#data importation
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(readxl)
Alberto_df<- read_excel("C:/Users/leefo/Downloads/Database Mass Loss.Final.xlsx")
tibble(Alberto_df)## # A tibble: 720 × 6
## `Wood specie` `Extractives source` `Fungus type` Treatment
## <chr> <chr> <chr> <chr>
## 1 BS AQ TV UN
## 2 BS AQ TV UN
## 3 BS AQ TV UN
## 4 BS AQ TV UN
## 5 BS AQ TV L
## 6 BS AQ TV L
## 7 BS AQ TV L
## 8 BS AQ TV L
## 9 BS AQ TV C
## 10 BS AQ TV C
## # ℹ 710 more rows
## # ℹ 2 more variables: `Concentration mg/mL` <chr>, `Mass loss (%)` <dbl>#looking on data structure
str(Alberto_df)## tibble [720 × 6] (S3: tbl_df/tbl/data.frame)
## $ Wood specie : chr [1:720] "BS" "BS" "BS" "BS" ...
## $ Extractives source : chr [1:720] "AQ" "AQ" "AQ" "AQ" ...
## $ Fungus type : chr [1:720] "TV" "TV" "TV" "TV" ...
## $ Treatment : chr [1:720] "UN" "UN" "UN" "UN" ...
## $ Concentration mg/mL: chr [1:720] "0.5mg/mL" "0.5mg/mL" "0.5mg/mL" "0.5mg/mL" ...
## $ Mass loss (%) : num [1:720] 12.1 18.1 11 13 48.4 ...#assigning variables into factors
Alberto_df$`Wood specie`<-as.factor(Alberto_df$`Wood specie`)
Alberto_df$`Extractives source`<-as.factor(Alberto_df$`Extractives source`)
Alberto_df$`Fungus type`<-as.factor(Alberto_df$`Fungus type`)
Alberto_df$Treatment<-as.factor(Alberto_df$Treatment)
Alberto_df$`Concentration mg/mL`<-as.factor(Alberto_df$`Concentration mg/mL`)attach(Alberto_df) #new data structure
str(Alberto_df)## tibble [720 × 6] (S3: tbl_df/tbl/data.frame)
## $ Wood specie : Factor w/ 3 levels "BS","JG","SA": 1 1 1 1 1 1 1 1 1 1 ...
## $ Extractives source : Factor w/ 2 levels "AJ","AQ": 2 2 2 2 2 2 2 2 2 2 ...
## $ Fungus type : Factor w/ 2 levels "PP","TV": 2 2 2 2 2 2 2 2 2 2 ...
## $ Treatment : Factor w/ 3 levels "C","L","UN": 3 3 3 3 2 2 2 2 1 1 ...
## $ Concentration mg/mL: Factor w/ 5 levels "0.5mg/mL","1.0mg/mL",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Mass loss (%) : num [1:720] 12.1 18.1 11 13 48.4 ...#performing normality check by observation
qqnorm(`Mass loss (%)`)
qqline(`Mass loss (%)`)
plot(density(`Mass loss (%)`))
hist(`Mass loss (%)`,freq = FALSE, main = '')
lines(density(`Mass loss (%)`))
#normality check by statistical tests
shapiro.test(`Mass loss (%)`)##
## Shapiro-Wilk normality test
##
## data: Mass loss (%)
## W = 0.96064, p-value = 5.57e-13ks.test(Alberto_df$`Mass loss (%)`, "pnorm", mean=mean(Alberto_df$`Mass loss (%)`), sd=sd(Alberto_df$`Mass loss (%)`))## Warning in ks.test.default(Alberto_df$`Mass loss (%)`, "pnorm", mean =
## mean(Alberto_df$`Mass loss (%)`), : ties should not be present for the
## one-sample Kolmogorov-Smirnov test##
## Asymptotic one-sample Kolmogorov-Smirnov test
##
## data: Alberto_df$`Mass loss (%)`
## D = 0.091874, p-value = 1.052e-05
## alternative hypothesis: two-sided#performing anova test
library(agricolae)
library(sjPlot)## #refugeeswelcomelibrary(ggplot2)
library(tidyverse)
library(ggpubr)
library(rstatix)##
## Attaching package: 'rstatix'## The following object is masked from 'package:stats':
##
## filterlibrary(datasets)
library(ggplot2)
library(multcompView)
library(dplyr)MAIN EFFECT
modelM1<-aov(`Mass loss (%)`~`Wood specie`)
summary.aov(modelM1)## Df Sum Sq Mean Sq F value Pr(>F)
## `Wood specie` 2 4590 2294.9 19.9 3.88e-09 ***
## Residuals 717 82683 115.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mainws<-LSD.test(modelM1,c("Wood specie"))
# Combine the data
means <- data.frame(
Variable = c("BS", "JG", "SA"),
Mean_Mass_Loss = c(20.72104, 14.78121, 19.24279),
SE = c(0.693175, 0.693175, 0.693175)
)
groups <- data.frame(
Variable = c("BS", "SA", "JG"),
Groups = c("a", "a", "b")
)
# Merge the data frames
resultws <- merge(means, groups, by = "Variable")
# Rename columns
colnames(resultws) <- c("Variable", "Mean_Mass_Loss", "SE", "Groups")
# Display the result
# For first factor
p1 = ggplot(resultws, aes(x = Variable,y = Mean_Mass_Loss))
# Adding layers to p1 object
# Plotting bars
plotA = p1 +
geom_bar(stat = "identity",
color = "black",
position = position_dodge(width=0.9),
width = 0.4)+theme_classic()
# Adding error bars
plotB = plotA +
geom_errorbar(aes(ymax = Mean_Mass_Loss+ SE,
ymin = Mean_Mass_Loss - SE),
position = position_dodge(width=0.9),
width = 0.2)
# Changing main title, X & Y labels
plotC = plotB +
labs(title = "",
x = "Wood species",
y = "Mean Mass Loss (%)")
# Adding lettering from the test applied (LSD$group)
plotD = plotC +
geom_text(aes(x = Variable,
y = Mean_Mass_Loss + SE,
label = Groups),
position = position_dodge(width = 0.9),
vjust = -(0.5))
plotD
modelM2<-aov(`Mass loss (%)`~`Extractives source`)
summary.aov(modelM2)## Df Sum Sq Mean Sq F value Pr(>F)
## `Extractives source` 1 1683 1683.2 14.12 0.000185 ***
## Residuals 718 85590 119.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mainext<-LSD.test(modelM2,c("Extractives source"))
# Combine the data
means <- data.frame(
Variable = c("AQ", "AJ"),
Mean_Mass_Loss = c(19.77733, 16.71936),
SE = c(0.575436, 0.575436)
)
groups <- data.frame(
Variable = c("AQ", "AJ"),
Groups = c("a","b")
)
# Merge the data frames
resultws <- merge(means, groups, by = "Variable")
# Rename columns
colnames(resultws) <- c("Variable", "Mean_Mass_Loss", "SE", "Groups")
# Display the result
# For first factor
p1 = ggplot(resultws, aes(x = Variable,y = Mean_Mass_Loss))
# Adding layers to p1 object
# Plotting bars
plotA = p1 +
geom_bar(stat = "identity",
color = "black",
position = position_dodge(width=0.5),
width = 0.3)+theme_classic2()
# Adding error bars
plotB = plotA +
geom_errorbar(aes(ymax = Mean_Mass_Loss+ SE,
ymin = Mean_Mass_Loss - SE),
position = position_dodge(width=0.9),
width = 0.1)
# Changing main title, X & Y labels
plotC = plotB +
labs(title = "",
x = "Extractives source",
y = "Mean Mass Loss (%)")
# Adding lettering from the test applied (LSD$group)
plotD = plotC +
geom_text(aes(x = Variable,
y = Mean_Mass_Loss + SE,
label = Groups),
position = position_dodge(width = 0.9),
vjust = -(0.5))
plotD
modelM3<-aov(`Mass loss (%)`~`Fungus type`)
summary.aov(modelM3)## Df Sum Sq Mean Sq F value Pr(>F)
## `Fungus type` 1 416 415.6 3.436 0.0642 .
## Residuals 718 86857 121.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1mainfu<-LSD.test(modelM3,c("Fungus type"))
# Combine the data
means <- data.frame(
Variable = c("TV", "PP"),
Mean_Mass_Loss = c(19.00811, 17.48858),
SE = c(0.5796, 0.5796)
)
groups <- data.frame(
Variable = c("TV", "PP"),
Groups = c("a","a")
)
# Merge the data frames
resultws <- merge(means, groups, by = "Variable")
# Rename columns
colnames(resultws) <- c("Variable", "Mean_Mass_Loss", "SE", "Groups")
# Display the result
# For first factor
p1 = ggplot(resultws, aes(x = Variable,y = Mean_Mass_Loss))
# Adding layers to p1 object
# Plotting bars
plotA = p1 +
geom_bar(stat = "identity",
color = "black",
position = position_dodge(width=0.5),
width = 0.3)+theme_classic2()
# Adding error bars
plotB = plotA +
geom_errorbar(aes(ymax = Mean_Mass_Loss+ SE,
ymin = Mean_Mass_Loss - SE),
position = position_dodge(width=0.9),
width = 0.1)
# Changing main title, X & Y labels
plotC = plotB +
labs(title = "",
x = "Fungus type",
y = "Mean Mass Loss (%)")
# Adding lettering from the test applied (LSD$group)
plotD = plotC +
geom_text(aes(x = Variable,
y = Mean_Mass_Loss + SE,
label = Groups),
position = position_dodge(width = 0.9),
vjust = -(0.5))
plotD
modelM4<-aov(`Mass loss (%)`~`Treatment`)
summary.aov(modelM4)## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 2 39731 19866 299.6 <2e-16 ***
## Residuals 717 47542 66
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1maintr<-LSD.test(modelM4,c("Treatment"))
# Combine the data
means <- data.frame(
Variable = c("C", "L", "UN"),
Mean_Mass_Loss = c(28.02021, 16.70250, 10.02233),
SE = c(0.5256204, 0.5256204, 0.5256204)
)
groups <- data.frame(
Variable = c("C", "L", "UN"),
Groups = c("a", "b", "c")
)
# Merge the data frames
resultws <- merge(means, groups, by = "Variable")
# Rename columns
colnames(resultws) <- c("Variable", "Mean_Mass_Loss", "SE", "Groups")
# Display the result
# For first factor
p1 = ggplot(resultws, aes(x = Variable,y = Mean_Mass_Loss))
# Adding layers to p1 object
# Plotting bars
plotA = p1 +
geom_bar(stat = "identity",
color = "black",
position = position_dodge(width=0.9),
width = 0.4)+theme_classic()
# Adding error bars
plotB = plotA +
geom_errorbar(aes(ymax = Mean_Mass_Loss+ SE,
ymin = Mean_Mass_Loss - SE),
position = position_dodge(width=0.9),
width = 0.2)
# Changing main title, X & Y labels
plotC = plotB +
labs(title = "",
x = "Treatment",
y = "Mean Mass Loss (%)")
# Adding lettering from the test applied (LSD$group)
plotD = plotC +
geom_text(aes(x = Variable,
y = Mean_Mass_Loss + SE,
label = Groups),
position = position_dodge(width = 0.9),
vjust = -(0.5))
plotD
modelM5<-aov(`Mass loss (%)`~`Concentration mg/mL`)
summary.aov(modelM5)## Df Sum Sq Mean Sq F value Pr(>F)
## `Concentration mg/mL` 4 6414 1603.5 14.18 3.84e-11 ***
## Residuals 715 80859 113.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1maincon<-LSD.test(modelM5,c("Concentration mg/mL"))
# Combine the data
means <- data.frame(
Variable = c("0.5mg/mL", "1.0mg/mL", "1.5mg/mL ","2.0mg/mL", "2.5mg/mL"),
Mean_Mass_Loss = c(23.06687, 19.25493, 18.55257, 16.03014, 14.33722),
