Linear_Nutri_Dis

library(readxl)
Linear_total <- read_excel("C:/Users/Admin/Desktop/Linear_total.xlsx", 
     sheet = "Distribution", col_types = c("text", 
         "numeric", "numeric", "numeric", 
         "numeric", "numeric", "numeric", 
       "numeric", "numeric", "numeric",          "numeric", "numeric", "numeric", 
         "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
         "numeric", "numeric", "numeric"))
attach(Linear_total)
View(Linear_total)
require(ggplot2)
require(car)
require(psych)
require(relaimpo)

1.Diet vs Compartment allocation

1a. Dietary protein vs compartment allocation

Linear1=lm(Body_fillet~Diet_pro)
summary(Linear1)

## 
## Call:
## lm(formula = Body_fillet ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5184 -1.0882 -0.1672  1.1916  4.1455 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  31.3360     5.4697   5.729 0.000191 ***
## Diet_pro      0.1011     0.1234   0.819 0.431860    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.602 on 10 degrees of freedom
## Multiple R-squared:  0.06286,    Adjusted R-squared:  -0.03085 
## F-statistic: 0.6708 on 1 and 10 DF,  p-value: 0.4319

p1=ggplot(Linear_total,aes(x=Diet_pro,y=Body_fillet))
p1+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear2=lm(Body_liver~Diet_pro)
summary(Linear2)

## 
## Call:
## lm(formula = Body_liver ~ Diet_pro)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.37836 -0.09937  0.02091  0.08780  0.49813 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.69830    0.51947   5.194 0.000405 ***
## Diet_pro    -0.01078    0.01172  -0.919 0.379523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2471 on 10 degrees of freedom
## Multiple R-squared:  0.07794,    Adjusted R-squared:  -0.01426 
## F-statistic: 0.8453 on 1 and 10 DF,  p-value: 0.3795

p2=ggplot(Linear_total,aes(x=Diet_pro,y=Body_liver))
p2+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear3=lm(Body_viscera~Diet_pro)
summary(Linear3)

## 
## Call:
## lm(formula = Body_viscera ~ Diet_pro)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.66141 -0.24040 -0.06915  0.25753  0.82407 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.63891    0.98924   5.700 0.000198 ***
## Diet_pro    -0.03699    0.02232  -1.657 0.128506    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4706 on 10 degrees of freedom
## Multiple R-squared:  0.2154, Adjusted R-squared:  0.137 
## F-statistic: 2.746 on 1 and 10 DF,  p-value: 0.1285

p3=ggplot(Linear_total,aes(x=Diet_pro,y=Body_viscera))
p3+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear4=lm(Body_rest~Diet_pro)
summary(Linear4)

## 
## Call:
## lm(formula = Body_rest ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4261 -0.9719 -0.0572  0.8374  5.2444 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 60.32683    5.79746  10.406  1.1e-06 ***
## Diet_pro    -0.05332    0.13081  -0.408    0.692    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.758 on 10 degrees of freedom
## Multiple R-squared:  0.01634,    Adjusted R-squared:  -0.08202 
## F-statistic: 0.1661 on 1 and 10 DF,  p-value: 0.6922

p4=ggplot(Linear_total,aes(x=Diet_pro,y=Body_rest))
p4+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

1b. Dietary fat vs compartment allocation

Linear5=lm(Body_fillet~Diet_fat)
summary(Linear5)

## 
## Call:
## lm(formula = Body_fillet ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7344 -1.1868 -0.0806  1.0246  4.5056 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  37.1067     1.6907  21.947 8.64e-10 ***
## Diet_fat     -0.1107     0.1258  -0.879      0.4    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.59 on 10 degrees of freedom
## Multiple R-squared:  0.07176,    Adjusted R-squared:  -0.02106 
## F-statistic: 0.7731 on 1 and 10 DF,  p-value: 0.3999

p5=ggplot(Linear_total,aes(x=Diet_fat,y=Body_fillet))
p5+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear6=lm(Body_liver~Diet_fat)
summary(Linear6)

## 
## Call:
## lm(formula = Body_liver ~ Diet_fat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.41457 -0.09952  0.03840  0.12376  0.42465 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.09940    0.16206  12.955 1.42e-07 ***
## Diet_fat     0.01044    0.01206   0.866    0.407    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2482 on 10 degrees of freedom
## Multiple R-squared:  0.0697, Adjusted R-squared:  -0.02333 
## F-statistic: 0.7492 on 1 and 10 DF,  p-value: 0.407

p6=ggplot(Linear_total,aes(x=Diet_fat,y=Body_liver))
p6+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear7=lm(Body_viscera~Diet_fat)
summary(Linear7)

