ggplot(datos, aes(x = SPAD, y = PhiNO, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = SPAD, y = PhiNO),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_y_continuous(breaks = c(0, 0.1,0.2,0.3, 0.4), limits = c(0, 0.4)) +
scale_x_continuous(breaks = c(0, 10, 20, 30, 40, 50, 60), limits = c(0, 60)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("SPAD") + ylab(PhiNO) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position = "none")+
labs(x = "SPAD", y = "PhiNO", color = "Tratamiento") +
annotate("text", x = 30, y = 0.04, label = annotation_text,
size = 5, hjust = 0, color = "black")
F3
OE3: Correlación de parámetros fotosintéticos con la altura de los tallos de la especie Calycophyllum spruceanum en función de tres niveles de radiación fotosintética activa.
GLMM: Incremento promedio de altura (cm) de la especie Calycophyllum spruceanum
## [1] "Expe" "Codigo" "M1"
## [4] "M2" "M3" "I1"
## [7] "I2" "Phi2" "vH"
## [10] "gH" "LEF" "qL"
## [13] "SPAD" "Fv_Fm" "PAR"
## [16] "Ambient_Humidity" "Ambient_Temperature" "Ambient_Pressure"
## [19] "Leaf_Temperature"Height.Model<-glmer(I2~Expe+(1|Expe),family=Gamma(link="log"), data=datos, na.action = na.fail)
shapiro.test(resid(Height.Model))##
## Shapiro-Wilk normality test
##
## data: resid(Height.Model)
## W = 0.99378, p-value = 0.6483
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: Gamma ( log )
## Formula: I2 ~ Expe + (1 | Expe)
## Data: datos
##
## AIC BIC logLik deviance df.resid
## 497.6 520.0 -241.8 483.6 173
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4175 -0.7285 -0.2236 0.4935 4.0066
##
## Random effects:
## Groups Name Variance Std.Dev.
## Expe (Intercept) 0.0000 0.0000
## Residual 0.3645 0.6037
## Number of obs: 180, groups: Expe, 5
##
## Fixed effects:
## Estimate Std. Error t value Pr(>|z|)
## (Intercept) 0.57942 0.07559 7.665 1.78e-14 ***
## ExpeIA14h 0.44075 0.13092 3.366 0.000761 ***
## ExpeIA18h -0.17173 0.13092 -1.312 0.189620
## ExpeIB14h 0.13679 0.13092 1.045 0.296099
## ExpeIB18h -0.42241 0.13092 -3.226 0.001254 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ExIA14 ExIA18 ExIB14
## ExpeIA14h -0.577
## ExpeIA18h -0.577 0.333
## ExpeIB14h -0.577 0.333 0.333
## ExpeIB18h -0.577 0.333 0.333 0.333
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## R2m R2c
## delta 0.1621476 0.1621476
## lognormal 0.1849849 0.1849849
## trigamma 0.1384952 0.1384952##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: glmer(formula = I2 ~ Expe + (1 | Expe), data = datos, family = Gamma(link = "log"),
## na.action = na.fail)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## IA14h - Control == 0 0.4408 0.1309 3.366 0.00665 **
## IA18h - Control == 0 -0.1717 0.1309 -1.312 0.68143
## IB14h - Control == 0 0.1368 0.1309 1.045 0.83278
## IB18h - Control == 0 -0.4224 0.1309 -3.226 0.01083 *
## IA18h - IA14h == 0 -0.6125 0.1512 -4.051 < 0.001 ***
## IB14h - IA14h == 0 -0.3040 0.1512 -2.011 0.25844
## IB18h - IA14h == 0 -0.8632 0.1512 -5.710 < 0.001 ***
## IB14h - IA18h == 0 0.3085 0.1512 2.041 0.24425
## IB18h - IA18h == 0 -0.2507 0.1512 -1.658 0.45723
## IB18h - IB14h == 0 -0.5592 0.1512 -3.699 0.00198 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)## Control IA14h IA18h IB14h IB18h
## "c" "b" "ac" "bc" "a"
## Expe lsmean SE df asymp.LCL asymp.UCL
## Control 1.78 0.135 Inf 1.539 2.07
## IA14h 2.77 0.297 Inf 2.249 3.42
## IA18h 1.50 0.161 Inf 1.219 1.85
## IB14h 2.05 0.219 Inf 1.660 2.52
## IB18h 1.17 0.125 Inf 0.949 1.44
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scaleIndice <- c("I2","I2","I2","I2","I2")
Section <- c("Control","IA14h","IA18h","IB14h","IB18h")
Promedio <- c(1.78 ,2.77 ,1.50 ,2.05 ,1.17 )
SE <- c(0.135,0.297,0.161,0.219,0.125)
tgc <- data.frame(Indice,Section,Promedio,SE)
head(tgc)pd <- position_dodge(0)
P1 <- ggplot(tgc, aes(x = reorder(Section, Promedio), y = Promedio, group = Indice)) +
geom_errorbar(aes(ymin = Promedio - SE, ymax = Promedio + SE), width = 0.2, size = 1, position = pd) +
geom_line(aes(linetype = Indice), position = pd, size = 1) +
geom_point(aes(shape=Indice,fill=Indice),size = 5,color = "black", position = pd)+
geom_point(size = 4, color= "#95D840FF",position = pd) +