SE = 0.88619
)
groups <- data.frame(
Variable = c("0.5mg/mL", "1.0mg/mL", "1.5mg/mL ","2.0mg/mL", "2.5mg/mL"),
Groups = c("a", "b", "b","c", "c")
)
# Merge the data frames
resultws <- merge(means, groups, by = "Variable")
# Rename columns
colnames(resultws) <- c("Variable", "Mean_Mass_Loss", "SE", "Groups")
# Display the result
# For first factor
p1 = ggplot(resultws, aes(x = Variable,y = Mean_Mass_Loss))
# Adding layers to p1 object
# Plotting bars
plotA = p1 +
geom_bar(stat = "identity",
color = "black",
position = position_dodge(width=0.9),
width = 0.4)+theme_classic()
# Adding error bars
plotB = plotA +
geom_errorbar(aes(ymax = Mean_Mass_Loss+ SE,
ymin = Mean_Mass_Loss - SE),
position = position_dodge(width=0.9),
width = 0.2)
# Changing main title, X & Y labels
plotC = plotB +
labs(title = "",
x = "Treatment",
y = "Mean Mass Loss (%)")
# Adding lettering from the test applied (LSD$group)
plotD = plotC +
geom_text(aes(x = Variable,
y = Mean_Mass_Loss + SE,
label = Groups),
position = position_dodge(width = 0.9),
vjust = -(0.5))
plotD
MODELLING RESULTS
model1<-aov(`Mass loss (%)`~`Wood specie`+`Extractives source`+`Fungus type`+`Treatment`+`Concentration mg/mL`)
summary.aov(model1)## Df Sum Sq Mean Sq F value Pr(>F)
## `Wood specie` 2 4590 2295 47.245 < 2e-16 ***
## `Extractives source` 1 1683 1683 34.652 6.08e-09 ***
## `Fungus type` 1 416 416 8.556 0.00355 **
## Treatment 2 39731 19866 408.973 < 2e-16 ***
## `Concentration mg/mL` 4 6414 1603 33.011 < 2e-16 ***
## Residuals 709 34439 49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model2<-aov(`Mass loss (%)`~(`Wood specie`+`Extractives source`+`Fungus type`+`Treatment`+`Concentration mg/mL`)^2)
summary.aov(model2)## Df Sum Sq Mean Sq F value Pr(>F)
## `Wood specie` 2 4590 2295 60.652 < 2e-16
## `Extractives source` 1 1683 1683 44.486 5.35e-11
## `Fungus type` 1 416 416 10.984 0.000968
## Treatment 2 39731 19866 525.025 < 2e-16
## `Concentration mg/mL` 4 6414 1603 42.378 < 2e-16
## `Wood specie`:`Extractives source` 2 1713 856 22.633 3.07e-10
## `Wood specie`:`Fungus type` 2 17 8 0.219 0.803354
## `Wood specie`:Treatment 4 1759 440 11.624 3.93e-09
## `Wood specie`:`Concentration mg/mL` 8 338 42 1.118 0.348586
## `Extractives source`:`Fungus type` 1 306 306 8.075 0.004624
## `Extractives source`:Treatment 2 535 267 7.067 0.000918
## `Extractives source`:`Concentration mg/mL` 4 239 60 1.578 0.178273
## `Fungus type`:Treatment 2 11 6 0.150 0.860763
## `Fungus type`:`Concentration mg/mL` 4 685 171 4.527 0.001293
## Treatment:`Concentration mg/mL` 8 3410 426 11.264 4.94e-15
## Residuals 672 25427 38
##
## `Wood specie` ***
## `Extractives source` ***
## `Fungus type` ***
## Treatment ***
## `Concentration mg/mL` ***
## `Wood specie`:`Extractives source` ***
## `Wood specie`:`Fungus type`
## `Wood specie`:Treatment ***
## `Wood specie`:`Concentration mg/mL`
## `Extractives source`:`Fungus type` **
## `Extractives source`:Treatment ***
## `Extractives source`:`Concentration mg/mL`
## `Fungus type`:Treatment
## `Fungus type`:`Concentration mg/mL` **
## Treatment:`Concentration mg/mL` ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model3<-aov(`Mass loss (%)`~(`Wood specie`+`Extractives source`+`Fungus type`+`Treatment`+`Concentration mg/mL`)^3)
summary.aov(model3)## Df Sum Sq Mean Sq
## `Wood specie` 2 4590 2295
## `Extractives source` 1 1683 1683
## `Fungus type` 1 416 416
## Treatment 2 39731 19866
## `Concentration mg/mL` 4 6414 1603
## `Wood specie`:`Extractives source` 2 1713 856
## `Wood specie`:`Fungus type` 2 17 8
## `Wood specie`:Treatment 4 1759 440
## `Wood specie`:`Concentration mg/mL` 8 338 42
## `Extractives source`:`Fungus type` 1 306 306
## `Extractives source`:Treatment 2 535 267
## `Extractives source`:`Concentration mg/mL` 4 239 60
## `Fungus type`:Treatment 2 11 6
## `Fungus type`:`Concentration mg/mL` 4 685 171
## Treatment:`Concentration mg/mL` 8 3410 426
## `Wood specie`:`Extractives source`:`Fungus type` 2 636 318
## `Wood specie`:`Extractives source`:Treatment 4 352 88
## `Wood specie`:`Extractives source`:`Concentration mg/mL` 8 2048 256
## `Wood specie`:`Fungus type`:Treatment 4 544 136
## `Wood specie`:`Fungus type`:`Concentration mg/mL` 8 445 56
## `Wood specie`:Treatment:`Concentration mg/mL` 16 574 36
## `Extractives source`:`Fungus type`:Treatment 2 343 171
## `Extractives source`:`Fungus type`:`Concentration mg/mL` 4 555 139
## `Extractives source`:Treatment:`Concentration mg/mL` 8 227 28
## `Fungus type`:Treatment:`Concentration mg/mL` 8 473 59
## Residuals 608 19229 32
## F value Pr(>F)
## `Wood specie` 72.563 < 2e-16 ***
## `Extractives source` 53.222 9.34e-13 ***
## `Fungus type` 13.141 0.000313 ***
## Treatment 628.130 < 2e-16 ***
## `Concentration mg/mL` 50.701 < 2e-16 ***
## `Wood specie`:`Extractives source` 27.078 5.43e-12 ***
## `Wood specie`:`Fungus type` 0.262 0.769563
## `Wood specie`:Treatment 13.907 7.33e-11 ***
## `Wood specie`:`Concentration mg/mL` 1.338 0.221649
## `Extractives source`:`Fungus type` 9.661 0.001970 **
## `Extractives source`:Treatment 8.455 0.000239 ***
## `Extractives source`:`Concentration mg/mL` 1.888 0.110859
## `Fungus type`:Treatment 0.179 0.835799
## `Fungus type`:`Concentration mg/mL` 5.416 0.000273 ***
## Treatment:`Concentration mg/mL` 13.476 < 2e-16 ***
## `Wood specie`:`Extractives source`:`Fungus type` 10.061 5.02e-05 ***
## `Wood specie`:`Extractives source`:Treatment 2.779 0.026190 *
## `Wood specie`:`Extractives source`:`Concentration mg/mL` 8.096 2.02e-10 ***
## `Wood specie`:`Fungus type`:Treatment 4.302 0.001937 **
## `Wood specie`:`Fungus type`:`Concentration mg/mL` 1.758 0.082502 .
## `Wood specie`:Treatment:`Concentration mg/mL` 1.135 0.318112
## `Extractives source`:`Fungus type`:Treatment 5.419 0.004648 **
## `Extractives source`:`Fungus type`:`Concentration mg/mL` 4.389 0.001662 **
## `Extractives source`:Treatment:`Concentration mg/mL` 0.898 0.517405
## `Fungus type`:Treatment:`Concentration mg/mL` 1.869 0.062278 .
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1model4<-aov(`Mass loss (%)`~(`Wood specie`+`Extractives source`+`Fungus type`+`Treatment`+`Concentration mg/mL`)^4)
summary.aov(model4)## Df
## `Wood specie` 2
## `Extractives source` 1
## `Fungus type` 1
## Treatment 2
## `Concentration mg/mL` 4
## `Wood specie`:`Extractives source` 2
## `Wood specie`:`Fungus type` 2
## `Wood specie`:Treatment 4
## `Wood specie`:`Concentration mg/mL` 8
## `Extractives source`:`Fungus type` 1
## `Extractives source`:Treatment 2
## `Extractives source`:`Concentration mg/mL` 4
## `Fungus type`:Treatment 2
## `Fungus type`:`Concentration mg/mL` 4
## Treatment:`Concentration mg/mL` 8
## `Wood specie`:`Extractives source`:`Fungus type` 2
## `Wood specie`:`Extractives source`:Treatment 4
## `Wood specie`:`Extractives source`:`Concentration mg/mL` 8
## `Wood specie`:`Fungus type`:Treatment 4
## `Wood specie`:`Fungus type`:`Concentration mg/mL` 8
## `Wood specie`:Treatment:`Concentration mg/mL` 16
## `Extractives source`:`Fungus type`:Treatment 2
## `Extractives source`:`Fungus type`:`Concentration mg/mL` 4
## `Extractives source`:Treatment:`Concentration mg/mL` 8
## `Fungus type`:Treatment:`Concentration mg/mL` 8
## `Wood specie`:`Extractives source`:`Fungus type`:Treatment 4
## `Wood specie`:`Extractives source`:`Fungus type`:`Concentration mg/mL` 8
## `Wood specie`:`Extractives source`:Treatment:`Concentration mg/mL` 16
## `Wood specie`:`Fungus type`:Treatment:`Concentration mg/mL` 16
## `Extractives source`:`Fungus type`:Treatment:`Concentration mg/mL` 8
## Residuals 556
## Sum Sq
## `Wood specie` 4590
## `Extractives source` 1683
## `Fungus type` 416
## Treatment 39731
## `Concentration mg/mL` 6414
## `Wood specie`:`Extractives source` 1713
## `Wood specie`:`Fungus type` 17
## `Wood specie`:Treatment 1759
## `Wood specie`:`Concentration mg/mL` 338
## `Extractives source`:`Fungus type` 306
## `Extractives source`:Treatment 535
## `Extractives source`:`Concentration mg/mL` 239
## `Fungus type`:Treatment 11
## `Fungus type`:`Concentration mg/mL` 685
## Treatment:`Concentration mg/mL` 3410
## `Wood specie`:`Extractives source`:`Fungus type` 636
## `Wood specie`:`Extractives source`:Treatment 352
## `Wood specie`:`Extractives source`:`Concentration mg/mL` 2048
## `Wood specie`:`Fungus type`:Treatment 544
## `Wood specie`:`Fungus type`:`Concentration mg/mL` 445
## `Wood specie`:Treatment:`Concentration mg/mL` 574
## `Extractives source`:`Fungus type`:Treatment 343
## `Extractives source`:`Fungus type`:`Concentration mg/mL` 555
## `Extractives source`:Treatment:`Concentration mg/mL` 227
## `Fungus type`:Treatment:`Concentration mg/mL` 473
## `Wood specie`:`Extractives source`:`Fungus type`:Treatment 555
## `Wood specie`:`Extractives source`:`Fungus type`:`Concentration mg/mL` 75
## `Wood specie`:`Extractives source`:Treatment:`Concentration mg/mL` 1434
## `Wood specie`:`Fungus type`:Treatment:`Concentration mg/mL` 578
## `Extractives source`:`Fungus type`:Treatment:`Concentration mg/mL` 554
## Residuals 16032
## Mean Sq
## `Wood specie` 2295
## `Extractives source` 1683
## `Fungus type` 416
## Treatment 19866
## `Concentration mg/mL` 1603
## `Wood