Analysis of Variance ANOVA for metabolites detected
ANOVATest.data <- read.csv("C:/Users/cesar/Desktop/Tesis Maestria/TRATAMIENTOS/Hierro/Hierro Espinaca Anovas.csv")
attach(ANOVATest.data)
str(ANOVATest.data)
## 'data.frame': 30 obs. of 28 variables:
## $ Sample : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Condition : chr "Standard S4" "Standard S4" "Standard S4" "Standard S4" ...
## $ Valine : num 78.8 80.6 86.3 34.7 38.6 ...
## $ Alanine : num 230.9 231.4 207.7 71.7 91.1 ...
## $ GABA : num 1057 1060 1136 300 625 ...
## $ Glutamate : num 1018 1073 766 514 398 ...
## $ Malate : num 693 795 822 162 790 ...
## $ Succinate : num 122.3 123.2 62.1 28.1 34.5 ...
## $ Citrate : num 190.5 119 308.2 99.2 212.4 ...
## $ Aspartate : num 751 616 448 253 272 ...
## $ Betaine : num 1069 942 1187 636 471 ...
## $ Glucose : num 1070 1060 1154 408 448 ...
## $ Fructose : num 700 740 907 322 332 ...
## $ Sucrose : num 1082 1151 1162 194 329 ...
## $ Ascorbate : num 33.5 28.2 28.9 17.1 0 ...
## $ Uridine : num 165.9 178.5 137.9 85.9 86.6 ...
## $ Adenosine : num 155.9 149.9 128.3 86.1 80.4 ...
## $ Fumarate : num 233.1 262.4 248 40.9 128.3 ...
## $ Tyrosine : num 40.1 40 47.2 17.2 20.4 32.4 64.6 68.6 93.2 55.8 ...
## $ Phenylalanine: num 42.4 39 60.6 22.8 23.8 26.2 41.4 59.7 71.2 43.9 ...
## $ Guanosine : num 110.5 116.5 94.6 85 79.4 ...
## $ Formate : num 72.8 78.8 75.9 14.7 9.7 ...
## $ Choline : num 240 202 207 146 148 ...
## $ Ferulate : num 36.2 43.6 25.6 26 11.7 20 69.5 67.3 60.2 50.5 ...
## $ Glycerol : num 282 295 312 160 188 ...
## $ Isoleucine : num 37.3 37.9 46.5 19.8 19.2 32.2 55.3 61.3 71.5 52.7 ...
## $ Leucine : num 85 85.7 116.7 35 46.8 ...
## $ p.Cumarate : num 14.5 18 13.2 11.3 5.6 52.8 36 53.8 26.4 17.8 ...
Alanine
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Alanine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Alanine[Condition == "Standard S4"]
## W = 0.79129, p-value = 0.06869
with(ANOVATest.data, shapiro.test(Alanine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Alanine[Condition == "Standard S5"]
## W = 0.7975, p-value = 0.07731
with(ANOVATest.data, shapiro.test(Alanine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Alanine[Condition == "Deficiency S4"]
## W = 0.83241, p-value = 0.145
with(ANOVATest.data, shapiro.test(Alanine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Alanine[Condition == "Deficiency S5"]
## W = 0.90223, p-value = 0.4223
with(ANOVATest.data, shapiro.test(Alanine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Alanine[Condition == "Excess S4"]
## W = 0.9227, p-value = 0.5475
with(ANOVATest.data, shapiro.test(Alanine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Alanine[Condition == "Excess S5"]
## W = 0.94153, p-value = 0.6767
Hm_var <- bartlett.test(Alanine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Alanine by Condition
## Bartlett's K-squared = 10.231, df = 5, p-value = 0.06894
#### ONE WAY - ANOVA
OneWay_test <- aov(Alanine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 658135 131627 16.35 4.75e-07 ***
## Residuals 24 193179 8049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Alanine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 81.76 -93.6823549 257.20235 0.7027974
## Excess S4-Deficiency S4 84.04 -91.4023549 259.48235 0.6788933
## Excess S5-Deficiency S4 443.74 268.2976451 619.18235 0.0000007
## Standard S4-Deficiency S4 30.30 -145.1423549 205.74235 0.9941562
## Standard S5-Deficiency S4 176.16 0.7176451 351.60235 0.0486554
## Excess S4-Deficiency S5 2.28 -173.1623549 177.72235 1.0000000
## Excess S5-Deficiency S5 361.98 186.5376451 537.42235 0.0000183
## Standard S4-Deficiency S5 -51.46 -226.9023549 123.98235 0.9409441
## Standard S5-Deficiency S5 94.40 -81.0423549 269.84235 0.5671192
## Excess S5-Excess S4 359.70 184.2576451 535.14235 0.0000202
## Standard S4-Excess S4 -53.74 -229.1823549 121.70235 0.9298531
## Standard S5-Excess S4 92.12 -83.3223549 267.56235 0.5918832
## Standard S4-Excess S5 -413.44 -588.8823549 -237.99765 0.0000022
## Standard S5-Excess S5 -267.58 -443.0223549 -92.13765 0.0010763
## Standard S5-Standard S4 145.86 -29.5823549 321.30235 0.1436309
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Alanine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Valine