theme_classic() +
annotate("text", x = "Control", y = 2.1, label = "c",parse = TRUE,size=6)+
annotate("text", x = "IA14h", y = 3.3, label = "b",parse = TRUE,size=6)+
annotate("text", x = "IA18h", y = 1.9, label = "ac",parse = TRUE,size=6)+
annotate("text", x = "IB14h", y = 2.5, label = "bc",parse = TRUE,size=6)+
annotate("text", x = "IB18h", y = 1.5, label = "a",parse = TRUE,size=6)+
scale_y_continuous(breaks = c(0,0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4), limits = c(0, 4)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 11, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 11, angle = 0)) +
guides(linetype = guide_legend("")) +
labs(title = paste("", sep = "\n"), x = "Tratamiento", y = "Incremento altura (cm)")+
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 1.5, fill = "NA")) +
theme(legend.position = "")
P1
Regresión lineal: Relación entre el incremento de altura con el Phi2, SPAD, Fv’/Fm’ y qL
Incremento de altura/Phi2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3458 0.4412 0.5020 0.4978 0.5548 0.6240## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~Phi2, data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.9024, p-value = 1.608e-09## R2m R2c
## [1,] 0.01031222 0.01031222## [1] 0.01036956
## attr(,"adj.r.squared")
## [1] 0.0108186##
## Call:
## glm(formula = I2 ~ Phi2, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.7714 0.6848 4.047 7.73e-05 ***
## Phi2 -1.8631 1.3642 -1.366 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.411661)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 251.28 on 178 degrees of freedom
## AIC: 576.86
##
## Number of Fisher Scoring iterations: 2mid <- mean(Phi2)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G01 <- ggplot(datos, aes(x = Phi2, y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = Phi2, y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(0.35,0.40,0.45,0.50,0.55,0.60,0.65), limits = c(0.35, 0.63)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("Phi2") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "Phi2", color = "Tratamiento") +
annotate("text", x = 0.35, y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G01
Incremento de altura/SPAD
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 23.80 29.84 32.64 32.78 35.97 42.49## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~SPAD, data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.92278, p-value = 3.641e-08## R2m R2c
## [1,] 0.03608977 0.03608977## [1] 0.03628516
## attr(,"adj.r.squared")
## [1] 0.03785647##
## Call:
## glm(formula = I2 ~ SPAD, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.82747 0.77116 4.963 1.61e-06 ***
## SPAD -0.06051 0.02337 -2.589 0.0104 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.374693)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 244.70 on 178 degrees of freedom
## AIC: 572.09
##
## Number of Fisher Scoring iterations: 2mid <- mean(SPAD)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G02 <- ggplot(datos, aes(x = SPAD, y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = SPAD, y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(25,30,35,40), limits = c(23,43)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("SPAD") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "SPAD", color = "Tratamiento") +
annotate("text", x = 36,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G02
Incremento de altura/Fv’/Fm’
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.4905 0.5957 0.6238 0.6234 0.6542 0.7345## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~Fv_Fm, data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.91441, p-value = 9.577e-09## R2m R2c
## [1,] 0.01543139 0.01543139## [1] 0.01551674
## attr(,"adj.r.squared")
## [1] 0.01618868##
## Call:
## glm(formula = I2 ~ Fv_Fm, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.015 1.299 3.091 0.00232 **
## Fv_Fm -3.482 2.079 -1.675 0.09570 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.404318)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 249.97 on 178 degrees of freedom
## AIC: 575.93
##
## Number of Fisher Scoring iterations: 2mid <- mean(Fv_Fm)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G03 <- ggplot(datos, aes(x = Fv_Fm, y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = Fv_Fm, y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(0.50,0.55,0.60,0.65,0.70), limits = c(0.48,0.72)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("Fv_Fm") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "Fv_Fm", color = "Tratamiento") +
annotate("text", x = 0.50,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G03
