Linear_energy ration
La Nguyễn Thế Hiển
7/6/2021
library(readxl)
Linear_total <- read_excel("C:/Users/Admin/Desktop/Linear_total.xlsx",
sheet = "Energy ratio", col_types = c("text", "numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric"))
View(Linear_total)
attach(Linear_total)
View(Linear_total)
require(ggplot2)
require(car)
require(psych)
require(relaimpo)1.Energy ratio vs nutrients allocations
1a. Ep/Ef vs nutrient compartment allocations
1a1. Ep/Ef vs nutrient allocation in the fillet
Linear13=lm(Fillet_pro~`Ep/Ef`)
summary(Linear13)##
## Call:
## lm(formula = Fillet_pro ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8467 -0.3022 -0.1819 0.4915 4.3877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.7592 1.4563 25.242 2.18e-10 ***
## `Ep/Ef` 0.4602 0.4333 1.062 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.414 on 10 degrees of freedom
## Multiple R-squared: 0.1014, Adjusted R-squared: 0.01151
## F-statistic: 1.128 on 1 and 10 DF, p-value: 0.3132p13=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Fillet_pro))
p13+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear14=lm(Fillet_fat~`Ep/Ef`)
summary(Linear14)##
## Call:
## lm(formula = Fillet_fat ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2770 -1.9539 -0.5782 1.8972 7.3228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.9845 2.1488 8.835 4.88e-06 ***
## `Ep/Ef` -3.0952 0.6394 -4.841 0.000681 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.561 on 10 degrees of freedom
## Multiple R-squared: 0.7009, Adjusted R-squared: 0.671
## F-statistic: 23.43 on 1 and 10 DF, p-value: 0.0006805p14=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Fillet_fat))
p14+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear15=lm(Fillet_ash~`Ep/Ef`)
summary(Linear15)##
## Call:
## lm(formula = Fillet_ash ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5481 -0.6005 -0.1611 0.5662 2.2067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.42009 0.70575 11.931 3.08e-07 ***
## `Ep/Ef` 0.02041 0.21000 0.097 0.924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.17 on 10 degrees of freedom
## Multiple R-squared: 0.0009439, Adjusted R-squared: -0.09896
## F-statistic: 0.009448 on 1 and 10 DF, p-value: 0.9245p15=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Fillet_ash))
p15+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear16=lm(Fillet_ener~`Ep/Ef`)
summary(Linear16)##
## Call:
## lm(formula = Fillet_ener ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4718 -1.3917 -0.1972 0.9497 4.3118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.4318 1.4313 19.86 2.29e-09 ***
## `Ep/Ef` 0.6859 0.4259 1.61 0.138
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.372 on 10 degrees of freedom
## Multiple R-squared: 0.2059, Adjusted R-squared: 0.1265
## F-statistic: 2.594 on 1 and 10 DF, p-value: 0.1384p16=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Fillet_ener))
p16+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
### 1a2. Ep/Ef vs nutrient allocation in the viscera
Linear17=lm(Vis_pro~`Ep/Ef`)
summary(Linear17)##
## Call:
## lm(formula = Vis_pro ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45236 -0.23782 0.01568 0.25397 0.38288
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.29119 0.18030 18.254 5.23e-09 ***
## `Ep/Ef` -0.08564 0.05365 -1.596 0.142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2988 on 10 degrees of freedom
## Multiple R-squared: 0.2031, Adjusted R-squared: 0.1234
## F-statistic: 2.548 on 1 and 10 DF, p-value: 0.1415p17=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Vis_pro))
p17+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear18=lm(Vis_fat~`Ep/Ef`)
summary(Linear18)##
## Call:
## lm(formula = Vis_fat ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.544 -3.130 0.861 2.414 4.414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.3407 2.0745 8.359 8e-06 ***
## `Ep/Ef` -0.1375 0.6173 -0.223 0.828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.438 on 10 degrees of freedom
## Multiple R-squared: 0.004939, Adjusted R-squared: -0.09457
## F-statistic: 0.04964 on 1 and 10 DF, p-value: 0.8282p18=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Vis_fat))
p18+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear19=lm(Vis_ash~`Ep/Ef`)
summary(Linear15)##