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Valine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Valine[Condition == "Standard S4"]
## W = 0.79117, p-value = 0.06853
with(ANOVATest.data, shapiro.test(Valine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Valine[Condition == "Standard S5"]
## W = 0.92225, p-value = 0.5446
with(ANOVATest.data, shapiro.test(Valine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Valine[Condition == "Deficiency S4"]
## W = 0.86262, p-value = 0.2378
with(ANOVATest.data, shapiro.test(Valine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Valine[Condition == "Deficiency S5"]
## W = 0.90025, p-value = 0.4113
with(ANOVATest.data, shapiro.test(Valine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Valine[Condition == "Excess S4"]
## W = 0.80878, p-value = 0.09536
with(ANOVATest.data, shapiro.test(Valine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Valine[Condition == "Excess S5"]
## W = 0.90973, p-value = 0.466
Hm_var <- bartlett.test(Valine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Valine by Condition
## Bartlett's K-squared = 11.291, df = 5, p-value = 0.0459
#### ONE WAY - ANOVA
OneWay_test <- aov(Valine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 169844 33969 12.91 3.7e-06 ***
## Residuals 24 63147 2631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Valine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 90.30 -10.0071901 190.60719 0.0950115
## Excess S4-Deficiency S4 29.48 -70.8271901 129.78719 0.9404735
## Excess S5-Deficiency S4 191.26 90.9528099 291.56719 0.0000589
## Standard S4-Deficiency S4 -36.44 -136.7471901 63.86719 0.8669316
## Standard S5-Deficiency S4 4.50 -95.8071901 104.80719 0.9999918
## Excess S4-Deficiency S5 -60.82 -161.1271901 39.48719 0.4405353
## Excess S5-Deficiency S5 100.96 0.6528099 201.26719 0.0478762
## Standard S4-Deficiency S5 -126.74 -227.0471901 -26.43281 0.0077497
## Standard S5-Deficiency S5 -85.80 -186.1071901 14.50719 0.1247567
## Excess S5-Excess S4 161.78 61.4728099 262.08719 0.0005506
## Standard S4-Excess S4 -65.92 -166.2271901 34.38719 0.3545693
## Standard S5-Excess S4 -24.98 -125.2871901 75.32719 0.9700193
## Standard S4-Excess S5 -227.70 -328.0071901 -127.39281 0.0000041
## Standard S5-Excess S5 -186.76 -287.0671901 -86.45281 0.0000826
## Standard S5-Standard S4 40.94 -59.3671901 141.24719 0.8019837
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Valine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Leucine
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Leucine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Leucine[Condition == "Standard S4"]
## W = 0.93425, p-value = 0.6256
with(ANOVATest.data, shapiro.test(Leucine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Leucine[Condition == "Standard S5"]
## W = 0.95138, p-value = 0.747
with(ANOVATest.data, shapiro.test(Leucine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Leucine[Condition == "Deficiency S4"]
## W = 0.95775, p-value = 0.7922
with(ANOVATest.data, shapiro.test(Leucine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Leucine[Condition == "Deficiency S5"]