## 
## Call:
## lm(formula = Body_viscera ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6705 -0.2833 -0.1135  0.2263  0.9231 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  3.52952    0.30165  11.701  3.7e-07 ***
## Diet_fat     0.04031    0.02245   1.795    0.103    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.462 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

p7=ggplot(Linear_total,aes(x=Diet_fat,y=Body_viscera))
p7+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear8=lm(Body_rest~Diet_fat)
summary(Linear8)

## 
## Call:
## lm(formula = Body_rest ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8510 -0.8240 -0.0513  0.8865  4.8195 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  57.2644     1.7976  31.857 2.19e-11 ***
## Diet_fat      0.0599     0.1338   0.448    0.664    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.753 on 10 degrees of freedom
## Multiple R-squared:  0.01965,    Adjusted R-squared:  -0.07838 
## F-statistic: 0.2004 on 1 and 10 DF,  p-value: 0.6639

p8=ggplot(Linear_total,aes(x=Diet_fat,y=Body_rest))
p8+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

1c. Dietary carb vs compartment allocation

Linear9=lm(Body_fillet~Diet_carb)
summary(Linear9)

## 
## Call:
## lm(formula = Body_fillet ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6860 -1.2404 -0.4549  1.3718  4.1058 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 36.26221    2.99342  12.114 2.67e-07 ***
## Diet_carb   -0.01600    0.09465  -0.169    0.869    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.684 on 10 degrees of freedom
## Multiple R-squared:  0.002851,   Adjusted R-squared:  -0.09686 
## F-statistic: 0.02859 on 1 and 10 DF,  p-value: 0.8691

p9=ggplot(Linear_total,aes(x=Diet_carb,y=Body_fillet))
p9+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear10=lm(Body_liver~Diet_carb)
summary(Linear10)

## 
## Call:
## lm(formula = Body_liver ~ Diet_carb)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.31829 -0.13329 -0.00902  0.11386  0.52093 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 2.154156   0.286071   7.530 1.99e-05 ***
## Diet_carb   0.002326   0.009045   0.257    0.802    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2565 on 10 degrees of freedom
## Multiple R-squared:  0.006571,   Adjusted R-squared:  -0.09277 
## F-statistic: 0.06614 on 1 and 10 DF,  p-value: 0.8023

p10=ggplot(Linear_total,aes(x=Diet_carb,y=Body_liver))
p10+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear11=lm(Body_viscera~Diet_carb)
summary(Linear11)

## 
## Call:
## lm(formula = Body_viscera ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6514 -0.3522 -0.0725  0.2824  1.0528 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 3.822578   0.589167   6.488    7e-05 ***
## Diet_carb   0.006306   0.018628   0.339    0.742    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5282 on 10 degrees of freedom
## Multiple R-squared:  0.01133,    Adjusted R-squared:  -0.08754 
## F-statistic: 0.1146 on 1 and 10 DF,  p-value: 0.742

p11=ggplot(Linear_total,aes(x=Diet_carb,y=Body_viscera))
p11+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear12=lm(Body_rest~Diet_carb)
summary(Linear12)

## 
## Call:
## lm(formula = Body_rest ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3462 -1.0609  0.1023  0.9794  5.3244 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 57.761061   3.100417  18.630 4.29e-09 ***
## Diet_carb    0.007371   0.098028   0.075    0.942    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.78 on 10 degrees of freedom
## Multiple R-squared:  0.0005651,  Adjusted R-squared:  -0.09938 
## F-statistic: 0.005654 on 1 and 10 DF,  p-value: 0.9415

p12=ggplot(Linear_total,aes(x=Diet_carb,y=Body_rest))
p12+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2.Dietary nutrients vs nutrients allocations

2a. Dietary protein vs nutrient compartment allocations

2a1. Dietary protein vs nutrient allocation in the fillet

Linear13=lm(Fillet_pro~Diet_pro)
summary(Linear13)

## 
## Call:
## lm(formula = Fillet_pro ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5430 -0.6618 -0.0836  0.7933  3.6914 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  32.8030     5.0764   6.462 7.24e-05 ***
## Diet_pro      0.1211     0.1145   1.057    0.315    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.415 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

p13=ggplot(Linear_total,aes(x=Diet_pro,y=Fillet_pro))
p13+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear14=lm(Fillet_fat~Diet_pro)
summary(Linear14)