## Call:
## lm(formula = Fillet_ash ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5481 -0.6005 -0.1611 0.5662 2.2067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.42009 0.70575 11.931 3.08e-07 ***
## `Ep/Ef` 0.02041 0.21000 0.097 0.924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.17 on 10 degrees of freedom
## Multiple R-squared: 0.0009439, Adjusted R-squared: -0.09896
## F-statistic: 0.009448 on 1 and 10 DF, p-value: 0.9245p19=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Vis_ash))
p19+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear20=lm(Vis_ener~`Ep/Ef`)
summary(Linear20)##
## Call:
## lm(formula = Vis_ener ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.69146 -0.59791 -0.07941 0.80771 1.55182
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.0274 0.6218 14.518 4.78e-08 ***
## `Ep/Ef` -0.7038 0.1850 -3.804 0.00346 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.031 on 10 degrees of freedom
## Multiple R-squared: 0.5914, Adjusted R-squared: 0.5505
## F-statistic: 14.47 on 1 and 10 DF, p-value: 0.003462p20=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Vis_ener))
p20+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1a3. Ep/Ef vs nutrient allocation in the remaining liver
Linear21=lm(Liver_pro~`Ep/Ef`)
summary(Linear21)##
## Call:
## lm(formula = Liver_pro ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.35658 -0.20074 -0.02666 0.07314 0.80615
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.46471 0.20703 7.075 3.4e-05 ***
## `Ep/Ef` 0.02855 0.06160 0.463 0.653
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3431 on 10 degrees of freedom
## Multiple R-squared: 0.02102, Adjusted R-squared: -0.07688
## F-statistic: 0.2147 on 1 and 10 DF, p-value: 0.653p21=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Liver_pro))
p21+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear22=lm(Liver_energy~`Ep/Ef`)
summary(Linear22)##
## Call:
## lm(formula = Liver_energy ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.95464 -0.36965 -0.01656 0.18108 1.53912
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.2854 0.4638 9.239 3.27e-06 ***
## `Ep/Ef` -0.2580 0.1380 -1.869 0.0911 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7687 on 10 degrees of freedom
## Multiple R-squared: 0.2589, Adjusted R-squared: 0.1848
## F-statistic: 3.494 on 1 and 10 DF, p-value: 0.09112p22=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Liver_energy))
p22+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1a4. Ep/Ef vs nutrient allocation in the remaining
Linear23=lm(Rest_pro~`Ep/Ef`)
summary(Linear23)##
## Call:
## lm(formula = Rest_pro ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0262 -1.1553 0.2981 0.4600 4.1409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58.4849 1.4986 39.027 2.91e-12 ***
## `Ep/Ef` -0.4031 0.4459 -0.904 0.387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.484 on 10 degrees of freedom
## Multiple R-squared: 0.07556, Adjusted R-squared: -0.01688
## F-statistic: 0.8174 on 1 and 10 DF, p-value: 0.3872p23=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Rest_pro))
p23+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear24=lm(Rest_fat~`Ep/Ef`)
summary(Linear24)##
## Call:
## lm(formula = Rest_fat ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.2005 -2.2564 0.7419 2.5689 6.4866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 63.6749 2.9502 21.583 1.02e-09 ***
## `Ep/Ef` 3.2327 0.8779 3.683 0.00423 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.89 on 10 degrees of freedom
## Multiple R-squared: 0.5756, Adjusted R-squared: 0.5331
## F-statistic: 13.56 on 1 and 10 DF, p-value: 0.004229p24=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Rest_fat))
p24+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear25=lm(Rest_ash~`Ep/Ef`)
summary(Linear25)##
## Call:
## lm(formula = Rest_ash ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7556 -0.3915 0.1435 0.6969 1.6423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 90.66078 0.79351 114.253 <2e-16 ***
## `Ep/Ef` -0.02973 0.23611 -0.126 0.902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.315 on 10 degrees of freedom
## Multiple R-squared: 0.001582, Adjusted R-squared: -0.09826
## F-statistic: 0.01585 on 1 and 10 DF, p-value: 0.9023p25=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Rest_ash))
p25+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear26=lm(Rest_ener~`Ep/Ef`)
summary(Linear26)##
## Call:
## lm(formula = Rest_ener ~ `Ep/Ef`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.8011 -1.4677 -0.5191 2.9219 4.0298
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58.2554 1.9007 30.650 3.2e-11 ***
## `Ep/Ef` 0.2760 0.5656 0.488 0.636
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.15 on 10 degrees of freedom
