library(readxl)
Snakehead_compartments_agreed <- read_excel("C:/Users/Admin/Desktop/Luan van Offical/Snakehead compartments agreed.xlsx",
sheet = "Linear", col_types = c("text",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric"))
View(Snakehead_compartments_agreed)
attach(Snakehead_compartments_agreed)
require(ggplot2)
require(car)
require(psych)
require(relaimpo)
Fat diet vs fat_fillet
Linear1=lm(Diet_fat~Fillet_fat)
summary(Linear1)
##
## Call:
## lm(formula = Diet_fat ~ Fillet_fat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7983 -2.3627 0.4066 1.3204 4.4531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0650 1.4841 2.065 0.0658 .
## Fillet_fat 0.9122 0.1290 7.071 3.41e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.656 on 10 degrees of freedom
## Multiple R-squared: 0.8333, Adjusted R-squared: 0.8167
## F-statistic: 50 on 1 and 10 DF, p-value: 3.411e-05
p1=ggplot(Snakehead_compartments_agreed,aes(x=Diet_fat,y=Fillet_fat))
p1+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Fat diet vs Pro_fillet
Linear2=lm(Diet_fat~Fillet_pro)
summary(Linear2)
##
## Call:
## lm(formula = Diet_fat ~ Fillet_pro)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.6876 -4.6602 0.1294 3.6532 10.1197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.9835 29.5659 1.352 0.206
## Fillet_pro -0.7328 0.7742 -0.947 0.366
##
## Residual standard error: 6.234 on 10 degrees of freedom
## Multiple R-squared: 0.08223, Adjusted R-squared: -0.00955
## F-statistic: 0.8959 on 1 and 10 DF, p-value: 0.3662
p2=ggplot(Snakehead_compartments_agreed,aes(x=Diet_fat,y=Fillet_pro))
p2+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Fat diet vs Viscera
Linear3=lm(Diet_fat~Body_viscera)
summary(Linear3)
##
## Call:
## lm(formula = Diet_fat ~ Body_viscera)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.648 -3.234 -2.235 4.302 9.688
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -12.228 13.622 -0.898 0.390
## Body_viscera 6.047 3.368 1.795 0.103
##
## Residual standard error: 5.659 on 10 degrees of freedom
## Multiple R-squared: 0.2437, Adjusted R-squared: 0.1681
## F-statistic: 3.223 on 1 and 10 DF, p-value: 0.1029
p3=ggplot(Snakehead_compartments_agreed,aes(x=Diet_fat,y=Body_viscera))
p3+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Protein diet vs Protein_fillet
Linear4=lm(Diet_pro~Fillet_pro)
summary(Linear4)
##
## Call:
## lm(formula = Diet_pro ~ Fillet_pro)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.474 -5.205 -1.049 4.421 9.200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.2619 29.9906 0.409 0.691
## Fillet_pro 0.8300 0.7853 1.057 0.315
##
## Residual standard error: 6.323 on 10 degrees of freedom
## Multiple R-squared: 0.1005, Adjusted R-squared: 0.01053
## F-statistic: 1.117 on 1 and 10 DF, p-value: 0.3154
p4=ggplot(Snakehead_compartments_agreed,aes(x=Diet_pro,y=Fillet_pro))
p4+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Protein diet vs Protein_Viscera
Linear5=lm(Diet_pro~Vis_pro)
summary(Linear5)
##
## Call:
## lm(formula = Diet_pro ~ Vis_pro)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1937 -3.8235 -0.8704 2.5047 10.8466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 83.53 14.54 5.747 0.000186 ***
## Vis_pro -13.04 4.76 -2.740 0.020829 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.039 on 10 degrees of freedom
## Multiple R-squared: 0.4288, Adjusted R-squared: 0.3717
## F-statistic: 7.508 on 1 and 10 DF, p-value: 0.02083
p5=ggplot(Snakehead_compartments_agreed,aes(x=Diet_pro,y=Vis_pro))
p5+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Energy diet vs Energy_fillet
Linear6=lm(Diet_ene~Energy_fillet)
summary(Linear6)
##
## Call:
## lm(formula = Diet_ene ~ Energy_fillet)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0109 -1.1264 0.3336 0.9046 2.1042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 27.6376 4.9878 5.541 0.000247 ***
## Energy_fillet -0.2573 0.1633 -1.576 0.146023
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.374 on 10 degrees of freedom
## Multiple R-squared: 0.199, Adjusted R-squared: 0.1189
## F-statistic: 2.485 on 1 and 10 DF, p-value: 0.146
p6=ggplot(Snakehead_compartments_agreed,aes(x=Diet_ene,y=Energy_fillet))
p6+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Energy diet vs Energy_vis+liver
Linear7=lm(Diet_ene~`Energy liver+vis`)
summary(Linear7)
##
## Call:
## lm(formula = Diet_ene ~ `Energy liver+vis`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.27230 -0.61624 -0.08864 0.25581 1.98617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.1760 1.4942 9.487 2.57e-06 ***
## `Energy liver+vis` 0.5369 0.1401 3.833 0.0033 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9773 on 10 degrees of freedom
## Multiple R-squared: 0.595, Adjusted R-squared: 0.5545
## F-statistic: 14.69 on 1 and 10 DF, p-value: 0.003304
p7=ggplot(Snakehead_compartments_agreed,aes(x=Diet_ene,y=`Energy liver+vis`))
p7+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