## 
## Call:
## lm(formula = Fillet_fat ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3850 -4.4600 -0.4884  2.4049 12.2271 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  21.3193    13.1907   1.616    0.137
## Diet_pro     -0.2613     0.2976  -0.878    0.401
## 
## Residual standard error: 6.275 on 10 degrees of freedom
## Multiple R-squared:  0.07154,    Adjusted R-squared:  -0.02131 
## F-statistic: 0.7705 on 1 and 10 DF,  p-value: 0.4007

p14=ggplot(Linear_total,aes(x=Diet_pro,y=Fillet_fat))
p14+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear15=lm(Fillet_ash~Diet_pro)
summary(Linear15)

## 
## Call:
## lm(formula = Fillet_ash ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5949 -0.7715 -0.2535  0.6217  1.9271 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  9.98611    2.41271   4.139  0.00202 **
## Diet_pro    -0.03430    0.05444  -0.630  0.54277   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.148 on 10 degrees of freedom
## Multiple R-squared:  0.03818,    Adjusted R-squared:  -0.058 
## F-statistic: 0.397 on 1 and 10 DF,  p-value: 0.5428

p15=ggplot(Linear_total,aes(x=Diet_pro,y=Fillet_ash))
p15+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear16=lm(Fillet_ener~Diet_pro)
summary(Linear16)

## 
## Call:
## lm(formula = Fillet_ener ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6469 -1.8314 -0.5533  0.8957  5.5640 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 27.06707    5.49061   4.930 0.000596 ***
## Diet_pro     0.07719    0.12389   0.623 0.547166    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.612 on 10 degrees of freedom
## Multiple R-squared:  0.03737,    Adjusted R-squared:  -0.05889 
## F-statistic: 0.3883 on 1 and 10 DF,  p-value: 0.5472

p16=ggplot(Linear_total,aes(x=Diet_pro,y=Fillet_ener))
p16+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2a2. Dietary protein vs nutrient allocation in the viscera

Linear17=lm(Vis_pro~Diet_pro)
summary(Linear17)

## 
## Call:
## lm(formula = Vis_pro ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.3269 -0.1887 -0.0589  0.1898  0.4112 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.48193    0.53183   8.427 7.44e-06 ***
## Diet_pro    -0.03288    0.01200  -2.740   0.0208 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.253 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

p17=ggplot(Linear_total,aes(x=Diet_pro,y=Vis_pro))
p17+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear18=lm(Vis_fat~Diet_pro)
summary(Linear18)

## 
## Call:
## lm(formula = Vis_fat ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5740 -2.3877  0.2746  1.7927  4.6664 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.76917    7.12288   1.793    0.103
## Diet_pro     0.09489    0.16072   0.590    0.568
## 
## Residual standard error: 3.388 on 10 degrees of freedom
## Multiple R-squared:  0.03369,    Adjusted R-squared:  -0.06295 
## F-statistic: 0.3486 on 1 and 10 DF,  p-value: 0.568

p18=ggplot(Linear_total,aes(x=Diet_pro,y=Vis_fat))
p18+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear19=lm(Vis_ash~Diet_pro)
summary(Linear15)

## 
## Call:
## lm(formula = Fillet_ash ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5949 -0.7715 -0.2535  0.6217  1.9271 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  9.98611    2.41271   4.139  0.00202 **
## Diet_pro    -0.03430    0.05444  -0.630  0.54277   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.148 on 10 degrees of freedom
## Multiple R-squared:  0.03818,    Adjusted R-squared:  -0.058 
## F-statistic: 0.397 on 1 and 10 DF,  p-value: 0.5428

p19=ggplot(Linear_total,aes(x=Diet_pro,y=Vis_ash))
p19+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear20=lm(Vis_ener~Diet_pro)
summary(Linear20)

## 
## Call:
## lm(formula = Vis_ener ~ Diet_pro)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -2.438 -0.834 -0.187  1.267  1.904 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 11.24443    3.09942   3.628  0.00463 **
## Diet_pro    -0.09782    0.06993  -1.399  0.19214   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.474 on 10 degrees of freedom
## Multiple R-squared:  0.1636, Adjusted R-squared:  0.07999 
## F-statistic: 1.956 on 1 and 10 DF,  p-value: 0.1921

p20=ggplot(Linear_total,aes(x=Diet_pro,y=Vis_ener))
p20+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2a3. Dietary protein vs nutrient allocation in the remaining liver

Linear21=lm(Liver_pro~Diet_pro)
summary(Linear21)