specie`:`Extractives source` 856
## `Wood specie`:`Fungus type` 8
## `Wood specie`:Treatment 440
## `Wood specie`:`Concentration mg/mL` 42
## `Extractives source`:`Fungus type` 306
## `Extractives source`:Treatment 267
## `Extractives source`:`Concentration mg/mL` 60
## `Fungus type`:Treatment 6
## `Fungus type`:`Concentration mg/mL` 171
## Treatment:`Concentration mg/mL` 426
## `Wood specie`:`Extractives source`:`Fungus type` 318
## `Wood specie`:`Extractives source`:Treatment 88
## `Wood specie`:`Extractives source`:`Concentration mg/mL` 256
## `Wood specie`:`Fungus type`:Treatment 136
## `Wood specie`:`Fungus type`:`Concentration mg/mL` 56
## `Wood specie`:Treatment:`Concentration mg/mL` 36
## `Extractives source`:`Fungus type`:Treatment 171
## `Extractives source`:`Fungus type`:`Concentration mg/mL` 139
## `Extractives source`:Treatment:`Concentration mg/mL` 28
## `Fungus type`:Treatment:`Concentration mg/mL` 59
## `Wood specie`:`Extractives source`:`Fungus type`:Treatment 139
## `Wood specie`:`Extractives source`:`Fungus type`:`Concentration mg/mL` 9
## `Wood specie`:`Extractives source`:Treatment:`Concentration mg/mL` 90
## `Wood specie`:`Fungus type`:Treatment:`Concentration mg/mL` 36
## `Extractives source`:`Fungus type`:Treatment:`Concentration mg/mL` 69
## Residuals 29
## F value
## `Wood specie` 79.588
## `Extractives source` 58.375
## `Fungus type` 14.414
## Treatment 688.946
## `Concentration mg/mL` 55.610
## `Wood specie`:`Extractives source` 29.700
## `Wood specie`:`Fungus type` 0.287
## `Wood specie`:Treatment 15.253
## `Wood specie`:`Concentration mg/mL` 1.467
## `Extractives source`:`Fungus type` 10.596
## `Extractives source`:Treatment 9.274
## `Extractives source`:`Concentration mg/mL` 2.071
## `Fungus type`:Treatment 0.197
## `Fungus type`:`Concentration mg/mL` 5.941
## Treatment:`Concentration mg/mL` 14.781
## `Wood specie`:`Extractives source`:`Fungus type` 11.036
## `Wood specie`:`Extractives source`:Treatment 3.048
## `Wood specie`:`Extractives source`:`Concentration mg/mL` 8.880
## `Wood specie`:`Fungus type`:Treatment 4.718
## `Wood specie`:`Fungus type`:`Concentration mg/mL` 1.928
## `Wood specie`:Treatment:`Concentration mg/mL` 1.245
## `Extractives source`:`Fungus type`:Treatment 5.944
## `Extractives source`:`Fungus type`:`Concentration mg/mL` 4.814
## `Extractives source`:Treatment:`Concentration mg/mL` 0.985
## `Fungus type`:Treatment:`Concentration mg/mL` 2.050
## `Wood specie`:`Extractives source`:`Fungus type`:Treatment 4.816
## `Wood specie`:`Extractives source`:`Fungus type`:`Concentration mg/mL` 0.325
## `Wood specie`:`Extractives source`:Treatment:`Concentration mg/mL` 3.109
## `Wood specie`:`Fungus type`:Treatment:`Concentration mg/mL` 1.252
## `Extractives source`:`Fungus type`:Treatment:`Concentration mg/mL` 2.403
## Residuals
## Pr(>F)
## `Wood specie` < 2e-16
## `Extractives source` 9.54e-14
## `Fungus type` 0.000163
## Treatment < 2e-16
## `Concentration mg/mL` < 2e-16
## `Wood specie`:`Extractives source` 5.56e-13
## `Wood specie`:`Fungus type` 0.750311
## `Wood specie`:Treatment 7.65e-12
## `Wood specie`:`Concentration mg/mL` 0.166135
## `Extractives source`:`Fungus type` 0.001202
## `Extractives source`:Treatment 0.000109
## `Extractives source`:`Concentration mg/mL` 0.083220
## `Fungus type`:Treatment 0.821419
## `Fungus type`:`Concentration mg/mL` 0.000110
## Treatment:`Concentration mg/mL` < 2e-16
## `Wood specie`:`Extractives source`:`Fungus type` 2.00e-05
## `Wood specie`:`Extractives source`:Treatment 0.016752
## `Wood specie`:`Extractives source`:`Concentration mg/mL` 1.76e-11
## `Wood specie`:`Fungus type`:Treatment 0.000945
## `Wood specie`:`Fungus type`:`Concentration mg/mL` 0.053621
## `Wood specie`:Treatment:`Concentration mg/mL` 0.228666
## `Extractives source`:`Fungus type`:Treatment 0.002792
## `Extractives source`:`Fungus type`:`Concentration mg/mL` 0.000798
## `Extractives source`:Treatment:`Concentration mg/mL` 0.446375
## `Fungus type`:Treatment:`Concentration mg/mL` 0.038963
## `Wood specie`:`Extractives source`:`Fungus type`:Treatment 0.000796
## `Wood specie`:`Extractives source`:`Fungus type`:`Concentration mg/mL` 0.956609
## `Wood specie`:`Extractives source`:Treatment:`Concentration mg/mL` 4.36e-05
## `Wood specie`:`Fungus type`:Treatment:`Concentration mg/mL` 0.223421
## `Extractives source`:`Fungus type`:Treatment:`Concentration mg/mL` 0.014848
## Residuals
##
## `Wood specie` ***
## `Extractives source` ***
## `Fungus type` ***
## Treatment ***
## `Concentration mg/mL` ***
## `Wood specie`:`Extractives source` ***
## `Wood specie`:`Fungus type`
## `Wood specie`:Treatment ***
## `Wood specie`:`Concentration mg/mL`
## `Extractives source`:`Fungus type` **
## `Extractives source`:Treatment ***
## `Extractives source`:`Concentration mg/mL` .
## `Fungus type`:Treatment
## `Fungus type`:`Concentration mg/mL` ***
## Treatment:`Concentration mg/mL` ***
## `Wood specie`:`Extractives source`:`Fungus type` ***
## `Wood specie`:`Extractives source`:Treatment *
## `Wood specie`:`Extractives source`:`Concentration mg/mL` ***
## `Wood specie`:`Fungus type`:Treatment ***
## `Wood specie`:`Fungus type`:`Concentration mg/mL` .
## `Wood specie`:Treatment:`Concentration mg/mL`
## `Extractives source`:`Fungus type`:Treatment **
## `Extractives source`:`Fungus type`:`Concentration mg/mL` ***
## `Extractives source`:Treatment:`Concentration mg/mL`
## `Fungus type`:Treatment:`Concentration mg/mL` *
## `Wood specie`:`Extractives source`:`Fungus type`:Treatment ***
## `Wood specie`:`Extractives source`:`Fungus type`:`Concentration mg/mL`
## `Wood specie`:`Extractives source`:Treatment:`Concentration mg/mL` ***
## `Wood specie`:`Fungus type`:Treatment:`Concentration mg/mL`
## `Extractives source`:`Fungus type`:Treatment:`Concentration mg/mL` *
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1WOOD SPECIES
#PLOTING RESULTS
library(agricolae)
lsdmodel1<-LSD.test(model1,c("Wood specie","Treatment"))
lsdmodel1## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 2.163531
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Wood specie:Treatment 9 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## BS:C 27.940000 4.900342 80 0.7792162 26.410153 29.469847 15.80 38.83
## BS:L 21.731875 12.482900 80 0.7792162 20.202028 23.261722 1.09 51.62
## BS:UN 12.491250 9.690714 80 0.7792162 10.961403 14.021097 1.19 52.24
## JG:C 25.308125 5.718979 80 0.7792162 23.778278 26.837972 15.80 36.40
## JG:L 11.586375 7.448256 80 0.7792162 10.056528 13.116222 3.63 39.87
## JG:UN 7.449125 4.404520 80 0.7792162 5.919278 8.978972 0.11 21.29
## SA:C 30.812500 3.173726 80 0.7792162 29.282653 32.342347 25.07 35.86
## SA:L 16.789250 9.702965 80 0.7792162 15.259403 18.319097 3.79 58.08
## SA:UN 10.126625 5.989710 80 0.7792162 8.596778 11.656472 -8.27 27.18
## Q25 Q50 Q75
## BS:C 25.6800 27.830 29.9600
## BS:L 12.2100 19.175 31.1825
## BS:UN 4.7250 10.900 15.6225
## JG:C 21.2400 25.530 28.0200
## JG:L 6.8475 10.390 13.3475
## JG:UN 4.0950 7.440 10.0825
## SA:C 28.7075 31.080 32.7450
## SA:L 11.2325 14.185 21.3875
## SA:UN 5.3375 9.990 14.5325
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## SA:C 30.812500 a
## BS:C 27.940000 b
## JG:C 25.308125 c
## BS:L 21.731875 d
## SA:L 16.789250 e
## BS:UN 12.491250 f
## JG:L 11.586375 fg
## SA:UN 10.126625 g
## JG:UN 7.449125 h
##
## attr(,"class")
## [1] "group"# Creating the data frame with the given data
# Define the data
mean_mass_loss <- c(30.812500, 27.940000, 25.308125, 21.731875, 16.789250, 12.491250, 11.586375, 10.126625, 7.449125)
variable_used <- c("SA:C", "BS:C", "JG:C", "BS:L", "SA:L", "BS:UN", "JG:L", "SA:UN", "JG:UN")
groups_letters <- c("a", "b", "c", "d", "e", "f", "fg", "g", "h")
standard_error <- c(0.7792162, 0.7792162, 0.7792162, 0.7792162, 0.7792162, 0.7792162, 0.7792162, 0.7792162, 0.7792162)
# Combine the data into a data frame
df <- data.frame(
`Mean Mass Loss (%)` = mean_mass_loss,
`Variable Used` = variable_used,
`Groups Letters` = groups_letters,
`Standard Error (SE)` = standard_error
)
#GENERAL PLOT NO SPLITTING THE INTERACTION VARIABLES
# Create the bar chart with error bars and group letters
ggplot(df, aes(x = Variable.Used, y = Mean.Mass.Loss....)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = Mean.Mass.Loss.... - Standard.Error..SE., ymax = Mean.Mass.Loss.... + Standard.Error..SE.), width = 0.2) +
geom_text(aes(label = Groups.Letters, y = Mean.Mass.Loss.... + Standard.Error..SE. + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into WoodSpecies and Treatment
df <- df %>%
separate(Variable.Used, into = c("WoodSpecies", "Treatment"), sep = ":")
#USING FACE WARAPPING PLOTTING BASED ON EACH VARIABLE ON INTERACTION SPLITTED
#FACE WRAP 1
# Create the bar chart with error bars and group letters, facetted by Wood Species
ggplot(df, aes(x = WoodSpecies, y = Mean.Mass.Loss....)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = Mean.Mass.Loss.... - Standard.Error..SE., ymax = Mean.Mass.Loss.... + Standard.Error..SE.), width = 0.2) +
geom_text(aes(label = Groups.Letters, y = Mean.Mass.Loss.... + Standard.Error..SE. + 1), vjust = 0) +
labs( x = "Wood specie",
y = "Mean Mass Loss (%)") +
facet_wrap(~ Treatment) +