## Multiple R-squared: 0.02326, Adjusted R-squared: -0.07442
## F-statistic: 0.2381 on 1 and 10 DF, p-value: 0.6361p26=ggplot(Linear_total,aes(x=`Ep/Ef`,y=Rest_ener))
p26+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1b. Ep/Echo vs nutrient compartment allocations
1b1. Ep/Echo vs nutrient allocation in the fillet
Linear27=lm(Fillet_pro~`Ep/Ec`)
summary(Linear27)##
## Call:
## lm(formula = Fillet_pro ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8722 -0.6383 -0.2874 1.0833 3.8591
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.7983 2.0530 17.924 6.25e-09 ***
## `Ep/Ec` 0.6163 0.8986 0.686 0.508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.488 on 10 degrees of freedom
## Multiple R-squared: 0.04492, Adjusted R-squared: -0.05058
## F-statistic: 0.4704 on 1 and 10 DF, p-value: 0.5084p27=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Fillet_pro))
p27+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear28=lm(Fillet_fat~`Ep/Ec`)
summary(Linear28)##
## Call:
## lm(formula = Fillet_fat ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.081 -4.567 -1.460 4.428 11.769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.206 5.298 1.360 0.204
## `Ep/Ec` 1.235 2.319 0.533 0.606
##
## Residual standard error: 6.422 on 10 degrees of freedom
## Multiple R-squared: 0.0276, Adjusted R-squared: -0.06964
## F-statistic: 0.2838 on 1 and 10 DF, p-value: 0.6059p28=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Fillet_fat))
p28+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear29=lm(Fillet_ash~`Ep/Ec`)
summary(Linear29)##
## Call:
## lm(formula = Fillet_ash ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4142 -0.9013 -0.2667 0.7276 1.8866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.2648 0.9285 9.978 1.62e-06 ***
## `Ep/Ec` -0.3666 0.4064 -0.902 0.388
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.125 on 10 degrees of freedom
## Multiple R-squared: 0.07522, Adjusted R-squared: -0.01725
## F-statistic: 0.8134 on 1 and 10 DF, p-value: 0.3883p29=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Fillet_ash))
p29+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear30=lm(Fillet_ener~`Ep/Ec`)
summary(Linear30)##
## Call:
## lm(formula = Fillet_ener ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6732 -2.3199 -0.7207 1.4529 5.4022
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.476975 2.196398 13.88 7.37e-08 ***
## `Ep/Ec` -0.009866 0.961382 -0.01 0.992
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.662 on 10 degrees of freedom
## Multiple R-squared: 1.053e-05, Adjusted R-squared: -0.09999
## F-statistic: 0.0001053 on 1 and 10 DF, p-value: 0.992p30=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Fillet_ener))
p30+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1b2. Ep/Echo vs nutrient allocation in the viscera
Linear31=lm(Vis_pro~`Ep/Ec`)
summary(Linear31)##
## Call:
## lm(formula = Vis_pro ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.38329 -0.21448 -0.04762 0.28152 0.34548
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.51388 0.22478 15.632 2.35e-08 ***
## `Ep/Ec` -0.22213 0.09839 -2.258 0.0476 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2724 on 10 degrees of freedom
## Multiple R-squared: 0.3376, Adjusted R-squared: 0.2714
## F-statistic: 5.097 on 1 and 10 DF, p-value: 0.04755p31=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Vis_pro))
p31+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear32=lm(Vis_fat~`Ep/Ec`)
summary(Linear32)##
## Call:
## lm(formula = Vis_fat ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1771 -2.3251 0.1489 2.1505 4.7104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.9503 2.7639 5.409 0.000298 ***
## `Ep/Ec` 0.9273 1.2098 0.766 0.461103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.35 on 10 degrees of freedom
## Multiple R-squared: 0.05549, Adjusted R-squared: -0.03896
## F-statistic: 0.5875 on 1 and 10 DF, p-value: 0.4611p32=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Vis_fat))
p32+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear33=lm(Vis_ash~`Ep/Ec`)
summary(Linear33)##
## Call:
## lm(formula = Vis_ash ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.20859 -0.12582 -0.02226 0.07456 0.47152
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.11717 0.15827 7.059 3.46e-05 ***
## `Ep/Ec` -0.07969 0.06928 -1.150 0.277
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1918 on 10 degrees of freedom