## 
## Call:
## lm(formula = Liver_pro ~ Diet_pro)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.47636 -0.18310 -0.01326  0.14196  0.65180 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  2.21198    0.69762   3.171  0.00997 **
## Diet_pro    -0.01510    0.01574  -0.960  0.35993   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3319 on 10 degrees of freedom
## Multiple R-squared:  0.0843, Adjusted R-squared:  -0.007267 
## F-statistic: 0.9206 on 1 and 10 DF,  p-value: 0.3599

p21=ggplot(Linear_total,aes(x=Diet_pro,y=Liver_pro))
p21+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear22=lm(Liver_energy~Diet_pro)
summary(Linear22)

## 
## Call:
## lm(formula = Liver_energy ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.6777 -0.4727 -0.3800  0.1254  1.9491 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  4.34839    1.85873   2.339   0.0414 *
## Diet_pro    -0.01878    0.04194  -0.448   0.6639  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8842 on 10 degrees of freedom
## Multiple R-squared:  0.01965,    Adjusted R-squared:  -0.07838 
## F-statistic: 0.2005 on 1 and 10 DF,  p-value: 0.6639

p22=ggplot(Linear_total,aes(x=Diet_pro,y=Liver_energy))
p22+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2a4. Dietary protein vs nutrient allocation in the remaining

Linear23=lm(Rest_pro~Diet_pro)
summary(Linear23)

## 
## Call:
## lm(formula = Rest_pro ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1630 -1.0543  0.3597  0.7398  4.7640 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 60.50310    5.33301  11.345 4.94e-07 ***
## Diet_pro    -0.07307    0.12033  -0.607    0.557    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.537 on 10 degrees of freedom
## Multiple R-squared:  0.03557,    Adjusted R-squared:  -0.06088 
## F-statistic: 0.3688 on 1 and 10 DF,  p-value: 0.5572

p23=ggplot(Linear_total,aes(x=Diet_pro,y=Rest_pro))
p23+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear24=lm(Rest_fat~Diet_pro)
summary(Linear24)

## 
## Call:
## lm(formula = Rest_fat ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -13.365  -4.050  -1.445   4.917  11.558 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  65.9115    15.6047   4.224  0.00176 **
## Diet_pro      0.1664     0.3521   0.472  0.64672   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.423 on 10 degrees of freedom
## Multiple R-squared:  0.02184,    Adjusted R-squared:  -0.07598 
## F-statistic: 0.2233 on 1 and 10 DF,  p-value: 0.6467

p24=ggplot(Linear_total,aes(x=Diet_pro,y=Rest_fat))
p24+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear25=lm(Rest_ash~Diet_pro)
summary(Linear25)

## 
## Call:
## lm(formula = Rest_ash ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4126 -0.6790  0.2338  0.8656  1.6882 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 88.77096    2.70645  32.800 1.64e-11 ***
## Diet_pro     0.04105    0.06107   0.672    0.517    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.287 on 10 degrees of freedom
## Multiple R-squared:  0.04323,    Adjusted R-squared:  -0.05244 
## F-statistic: 0.4519 on 1 and 10 DF,  p-value: 0.5167

p25=ggplot(Linear_total,aes(x=Diet_pro,y=Rest_ash))
p25+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear26=lm(Rest_ener~Diet_pro)
summary(Linear26)

## 
## Call:
## lm(formula = Rest_ener ~ Diet_pro)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2319 -1.2869 -0.7007  2.8417  4.3439 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  57.3401     6.6780   8.586  6.3e-06 ***
## Diet_pro      0.0394     0.1507   0.261    0.799    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.177 on 10 degrees of freedom
## Multiple R-squared:  0.006791,   Adjusted R-squared:  -0.09253 
## F-statistic: 0.06837 on 1 and 10 DF,  p-value: 0.799

p26=ggplot(Linear_total,aes(x=Diet_pro,y=Rest_ener))
p26+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2b. Dietary fat vs nutrient compartment allocations

2b1. Dietary fat vs nutrient allocation in the fillet

Linear27=lm(Fillet_pro~Diet_fat)
summary(Linear27)

## 
## Call:
## lm(formula = Fillet_pro ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7555 -0.4640 -0.2394  0.6040  4.4789 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  39.4694     1.5926  24.784 2.61e-10 ***
## Diet_fat     -0.1122     0.1185  -0.947    0.366    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.439 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

p27=ggplot(Linear_total,aes(x=Diet_fat,y=Fillet_pro))
p27+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear28=lm(Fillet_fat~Diet_fat)
summary(Linear28)