theme_classic()
#FACE WRAP 2
# Create the bar chart with error bars and group letters, facetted by Wood Species
ggplot(df, aes(x = Treatment, y = Mean.Mass.Loss....)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = Mean.Mass.Loss.... - Standard.Error..SE., ymax = Mean.Mass.Loss.... + Standard.Error..SE.), width = 0.2) +
geom_text(aes(label = Groups.Letters, y = Mean.Mass.Loss.... + Standard.Error..SE. + 1), vjust = 0) +
labs( x = "Treatment",
y = "Mean Mass Loss (%)") +
facet_wrap(~ WoodSpecies) +
theme_classic()
#PLOTING RESULTS
library(agricolae)
lsdmodel2<-LSD.test(model1,c("Wood specie","Concentration mg/mL"))
lsdmodel2## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 2.793106
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Wood specie:Concentration mg/mL 15 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## BS:0.5mg/mL 26.40396 11.676617 48 1.005964 24.428934 28.37898 9.70 52.24
## BS:1.0mg/mL 20.79312 10.869561 48 1.005964 18.818101 22.76815 1.09 49.83
## BS:1.5mg/mL 22.15000 10.641152 48 1.005964 20.174976 24.12502 1.19 45.70
## BS:2.0mg/mL 17.55375 10.081168 48 1.005964 15.578726 19.52877 3.54 38.83
## BS:2.5mg/mL 16.70437 11.606170 48 1.005964 14.729351 18.67940 3.01 38.83
## JG:0.5mg/mL 19.56125 8.071015 48 1.005964 17.586226 21.53627 6.29 38.48
## JG:1.0mg/mL 16.23729 8.763909 48 1.005964 14.262267 18.21232 4.48 39.87
## JG:1.5mg/mL 14.20396 8.765551 48 1.005964 12.228934 16.17898 5.75 36.40
## JG:2.0mg/mL 12.55146 10.055294 48 1.005964 10.576434 14.52648 0.11 36.40
## JG:2.5mg/mL 11.35208 10.747772 48 1.005964 9.377059 13.32711 0.25 36.40
## SA:0.5mg/mL 23.23542 11.647517 48 1.005964 21.260392 25.21044 3.79 58.08
## SA:1.0mg/mL 20.73438 8.628529 48 1.005964 18.759351 22.70940 6.04 35.86
## SA:1.5mg/mL 19.30375 9.593036 48 1.005964 17.328726 21.27877 6.04 35.86
## SA:2.0mg/mL 17.98521 11.589751 48 1.005964 16.010184 19.96023 -8.27 35.86
## SA:2.5mg/mL 14.95521 11.815908 48 1.005964 12.980184 16.93023 3.03 35.86
## Q25 Q50 Q75
## BS:0.5mg/mL 16.9975 26.395 35.3600
## BS:1.0mg/mL 12.1650 17.655 28.0275
## BS:1.5mg/mL 13.1450 21.355 29.4500
## BS:2.0mg/mL 8.9850 15.585 26.7875
## BS:2.5mg/mL 5.1900 14.570 26.7875
## JG:0.5mg/mL 13.5875 17.435 25.2825
## JG:1.0mg/mL 9.9875 13.115 22.0025
## JG:1.5mg/mL 7.6175 9.595 20.8000
## JG:2.0mg/mL 4.6025 8.425 20.8000
## JG:2.5mg/mL 3.7275 5.160 20.8000
## SA:0.5mg/mL 14.4300 23.480 30.4050
## SA:1.0mg/mL 13.9400 17.255 28.8725
## SA:1.5mg/mL 11.8125 15.410 28.8525
## SA:2.0mg/mL 9.1675 16.040 29.0050
## SA:2.5mg/mL 5.3025 8.490 28.4225
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## BS:0.5mg/mL 26.40396 a
## SA:0.5mg/mL 23.23542 b
## BS:1.5mg/mL 22.15000 bc
## BS:1.0mg/mL 20.79312 bcd
## SA:1.0mg/mL 20.73438 bcde
## JG:0.5mg/mL 19.56125 cdef
## SA:1.5mg/mL 19.30375 defg
## SA:2.0mg/mL 17.98521 efgh
## BS:2.0mg/mL 17.55375 fghi
## BS:2.5mg/mL 16.70437 ghij
## JG:1.0mg/mL 16.23729 hij
## SA:2.5mg/mL 14.95521 ijk
## JG:1.5mg/mL 14.20396 jk
## JG:2.0mg/mL 12.55146 kl
## JG:2.5mg/mL 11.35208 l
##
## attr(,"class")
## [1] "group"# Define the new data
mean_mass_loss <- c(26.40396, 23.23542, 22.15000, 20.79312, 20.73438, 19.56125, 19.30375, 17.98521,
17.55375, 16.70437, 16.23729, 14.95521, 14.20396, 12.55146, 11.35208)
variable_used <- c("BS:0.5", "SA:0.5", "BS:1.5", "BS:1.0", "SA:1.0",
"JG:0.5", "SA:1.5", "SA:2.0", "BS:2.0", "BS:2.5",
"JG:1.0", "SA:2.5", "JG:1.5", "JG:2.0", "JG:2.5")
groups_letters <- c("a", "b", "bc", "bcd", "bcde", "cdef", "defg", "efgh", "fghi", "ghij", "hij", "ijk", "jk", "kl", "l")
standard_error <- c(1.005964, 1.005964, 1.005964, 1.005964, 1.005964, 1.005964, 1.005964, 1.005964,
1.005964, 1.005964, 1.005964, 1.005964, 1.005964, 1.005964, 1.005964)
# Combine the data into a data frame
df <- data.frame(
MeanMassLoss = mean_mass_loss,
VariableUsed = variable_used,
GroupsLetters = groups_letters,
StandardError = standard_error
)
# Plotting the general bar chart with error bars and group letters (no splitting of variables)
ggplot(df, aes(x = VariableUsed, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into WoodSpecies and Concentration
df <- df %>%
separate(VariableUsed, into = c("WoodSpecies", "Concentration"), sep = ":")
# Plotting with facet wrap by Wood Species
ggplot(df, aes(x = Concentration, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Concentration (mg/mL)",
y = "Mean Mass Loss (%)") +
facet_wrap(~ WoodSpecies) +
theme_classic()
# Plotting with facet wrap by Concentration
ggplot(df, aes(x = WoodSpecies, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Wood Species",
y = "Mean Mass Loss (%)") +
facet_wrap(~ Concentration) +
theme_classic()
#PLOTING RESULTS
library(agricolae)
lsdmodel3<-LSD.test(model1,c("Wood specie","Fungus type"))
lsdmodel3## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 1.766515
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Wood specie:Fungus type 6 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## BS:PP 19.74767 12.590837 120 0.6362274 18.49855 20.99678 1.19 52.24
## BS:TV 21.69442 10.126406 120 0.6362274 20.44530 22.94353 1.09 49.83
## JG:PP 14.11067 9.464351 120 0.6362274 12.86155 15.35978 0.11 39.87
## JG:TV 15.45175 9.921728 120 0.6362274 14.20263 16.70087 3.07 36.40
## SA:PP 18.60742 11.359712 120 0.6362274 17.35830 19.85653 -8.27 58.08
## SA:TV 19.87817 10.632607 120 0.6362274 18.62905 21.12728 3.21 58.08
## Q25 Q50 Q75
## BS:PP 9.2800 19.480 28.675
## BS:TV 13.9975 21.740 28.210
## JG:PP 6.5550 11.190 21.875
## JG:TV 7.9450 12.685 24.400
## SA:PP 8.4775 16.135 28.850
## SA:TV 12.1650 17.560 29.690
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## BS:TV 21.69442 a
## SA:TV 19.87817 b
## BS:PP 19.74767 b
## SA:PP 18.60742 b
## JG:TV 15.45175 c
## JG:PP 14.11067 c
##
## attr(,"class")
## [1] "group"# Define the new data
mean_mass_loss <- c(21.69442, 19.87817, 19.74767, 18.60742, 15.45175, 14.11067)
variable_used <- c("BS:TV", "SA:TV", "BS:PP", "SA:PP", "JG:TV", "JG:PP")
groups_letters <- c("a", "b", "b", "b", "c", "c")
standard_error <- c(0.6362274, 0.6362274, 0.6362274, 0.6362274, 0.6362274, 0.6362274)
# Combine the data into a data frame
df <- data.frame(
MeanMassLoss = mean_mass_loss,
VariableUsed = variable_used,
GroupsLetters = groups_letters,
StandardError = standard_error
)
# General Plot (No Splitting)
ggplot(df, aes(x = VariableUsed, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into WoodSpecies and FungusType
df <- df %>%
separate(VariableUsed, into = c("WoodSpecies", "FungusType"), sep = ":")
# Facet Wrap by Wood Species
ggplot(df, aes(x = FungusType, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Fungus Type",
y = "Mean Mass Loss (%)") +
facet_wrap(~ WoodSpecies) +
theme_classic()
# Facet Wrap by Fungus Type
ggplot(df, aes(x = WoodSpecies, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Wood Species",
y = "Mean Mass Loss (%)") +
facet_wrap(~ FungusType) +
theme_classic()
#PLOTING RESULTS
library(agricolae)
lsdmodel4<-LSD.test(model1,c("Wood specie","Extractives source"))
lsdmodel4## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 1.766515
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Wood specie:Extractives source 6 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## BS:AJ 17.86158 8.366670 120 0.6362274 16.61247 19.11070 1.09 40.32
## BS:AQ 23.58050 13.285049 120 0.6362274 22.33138 24.82962 1.19 52.24
## JG:AJ 12.42058 7.708017 120 0.6362274 11.17147 13.66970 0.11 25.91
## JG:AQ 17.14183 10.875162 120 0.6362274 15.89272 18.39095 0.25 39.87
## SA:AJ 19.87592 11.734626 120 0.6362274 18.62680 21.12503 -8.27 58.08
## SA:AQ 18.60967 10.217617 120 0.6362274 17.36055 19.85878 3.03 34.78
## Q25 Q50 Q75
## BS:AJ 11.0275 19.165 25.5675
## BS:AQ 11.8550 27.830 32.8150
## JG:AJ 6.4100 10.140 19.2500
## JG:AQ 8.0500 13.700 26.0200
## SA:AJ 12.2500 16.945 28.8800
## SA:AQ 8.4775 15.925 29.7525
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## BS:AQ 23.58050 a
## SA:AJ 19.87592 b
## SA:AQ 18.60967 bc
## BS:AJ 17.86158 c
## JG:AQ 17.14183 c
## JG:AJ 12.42058 d
##
## attr(,"class")
## [1] "group"# Define the new data
mean_mass_loss <- c(23.58050, 19.87592, 18.60967, 17.86158, 17.14183, 12.42058)
variable_used <- c("BS:AQ", "SA:AJ", "SA:AQ", "BS:AJ", "JG:AQ", "JG:AJ")
groups_letters <- c("a", "b", "bc", "c", "c", "d")
standard_error <- c(0.6362274, 0.6362274, 0.6362274, 0.6362274, 0.6362274, 0.6362274)
# Combine the data into a data frame
df <- data.frame(
MeanMassLoss = mean_mass_loss,
VariableUsed = variable_used,
GroupsLetters = groups_letters,
StandardError = standard_error
)
# General Plot (No Splitting)
ggplot(df, aes(x = VariableUsed, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into WoodSpecies and ExtractivesSource
df <- df %>%
separate(VariableUsed, into = c("WoodSpecies", "ExtractivesSource"), sep = ":")
# Facet Wrap by Wood Species
ggplot(df, aes(x = ExtractivesSource, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Extractives Source",
y = "Mean Mass Loss (%)") +
facet_wrap(~ WoodSpecies) +
theme_classic()
# Facet Wrap by Extractives Source
ggplot(df, aes(x = WoodSpecies, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Wood Species",
y = "Mean Mass Loss (%)") +
facet_wrap(~ ExtractivesSource) +
theme_classic()