## W = 0.93432, p-value = 0.6261
with(ANOVATest.data, shapiro.test(Leucine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Leucine[Condition == "Excess S4"]
## W = 0.92917, p-value = 0.5907
with(ANOVATest.data, shapiro.test(Leucine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Leucine[Condition == "Excess S5"]
## W = 0.96759, p-value = 0.8596
Hm_var <- bartlett.test(Leucine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Leucine by Condition
## Bartlett's K-squared = 8.6674, df = 5, p-value = 0.1231
#### ONE WAY - ANOVA
OneWay_test <- aov(Leucine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 187485 37497 11.25 1.14e-05 ***
## Residuals 24 79999 3333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Leucine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 126.66 13.75927 239.56073 0.0216263
## Excess S4-Deficiency S4 -26.26 -139.16073 86.64073 0.9776628
## Excess S5-Deficiency S4 30.22 -82.68073 143.12073 0.9593674
## Standard S4-Deficiency S4 -124.06 -236.96073 -11.15927 0.0254325
## Standard S5-Deficiency S4 -71.92 -184.82073 40.98073 0.3875356
## Excess S4-Deficiency S5 -152.92 -265.82073 -40.01927 0.0039325
## Excess S5-Deficiency S5 -96.44 -209.34073 16.46073 0.1256274
## Standard S4-Deficiency S5 -250.72 -363.62073 -137.81927 0.0000058
## Standard S5-Deficiency S5 -198.58 -311.48073 -85.67927 0.0001805
## Excess S5-Excess S4 56.48 -56.42073 169.38073 0.6390570
## Standard S4-Excess S4 -97.80 -210.70073 15.10073 0.1169065
## Standard S5-Excess S4 -45.66 -158.56073 67.24073 0.8078256
## Standard S4-Excess S5 -154.28 -267.18073 -41.37927 0.0035917
## Standard S5-Excess S5 -102.14 -215.04073 10.76073 0.0924216
## Standard S5-Standard S4 52.14 -60.76073 165.04073 0.7104258
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Leucine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Isoleucine
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Isoleucine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Isoleucine[Condition == "Standard S4"]
## W = 0.86532, p-value = 0.248
with(ANOVATest.data, shapiro.test(Isoleucine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Isoleucine[Condition == "Standard S5"]
## W = 0.95057, p-value = 0.7412
with(ANOVATest.data, shapiro.test(Isoleucine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Isoleucine[Condition == "Deficiency S4"]
## W = 0.79115, p-value = 0.06849
with(ANOVATest.data, shapiro.test(Isoleucine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Isoleucine[Condition == "Deficiency S5"]
## W = 0.9121, p-value = 0.4803
with(ANOVATest.data, shapiro.test(Isoleucine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Isoleucine[Condition == "Excess S4"]
## W = 0.85569, p-value = 0.2132
with(ANOVATest.data, shapiro.test(Isoleucine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Isoleucine[Condition == "Excess S5"]
## W = 0.95008, p-value = 0.7378
Hm_var <- bartlett.test(Isoleucine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Isoleucine by Condition
## Bartlett's K-squared = 13.832, df = 5, p-value = 0.01671
#### ONE WAY - ANOVA
OneWay_test <- aov(Isoleucine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 46954 9391 8.499 9.66e-05 ***