## 
## Call:
## lm(formula = Fillet_fat ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1237 -1.2355 -0.2407  1.4620  5.5253 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -1.1585     1.7357  -0.667     0.52    
## Diet_fat      0.9136     0.1292   7.071 3.41e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.658 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

p28=ggplot(Linear_total,aes(x=Diet_fat,y=Fillet_fat))
p28+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear29=lm(Fillet_ash~Diet_fat)
summary(Linear29)

## 
## Call:
## lm(formula = Fillet_ash ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5937 -0.5896 -0.1832  0.6296  2.2508 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.70287    0.76004  11.451 4.53e-07 ***
## Diet_fat    -0.01847    0.05657  -0.326    0.751    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.164 on 10 degrees of freedom
## Multiple R-squared:  0.01054,    Adjusted R-squared:  -0.0884 
## F-statistic: 0.1066 on 1 and 10 DF,  p-value: 0.7508

p29=ggplot(Linear_total,aes(x=Diet_fat,y=Fillet_ash))
p29+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear30=lm(Fillet_ener~Diet_fat)
summary(Linear30)

## 
## Call:
## lm(formula = Fillet_ener ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5316 -1.5925  0.0139  0.6631  4.2520 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  32.5855     1.5675  20.788 1.47e-09 ***
## Diet_fat     -0.1767     0.1167  -1.515    0.161    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.401 on 10 degrees of freedom
## Multiple R-squared:  0.1866, Adjusted R-squared:  0.1053 
## F-statistic: 2.294 on 1 and 10 DF,  p-value: 0.1608

p30=ggplot(Linear_total,aes(x=Diet_fat,y=Fillet_ener))
p30+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2b2. Dietary fat vs nutrient allocation in the viscera

Linear31=lm(Vis_pro~Diet_fat)
summary(Linear31)

## 
## Call:
## lm(formula = Vis_pro ~ Diet_fat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.43392 -0.27587  0.02877  0.21536  0.45277 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.84412    0.20754  13.704 8.31e-08 ***
## Diet_fat     0.01613    0.01545   1.044    0.321    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3179 on 10 degrees of freedom
## Multiple R-squared:  0.09828,    Adjusted R-squared:  0.008104 
## F-statistic:  1.09 on 1 and 10 DF,  p-value: 0.3211

p31=ggplot(Linear_total,aes(x=Diet_fat,y=Vis_pro))
p31+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear32=lm(Vis_fat~Diet_fat)
summary(Linear32)

## 
## Call:
## lm(formula = Vis_fat ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5821 -3.0476  0.8279  2.3791  4.3316 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 16.60049    2.24739   7.387 2.35e-05 ***
## Diet_fat     0.02775    0.16729   0.166    0.872    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.442 on 10 degrees of freedom
## Multiple R-squared:  0.002743,   Adjusted R-squared:  -0.09698 
## F-statistic: 0.02751 on 1 and 10 DF,  p-value: 0.8716

p32=ggplot(Linear_total,aes(x=Diet_fat,y=Vis_fat))
p32+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear33=lm(Vis_ash~Diet_fat)
summary(Linear33)

## 
## Call:
## lm(formula = Vis_ash ~ Diet_fat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.20305 -0.09358 -0.04001  0.01030  0.55732 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.014283   0.131120   7.736 1.58e-05 ***
## Diet_fat    -0.005615   0.009760  -0.575    0.578    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2008 on 10 degrees of freedom
## Multiple R-squared:  0.03204,    Adjusted R-squared:  -0.06476 
## F-statistic: 0.331 on 1 and 10 DF,  p-value: 0.5778

p33=ggplot(Linear_total,aes(x=Diet_fat,y=Vis_ash))
p33+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear34=lm(Vis_ener~Diet_fat)
summary(Linear34)

## 
## Call:
## lm(formula = Vis_ener ~ Diet_fat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.91312 -0.58029 -0.08325  0.73308  1.57904 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.74949    0.71126   6.678 5.52e-05 ***
## Diet_fat     0.18264    0.05294   3.450  0.00623 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.089 on 10 degrees of freedom
## Multiple R-squared:  0.5434, Adjusted R-squared:  0.4977 
## F-statistic:  11.9 on 1 and 10 DF,  p-value: 0.006229

p34=ggplot(Linear_total,aes(x=Diet_fat,y=Vis_ener))
p34+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2b3. Dietary fat vs nutrient allocation in the remaining liver