## Multiple R-squared: 0.1169, Adjusted R-squared: 0.02854
## F-statistic: 1.323 on 1 and 10 DF, p-value: 0.2768p33=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Vis_ash))
p33+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear34=lm(Vis_ener~`Ep/Ec`)
summary(Linear34)##
## Call:
## lm(formula = Vis_ener ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3518 -1.2450 -0.1896 1.1910 2.2226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.2625 1.3260 5.477 0.00027 ***
## `Ep/Ec` -0.1459 0.5804 -0.251 0.80664
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.607 on 10 degrees of freedom
## Multiple R-squared: 0.006278, Adjusted R-squared: -0.09309
## F-statistic: 0.06317 on 1 and 10 DF, p-value: 0.8066p34=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Vis_ener))
p34+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1b3. Ep/Echo vs nutrient allocation in the remaining liver
Linear35=lm(Liver_pro~`Ep/Ec`)
summary(Linear35)##
## Call:
## lm(formula = Liver_pro ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.50746 -0.15509 0.00599 0.11697 0.62070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.9306 0.2555 7.557 1.93e-05 ***
## `Ep/Ec` -0.1783 0.1118 -1.595 0.142
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3097 on 10 degrees of freedom
## Multiple R-squared: 0.2027, Adjusted R-squared: 0.123
## F-statistic: 2.543 on 1 and 10 DF, p-value: 0.1419p35=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Liver_pro))
p35+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear36=lm(Liver_energy~`Ep/Ec`)
summary(Linear36)##
## Call:
## lm(formula = Liver_energy ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.81369 -0.42206 -0.29337 0.00637 1.88877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2053 0.7289 4.398 0.00134 **
## `Ep/Ec` 0.1489 0.3190 0.467 0.65065
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8834 on 10 degrees of freedom
## Multiple R-squared: 0.02132, Adjusted R-squared: -0.07654
## F-statistic: 0.2179 on 1 and 10 DF, p-value: 0.6507p36=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Liver_energy))
p36+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1b4. Ep/Echo vs nutrient allocation in the remaining
Linear37=lm(Rest_pro~`Ep/Ec`)
summary(Linear37)##
## Call:
## lm(formula = Rest_pro ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1791 -0.9037 0.0356 0.8570 4.8916
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.7572 2.1257 27.172 1.05e-10 ***
## `Ep/Ec` -0.2158 0.9304 -0.232 0.821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.576 on 10 degrees of freedom
## Multiple R-squared: 0.005353, Adjusted R-squared: -0.09411
## F-statistic: 0.05382 on 1 and 10 DF, p-value: 0.8212p37=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Rest_pro))
p37+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear38=lm(Rest_fat~`Ep/Ec`)
summary(Linear38)##
## Call:
## lm(formula = Rest_fat ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.4468 -4.7729 -0.0034 4.9151 9.5993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 77.843 5.992 12.991 1.38e-07 ***
## `Ep/Ec` -2.163 2.623 -0.825 0.429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.263 on 10 degrees of freedom
## Multiple R-squared: 0.06366, Adjusted R-squared: -0.02997
## F-statistic: 0.6799 on 1 and 10 DF, p-value: 0.4288p38=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Rest_fat))
p38+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear39=lm(Rest_ash~`Ep/Ec`)
summary(Linear39)##
## Call:
## lm(formula = Rest_ash ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3582 -0.7013 0.2506 1.0670 1.4685
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 89.6180 1.0370 86.419 1.05e-15 ***
## `Ep/Ec` 0.4462 0.4539 0.983 0.349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.257 on 10 degrees of freedom
## Multiple R-squared: 0.08813, Adjusted R-squared: -0.003053
## F-statistic: 0.9665 on 1 and 10 DF, p-value: 0.3487p39=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Rest_ash))
p39+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear40=lm(Rest_ener~`Ep/Ec`)
summary(Linear40)##
## Call:
## lm(formula = Rest_ener ~ `Ep/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2508 -1.3852 -0.4113 2.7257 4.2702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 59.055176 2.629931 22.455 6.9e-10 ***
## `Ep/Ec` 0.006816 1.151143 0.006 0.995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.188 on 10 degrees of freedom
## Multiple R-squared: 3.505e-06, Adjusted R-squared: -0.1
## F-statistic: 3.505e-05 on 1 and 10 DF, p-value: 0.9954p40=ggplot(Linear_total,aes(x=`Ep/Ec`,y=Rest_ener))
p40+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1c.Ef/Echo vs nutrient compartment allocations
