library(readxl)
Fat_allo <- read_excel("C:/Users/Admin/Desktop/Fat_allo.xlsx",
sheet = "Pro_Energy", col_types = c("text",
"text", "numeric", "numeric"))
View(Fat_allo)
require(multcomp)
require(lsmeans)
require(ggpubr)
require(car)
attach(Fat_allo)
attach(Fat_allo)
Protein
Treatment=as.factor(Fat_allo$Treatment)
Compartment=as.factor(Fat_allo$Compartment)
plot=ggline(Fat_allo,x="Treatment",y="Allo_Pro",color="Compartment",add=c("mean_se","dotplot"),palette = c("black","brown","blue","red"))
plot
modelpro=lm(Allo_Pro~Treatment*Compartment,data = Fat_allo)
anova(modelpro)
## Analysis of Variance Table
##
## Response: Allo_Pro
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 0.00000 0.00000 0.0026 0.9998
## Compartment 3 3.02977 1.00992 2028.5760 <2e-16 ***
## Treatment:Compartment 9 0.00187 0.00021 0.4163 0.9168
## Residuals 32 0.01593 0.00050
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
modelpro1=lm(Allo_Pro~Treatment+Compartment,data = Fat_allo)
anova(modelpro1)
## Analysis of Variance Table
##
## Response: Allo_Pro
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 0.0000 0.00000 0.003 0.9998
## Compartment 3 3.0298 1.00992 2326.710 <2e-16 ***
## Residuals 41 0.0178 0.00043
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
posthoc=lsmeans(modelpro1,pairwise~Treatment*Compartment,adjust="Tukey")
cld(posthoc[[1]],alpha=0.05,Letters=letters)
## Treatment Compartment lsmean SE df lower.CL upper.CL .group
## Control Liver 0.0152 0.00796 41 -0.000865 0.0313 a
## High_carb Liver 0.0153 0.00796 41 -0.000731 0.0314 a
## Mixed Liver 0.0155 0.00796 41 -0.000603 0.0315 a
## High_fat Liver 0.0160 0.00796 41 -0.000111 0.0320 a
## Control Viscera 0.0301 0.00796 41 0.014035 0.0462 a
## High_carb Viscera 0.0302 0.00796 41 0.014168 0.0463 a
## Mixed Viscera 0.0304 0.00796 41 0.014296 0.0464 a
## High_fat Viscera 0.0309 0.00796 41 0.014788 0.0469 a
## Control Fillet 0.3909 0.00796 41 0.374841 0.4070 b
## High_carb Fillet 0.3910 0.00796 41 0.374974 0.4071 b
## Mixed Fillet 0.3912 0.00796 41 0.375102 0.4072 b
## High_fat Fillet 0.3917 0.00796 41 0.375594 0.4077 b
## Control Remaining 0.6101 0.00796 41 0.594041 0.6262 c
## High_carb Remaining 0.6102 0.00796 41 0.594174 0.6263 c
## Mixed Remaining 0.6104 0.00796 41 0.594302 0.6264 c
## High_fat Remaining 0.6109 0.00796 41 0.594794 0.6269 c
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 16 estimates
## significance level used: alpha = 0.05
Energy
plot=ggline(Fat_allo,x="Treatment",y="Allo_Energy",color="Compartment",add=c("mean_se","dotplot"),palette = c("black","brown","blue","red"))
plot
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
modelener=lm(Allo_Energy~Treatment*Compartment,data = Fat_allo)
anova(modelener)
## Analysis of Variance Table
##
## Response: Allo_Energy
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 0.00001 0.00000 0.0048 0.9995
## Compartment 3 2.73483 0.91161 1374.2745 <2e-16 ***
## Treatment:Compartment 9 0.00556 0.00062 0.9313 0.5119
## Residuals 32 0.02123 0.00066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
modelener1=lm(Allo_Energy~Treatment+Compartment,data = Fat_allo)
anova(modelener1)
## Analysis of Variance Table
##
## Response: Allo_Energy
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 0.00001 0.00000 0.0049 0.9995
## Compartment 3 2.73483 0.91161 1395.3059 <2e-16 ***
## Residuals 41 0.02679 0.00065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
posthoc=lsmeans(modelener1,pairwise~Treatment*Compartment,adjust="Tukey")
cld(posthoc[[1]],alpha=0.05,Letters=letters)
## Treatment Compartment lsmean SE df lower.CL upper.CL .group
## High_fat Liver 0.0345 0.00976 41 0.0148 0.0543 a
## Mixed Liver 0.0352 0.00976 41 0.0155 0.0549 a
## High_carb Liver 0.0356 0.00976 41 0.0158 0.0553 a
## Control Liver 0.0357 0.00976 41 0.0160 0.0554 a
## High_fat Viscera 0.0689 0.00976 41 0.0491 0.0886 a
## Mixed Viscera 0.0695 0.00976 41 0.0498 0.0892 a
## High_carb Viscera 0.0699 0.00976 41 0.0502 0.0896 a
## Control Viscera 0.0700 0.00976 41 0.0503 0.0897 a
## High_fat Fillet 0.3089 0.00976 41 0.2892 0.3286 b
## Mixed Fillet 0.3095 0.00976 41 0.2898 0.3292 b
## High_carb Fillet 0.3099 0.00976 41 0.2902 0.3296 b
## Control Fillet 0.3100 0.00976 41 0.2903 0.3298 b
## High_fat Remaining 0.6317 0.00976 41 0.6120 0.6514 c
## Mixed Remaining 0.6323 0.00976 41 0.6126 0.6521 c
## High_carb Remaining 0.6327 0.00976 41 0.6130 0.6524 c
## Control Remaining 0.6329 0.00976 41 0.6131 0.6526 c
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 16 estimates