TREATMENTS
#PLOTING RESULTS
library(agricolae)
lsdmodel5<-LSD.test(model1,c("Treatment","Concentration mg/mL"))
lsdmodel5## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 2.793106
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Treatment:Concentration mg/mL 15 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## C:0.5mg/mL 28.133125 5.133971 48 1.005964 26.158101 30.108149 15.80 38.83
## C:1.0mg/mL 27.568542 5.690293 48 1.005964 25.593517 29.543566 15.80 38.83
## C:1.5mg/mL 28.133125 5.133971 48 1.005964 26.158101 30.108149 15.80 38.83
## C:2.0mg/mL 28.133125 5.133971 48 1.005964 26.158101 30.108149 15.80 38.83
## C:2.5mg/mL 28.133125 5.133971 48 1.005964 26.158101 30.108149 15.80 38.83
## L:0.5mg/mL 24.440000 13.368188 48 1.005964 22.464976 26.415024 3.79 58.08
## L:1.0mg/mL 17.631875 10.471087 48 1.005964 15.656851 19.606899 1.09 49.83
## L:1.5mg/mL 17.065625 9.654661 48 1.005964 15.090601 19.040649 6.04 45.70
## L:2.0mg/mL 13.624792 7.144039 48 1.005964 11.649767 15.599816 3.63 32.24
## L:2.5mg/mL 10.750208 7.772521 48 1.005964 8.775184 12.725233 3.35 37.53
## UN:0.5mg/mL 16.627500 9.249314 48 1.005964 14.652476 18.602524 3.98 52.24
## UN:1.0mg/mL 12.564375 4.726571 48 1.005964 10.589351 14.539399 2.14 27.10
## UN:1.5mg/mL 10.458958 5.721258 48 1.005964 8.483934 12.433983 1.19 32.87
## UN:2.0mg/mL 6.332500 5.171703 48 1.005964 4.357476 8.307524 -8.27 18.23
## UN:2.5mg/mL 4.128333 2.078769 48 1.005964 2.153309 6.103358 0.25 10.81
## Q25 Q50 Q75
## C:0.5mg/mL 25.6125 28.245 31.2200
## C:1.0mg/mL 24.4000 28.055 31.2200
## C:1.5mg/mL 25.6125 28.245 31.2200
## C:2.0mg/mL 25.6125 28.245 31.2200
## C:2.5mg/mL 25.6125 28.245 31.2200
## L:0.5mg/mL 13.6825 20.690 30.3400
## L:1.0mg/mL 10.9775 14.025 21.7575
## L:1.5mg/mL 9.9100 13.840 21.0225
## L:2.0mg/mL 8.4925 12.320 15.9000
## L:2.5mg/mL 5.5375 8.230 12.4275
## UN:0.5mg/mL 10.9975 14.820 18.2100
## UN:1.0mg/mL 9.7550 12.150 14.8100
## UN:1.5mg/mL 7.1100 8.500 12.4675
## UN:2.0mg/mL 3.7650 5.060 9.1425
## UN:2.5mg/mL 3.3000 4.025 4.9650
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## C:0.5mg/mL 28.133125 a
## C:1.5mg/mL 28.133125 a
## C:2.0mg/mL 28.133125 a
## C:2.5mg/mL 28.133125 a
## C:1.0mg/mL 27.568542 a
## L:0.5mg/mL 24.440000 b
## L:1.0mg/mL 17.631875 c
## L:1.5mg/mL 17.065625 c
## UN:0.5mg/mL 16.627500 c
## L:2.0mg/mL 13.624792 d
## UN:1.0mg/mL 12.564375 de
## L:2.5mg/mL 10.750208 e
## UN:1.5mg/mL 10.458958 e
## UN:2.0mg/mL 6.332500 f
## UN:2.5mg/mL 4.128333 f
##
## attr(,"class")
## [1] "group"# Define the new data
mean_mass_loss <- c(
28.133125, 27.568542, 28.133125, 28.133125, 28.133125,
24.44, 17.631875, 17.065625, 16.6275, 13.624792,
12.564375, 10.750208, 10.458958, 6.3325, 4.128333
)
variable_used <- c(
"C:0.5", "C:1.0", "C:1.5", "C:2.0", "C:2.5",
"L:0.5", "L:1.0", "L:1.5", "UN:0.5", "L:2.0",
"UN:1.0", "L:2.5", "UN:1.5", "UN:2.0", "UN:2.5"
)
groups_letters <- c("a", "a", "a", "a", "a", "b", "c", "c", "c", "d", "de", "e", "e", "f", "f")
standard_error <- c(
1.005964, 1.005964, 1.005964, 1.005964, 1.005964,
1.005964, 1.005964, 1.005964, 1.005964, 1.005964,
1.005964, 1.005964, 1.005964, 1.005964, 1.005964
)
# Combine the data into a data frame
df <- data.frame(
MeanMassLoss = mean_mass_loss,
VariableUsed = variable_used,
GroupsLetters = groups_letters,
StandardError = standard_error
)
# General Plot (No Splitting)
ggplot(df, aes(x = VariableUsed, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment:Concentration",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into Treatment and Concentration
df <- df %>%
separate(VariableUsed, into = c("Treatment", "Concentration"), sep = ":")
# Facet Wrap by Treatment
ggplot(df, aes(x = Concentration, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Concentration (mg/mL)",
y = "Mean Mass Loss (%)") +
facet_wrap(~ Treatment) +
theme_classic()
# Facet Wrap by Treatment
ggplot(df, aes(x = Treatment, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
facet_wrap(~ Concentration) +
theme_classic()
#PLOTING RESULTS
library(agricolae)
lsdmodel6<-LSD.test(model1,c("Treatment","Extractives source"))
lsdmodel6## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 1.766515
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Treatment:Extractives source 6 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## C:AJ 25.692083 5.224917 120 0.6362274 24.442968 26.94120 15.80 35.86
## C:AQ 30.348333 4.037433 120 0.6362274 29.099218 31.59745 20.58 38.83
## L:AJ 14.776083 9.891883 120 0.6362274 13.526968 16.02520 1.09 58.08
## L:AQ 18.628917 11.491217 120 0.6362274 17.379802 19.87803 3.79 51.62
## UN:AJ 9.689917 6.094452 120 0.6362274 8.440802 10.93903 -8.27 27.18
## UN:AQ 10.354750 8.380394 120 0.6362274 9.105635 11.60387 0.25 52.24
## Q25 Q50 Q75
## C:AJ 21.4600 25.88 28.2275
## C:AQ 28.4225 30.08 31.9175
## L:AJ 8.1350 12.87 19.1275
## L:AQ 10.7650 14.77 23.9075
## UN:AJ 5.3900 9.58 13.0925
## UN:AQ 4.5550 8.32 13.3525
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## C:AQ 30.348333 a
## C:AJ 25.692083 b
## L:AQ 18.628917 c
## L:AJ 14.776083 d
## UN:AQ 10.354750 e
## UN:AJ 9.689917 e
##
## attr(,"class")
## [1] "group"# Define the new data
mean_mass_loss <- c(
25.692083, 30.348333, 14.776083, 18.628917, 9.689917, 10.354750
)
variable_used <- c(
"C:AJ", "C:AQ", "L:AJ", "L:AQ", "UN:AJ", "UN:AQ"
)
groups_letters <- c("b", "a", "d", "c", "e", "e")
standard_error <- c(
0.6362274, 0.6362274, 0.6362274, 0.6362274, 0.6362274, 0.6362274
)
# Combine the data into a data frame
df <- data.frame(
MeanMassLoss = mean_mass_loss,
VariableUsed = variable_used,
GroupsLetters = groups_letters,
StandardError = standard_error
)
# General Plot (No Splitting)
ggplot(df, aes(x = VariableUsed, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into Treatment and Extractives Source
df <- df %>%
separate(VariableUsed, into = c("Treatment", "ExtractivesSource"), sep = ":")
# Facet Wrap by Treatment
ggplot(df, aes(x = ExtractivesSource, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Extractives Source",
y = "Mean Mass Loss (%)") +
facet_wrap(~ Treatment) +
theme_classic()
# Facet Wrap by Treatment
ggplot(df, aes(x = Treatment, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
facet_wrap(~ ExtractivesSource) +
theme_classic()
#PLOTING RESULTS
library(agricolae)
lsdmodel7<-LSD.test(model1,c("Treatment","Fungus type"))
lsdmodel7## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 1.766515
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Treatment:Fungus type 6 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## C:PP 27.13833 5.580264 120 0.6362274 25.889218 28.38745 15.80 38.83
## C:TV 28.90208 4.671640 120 0.6362274 27.652968 30.15120 19.25 36.40
## L:PP 15.89217 11.195055 120 0.6362274 14.643052 17.14128 3.35 58.08
## L:TV 17.51283 10.522755 120 0.6362274 16.263718 18.76195 1.09 58.08
## UN:PP 9.43525 8.759057 120 0.6362274 8.186135 10.68437 -8.27 52.24
## UN:TV 10.60942 5.493632 120 0.6362274 9.360302 11.85853 3.07 27.18
## Q25 Q50 Q75
## C:PP 23.6300 27.560 30.8525
## C:TV 25.6800 28.840 31.6125
## L:PP 8.0425 12.370 19.6550
## L:TV 10.6100 14.770 21.9700
## UN:PP 4.4600 7.535 11.9025
## UN:TV 5.6600 10.210 14.3800
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## C:TV 28.90208 a
## C:PP 27.13833 a
## L:TV 17.51283 b
## L:PP 15.89217 b
## UN:TV 10.60942 c
## UN:PP 9.43525 c
##
## attr(,"class")
## [1] "group"# Define the new data
mean_mass_loss <- c(
27.13833, 28.90208, 15.89217, 17.51283, 9.43525, 10.60942
)
variable_used <- c(
"C:PP", "C:TV", "L:PP", "L:TV", "UN:PP", "UN:TV"
)
groups_letters <- c("a", "a", "b", "b", "c", "c")
standard_error <- c(
0.6362274, 0.6362274, 0.6362274, 0.6362274, 0.6362274, 0.6362274
)
# Combine the data into a data frame
df <- data.frame(
MeanMassLoss = mean_mass_loss,
VariableUsed = variable_used,
GroupsLetters = groups_letters,
StandardError = standard_error
)
# General Plot (No Splitting)
ggplot(df, aes(x = VariableUsed, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment:Fungus Type",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into Treatment and Fungus Type
df <- df %>%
separate(VariableUsed, into = c("Treatment", "FungusType"), sep = ":")
# Facet Wrap by Treatment
ggplot(df, aes(x = FungusType, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Fungus Type",
y = "Mean Mass Loss (%)") +
facet_wrap(~ Treatment) +
theme_classic()
# Facet Wrap by Treatment
ggplot(df, aes(x = Treatment, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
facet_wrap(~ FungusType) +
theme_classic()
FUNGUS TYPE
#PLOTING RESULTS
library(agricolae)
lsdmodel8<-LSD.test(model1,c("Fungus type","Concentration mg/mL"))
lsdmodel8## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 2.280562
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Fungus type:Concentration mg/mL 10 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## PP:0.5mg/mL 24.06653 11.358371 72 0.821366 22.45393 25.67913 6.29 58.08