1c1. Ef/Echo vs nutrient allocation in the fillet
Linear41=lm(Fillet_pro~`Ef/Ec`)
summary(Linear41)##
## Call:
## lm(formula = Fillet_pro ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8939 -0.9210 -0.0825 0.8510 4.3405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.8385 1.3827 28.088 7.6e-11 ***
## `Ef/Ec` -0.7284 1.1915 -0.611 0.555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.5 on 10 degrees of freedom
## Multiple R-squared: 0.03603, Adjusted R-squared: -0.06037
## F-statistic: 0.3738 on 1 and 10 DF, p-value: 0.5546p41=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Fillet_pro))
p41+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear42=lm(Fillet_fat~`Ef/Ec`)
summary(Linear42)##
## Call:
## lm(formula = Fillet_fat ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1228 -0.9079 -0.2169 1.3250 3.5037
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7245 1.2366 0.586 0.571
## `Ef/Ec` 9.2188 1.0656 8.651 5.89e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.236 on 10 degrees of freedom
## Multiple R-squared: 0.8821, Adjusted R-squared: 0.8704
## F-statistic: 74.85 on 1 and 10 DF, p-value: 5.894e-06p42=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Fillet_fat))
p42+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear43=lm(Fillet_ash~`Ef/Ec`)
summary(Linear43)##
## Call:
## lm(formula = Fillet_ash ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6151 -0.6822 -0.1658 0.8105 2.1789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.8794 0.6302 14.090 6.37e-08 ***
## `Ef/Ec` -0.4031 0.5430 -0.742 0.475
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.139 on 10 degrees of freedom
## Multiple R-squared: 0.05223, Adjusted R-squared: -0.04255
## F-statistic: 0.5511 on 1 and 10 DF, p-value: 0.475p43=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Fillet_ash))
p43+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear44=lm(Fillet_ener~`Ef/Ec`)
summary(Linear44)##
## Call:
## lm(formula = Fillet_ener ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2667 -1.6546 -0.1629 1.0337 4.5169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 31.746 1.393 22.795 5.95e-10 ***
## `Ef/Ec` -1.303 1.200 -1.086 0.303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.518 on 10 degrees of freedom
## Multiple R-squared: 0.1055, Adjusted R-squared: 0.01605
## F-statistic: 1.179 on 1 and 10 DF, p-value: 0.3029p44=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Fillet_ener))
p44+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1c2. Ef/Echo vs nutrient allocation in the viscera
Linear45=lm(Vis_pro~`Ef/Ec`)
summary(Linear45)##
## Call:
## lm(formula = Vis_pro ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45936 -0.23986 -0.01218 0.20069 0.51966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.03563 0.18516 16.395 1.48e-08 ***
## `Ef/Ec` 0.00286 0.15955 0.018 0.986
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3347 on 10 degrees of freedom
## Multiple R-squared: 3.212e-05, Adjusted R-squared: -0.09996
## F-statistic: 0.0003212 on 1 and 10 DF, p-value: 0.9861p45=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Vis_pro))
p45+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear46=lm(Vis_fat~`Ef/Ec`)
summary(Linear46)##
## Call:
## lm(formula = Vis_fat ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7826 -2.9028 0.8513 2.2971 4.1837
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.96200 1.90658 8.897 4.59e-06 ***
## `Ef/Ec` -0.02745 1.64289 -0.017 0.987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.447 on 10 degrees of freedom
## Multiple R-squared: 2.791e-05, Adjusted R-squared: -0.09997
## F-statistic: 0.0002791 on 1 and 10 DF, p-value: 0.987p46=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Vis_fat))
p46+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear47=lm(Vis_ash~`Ef/Ec`)
summary(Linear47)##
## Call:
## lm(formula = Vis_ash ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14239 -0.12002 -0.01280 0.01626 0.53529
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.05258 0.10586 9.944 1.67e-06 ***
## `Ef/Ec` -0.10703 0.09122 -1.173 0.268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1914 on 10 degrees of freedom
## Multiple R-squared: 0.121, Adjusted R-squared: 0.03313
## F-statistic: 1.377 on 1 and 10 DF, p-value: 0.2678p47=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Vis_ash))
p47+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear48=lm(Vis_ener~`Ef/Ec`)