## Residuals 24 26520 1105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Isoleucine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 65.52 0.5163819 130.523618 0.0474188
## Excess S4-Deficiency S4 -12.44 -77.4436181 52.563618 0.9906370
## Excess S5-Deficiency S4 43.74 -21.2636181 108.743618 0.3300275
## Standard S4-Deficiency S4 -49.00 -114.0036181 16.003618 0.2208501
## Standard S5-Deficiency S4 -26.54 -91.5436181 38.463618 0.8017643
## Excess S4-Deficiency S5 -77.96 -142.9636181 -12.956382 0.0124077
## Excess S5-Deficiency S5 -21.78 -86.7836181 43.223618 0.9010008
## Standard S4-Deficiency S5 -114.52 -179.5236181 -49.516382 0.0001766
## Standard S5-Deficiency S5 -92.06 -157.0636181 -27.056382 0.0024668
## Excess S5-Excess S4 56.18 -8.8236181 121.183618 0.1183098
## Standard S4-Excess S4 -36.56 -101.5636181 28.443618 0.5210115
## Standard S5-Excess S4 -14.10 -79.1036181 50.903618 0.9835614
## Standard S4-Excess S5 -92.74 -157.7436181 -27.736382 0.0022787
## Standard S5-Excess S5 -70.28 -135.2836181 -5.276382 0.0287701
## Standard S5-Standard S4 22.46 -42.5436181 87.463618 0.8890266
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Isoleucine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
GABA
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(GABA[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: GABA[Condition == "Standard S4"]
## W = 0.83798, p-value = 0.1594
with(ANOVATest.data, shapiro.test(GABA[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: GABA[Condition == "Standard S5"]
## W = 0.73635, p-value = 0.02214
with(ANOVATest.data, shapiro.test(GABA[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: GABA[Condition == "Deficiency S4"]
## W = 0.92452, p-value = 0.5595
with(ANOVATest.data, shapiro.test(GABA[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: GABA[Condition == "Deficiency S5"]
## W = 0.85378, p-value = 0.2068
with(ANOVATest.data, shapiro.test(GABA[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: GABA[Condition == "Excess S4"]
## W = 0.85985, p-value = 0.2277
with(ANOVATest.data, shapiro.test(GABA[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: GABA[Condition == "Excess S5"]
## W = 0.94691, p-value = 0.7151
Hm_var <- bartlett.test(GABA ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: GABA by Condition
## Bartlett's K-squared = 2.7587, df = 5, p-value = 0.7371
#### ONE WAY - ANOVA
OneWay_test <- aov(GABA ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 1944588 388918 4.945 0.00298 **
## Residuals 24 1887654 78652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = GABA ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 183.58 -364.84264 732.0026 0.9013509
## Excess S4-Deficiency S4 231.16 -317.26264 779.5826 0.7804117
## Excess S5-Deficiency S4 799.00 250.57736 1347.4226 0.0018113
## Standard S4-Deficiency S4 392.00 -156.42264 940.4226 0.2698041
## Standard S5-Deficiency S4 495.72 -52.70264 1044.1426 0.0928748
## Excess S4-Deficiency S5 47.58 -500.84264 596.0026 0.9997850
## Excess S5-Deficiency S5 615.42 66.99736 1163.8426 0.0215815
## Standard S4-Deficiency S5 208.42 -340.00264 756.8426 0.8441444
## Standard S5-Deficiency S5 312.14 -236.28264 860.5626 0.5084109
## Excess S5-Excess S4 567.84 19.41736 1116.2626 0.0394050
## Standard S4-Excess S4 160.84 -387.58264 709.2626 0.9409749
## Standard S5-Excess S4 264.56 -283.86264 812.9826 0.6726031
## Standard S4-Excess S5 -407.00 -955.42264 141.4226 0.2347665
## Standard S5-Excess S5 -303.28 -851.70264 245.1426 0.5387754
## Standard S5-Standard S4 103.72 -444.70264 652.1426 0.9911281
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = GABA, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Betaine
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Betaine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Betaine[Condition == "Standard S4"]
## W = 0.93899, p-value = 0.6588
with(ANOVATest.data, shapiro.test(Betaine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Betaine[Condition == "Standard S5"]
## W = 0.80332, p-value = 0.08622
with(ANOVATest.data, shapiro.test(Betaine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Betaine[Condition == "Deficiency S4"]
## W = 0.73727, p-value = 0.02259
with(ANOVATest.data, shapiro.test(Betaine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Betaine[Condition == "Deficiency S5"]