## PP:1.0mg/mL 18.51472 9.742311 72 0.821366 16.90212 20.12732 2.14 40.74
## PP:1.5mg/mL 17.83125 10.610654 72 0.821366 16.21865 19.44385 1.19 45.70
## PP:2.0mg/mL 14.29292 11.091504 72 0.821366 12.68032 15.90552 -8.27 38.83
## PP:2.5mg/mL 12.73750 11.148963 72 0.821366 11.12490 14.35010 0.25 38.83
## TV:0.5mg/mL 22.06722 10.392314 72 0.821366 20.45462 23.67982 3.79 58.08
## TV:1.0mg/mL 19.99514 9.568186 72 0.821366 18.38254 21.60774 1.09 49.83
## TV:1.5mg/mL 19.27389 9.747467 72 0.821366 17.66129 20.88649 6.09 40.32
## TV:2.0mg/mL 17.76736 10.311026 72 0.821366 16.15476 19.37996 3.07 36.40
## TV:2.5mg/mL 15.93694 11.774543 72 0.821366 14.32434 17.54955 3.18 37.53
## Q25 Q50 Q75
## PP:0.5mg/mL 14.4300 23.540 30.4050
## PP:1.0mg/mL 11.1525 15.240 26.1025
## PP:1.5mg/mL 7.7950 15.605 27.6050
## PP:2.0mg/mL 5.5000 11.190 25.3475
## PP:2.5mg/mL 4.1575 7.310 24.0175
## TV:0.5mg/mL 14.4600 20.440 28.3450
## TV:1.0mg/mL 13.1750 16.660 25.9375
## TV:1.5mg/mL 10.7100 16.555 27.9250
## TV:2.0mg/mL 9.1675 14.990 26.8100
## TV:2.5mg/mL 4.8225 10.590 26.1325
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## PP:0.5mg/mL 24.06653 a
## TV:0.5mg/mL 22.06722 ab
## TV:1.0mg/mL 19.99514 bc
## TV:1.5mg/mL 19.27389 c
## PP:1.0mg/mL 18.51472 c
## PP:1.5mg/mL 17.83125 cd
## TV:2.0mg/mL 17.76736 cd
## TV:2.5mg/mL 15.93694 de
## PP:2.0mg/mL 14.29292 ef
## PP:2.5mg/mL 12.73750 f
##
## attr(,"class")
## [1] "group"# Define the new data
mean_mass_loss <- c(
24.06653, 18.51472, 17.83125, 14.29292, 12.73750,
22.06722, 19.99514, 19.27389, 17.76736, 15.93694
)
variable_used <- c(
"PP:0.5", "PP:1.0", "PP:1.5", "PP:2.0", "PP:2.5",
"TV:0.5", "TV:1.0", "TV:1.5", "TV:2.0", "TV:2.5"
)
groups_letters <- c("a", "c", "cd", "ef", "f", "ab", "bc", "c", "cd", "de")
standard_error <- c(
0.821366, 0.821366, 0.821366, 0.821366, 0.821366,
0.821366, 0.821366, 0.821366, 0.821366, 0.821366
)
# Combine the data into a data frame
df <- data.frame(
MeanMassLoss = mean_mass_loss,
VariableUsed = variable_used,
GroupsLetters = groups_letters,
StandardError = standard_error
)
# General Plot (No Splitting)
ggplot(df, aes(x = VariableUsed, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into FungusType and Concentration
df <- df %>%
separate(VariableUsed, into = c("FungusType", "Concentration"), sep = ":")
# Facet Wrap by FungusType
ggplot(df, aes(x = Concentration, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Concentration (mg/mL)",
y = "Mean Mass Loss (%)") +
facet_wrap(~ FungusType) +
theme_classic()
# Facet Wrap by FungusType
ggplot(df, aes(x = FungusType, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Fungus Type",
y = "Mean Mass Loss (%)") +
facet_wrap(~ Concentration) +
theme_classic()
#PLOTING RESULTS
library(agricolae)
lsdmodel9<-LSD.test(model1,c("Fungus type","Extractives source"))
lsdmodel9## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 1.442354
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Fungus type:Extractives source 4 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## PP:AJ 15.30817 10.09303 180 0.5194775 14.28827 16.32806 -8.27 58.08
## PP:AQ 19.66900 12.29664 180 0.5194775 18.64910 20.68890 0.25 52.24
## TV:AJ 18.13056 9.57331 180 0.5194775 17.11066 19.15045 1.09 58.08
## TV:AQ 19.88567 11.37543 180 0.5194775 18.86577 20.90556 3.18 49.83
## Q25 Q50 Q75
## PP:AJ 6.9725 13.29 23.63
## PP:AQ 8.4550 17.31 29.59
## TV:AJ 10.2900 17.04 25.68
## TV:AQ 10.6100 17.16 29.84
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## TV:AQ 19.88567 a
## PP:AQ 19.66900 a
## TV:AJ 18.13056 b
## PP:AJ 15.30817 c
##
## attr(,"class")
## [1] "group"# Define the new data
mean_mass_loss <- c(15.30817, 19.66900, 18.13056, 19.88567)
std_dev <- c(10.09303, 12.29664, 9.57331, 11.37543)
variable_used <- c("PP:AJ", "PP:AQ", "TV:AJ", "TV:AQ")
groups_letters <- c("c", "a", "b", "a")
standard_error <- c(0.5194775, 0.5194775, 0.5194775, 0.5194775)
# Combine the data into a data frame
df <- data.frame(
MeanMassLoss = mean_mass_loss,
StdDev = std_dev,
VariableUsed = variable_used,
GroupsLetters = groups_letters,
StandardError = standard_error
)
# General Plot (No Splitting)
ggplot(df, aes(x = VariableUsed, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into FungusType and ExtractivesSource
df <- df %>%
separate(VariableUsed, into = c("FungusType", "ExtractivesSource"), sep = ":")
# Facet Wrap by ExtractivesSource
ggplot(df, aes(x = FungusType, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Fungus Type",
y = "Mean Mass Loss (%)") +
facet_wrap(~ ExtractivesSource) +
theme_classic()
# Facet Wrap by ExtractivesSource
ggplot(df, aes(x = ExtractivesSource, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Extractives Source",
y = "Mean Mass Loss (%)") +
facet_wrap(~ FungusType) +
theme_classic()
EXTRACTIVES SOURCE
#PLOTING RESULTS
library(agricolae)
lsdmodel10<-LSD.test(model1,c("Extractives source","Concentration mg/mL"))
lsdmodel10## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 2.280562
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Extractives source:Concentration mg/mL 10 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## AJ:0.5mg/mL 20.82069 9.665313 72 0.821366 19.20809 22.43330 6.29 58.08
## AJ:1.0mg/mL 17.26681 8.287623 72 0.821366 15.65420 18.87941 1.09 36.26
## AJ:1.5mg/mL 16.91597 9.126942 72 0.821366 15.30337 18.52857 5.56 40.32
## AJ:2.0mg/mL 15.36444 10.419875 72 0.821366 13.75184 16.97705 -8.27 35.86
## AJ:2.5mg/mL 13.22889 10.601159 72 0.821366 11.61629 14.84149 0.43 35.86
## AQ:0.5mg/mL 25.31306 11.635656 72 0.821366 23.70045 26.92566 3.79 52.24
## AQ:1.0mg/mL 21.24306 10.529613 72 0.821366 19.63045 22.85566 2.14 49.83
## AQ:1.5mg/mL 20.18917 10.950841 72 0.821366 18.57657 21.80177 1.19 45.70
## AQ:2.0mg/mL 16.69583 11.224395 72 0.821366 15.08323 18.30843 3.24 38.83
## AQ:2.5mg/mL 15.44556 12.379563 72 0.821366 13.83295 17.05816 0.25 38.83
## Q25 Q50 Q75
## AJ:0.5mg/mL 13.3400 19.380 26.3575
## AJ:1.0mg/mL 11.5700 14.510 24.3100
## AJ:1.5mg/mL 8.8350 15.025 25.4775
## AJ:2.0mg/mL 6.4300 15.035 24.6525
## AJ:2.5mg/mL 4.1775 7.935 24.6525
## AQ:0.5mg/mL 15.1400 25.825 33.1450
## AQ:1.0mg/mL 13.0050 20.080 29.4475
## AQ:1.5mg/mL 10.7725 18.785 29.6525
## AQ:2.0mg/mL 6.9975 11.920 28.9675
## AQ:2.5mg/mL 4.9975 9.435 28.9675
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## AQ:0.5mg/mL 25.31306 a
## AQ:1.0mg/mL 21.24306 b
## AJ:0.5mg/mL 20.82069 b
## AQ:1.5mg/mL 20.18917 b
## AJ:1.0mg/mL 17.26681 c
## AJ:1.5mg/mL 16.91597 c
## AQ:2.0mg/mL 16.69583 c
## AQ:2.5mg/mL 15.44556 cd
## AJ:2.0mg/mL 15.36444 cd
## AJ:2.5mg/mL 13.22889 d
##
## attr(,"class")
## [1] "group"# Define the new data
mean_mass_loss <- c(20.82069, 17.26681, 16.91597, 15.36444, 13.22889, 25.31306, 21.24306, 20.18917, 16.69583, 15.44556)
std_dev <- c(9.665313, 8.287623, 9.126942, 10.419875, 10.601159, 11.635656, 10.529613, 10.950841, 11.224395, 12.379563)
variable_used <- c("AJ:0.5", "AJ:1.0", "AJ:1.5", "AJ:2.0", "AJ:2.5", "AQ:0.5", "AQ:1.0", "AQ:1.5", "AQ:2.0", "AQ:2.5")
groups_letters <- c("b", "c", "c", "cd", "d", "a", "b", "b", "c", "cd")
standard_error <- c(0.821366, 0.821366, 0.821366, 0.821366, 0.821366, 0.821366, 0.821366, 0.821366, 0.821366, 0.821366)
# Combine the data into a data frame
df <- data.frame(
MeanMassLoss = mean_mass_loss,
StdDev = std_dev,
VariableUsed = variable_used,
GroupsLetters = groups_letters,
StandardError = standard_error
)
# General Plot (No Splitting)
ggplot(df, aes(x = VariableUsed, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Treatment",
y = "Mean Mass Loss (%)") +
theme_classic()
# Split the VariableUsed column into FungusType and ExtractivesSource
df <- df %>%
separate(VariableUsed, into = c("ExtractivesSource", "Concentration"), sep = ":")
# Facet Wrap by ExtractivesSource
ggplot(df, aes(x = Concentration, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Concentration",
y = "Mean Mass Loss (%)") +
facet_wrap(~ ExtractivesSource) +
theme_classic()
# Facet Wrap by ExtractivesSource
ggplot(df, aes(x = ExtractivesSource, y = MeanMassLoss)) +
geom_bar(stat = "identity", fill = "gray", color = "black") +
geom_errorbar(aes(ymin = MeanMassLoss - StandardError, ymax = MeanMassLoss + StandardError), width = 0.2) +
geom_text(aes(label = GroupsLetters, y = MeanMassLoss + StandardError + 1), vjust = 0) +
labs(x = "Extractives Source",
y = "Mean Mass Loss (%)") +
facet_wrap(~ Concentration) +
theme_classic()
THREE WAYS
#PLOTING RESULTS
library(agricolae)
lsdmodel11<-LSD.test(model1,c("Wood specie", "Extractives source", "Fungus type"))
lsdmodel11## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 2.49823
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Wood specie:Extractives source:Fungus type 12 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## BS:AJ:PP 14.97617 8.146246 60 0.8997614 13.209651 16.74268 3.33 30.00
## BS:AJ:TV 20.74700 7.608809 60 0.8997614 18.980485 22.51352 1.09 40.32
## BS:AQ:PP 24.51917 14.390197 60 0.8997614 22.752651 26.28568 1.19 52.24
## BS:AQ:TV 22.64183 12.128752 60 0.8997614 20.875318 24.40835 3.51 49.83
## JG:AJ:PP 11.36283 7.715905 60 0.8997614 9.596318 13.12935 0.11 25.88
## JG:AJ:TV 13.47833 7.617300 60 0.8997614 11.711818 15.24485 3.07 25.91
## JG:AQ:PP 16.85850 10.284636 60 0.8997614 15.091985 18.62502 0.25 39.87
## JG:AQ:TV 17.42517 11.515461 60 0.8997614 15.658651 19.19168 3.18 36.40
## SA:AJ:PP 19.58550 12.190877 60 0.8997614 17.818985 21.35202 -8.27 58.08
## SA:AJ:TV 20.16633 11.355519 60 0.8997614 18.399818 21.93285 3.21 58.08
## SA:AQ:PP 17.62933 10.474258 60 0.8997614 15.862818 19.39585 3.03 32.60
## SA:AQ:TV 19.59000 9.945055 60 0.8997614 17.823485 21.35652 3.72 34.78
## Q25 Q50 Q75
## BS:AJ:PP 7.0400 12.695 20.6900
## BS:AJ:TV 15.4400 21.330 25.7375
## BS:AQ:PP 10.0975 28.620 36.4375
## BS:AQ:TV 12.3575 26.215 30.3200
## JG:AJ:PP 6.1550 8.870 17.2000
## JG:AJ:TV 7.7775 11.555 19.9825
## JG:AQ:PP 8.3050 15.095 25.1800
## JG:AQ:TV 8.0125 13.335 27.5025
## SA:AJ:PP 12.1200 18.150 28.3425
## SA:AJ:TV 12.2725 15.680 29.6900
## SA:AQ:PP 7.9750 14.850 29.2875
## SA:AQ:TV 11.4675 20.360 29.8700
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## BS:AQ:PP 24.51917 a