Incremento de altura/qL
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3598 0.4701 0.6125 0.6086 0.7219 0.9213## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~qL, data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.90607, p-value = 2.731e-09## R2m R2c
## [1,] 0.0008589163 0.0008589163## [1] 0.0008637375
## attr(,"adj.r.squared")
## [1] 0.0009011411##
## Call:
## glm(formula = I2 ~ qL, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9927 0.3895 5.116 8.01e-07 ***
## qL -0.2444 0.6231 -0.392 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.42522)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 253.69 on 178 degrees of freedom
## AIC: 578.59
##
## Number of Fisher Scoring iterations: 2mid <- mean(qL)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 5) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G04 <- ggplot(datos, aes(x = qL, y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = qL, y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(0.4,0.5,0.6,0.7,0.8,0.9), limits = c(0.35,0.95)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("qL") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "qL", color = "Tratamiento") +
annotate("text", x = 0.35,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G04
Regresión lineal: Relación entre el incremento de altura con el vH+, gH+, LEF y PAR
Incremento de altura/vH+
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.02000 0.02975 0.04312 0.04704 0.06263 0.09875## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~vH, data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.91334, p-value = 8.12e-09## R2m R2c
## [1,] 0.001128975 0.001128975## [1] 0.00113531
## attr(,"adj.r.squared")
## [1] 0.001184474##
## Call:
## glm(formula = I2 ~ vH, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9399 0.2312 8.391 1.45e-14 ***
## vH -2.0404 4.5363 -0.450 0.653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.424833)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 253.62 on 178 degrees of freedom
## AIC: 578.54
##
## Number of Fisher Scoring iterations: 2mid <- mean(vH)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G05 <- ggplot(datos, aes(x = vH, y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = vH, y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(0.02,0.04,0.06,0.06,0.08,0.10), limits = c(0.02, 0.10)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("vH") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "vH+", color = "Tratamiento") +
annotate("text", x = 0.07,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G05
Incremento de altura/gH+
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 31.98 62.66 75.48 81.65 97.48 174.38## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~gH, data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.91777, p-value = 1.621e-08## R2m R2c
## [1,] 0.009253152 0.009253152## [1] 0.009304652
## attr(,"adj.r.squared")
## [1] 0.009707584##
## Call:
## glm(formula = I2 ~ gH, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.182977 0.276777 7.887 3.01e-13 ***
## gH -0.004152 0.003211 -1.293 0.198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.41318)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 251.55 on 178 degrees of freedom
## AIC: 577.06
##
## Number of Fisher Scoring iterations: 2mid <- mean(gH)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G06 <- ggplot(datos, aes(x = gH, y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = gH, y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(40,60,80,100,120,140,160), limits = c(30,175)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("gH") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "gH+", color = "Tratamiento") +
annotate("text", x = 125,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G06