Linear35=lm(Liver_pro~Diet_fat)
summary(Linear35)

## 
## Call:
## lm(formula = Liver_pro ~ Diet_fat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.37377 -0.20893 -0.01166  0.05056  0.81257 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  1.70000    0.22007   7.725  1.6e-05 ***
## Diet_fat    -0.01254    0.01638  -0.765    0.462    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3371 on 10 degrees of freedom
## Multiple R-squared:  0.05532,    Adjusted R-squared:  -0.03915 
## F-statistic: 0.5856 on 1 and 10 DF,  p-value: 0.4618

p35=ggplot(Linear_total,aes(x=Diet_fat,y=Liver_pro))
p35+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear36=lm(Liver_energy~Diet_fat)
summary(Linear36)

## 
## Call:
## lm(formula = Liver_energy ~ Diet_fat)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.95987 -0.37598  0.08081  0.27022  1.34974 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.54251    0.46924   5.418 0.000294 ***
## Diet_fat     0.08146    0.03493   2.332 0.041895 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7187 on 10 degrees of freedom
## Multiple R-squared:  0.3523, Adjusted R-squared:  0.2875 
## F-statistic: 5.439 on 1 and 10 DF,  p-value: 0.0419

p36=ggplot(Linear_total,aes(x=Diet_fat,y=Liver_energy))
p36+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2b4. Dietary fat vs nutrient allocation in the remaining

Linear37=lm(Rest_pro~Diet_fat)
summary(Linear37)

## 
## Call:
## lm(formula = Rest_pro ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1830 -1.0360  0.3435  0.4757  3.9841 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  55.9865     1.6223  34.511 9.88e-12 ***
## Diet_fat      0.1086     0.1208   0.899     0.39    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.485 on 10 degrees of freedom
## Multiple R-squared:  0.07485,    Adjusted R-squared:  -0.01767 
## F-statistic: 0.809 on 1 and 10 DF,  p-value: 0.3896

p37=ggplot(Linear_total,aes(x=Diet_fat,y=Rest_pro))
p37+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear38=lm(Rest_fat~Diet_fat)
summary(Linear38)

## 
## Call:
## lm(formula = Rest_fat ~ Diet_fat)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.724 -3.083  0.813  3.041  5.229 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  84.5580     2.8320  29.858 4.15e-11 ***
## Diet_fat     -0.9413     0.2108  -4.466  0.00121 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.338 on 10 degrees of freedom
## Multiple R-squared:  0.666,  Adjusted R-squared:  0.6326 
## F-statistic: 19.94 on 1 and 10 DF,  p-value: 0.001206

p38=ggplot(Linear_total,aes(x=Diet_fat,y=Rest_fat))
p38+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear39=lm(Rest_ash~Diet_fat)
summary(Linear39)

## 
## Call:
## lm(formula = Rest_ash ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.8081 -0.4716  0.1724  0.6851  1.7159 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 90.28285    0.85325 105.811   <2e-16 ***
## Diet_fat     0.02408    0.06351   0.379    0.712    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.307 on 10 degrees of freedom
## Multiple R-squared:  0.01417,    Adjusted R-squared:  -0.08441 
## F-statistic: 0.1438 on 1 and 10 DF,  p-value: 0.7125

p39=ggplot(Linear_total,aes(x=Diet_fat,y=Rest_ash))
p39+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear40=lm(Rest_ener~Diet_fat)
summary(Linear40)

## 
## Call:
## lm(formula = Rest_ener ~ Diet_fat)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.5968 -1.4969 -0.4146  2.8851  4.2341 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 60.12255    2.04778  29.360  4.9e-11 ***
## Diet_fat    -0.08737    0.15243  -0.573    0.579    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.136 on 10 degrees of freedom
## Multiple R-squared:  0.03181,    Adjusted R-squared:  -0.06501 
## F-statistic: 0.3285 on 1 and 10 DF,  p-value: 0.5792

p40=ggplot(Linear_total,aes(x=Diet_fat,y=Rest_ener))
p40+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2c. Dietary carb vs nutrient compartment allocations

2c1. Dietary carb vs nutrient allocation in the fillet

Linear41=lm(Fillet_pro~Diet_carb)
summary(Linear41)