## BS:AQ:TV 22.64183 ab
## BS:AJ:TV 20.74700 bc
## SA:AJ:TV 20.16633 bc
## SA:AQ:TV 19.59000 cd
## SA:AJ:PP 19.58550 cd
## SA:AQ:PP 17.62933 de
## JG:AQ:TV 17.42517 def
## JG:AQ:PP 16.85850 ef
## BS:AJ:PP 14.97617 fg
## JG:AJ:TV 13.47833 gh
## JG:AJ:PP 11.36283 h
##
## attr(,"class")
## [1] "group"#PLOTING RESULTS
library(agricolae)
lsdmodel2<-LSD.test(model1,c("Wood specie", "Extractives source", "Treatment"))
lsdmodel2## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 3.059695
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Wood specie:Extractives source:Treatment 18 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## BS:AJ:C 24.43500 3.309154 40 1.101978 22.271469 26.598531 15.80 28.21
## BS:AJ:L 17.47750 9.278150 40 1.101978 15.313969 19.641031 1.09 40.32
## BS:AJ:UN 11.67225 5.727092 40 1.101978 9.508719 13.835781 3.33 25.53
## BS:AQ:C 31.44500 3.534384 40 1.101978 29.281469 33.608531 27.83 38.83
## BS:AQ:L 25.98625 13.871892 40 1.101978 23.822719 28.149781 7.81 51.62
## BS:AQ:UN 13.31025 12.492089 40 1.101978 11.146719 15.473781 1.19 52.24
## JG:AJ:C 21.72500 3.668462 40 1.101978 19.561469 23.888531 15.80 25.91
## JG:AJ:L 8.90075 4.236706 40 1.101978 6.737219 11.064281 3.63 19.49
## JG:AJ:UN 6.63600 3.749915 40 1.101978 4.472469 8.799531 0.11 13.25
## JG:AQ:C 28.89125 5.143782 40 1.101978 26.727719 31.054781 20.58 36.40
## JG:AQ:L 14.27200 8.923609 40 1.101978 12.108469 16.435531 4.29 39.87
## JG:AQ:UN 8.26225 4.886605 40 1.101978 6.098719 10.425781 0.25 21.29
## SA:AJ:C 30.91625 3.624553 40 1.101978 28.752719 33.079781 25.85 35.86
## SA:AJ:L 17.95000 11.887419 40 1.101978 15.786469 20.113531 4.89 58.08
## SA:AJ:UN 10.76150 7.190036 40 1.101978 8.597969 12.925031 -8.27 27.18
## SA:AQ:C 30.70875 2.691446 40 1.101978 28.545219 32.872281 25.07 34.78
## SA:AQ:L 15.62850 6.828952 40 1.101978 13.464969 17.792031 3.79 29.47
## SA:AQ:UN 9.49175 4.488848 40 1.101978 7.328219 11.655281 3.03 17.55
## Q25 Q50 Q75
## BS:AJ:C 20.6900 25.680 26.4700
## BS:AJ:L 11.4400 16.475 21.9725
## BS:AJ:UN 6.8950 11.015 15.2000
## BS:AQ:C 29.1450 30.080 32.5450
## BS:AQ:L 13.3850 22.125 39.4250
## BS:AQ:UN 4.0050 10.080 16.5875
## JG:AJ:C 18.8050 22.545 24.7700
## JG:AJ:L 5.1025 7.940 11.4075
## JG:AJ:UN 3.3900 7.115 9.5525
## JG:AQ:C 25.8100 28.480 31.2925
## JG:AQ:L 8.7525 11.960 15.0600
## JG:AQ:UN 4.3875 7.780 11.1875
## SA:AJ:C 27.9025 31.030 33.7175
## SA:AJ:L 12.0300 13.675 22.0750
## SA:AJ:UN 5.5475 12.240 15.3200
## SA:AQ:C 30.1625 31.080 31.8575
## SA:AQ:L 10.8775 15.055 21.1625
## SA:AQ:UN 5.3375 8.320 13.4675
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## BS:AQ:C 31.44500 a
## SA:AJ:C 30.91625 a
## SA:AQ:C 30.70875 a
## JG:AQ:C 28.89125 ab
## BS:AQ:L 25.98625 bc
## BS:AJ:C 24.43500 cd
## JG:AJ:C 21.72500 d
## SA:AJ:L 17.95000 e
## BS:AJ:L 17.47750 e
## SA:AQ:L 15.62850 ef
## JG:AQ:L 14.27200 fg
## BS:AQ:UN 13.31025 fgh
## BS:AJ:UN 11.67225 ghi
## SA:AJ:UN 10.76150 hij
## SA:AQ:UN 9.49175 ijk
## JG:AJ:L 8.90075 ijk
## JG:AQ:UN 8.26225 jk
## JG:AJ:UN 6.63600 k
##
## attr(,"class")
## [1] "group"#PLOTING RESULTS
library(agricolae)
lsdmodel4<-LSD.test(model1,c("Wood specie", "Fungus type", "Treatment"))
lsdmodel4## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 3.059695
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Wood specie:Fungus type:Treatment 18 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## BS:PP:C 27.64000 5.993264 40 1.101978 25.476469 29.803531 15.80 38.83
## BS:PP:L 19.32575 13.168639 40 1.101978 17.162219 21.489281 3.35 51.62
## BS:PP:UN 12.27725 12.372478 40 1.101978 10.113719 14.440781 1.19 52.24
## BS:TV:C 28.24000 3.540988 40 1.101978 26.076469 30.403531 19.25 35.11
## BS:TV:L 24.13800 11.417190 40 1.101978 21.974469 26.301531 1.09 49.83
## BS:TV:UN 12.70525 6.087335 40 1.101978 10.541719 14.868781 3.51 25.53
## JG:PP:C 23.21125 4.607487 40 1.101978 21.047719 25.374781 15.80 29.59
## JG:PP:L 12.04900 9.241440 40 1.101978 9.885469 14.212531 4.17 39.87
## JG:PP:UN 7.07175 5.153809 40 1.101978 4.908219 9.235281 0.11 21.29
## JG:TV:C 27.40500 6.000325 40 1.101978 25.241469 29.568531 19.25 36.40
## JG:TV:L 11.12375 5.150951 40 1.101978 8.960219 13.287281 3.63 26.87
## JG:TV:UN 7.82650 3.527484 40 1.101978 5.662969 9.990031 3.07 14.98
## SA:PP:C 30.56375 3.123607 40 1.101978 28.400219 32.727281 25.85 35.86
## SA:PP:L 16.30175 9.807403 40 1.101978 14.138219 18.465281 5.66 58.08
## SA:PP:UN 8.95675 6.341092 40 1.101978 6.793219 11.120281 -8.27 21.40
## SA:TV:C 31.06125 3.243378 40 1.101978 28.897719 33.224781 25.07 35.33
## SA:TV:L 17.27675 9.697239 40 1.101978 15.113219 19.440281 3.79 58.08
## SA:TV:UN 11.29650 5.445763 40 1.101978 9.132969 13.460031 3.21 27.18
## Q25 Q50 Q75
## BS:PP:C 22.8950 28.180 29.9125
## BS:PP:L 9.9025 13.735 24.4050
## BS:PP:UN 4.5125 6.730 12.1350
## BS:TV:C 25.6800 27.830 29.9600
## BS:TV:L 16.1550 20.825 32.8150
## BS:TV:UN 8.9575 12.355 16.3625
## JG:PP:C 19.8025 24.405 26.3000
## JG:PP:L 6.4100 10.040 12.8425
## JG:PP:UN 2.6425 7.095 9.9325
## JG:TV:C 23.6650 25.965 31.1500
## JG:TV:L 7.6150 10.695 14.1975
## JG:TV:UN 4.3275 8.085 10.1150
## SA:PP:C 28.3300 31.105 32.4275
## SA:PP:L 9.8600 13.860 19.8100
## SA:PP:UN 5.2250 7.960 12.8700
## SA:TV:C 29.3375 31.080 33.5800
## SA:TV:L 11.4675 14.800 21.9825
## SA:TV:UN 5.8175 12.255 15.2675
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## SA:TV:C 31.06125 a
## SA:PP:C 30.56375 ab
## BS:TV:C 28.24000 abc
## BS:PP:C 27.64000 bc
## JG:TV:C 27.40500 c
## BS:TV:L 24.13800 d
## JG:PP:C 23.21125 d
## BS:PP:L 19.32575 e
## SA:TV:L 17.27675 e
## SA:PP:L 16.30175 e
## BS:TV:UN 12.70525 f
## BS:PP:UN 12.27725 f
## JG:PP:L 12.04900 f
## SA:TV:UN 11.29650 fg
## JG:TV:L 11.12375 fg
## SA:PP:UN 8.95675 gh
## JG:TV:UN 7.82650 h
## JG:PP:UN 7.07175 h
##
## attr(,"class")
## [1] "group"#PLOTING RESULTS
library(agricolae)
lsdmodel7<-LSD.test(model1,c("Extractives source", "Fungus type", "Treatment"))
lsdmodel7## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 2.49823
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Extractives source:Fungus type:Treatment 12 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min Max
## AJ:PP:C 24.69083 5.727513 60 0.8997614 22.924318 26.457349 15.80 35.86
## AJ:PP:L 13.75667 9.530342 60 0.8997614 11.990151 15.523182 3.35 58.08
## AJ:PP:UN 7.47700 5.575690 60 0.8997614 5.710485 9.243515 -8.27 21.40
## AJ:TV:C 26.69333 4.496536 60 0.8997614 24.926818 28.459849 19.25 35.33
## AJ:TV:L 15.79550 10.218419 60 0.8997614 14.028985 17.562015 1.09 58.08
## AJ:TV:UN 11.90283 5.819441 60 0.8997614 10.136318 13.669349 3.07 27.18
## AQ:PP:C 29.58583 4.221198 60 0.8997614 27.819318 31.352349 20.58 38.83
## AQ:PP:L 18.02767 12.356375 60 0.8997614 16.261151 19.794182 4.29 51.62
## AQ:PP:UN 11.39350 10.763584 60 0.8997614 9.626985 13.160015 0.25 52.24
## AQ:TV:C 31.11083 3.725181 60 0.8997614 29.344318 32.877349 25.07 36.40
## AQ:TV:L 19.23017 10.626334 60 0.8997614 17.463651 20.996682 3.79 49.83
## AQ:TV:UN 9.31600 4.858297 60 0.8997614 7.549485 11.082515 3.18 24.60
## Q25 Q50 Q75
## AJ:PP:C 20.3800 25.850 26.7700
## AJ:PP:L 6.9500 11.890 16.5175
## AJ:PP:UN 4.5175 7.005 11.0450
## AJ:TV:C 24.4000 25.910 28.6325
## AJ:TV:L 9.4100 13.320 19.6175
## AJ:TV:UN 8.2950 11.645 15.3050
## AQ:PP:C 28.3550 29.455 31.6300
## AQ:PP:L 9.8800 12.995 23.1725
## AQ:PP:UN 4.4600 7.875 14.8475
## AQ:TV:C 29.0075 30.695 34.8625
## AQ:TV:L 11.5750 15.950 23.9075
## AQ:TV:UN 5.0450 9.155 13.3425
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## AQ:TV:C 31.11083 a
## AQ:PP:C 29.58583 a
## AJ:TV:C 26.69333 b
## AJ:PP:C 24.69083 b
## AQ:TV:L 19.23017 c
## AQ:PP:L 18.02767 cd
## AJ:TV:L 15.79550 de
## AJ:PP:L 13.75667 ef
## AJ:TV:UN 11.90283 f
## AQ:PP:UN 11.39350 fg
## AQ:TV:UN 9.31600 gh
## AJ:PP:UN 7.47700 h
##
## attr(,"class")
## [1] "group"#PLOTING RESULTS
library(agricolae)
lsdmodel9<-LSD.test(model1,c("Extractives source", "Concentration mg/mL","Treatment"))
lsdmodel9## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 3.950049