## significance level used: alpha = 0.05
Ash
library(readxl)
Fat_allo <- read_excel("C:/Users/Admin/Desktop/Fat_allo.xlsx",
sheet = "Fat_Ash", col_types = c("text",
"text", "numeric", "numeric"))
View(Fat_allo)
attach(Fat_allo)
Treatment=as.factor(Fat_allo$Treatment)
Compartment=as.factor(Fat_allo$Compartment)
plot=ggline(Fat_allo,x="Treatment",y="Allo_Ash",color="Compartment",add=c("mean_se","dotplot"),palette = c("black","blue","red"))
plot
modelash=lm(Allo_Ash~Treatment*Compartment,data = Fat_allo)
anova(modelash)
## Analysis of Variance Table
##
## Response: Allo_Ash
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 0.0001 0.0000 0.1239 0.9451
## Compartment 2 9.4822 4.7411 12288.5719 <2e-16 ***
## Treatment:Compartment 6 0.0014 0.0002 0.5903 0.7348
## Residuals 24 0.0093 0.0004
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
modelash1=lm(Allo_Ash~Treatment+Compartment,data = Fat_allo)
anova(modelash1)
## Analysis of Variance Table
##
## Response: Allo_Ash
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 0.0001 0.0000 0.135 0.9384
## Compartment 2 9.4822 4.7411 13385.282 <2e-16 ***
## Residuals 30 0.0106 0.0004
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
posthoc=lsmeans(modelash1,pairwise~Treatment*Compartment,adjust="Tukey")
cld(posthoc[[1]],alpha=0.05,Letters=letters)
## Treatment Compartment lsmean SE df lower.CL upper.CL .group
## Mixed Viscera 0.00717 0.00768 30 -0.00852 0.0229 a
## High_carb Viscera 0.00809 0.00768 30 -0.00760 0.0238 a
## Control Viscera 0.01029 0.00768 30 -0.00540 0.0260 a
## High_fat Viscera 0.01231 0.00768 30 -0.00338 0.0280 a
## Mixed Fillet 0.08261 0.00768 30 0.06692 0.0983 b
## High_carb Fillet 0.08354 0.00768 30 0.06785 0.0992 b
## Control Fillet 0.08573 0.00768 30 0.07004 0.1014 b
## High_fat Fillet 0.08776 0.00768 30 0.07207 0.1034 b
## Mixed Remaining 1.13163 0.00768 30 1.11594 1.1473 c
## High_carb Remaining 1.13256 0.00768 30 1.11687 1.1483 c
## Control Remaining 1.13475 0.00768 30 1.11906 1.1504 c
## High_fat Remaining 1.13678 0.00768 30 1.12109 1.1525 c
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 12 estimates
## significance level used: alpha = 0.05
Fat
plot=ggline(Fat_allo,x="Treatment",y="Allo_fat",color="Compartment",add=c("mean_se","dotplot"),palette = c("black","blue","red"))
plot
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
modelfat=lm(Allo_fat~Treatment*Compartment,data = Fat_allo)
anova(modelfat)
## Analysis of Variance Table
##
## Response: Allo_fat
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 3 0.0035 0.00116 0.6881 0.5682
## Compartment 2 3.8675 1.93373 1149.0027 < 2.2e-16 ***
## Treatment:Compartment 6 0.1360 0.02267 13.4679 1.177e-06 ***
## Residuals 24 0.0404 0.00168
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
posthoc=lsmeans(modelfat,pairwise~Treatment*Compartment,adjust="Tukey")
cld(posthoc[[1]],alpha=0.05,Letters=letters)
## Treatment Compartment lsmean SE df lower.CL upper.CL .group
## High_carb Fillet 0.0388 0.0237 24 -0.0101 0.0876 a
## Control Fillet 0.0590 0.0237 24 0.0101 0.1078 a
## Mixed Fillet 0.1115 0.0237 24 0.0626 0.1604 ab
## High_carb Viscera 0.1434 0.0237 24 0.0945 0.1923 ab
## High_fat Viscera 0.1569 0.0237 24 0.1080 0.2058 ab
## High_fat Fillet 0.1862 0.0237 24 0.1373 0.2351 b
## Mixed Viscera 0.1883 0.0237 24 0.1394 0.2372 b
## Control Viscera 0.1924 0.0237 24 0.1435 0.2413 b
## High_fat Remaining 0.7205 0.0237 24 0.6716 0.7694 c
## Mixed Remaining 0.7788 0.0237 24 0.7299 0.8277 cd
## Control Remaining 0.8493 0.0237 24 0.8005 0.8982 de
## High_carb Remaining 0.9597 0.0237 24 0.9108 1.0086 e
##
## Confidence level used: 0.95
## P value adjustment: tukey method for comparing a family of 12 estimates
## significance level used: alpha = 0.05