## 
## Call:
## lm(formula = Fillet_pro ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8347 -0.8549 -0.2297  1.0728  3.7062 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 39.05204    2.82318  13.833  7.6e-08 ***
## Diet_carb   -0.03060    0.08926  -0.343    0.739    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.531 on 10 degrees of freedom
## Multiple R-squared:  0.01161,    Adjusted R-squared:  -0.08723 
## F-statistic: 0.1175 on 1 and 10 DF,  p-value: 0.7389

p41=ggplot(Linear_total,aes(x=Diet_carb,y=Fillet_pro))
p41+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear42=lm(Fillet_fat~Diet_carb)
summary(Linear42)

## 
## Call:
## lm(formula = Fillet_fat ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -9.4714 -3.8381  0.2242  4.1539  9.6733 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  20.0655     6.4472   3.112    0.011 *
## Diet_carb    -0.3344     0.2038  -1.640    0.132  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.781 on 10 degrees of freedom
## Multiple R-squared:  0.212,  Adjusted R-squared:  0.1332 
## F-statistic: 2.691 on 1 and 10 DF,  p-value: 0.132

p42=ggplot(Linear_total,aes(x=Diet_carb,y=Fillet_fat))
p42+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear43=lm(Fillet_ash~Diet_carb)
summary(Linear43)

## 
## Call:
## lm(formula = Fillet_ash ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.2723 -0.8887 -0.2551  0.8043  1.9430 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  7.29355    1.24606   5.853 0.000161 ***
## Diet_carb    0.03885    0.03940   0.986 0.347377    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.117 on 10 degrees of freedom
## Multiple R-squared:  0.08861,    Adjusted R-squared:  -0.002529 
## F-statistic: 0.9722 on 1 and 10 DF,  p-value: 0.3474

p43=ggplot(Linear_total,aes(x=Diet_carb,y=Fillet_ash))
p43+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear44=lm(Fillet_ener~Diet_carb)
summary(Linear44)

## 
## Call:
## lm(formula = Fillet_ener ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7924 -2.3968 -0.6768  1.6824  4.9912 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 29.19664    2.94030   9.930  1.7e-06 ***
## Diet_carb    0.04122    0.09297   0.443    0.667    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.636 on 10 degrees of freedom
## Multiple R-squared:  0.01928,    Adjusted R-squared:  -0.07879 
## F-statistic: 0.1966 on 1 and 10 DF,  p-value: 0.6669

p44=ggplot(Linear_total,aes(x=Diet_carb,y=Fillet_ener))
p44+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2c2. Dietary carb vs nutrient allocation in the viscera

Linear45=lm(Vis_pro~Diet_carb)
summary(Linear45)

## 
## Call:
## lm(formula = Vis_pro ~ Diet_carb)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.38925 -0.23266  0.00559  0.25842  0.41829 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  2.51782    0.33217   7.580 1.88e-05 ***
## Diet_carb    0.01704    0.01050   1.623    0.136    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2978 on 10 degrees of freedom
## Multiple R-squared:  0.2084, Adjusted R-squared:  0.1293 
## F-statistic: 2.633 on 1 and 10 DF,  p-value: 0.1357

p45=ggplot(Linear_total,aes(x=Diet_carb,y=Vis_pro))
p45+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear46=lm(Vis_fat~Diet_carb)
summary(Linear46)

## 
## Call:
## lm(formula = Vis_fat ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4598 -2.4241  0.1473  2.3725  4.5108 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 19.64391    3.74065   5.251 0.000373 ***
## Diet_carb   -0.08868    0.11827  -0.750 0.470661    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.354 on 10 degrees of freedom
## Multiple R-squared:  0.05322,    Adjusted R-squared:  -0.04145 
## F-statistic: 0.5622 on 1 and 10 DF,  p-value: 0.4707

p46=ggplot(Linear_total,aes(x=Diet_carb,y=Vis_fat))
p46+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear47=lm(Vis_ash~Diet_carb)
summary(Linear47)

## 
## Call:
## lm(formula = Vis_ash ~ Diet_carb)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.17449 -0.14583 -0.01827  0.04861  0.48133 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) 0.676120   0.209732   3.224  0.00912 **
## Diet_carb   0.008854   0.006631   1.335  0.21140   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.188 on 10 degrees of freedom
## Multiple R-squared:  0.1513, Adjusted R-squared:  0.06644 
## F-statistic: 1.783 on 1 and 10 DF,  p-value: 0.2114

p47=ggplot(Linear_total,aes(x=Diet_carb,y=Vis_ash))
p47+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear48=lm(Vis_ener~Diet_carb)
summary(Linear48)