## W = 0.92761, p-value = 0.5802
with(ANOVATest.data, shapiro.test(Betaine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Betaine[Condition == "Excess S4"]
## W = 0.83782, p-value = 0.159
with(ANOVATest.data, shapiro.test(Betaine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Betaine[Condition == "Excess S5"]
## W = 0.88845, p-value = 0.3494
Hm_var <- bartlett.test(Betaine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Betaine by Condition
## Bartlett's K-squared = 62.658, df = 5, p-value = 3.427e-12
#### ONE WAY - ANOVA
OneWay_test <- aov(Betaine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 681243546 136248709 26.52 4.85e-09 ***
## Residuals 24 123322059 5138419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Betaine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 -331.64 -4764.403 4101.123 0.9998962
## Excess S4-Deficiency S4 -478.84 -4911.603 3953.923 0.9993734
## Excess S5-Deficiency S4 12798.54 8365.777 17231.303 0.0000001
## Standard S4-Deficiency S4 -172.18 -4604.943 4260.583 0.9999960
## Standard S5-Deficiency S4 2348.96 -2083.803 6781.723 0.5826636
## Excess S4-Deficiency S5 -147.20 -4579.963 4285.563 0.9999982
## Excess S5-Deficiency S5 13130.18 8697.417 17562.943 0.0000000
## Standard S4-Deficiency S5 159.46 -4273.303 4592.223 0.9999973
## Standard S5-Deficiency S5 2680.60 -1752.163 7113.363 0.4434058
## Excess S5-Excess S4 13277.38 8844.617 17710.143 0.0000000
## Standard S4-Excess S4 306.66 -4126.103 4739.423 0.9999295
## Standard S5-Excess S4 2827.80 -1604.963 7260.563 0.3860109
## Standard S4-Excess S5 -12970.72 -17403.483 -8537.957 0.0000000
## Standard S5-Excess S5 -10449.58 -14882.343 -6016.817 0.0000022
## Standard S5-Standard S4 2521.14 -1911.623 6953.903 0.5091732
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Betaine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Phenylalanine
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Phenylalanine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Phenylalanine[Condition == "Standard S4"]
## W = 0.91022, p-value = 0.4689
with(ANOVATest.data, shapiro.test(Phenylalanine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Phenylalanine[Condition == "Standard S5"]
## W = 0.97409, p-value = 0.9008
with(ANOVATest.data, shapiro.test(Phenylalanine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Phenylalanine[Condition == "Deficiency S4"]
## W = 0.91798, p-value = 0.517
with(ANOVATest.data, shapiro.test(Phenylalanine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Phenylalanine[Condition == "Deficiency S5"]
## W = 0.94345, p-value = 0.6904
with(ANOVATest.data, shapiro.test(Phenylalanine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Phenylalanine[Condition == "Excess S4"]
## W = 0.9189, p-value = 0.5229
with(ANOVATest.data, shapiro.test(Phenylalanine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Phenylalanine[Condition == "Excess S5"]
## W = 0.9455, p-value = 0.705
Hm_var <- bartlett.test(Phenylalanine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Phenylalanine by Condition
## Bartlett's K-squared = 9.0424, df = 5, p-value = 0.1074
#### ONE WAY - ANOVA
OneWay_test <- aov(Phenylalanine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 95897 19179 10.8 1.59e-05 ***
## Residuals 24 42633 1776
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Phenylalanine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 113.56 31.14094 195.979059 0.0032977