summary(Linear48)##
## Call:
## lm(formula = Vis_ener ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8793 -0.6894 -0.2621 0.6646 2.3205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5787 0.7326 7.615 1.81e-05 ***
## `Ef/Ec` 1.3855 0.6313 2.195 0.0529 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.324 on 10 degrees of freedom
## Multiple R-squared: 0.3251, Adjusted R-squared: 0.2576
## F-statistic: 4.817 on 1 and 10 DF, p-value: 0.0529p48=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Vis_ener))
p48+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1c3. Ef/Echo vs nutrient allocation in the remaining liver
Linear49=lm(Liver_pro~`Ef/Ec`)
summary(Linear49)##
## Call:
## lm(formula = Liver_pro ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36591 -0.17974 -0.02168 0.10391 0.76286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7418 0.1780 9.785 1.94e-06 ***
## `Ef/Ec` -0.1948 0.1534 -1.270 0.233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3218 on 10 degrees of freedom
## Multiple R-squared: 0.1389, Adjusted R-squared: 0.05277
## F-statistic: 1.613 on 1 and 10 DF, p-value: 0.2328p49=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Liver_pro))
p49+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear50=lm(Liver_energy~`Ef/Ec`)
summary(Linear50)##
## Call:
## lm(formula = Liver_energy ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.2356 -0.4060 0.1779 0.3639 1.0740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.6117 0.3600 7.255 2.74e-05 ***
## `Ef/Ec` 0.9217 0.3102 2.971 0.014 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6508 on 10 degrees of freedom
## Multiple R-squared: 0.4689, Adjusted R-squared: 0.4158
## F-statistic: 8.828 on 1 and 10 DF, p-value: 0.01402p50=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Liver_energy))
p50+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
1c4. Ef/Echo vs nutrient allocation in the remaining
Linear51=lm(Rest_pro~`Ef/Ec`)
summary(Linear51)##
## Call:
## lm(formula = Rest_pro ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2552 -0.7778 0.1940 0.8780 3.9119
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 56.3841 1.3884 40.611 1.96e-12 ***
## `Ef/Ec` 0.9204 1.1964 0.769 0.459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.51 on 10 degrees of freedom
## Multiple R-squared: 0.05588, Adjusted R-squared: -0.03854
## F-statistic: 0.5918 on 1 and 10 DF, p-value: 0.4595p51=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Rest_pro))
p51+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear52=lm(Rest_fat~`Ef/Ec`)
summary(Linear52)##
## Call:
## lm(formula = Rest_fat ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7101 -3.8028 0.4458 3.3035 5.7541
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 82.314 2.420 34.009 1.14e-11 ***
## `Ef/Ec` -9.191 2.086 -4.407 0.00132 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.376 on 10 degrees of freedom
## Multiple R-squared: 0.6601, Adjusted R-squared: 0.6261
## F-statistic: 19.42 on 1 and 10 DF, p-value: 0.001321p52=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Rest_fat))
p52+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear53=lm(Rest_ash~`Ef/Ec`)
summary(Linear53)##
## Call:
## lm(formula = Rest_ash ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7142 -0.6975 0.1919 0.8034 1.7395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 90.0680 0.7036 128.017 <2e-16 ***
## `Ef/Ec` 0.5101 0.6063 0.841 0.42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.272 on 10 degrees of freedom
## Multiple R-squared: 0.06612, Adjusted R-squared: -0.02726
## F-statistic: 0.7081 on 1 and 10 DF, p-value: 0.4198p53=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Rest_ash))
p53+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)
Linear54=lm(Rest_ener~`Ef/Ec`)
summary(Linear54)##
## Call:
## lm(formula = Rest_ener ~ `Ef/Ec`)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.2863 -1.5775 -0.1817 2.6610 4.5445
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 60.064 1.724 34.835 9.01e-12 ***
## `Ef/Ec` -1.004 1.486 -0.676 0.515
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.117 on 10 degrees of freedom
## Multiple R-squared: 0.04366, Adjusted R-squared: -0.05197
## F-statistic: 0.4566 on 1 and 10 DF, p-value: 0.5146p54=ggplot(Linear_total,aes(x=`Ef/Ec`,y=Rest_ener))
p54+geom_point()+theme_bw()+theme_classic()+geom_smooth(method="lm",formula= y~x)