## 
## Call:
## lm(formula = Vis_ener ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9504 -1.0890 -0.2931  0.9415  2.5080 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  7.82814    1.77496   4.410  0.00131 **
## Diet_carb   -0.02873    0.05612  -0.512  0.61976   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.591 on 10 degrees of freedom
## Multiple R-squared:  0.02555,    Adjusted R-squared:  -0.0719 
## F-statistic: 0.2622 on 1 and 10 DF,  p-value: 0.6198

p48=ggplot(Linear_total,aes(x=Diet_carb,y=Vis_ener))
p48+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2c3. Dietary fat vs nutrient allocation in the remaining liver

Linear49=lm(Liver_pro~Diet_carb)
summary(Linear49)

## 
## Call:
## lm(formula = Liver_pro ~ Diet_carb)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.48393 -0.17365  0.00231  0.12763  0.64423 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.95175    0.33373   2.852   0.0172 *
## Diet_carb    0.01955    0.01055   1.853   0.0936 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2992 on 10 degrees of freedom
## Multiple R-squared:  0.2555, Adjusted R-squared:  0.1811 
## F-statistic: 3.432 on 1 and 10 DF,  p-value: 0.09364

p49=ggplot(Linear_total,aes(x=Diet_carb,y=Liver_pro))
p49+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear50=lm(Liver_energy~Diet_carb)
summary(Linear50)

## 
## Call:
## lm(formula = Liver_energy ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.9290 -0.4392 -0.1659  0.2016  1.6872 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.55946    0.93653   4.868 0.000653 ***
## Diet_carb   -0.03389    0.02961  -1.145 0.279050    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8397 on 10 degrees of freedom
## Multiple R-squared:  0.1158, Adjusted R-squared:  0.02741 
## F-statistic:  1.31 on 1 and 10 DF,  p-value: 0.279

p50=ggplot(Linear_total,aes(x=Diet_carb,y=Liver_energy))
p50+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

2c4. Dietary carb vs nutrient allocation in the remaining

Linear51=lm(Rest_pro~Diet_carb)
summary(Linear51)

## 
## Call:
## lm(formula = Rest_pro ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9560 -0.8298 -0.1659  0.9742  4.7456 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 57.478389   2.880510  19.954 2.19e-09 ***
## Diet_carb   -0.005995   0.091075  -0.066    0.949    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.583 on 10 degrees of freedom
## Multiple R-squared:  0.0004332,  Adjusted R-squared:  -0.09952 
## F-statistic: 0.004333 on 1 and 10 DF,  p-value: 0.9488

p51=ggplot(Linear_total,aes(x=Diet_carb,y=Rest_pro))
p51+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear52=lm(Rest_fat~Diet_carb)
summary(Linear52)

## 
## Call:
## lm(formula = Rest_fat ~ Diet_carb)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.0686  -3.1945   0.8886   4.6054   9.5720 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  60.2906     7.2229   8.347  8.1e-06 ***
## Diet_carb     0.4231     0.2284   1.852   0.0937 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.476 on 10 degrees of freedom
## Multiple R-squared:  0.2555, Adjusted R-squared:  0.181 
## F-statistic: 3.432 on 1 and 10 DF,  p-value: 0.09367

p52=ggplot(Linear_total,aes(x=Diet_carb,y=Rest_fat))
p52+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear53=lm(Rest_ash~Diet_carb)
summary(Linear53)

## 
## Call:
## lm(formula = Rest_ash ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4243 -0.6694  0.2370  1.0512  1.2925 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  92.0303     1.3883  66.290 1.49e-14 ***
## Diet_carb    -0.0477     0.0439  -1.087    0.303    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.245 on 10 degrees of freedom
## Multiple R-squared:  0.1056, Adjusted R-squared:  0.01618 
## F-statistic: 1.181 on 1 and 10 DF,  p-value: 0.3027

p53=ggplot(Linear_total,aes(x=Diet_carb,y=Rest_ash))
p53+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)

Linear54=lm(Rest_ener~Diet_carb)
summary(Linear54)

## 
## Call:
## lm(formula = Rest_ener ~ Diet_carb)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0775 -1.5644 -0.3867  2.6716  4.0478 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 58.41576    3.54864  16.461 1.43e-08 ***
## Diet_carb    0.02141    0.11220   0.191    0.853    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.182 on 10 degrees of freedom
## Multiple R-squared:  0.003627,   Adjusted R-squared:  -0.09601 
## F-statistic: 0.0364 on 1 and 10 DF,  p-value: 0.8525

p54=ggplot(Linear_total,aes(x=Diet_carb,y=Rest_ener))
p54+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)