##
## $parameters
## test p.ajusted name.t ntr
## Fisher-LSD none Extractives source:Concentration mg/mL:Treatment 30
## alpha
## 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min
## AJ:0.5mg/mL:C 25.917917 5.172277 24 1.422648 23.124810 28.711023 15.80
## AJ:0.5mg/mL:L 21.637500 12.896456 24 1.422648 18.844394 24.430606 10.00
## AJ:0.5mg/mL:UN 14.906667 5.568055 24 1.422648 12.113560 17.699773 6.29
## AJ:1.0mg/mL:C 24.788750 5.759550 24 1.422648 21.995644 27.581856 15.80
## AJ:1.0mg/mL:L 14.302083 8.670485 24 1.422648 11.508977 17.095190 1.09
## AJ:1.0mg/mL:UN 12.709583 3.714076 24 1.422648 9.916477 15.502690 6.95
## AJ:1.5mg/mL:C 25.917917 5.172277 24 1.422648 23.124810 28.711023 15.80
## AJ:1.5mg/mL:L 15.010000 8.592060 24 1.422648 12.216894 17.803106 6.04
## AJ:1.5mg/mL:UN 9.820000 3.959776 24 1.422648 7.026894 12.613106 5.56
## AJ:2.0mg/mL:C 25.917917 5.172277 24 1.422648 23.124810 28.711023 15.80
## AJ:2.0mg/mL:L 13.539167 7.826742 24 1.422648 10.746060 16.332273 3.63
## AJ:2.0mg/mL:UN 6.636250 6.925600 24 1.422648 3.843144 9.429356 -8.27
## AJ:2.5mg/mL:C 25.917917 5.172277 24 1.422648 23.124810 28.711023 15.80
## AJ:2.5mg/mL:L 9.391667 6.958470 24 1.422648 6.598560 12.184773 3.35
## AJ:2.5mg/mL:UN 4.377083 2.572148 24 1.422648 1.583977 7.170190 0.43
## AQ:0.5mg/mL:C 30.348333 4.107049 24 1.422648 27.555227 33.141440 20.58
## AQ:0.5mg/mL:L 27.242500 13.508415 24 1.422648 24.449394 30.035606 3.79
## AQ:0.5mg/mL:UN 18.348333 11.731832 24 1.422648 15.555227 21.141440 3.98
## AQ:1.0mg/mL:C 30.348333 4.107049 24 1.422648 27.555227 33.141440 20.58
## AQ:1.0mg/mL:L 20.961667 11.213295 24 1.422648 18.168560 23.754773 4.48
## AQ:1.0mg/mL:UN 12.419167 5.640382 24 1.422648 9.626060 15.212273 2.14
## AQ:1.5mg/mL:C 30.348333 4.107049 24 1.422648 27.555227 33.141440 20.58
## AQ:1.5mg/mL:L 19.121250 10.384391 24 1.422648 16.328144 21.914356 7.48
## AQ:1.5mg/mL:UN 11.097917 7.096259 24 1.422648 8.304810 13.891023 1.19
## AQ:2.0mg/mL:C 30.348333 4.107049 24 1.422648 27.555227 33.141440 20.58
## AQ:2.0mg/mL:L 13.710417 6.558993 24 1.422648 10.917310 16.503523 7.01
## AQ:2.0mg/mL:UN 6.028750 2.549401 24 1.422648 3.235644 8.821856 3.24
## AQ:2.5mg/mL:C 30.348333 4.107049 24 1.422648 27.555227 33.141440 20.58
## AQ:2.5mg/mL:L 12.108750 8.436747 24 1.422648 9.315644 14.901856 4.29
## AQ:2.5mg/mL:UN 3.879583 1.444081 24 1.422648 1.086477 6.672690 0.25
## Max Q25 Q50 Q75
## AJ:0.5mg/mL:C 35.86 23.0875 25.895 28.2275
## AJ:0.5mg/mL:L 58.08 13.3700 16.840 27.5275
## AJ:0.5mg/mL:UN 27.18 10.5950 13.145 18.7800
## AJ:1.0mg/mL:C 35.86 20.9075 25.125 27.1475
## AJ:1.0mg/mL:L 36.26 9.5925 12.105 14.0025
## AJ:1.0mg/mL:UN 24.28 9.9875 12.480 14.5975
## AJ:1.5mg/mL:C 35.86 23.0875 25.895 28.2275
## AJ:1.5mg/mL:L 40.32 9.1125 12.995 19.2800
## AJ:1.5mg/mL:UN 21.51 6.9375 8.730 12.0650
## AJ:2.0mg/mL:C 35.86 23.0875 25.895 28.2275
## AJ:2.0mg/mL:L 32.24 6.7250 13.030 17.4400
## AJ:2.0mg/mL:UN 18.23 2.7550 6.345 12.2375
## AJ:2.5mg/mL:C 35.86 23.0875 25.895 28.2275
## AJ:2.5mg/mL:L 25.97 4.8525 6.905 10.3650
## AJ:2.5mg/mL:UN 10.81 3.3975 4.135 4.8575
## AQ:0.5mg/mL:C 38.83 28.4225 30.080 31.9175
## AQ:0.5mg/mL:L 51.62 15.1400 25.610 38.7150
## AQ:0.5mg/mL:UN 52.24 12.7850 15.245 17.5800
## AQ:1.0mg/mL:C 38.83 28.4225 30.080 31.9175
## AQ:1.0mg/mL:L 49.83 14.0725 16.370 23.8550
## AQ:1.0mg/mL:UN 27.10 9.1825 11.685 15.4475
## AQ:1.5mg/mL:C 38.83 28.4225 30.080 31.9175
## AQ:1.5mg/mL:L 45.70 12.0350 15.885 21.9025
## AQ:1.5mg/mL:UN 32.87 7.6000 8.475 13.3425
## AQ:2.0mg/mL:C 38.83 28.4225 30.080 31.9175
## AQ:2.0mg/mL:L 30.83 9.4675 11.485 14.6325
## AQ:2.0mg/mL:UN 12.36 4.3400 4.745 7.4250
## AQ:2.5mg/mL:C 38.83 28.4225 30.080 31.9175
## AQ:2.5mg/mL:L 37.53 7.0450 9.435 12.9350
## AQ:2.5mg/mL:UN 5.98 3.1425 3.725 5.2475
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## AQ:0.5mg/mL:C 30.348333 a
## AQ:1.0mg/mL:C 30.348333 a
## AQ:1.5mg/mL:C 30.348333 a
## AQ:2.0mg/mL:C 30.348333 a
## AQ:2.5mg/mL:C 30.348333 a
## AQ:0.5mg/mL:L 27.242500 ab
## AJ:0.5mg/mL:C 25.917917 b
## AJ:1.5mg/mL:C 25.917917 b
## AJ:2.0mg/mL:C 25.917917 b
## AJ:2.5mg/mL:C 25.917917 b
## AJ:1.0mg/mL:C 24.788750 bc
## AJ:0.5mg/mL:L 21.637500 cd
## AQ:1.0mg/mL:L 20.961667 cd
## AQ:1.5mg/mL:L 19.121250 d
## AQ:0.5mg/mL:UN 18.348333 de
## AJ:1.5mg/mL:L 15.010000 ef
## AJ:0.5mg/mL:UN 14.906667 ef
## AJ:1.0mg/mL:L 14.302083 f
## AQ:2.0mg/mL:L 13.710417 fg
## AJ:2.0mg/mL:L 13.539167 fg
## AJ:1.0mg/mL:UN 12.709583 fgh
## AQ:1.0mg/mL:UN 12.419167 fgh
## AQ:2.5mg/mL:L 12.108750 fgh
## AQ:1.5mg/mL:UN 11.097917 fgh
## AJ:1.5mg/mL:UN 9.820000 ghi
## AJ:2.5mg/mL:L 9.391667 hi
## AJ:2.0mg/mL:UN 6.636250 ij
## AQ:2.0mg/mL:UN 6.028750 ij
## AJ:2.5mg/mL:UN 4.377083 j
## AQ:2.5mg/mL:UN 3.879583 j
##
## attr(,"class")
## [1] "group"#PLOTING RESULTS
library(agricolae)
lsdmode20<-LSD.test(model1,c("Fungus type", "Concentration mg/mL","Treatment"))
lsdmode20## $statistics
## MSerror Df Mean CV t.value LSD
## 48.57423 709 18.24835 38.19262 1.963316 3.950049
##
## $parameters
## test p.ajusted name.t ntr alpha
## Fisher-LSD none Fungus type:Concentration mg/mL:Treatment 30 0.05
##
## $means
## Mass loss (%) std r se LCL UCL Min
## PP:0.5mg/mL:C 27.241250 5.561258 24 1.422648 24.4481438 30.03436 15.80
## PP:0.5mg/mL:L 26.221667 13.759434 24 1.422648 23.4285605 29.01477 10.00
## PP:0.5mg/mL:UN 18.736667 11.532584 24 1.422648 15.9435605 21.52977 6.29
## PP:1.0mg/mL:C 26.726667 6.097612 24 1.422648 23.9335605 29.51977 15.80
## PP:1.0mg/mL:L 17.037500 10.701468 24 1.422648 14.2443938 19.83061 4.48
## PP:1.0mg/mL:UN 11.780000 4.616843 24 1.422648 8.9868938 14.57311 2.14
## PP:1.5mg/mL:C 27.241250 5.561258 24 1.422648 24.4481438 30.03436 15.80
## PP:1.5mg/mL:L 16.880833 9.964167 24 1.422648 14.0877271 19.67394 6.04
## PP:1.5mg/mL:UN 9.371667 7.022760 24 1.422648 6.5785605 12.16477 1.19
## PP:2.0mg/mL:C 27.241250 5.561258 24 1.422648 24.4481438 30.03436 15.80
## PP:2.0mg/mL:L 11.601667 5.714171 24 1.422648 8.8085605 14.39477 4.52
## PP:2.0mg/mL:UN 4.035833 4.888146 24 1.422648 1.2427271 6.82894 -8.27
## PP:2.5mg/mL:C 27.241250 5.561258 24 1.422648 24.4481438 30.03436 15.80
## PP:2.5mg/mL:L 7.719167 3.166308 24 1.422648 4.9260605 10.51227 3.35
## PP:2.5mg/mL:UN 3.252083 1.756493 24 1.422648 0.4589771 6.04519 0.25
## TV:0.5mg/mL:C 29.025000 4.612338 24 1.422648 26.2318938 31.81811 19.25
## TV:0.5mg/mL:L 22.658333 13.009231 24 1.422648 19.8652271 25.45144 3.79
## TV:0.5mg/mL:UN 14.518333 5.703916 24 1.422648 11.7252271 17.31144 3.98
## TV:1.0mg/mL:C 28.410417 5.244680 24 1.422648 25.6173105 31.20352 19.25
## TV:1.0mg/mL:L 18.226250 10.430520 24 1.422648 15.4331438 21.01936 1.09
## TV:1.0mg/mL:UN 13.348750 4.801358 24 1.422648 10.5556438 16.14186 3.84
## TV:1.5mg/mL:C 29.025000 4.612338 24 1.422648 26.2318938 31.81811 19.25
## TV:1.5mg/mL:L 17.250417 9.545774 24 1.422648 14.4573105 20.04352 7.48
## TV:1.5mg/mL:UN 11.546250 3.886179 24 1.422648 8.7531438 14.33936 6.09
## TV:2.0mg/mL:C 29.025000 4.612338 24 1.422648 26.2318938 31.81811 19.25
## TV:2.0mg/mL:L 15.647917 7.943542 24 1.422648 12.8548105 18.44102 3.63
## TV:2.0mg/mL:UN 8.629167 4.444539 24 1.422648 5.8360605 11.42227 3.07
## TV:2.5mg/mL:C 29.025000 4.612338 24 1.422648 26.2318938 31.81811 19.25
## TV:2.5mg/mL:L 13.781250 9.708344 24 1.422648 10.9881438 16.57436 3.68
## TV:2.5mg/mL:UN 5.004583 2.035383 24 1.422648 2.2114771 7.79769 3.18
## Max Q25 Q50 Q75
## PP:0.5mg/mL:C 38.83 24.7925 27.650 30.8525
## PP:0.5mg/mL:L 58.08 13.4675 25.150 36.6775
## PP:0.5mg/mL:UN 52.24 10.9975 16.525 19.1825
## PP:1.0mg/mL:C 38.83 23.6300 27.165 30.8525
## PP:1.0mg/mL:L 40.74 10.5025 13.735 20.0825
## PP:1.0mg/mL:UN 27.10 9.1150 11.730 12.4650
## PP:1.5mg/mL:C 38.83 24.7925 27.650 30.8525
## PP:1.5mg/mL:L 45.70 10.5850 13.840 20.1825
## PP:1.5mg/mL:UN 32.87 6.4925 7.695 8.4825
## PP:2.0mg/mL:C 38.83 24.7925 27.650 30.8525
## PP:2.0mg/mL:L 28.47 7.3625 10.720 13.3850
## PP:2.0mg/mL:UN 17.71 2.7550 4.555 6.3175
## PP:2.5mg/mL:C 38.83 24.7925 27.650 30.8525
## PP:2.5mg/mL:L 14.59 5.1025 7.310 9.7500
## PP:2.5mg/mL:UN 5.77 1.6850 3.485 4.6725
## TV:0.5mg/mL:C 36.40 25.8525 28.840 31.6125
## TV:0.5mg/mL:L 58.08 14.9100 18.520 28.3450
## TV:0.5mg/mL:UN 27.18 10.8650 13.900 17.7075
## TV:1.0mg/mL:C 36.40 24.9025 28.840 31.6125
## TV:1.0mg/mL:L 49.83 12.0075 14.535 22.5850
## TV:1.0mg/mL:UN 24.60 9.9875 14.010 15.5925
## TV:1.5mg/mL:C 36.40 25.8525 28.840 31.6125
## TV:1.5mg/mL:L 40.32 9.6650 13.545 21.0500
## TV:1.5mg/mL:UN 21.51 8.5100 10.915 13.7900
## TV:2.0mg/mL:C 36.40 25.8525 28.840 31.6125
## TV:2.0mg/mL:L 32.24 11.3375 13.120 20.8300
## TV:2.0mg/mL:UN 18.23 4.4500 9.085 12.2375
## TV:2.5mg/mL:C 36.40 25.8525 28.840 31.6125
## TV:2.5mg/mL:L 37.53 6.1225 9.995 20.0800
## TV:2.5mg/mL:UN 10.81 3.6850 4.250 5.4400
##
## $comparison
## NULL
##
## $groups
## Mass loss (%) groups
## TV:0.5mg/mL:C 29.025000 a
## TV:1.5mg/mL:C 29.025000 a
## TV:2.0mg/mL:C 29.025000 a
## TV:2.5mg/mL:C 29.025000 a
## TV:1.0mg/mL:C 28.410417 a
## PP:0.5mg/mL:C 27.241250 a
## PP:1.5mg/mL:C 27.241250 a
## PP:2.0mg/mL:C 27.241250 a
## PP:2.5mg/mL:C 27.241250 a
## PP:1.0mg/mL:C 26.726667 a
## PP:0.5mg/mL:L 26.221667 ab
## TV:0.5mg/mL:L 22.658333 bc
## PP:0.5mg/mL:UN 18.736667 cd
## TV:1.0mg/mL:L 18.226250 de
## TV:1.5mg/mL:L 17.250417 def
## PP:1.0mg/mL:L 17.037500 def
## PP:1.5mg/mL:L 16.880833 def
## TV:2.0mg/mL:L 15.647917 defg
## TV:0.5mg/mL:UN 14.518333 efgh
## TV:2.5mg/mL:L 13.781250 fgh
## TV:1.0mg/mL:UN 13.348750 fgh
## PP:1.0mg/mL:UN 11.780000 ghi
## PP:2.0mg/mL:L 11.601667 hij
## TV:1.5mg/mL:UN 11.546250 hij
## PP:1.5mg/mL:UN 9.371667 ij
## TV:2.0mg/mL:UN 8.629167 ijk
## PP:2.5mg/mL:L 7.719167 jkl
## TV:2.5mg/mL:UN 5.004583 klm
## PP:2.0mg/mL:UN 4.035833 lm
## PP:2.5mg/mL:UN 3.252083 m
##
## attr(,"class")
## [1] "group"