Incremento de altura/LEF
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.805 7.644 17.579 20.003 30.733 51.792## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~LEF, data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.9095, p-value = 4.536e-09## R2m R2c
## [1,] 1.271662e-05 1.271662e-05## [1] 1.278806e-05
## attr(,"adj.r.squared")
## [1] 1.334184e-05##
## Call:
## glm(formula = I2 ~ LEF, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8508360 0.1696726 10.908 <2e-16 ***
## LEF -0.0003445 0.0072210 -0.048 0.962
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.426434)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 253.91 on 178 degrees of freedom
## AIC: 578.74
##
## Number of Fisher Scoring iterations: 2mid <- mean(LEF)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 7) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G07 <- ggplot(datos, aes(x = LEF, y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = LEF, y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(10,20,30,40,50), limits = c(5,55)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("LEF") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "LEF", color = "Tratamiento") +
annotate("text", x = 35,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G07
Incremento de altura/PAR
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 24.30 30.54 74.39 96.61 165.42 249.95## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~PAR, data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.90856, p-value = 3.944e-09## R2m R2c
## [1,] 1.446778e-05 1.446778e-05## [1] 1.454906e-05
## attr(,"adj.r.squared")
## [1] 1.51791e-05##
## Call:
## glm(formula = I2 ~ PAR, family = gaussian, data = datos, na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.838e+00 1.545e-01 11.892 <2e-16 ***
## PAR 6.653e-05 1.307e-03 0.051 0.959
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.426431)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 253.90 on 178 degrees of freedom
## AIC: 578.74
##
## Number of Fisher Scoring iterations: 2mid <- mean(PAR)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 9) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G08 <- ggplot(datos, aes(x = PAR, y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = PAR, y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(20, 50,80,110,140,170,200,230,260), limits = c(20,260)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("PAR") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "PAR", color = "Tratamiento") +
annotate("text", x = 165,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G08
Regresión lineal: Relación entre el incremento de altura con la humedad, temperatura ambiental y de la hoja
Incremento de altura/Humedad ambiental
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 35.99 41.93 43.82 43.53 45.27 51.39## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~Ambient_Humidity, data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.91118, p-value = 5.84e-09## R2m R2c
## [1,] 0.001885132 0.001885132## [1] 0.001895702
## attr(,"adj.r.squared")
## [1] 0.001977794##
## Call:
## glm(formula = I2 ~ Ambient_Humidity, family = gaussian, data = datos,
## na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.09460 1.29184 0.847 0.398
## Ambient_Humidity 0.01722 0.02961 0.581 0.562
##
## (Dispersion parameter for gaussian family taken to be 1.423748)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 253.43 on 178 degrees of freedom
## AIC: 578.4
##
## Number of Fisher Scoring iterations: 2mid <- mean(Ambient_Humidity)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G09 <- ggplot(datos, aes(x = Ambient_Humidity, y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = Ambient_Humidity, y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(35,40,45,50), limits = c(35,52)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("Ambient_Humidity") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "Humedad ambiental (%)", color = "Tratamiento") +
annotate("text", x = 35,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G09
Incremento de altura/Temperatura ambiental