## Excess S4-Deficiency S4 27.38 -55.03906 109.799059 0.9041314
## Excess S5-Deficiency S4 20.04 -62.37906 102.459059 0.9729375
## Standard S4-Deficiency S4 -58.22 -140.63906 24.199059 0.2812325
## Standard S5-Deficiency S4 -47.46 -129.87906 34.959059 0.4959859
## Excess S4-Deficiency S5 -86.18 -168.59906 -3.760941 0.0367537
## Excess S5-Deficiency S5 -93.52 -175.93906 -11.100941 0.0197473
## Standard S4-Deficiency S5 -171.78 -254.19906 -89.360941 0.0000157
## Standard S5-Deficiency S5 -161.02 -243.43906 -78.600941 0.0000414
## Excess S5-Excess S4 -7.34 -89.75906 75.079059 0.9997556
## Standard S4-Excess S4 -85.60 -168.01906 -3.180941 0.0385599
## Standard S5-Excess S4 -74.84 -157.25906 7.579059 0.0905119
## Standard S4-Excess S5 -78.26 -160.67906 4.159059 0.0695822
## Standard S5-Excess S5 -67.50 -149.91906 14.919059 0.1542664
## Standard S5-Standard S4 10.76 -71.65906 93.179059 0.9984381
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Phenylalanine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Tyrosine
#### PNORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Tyrosine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Tyrosine[Condition == "Standard S4"]
## W = 0.85953, p-value = 0.2266
with(ANOVATest.data, shapiro.test(Tyrosine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Tyrosine[Condition == "Standard S5"]
## W = 0.97832, p-value = 0.9255
with(ANOVATest.data, shapiro.test(Tyrosine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Tyrosine[Condition == "Deficiency S4"]
## W = 0.91713, p-value = 0.5116
with(ANOVATest.data, shapiro.test(Tyrosine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Tyrosine[Condition == "Deficiency S5"]
## W = 0.78671, p-value = 0.06287
with(ANOVATest.data, shapiro.test(Tyrosine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Tyrosine[Condition == "Excess S4"]
## W = 0.77156, p-value = 0.04657
with(ANOVATest.data, shapiro.test(Tyrosine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Tyrosine[Condition == "Excess S5"]
## W = 0.99172, p-value = 0.9854
Hm_var <- bartlett.test(Tyrosine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Tyrosine by Condition
## Bartlett's K-squared = 10.629, df = 5, p-value = 0.05924
#### ONE WAY - ANOVA
OneWay_test <- aov(Tyrosine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 43092 8618 9.674 3.71e-05 ***
## Residuals 24 21381 891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Tyrosine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 63.16 4.793044 121.526956 0.0285810
## Excess S4-Deficiency S4 12.46 -45.906956 70.826956 0.9846858
## Excess S5-Deficiency S4 62.32 3.953044 120.686956 0.0315785
## Standard S4-Deficiency S4 -41.10 -99.466956 17.266956 0.2843171
## Standard S5-Deficiency S4 -11.16 -69.526956 47.206956 0.9906747
## Excess S4-Deficiency S5 -50.70 -109.066956 7.666956 0.1152350
## Excess S5-Deficiency S5 -0.84 -59.206956 57.526956 1.0000000
## Standard S4-Deficiency S5 -104.26 -162.626956 -45.893044 0.0001465
## Standard S5-Deficiency S5 -74.32 -132.686956 -15.953044 0.0072074
## Excess S5-Excess S4 49.86 -8.506956 108.226956 0.1255910
## Standard S4-Excess S4 -53.56 -111.926956 4.806956 0.0852338
## Standard S5-Excess S4 -23.62 -81.986956 34.746956 0.8074277
## Standard S4-Excess S5 -103.42 -161.786956 -45.053044 0.0001635
## Standard S5-Excess S5 -73.48 -131.846956 -15.113044 0.0080168
## Standard S5-Standard S4 29.94 -28.426956 88.306956 0.6149579
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Tyrosine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Aspartate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Aspartate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Aspartate[Condition == "Standard S4"]
## W = 0.91633, p-value = 0.5065
with(ANOVATest.data, shapiro.test(Aspartate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Aspartate[Condition == "Standard S5"]