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 26.00 28.48 29.12 29.59 30.77 33.70## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~Ambient_Temperature , data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.91585, p-value = 1.198e-08## R2m R2c
## [1,] 0.004521536 0.004521536## [1] 0.004546822
## attr(,"adj.r.squared")
## [1] 0.00474372##
## Call:
## glm(formula = I2 ~ Ambient_Temperature, family = gaussian, data = datos,
## na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.25369 1.56599 2.078 0.0392 *
## Ambient_Temperature -0.04764 0.05284 -0.902 0.3684
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.419966)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 252.75 on 178 degrees of freedom
## AIC: 577.92
##
## Number of Fisher Scoring iterations: 2mid <- mean(Ambient_Temperature )
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G10 <- ggplot(datos, aes(x = Ambient_Temperature , y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = Ambient_Temperature , y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(26,28,30,32,34), limits = c(26,34)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("Ambient_Temperature ") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "Temperatura ambiental (°C)", color = "Tratamiento") +
annotate("text", x = 31,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G10
Incremento de altura/Temperatura de la hoja
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 24.61 26.47 26.90 27.68 28.74 31.71## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.900 1.600 1.844 2.400 5.400
MCap<-glm(I2~Leaf_Temperature , data=datos, family = gaussian,na.action = na.fail)
null = glm (I2~ 1, data = datos, family = gaussian,na.action = na.fail)
shapiro.test(resid(MCap))##
## Shapiro-Wilk normality test
##
## data: resid(MCap)
## W = 0.91753, p-value = 1.561e-08## R2m R2c
## [1,] 0.004888557 0.004888557## [1] 0.004915886
## attr(,"adj.r.squared")
## [1] 0.005128765##
## Call:
## glm(formula = I2 ~ Leaf_Temperature, family = gaussian, data = datos,
## na.action = na.fail)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.18337 1.43112 2.224 0.0274 *
## Leaf_Temperature -0.04838 0.05159 -0.938 0.3497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.41944)
##
## Null deviance: 253.91 on 179 degrees of freedom
## Residual deviance: 252.66 on 178 degrees of freedom
## AIC: 577.85
##
## Number of Fisher Scoring iterations: 2mid <- mean(Leaf_Temperature)
pd <- position_dodge(0.1)
R2_value <- round(r.squaredLR(MCap), 4) # Calcula y redondea el R^2
`E-value` <- signif(lrtest(MCap)$`Pr(>Chisq)`[2], digits = 3) # Obtiene y redondea el E-value
annotation_text <- paste0("R^2 = ", R2_value, "\nE-value = ", `E-value`)
G11 <- ggplot(datos, aes(x = Leaf_Temperature , y = I2, color = Expe)) +
scale_color_manual(values = color_values) +
geom_point(alpha = 0.9, size = 4, color = "gray10")+
geom_point(alpha = 0.60, size = 3, position = pd)+
geom_smooth(mapping = aes(x = Leaf_Temperature , y = I2),
method = "glm", formula = y ~ x, color = "black", alpha = 0.6, size = 1.3) +
theme_classic() +
scale_x_continuous(breaks = c(24,25,26,27,28,29,30,31,32), limits = c(24,32)) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6), limits = c(0, 6)) +
theme(axis.text.x = element_text(face = "bold", color = "black", size = 13, angle = 0),
axis.text.y = element_text(face = "bold", color = "black", size = 13, angle = 0)) +
xlab("Leaf_Temperature ") + ylab(I2) + # Automatización del eje Y
theme(axis.text = element_text(size = 15), axis.title = element_text(size = 15, face = "bold")) +
theme(axis.line = element_line(colour = "black", size = 0.5, linetype = "solid")) +
theme(panel.background = element_rect(fill = "white", colour = "grey1", linetype = "solid", size = 2)) +
theme(panel.border = element_rect(linetype = "solid", size = 0.5, fill = "NA")) +
theme(legend.key = element_blank()) +
theme(legend.background = element_blank()) +
theme(legend.position="none")+
labs(y = "Incremento de altura (cm)", x = "Temperatura de la hoja (°C)", color = "Tratamiento") +
annotate("text", x = 29,y = 5.5, label = annotation_text,
size = 5, hjust = 0, color = "black")
G11