## W = 0.8624, p-value = 0.237
with(ANOVATest.data, shapiro.test(Aspartate[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Aspartate[Condition == "Deficiency S4"]
## W = 0.84847, p-value = 0.1897
with(ANOVATest.data, shapiro.test(Aspartate[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Aspartate[Condition == "Deficiency S5"]
## W = 0.81641, p-value = 0.1095
with(ANOVATest.data, shapiro.test(Aspartate[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Aspartate[Condition == "Excess S4"]
## W = 0.92982, p-value = 0.5951
with(ANOVATest.data, shapiro.test(Aspartate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Aspartate[Condition == "Excess S5"]
## W = 0.95756, p-value = 0.7909
Hm_var <- bartlett.test(Aspartate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Aspartate by Condition
## Bartlett's K-squared = 11.294, df = 5, p-value = 0.04585
#### ONE WAY - ANOVA
OneWay_test <- aov(Aspartate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 3206630 641326 17.42 2.68e-07 ***
## Residuals 24 883530 36814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Aspartate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 103.26 -271.94177 478.46177 0.9544147
## Excess S4-Deficiency S4 119.86 -255.34177 495.06177 0.9174022
## Excess S5-Deficiency S4 920.32 545.11823 1295.52177 0.0000011
## Standard S4-Deficiency S4 200.04 -175.16177 575.24177 0.5764802
## Standard S5-Deficiency S4 597.84 222.63823 973.04177 0.0006390
## Excess S4-Deficiency S5 16.60 -358.60177 391.80177 0.9999923
## Excess S5-Deficiency S5 817.06 441.85823 1192.26177 0.0000079
## Standard S4-Deficiency S5 96.78 -278.42177 471.98177 0.9652028
## Standard S5-Deficiency S5 494.58 119.37823 869.78177 0.0051622
## Excess S5-Excess S4 800.46 425.25823 1175.66177 0.0000109
## Standard S4-Excess S4 80.18 -295.02177 455.38177 0.9846150
## Standard S5-Excess S4 477.98 102.77823 853.18177 0.0071752
## Standard S4-Excess S5 -720.28 -1095.48177 -345.07823 0.0000534
## Standard S5-Excess S5 -322.48 -697.68177 52.72177 0.1217383
## Standard S5-Standard S4 397.80 22.59823 773.00177 0.0332536
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Aspartate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Glutamate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Glutamate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Glutamate[Condition == "Standard S4"]
## W = 0.91425, p-value = 0.4935
with(ANOVATest.data, shapiro.test(Glutamate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Glutamate[Condition == "Standard S5"]
## W = 0.94069, p-value = 0.6708
with(ANOVATest.data, shapiro.test(Glutamate[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Glutamate[Condition == "Deficiency S4"]
## W = 0.82208, p-value = 0.1212
with(ANOVATest.data, shapiro.test(Glutamate[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Glutamate[Condition == "Deficiency S5"]
## W = 0.81567, p-value = 0.1081
with(ANOVATest.data, shapiro.test(Glutamate[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Glutamate[Condition == "Excess S4"]
## W = 0.90517, p-value = 0.4391
with(ANOVATest.data, shapiro.test(Glutamate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Glutamate[Condition == "Excess S5"]
## W = 0.94595, p-value = 0.7082
Hm_var <- bartlett.test(Glutamate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Glutamate by Condition
## Bartlett's K-squared = 14.106, df = 5, p-value = 0.01495
#### ONE WAY - ANOVA
OneWay_test <- aov(Glutamate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 3687860 737572 8.696 8.19e-05 ***
## Residuals 24 2035694 84821
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Glutamate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 217.68 -351.84196 787.202 0.8410454
## Excess S4-Deficiency S4 27.16 -542.36196 596.682 0.9999888
## Excess S5-Deficiency S4 898.62 329.09804 1468.142 0.0007196
## Standard S4-Deficiency S4 154.74 -414.78196 724.262 0.9567668
## Standard S5-Deficiency S4 743.44 173.91804 1312.962 0.0056797
## Excess S4-Deficiency S5 -190.52 -760.04196 379.002 0.9015890
## Excess S5-Deficiency S5 680.94 111.41804 1250.462 0.0127441
## Standard S4-Deficiency S5 -62.94 -632.46196 506.582 0.9993001
## Standard S5-Deficiency S5 525.76 -43.76196 1095.282 0.0823233
## Excess S5-Excess S4 871.46 301.93804 1440.982 0.0010361
## Standard S4-Excess S4 127.58 -441.94196 697.102 0.9810497
## Standard S5-Excess S4 716.28 146.75804 1285.802 0.0080908
## Standard S4-Excess S5 -743.88 -1313.40196 -174.358 0.0056470
## Standard S5-Excess S5 -155.18 -724.70196 414.342 0.9562562
## Standard S5-Standard S4 588.70 19.17804 1158.222 0.0398711
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Glutamate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Adenosine
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Adenosine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Adenosine[Condition == "Standard S4"]
## W = 0.86549, p-value = 0.2486
with(ANOVATest.data, shapiro.test(Adenosine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Adenosine[Condition == "Standard S5"]
## W = 0.77997, p-value = 0.05509
with(ANOVATest.data, shapiro.test(Adenosine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Adenosine[Condition == "Deficiency S4"]
## W = 0.82429, p-value = 0.126
with(ANOVATest.data, shapiro.test(Adenosine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Adenosine[Condition == "Deficiency S5"]
## W = 0.70483, p-value = 0.01075
with(ANOVATest.data, shapiro.test(Adenosine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Adenosine[Condition == "Excess S4"]
## W = 0.94655, p-value = 0.7125
with(ANOVATest.data, shapiro.test(Adenosine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Adenosine[Condition == "Excess S5"]
## W = 0.86618, p-value = 0.2513
Hm_var <- bartlett.test(Adenosine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Adenosine by Condition
## Bartlett's K-squared = 28.213, df = 5, p-value = 3.307e-05
#### ONE WAY - ANOVA
OneWay_test <- aov(Adenosine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 688449 137690 18.83 1.31e-07 ***
## Residuals 24 175504 7313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Adenosine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 31.22 -136.003561 198.44356 0.9916365
## Excess S4-Deficiency S4 14.90 -152.323561 182.12356 0.9997550
## Excess S5-Deficiency S4 419.62 252.396439 586.84356 0.0000008
## Standard S4-Deficiency S4 47.48 -119.743561 214.70356 0.9482101
## Standard S5-Deficiency S4 216.68 49.456439 383.90356 0.0061018
## Excess S4-Deficiency S5 -16.32 -183.543561 150.90356 0.9996177
## Excess S5-Deficiency S5 388.40 221.176439 555.62356 0.0000028
## Standard S4-Deficiency S5 16.26 -150.963561 183.48356 0.9996245
## Standard S5-Deficiency S5 185.46 18.236439 352.68356 0.0236728
## Excess S5-Excess S4 404.72 237.496439 571.94356 0.0000014
## Standard S4-Excess S4 32.58 -134.643561 199.80356 0.9898439
## Standard S5-Excess S4 201.78 34.556439 369.00356 0.0117634
## Standard S4-Excess S5 -372.14 -539.363561 -204.91644 0.0000056
## Standard S5-Excess S5 -202.94 -370.163561 -35.71644 0.0111834
## Standard S5-Standard S4 169.20 1.976439 336.42356 0.0462043
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Adenosine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Guanosine
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Guanosine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Guanosine[Condition == "Standard S4"]
## W = 0.92915, p-value = 0.5906
with(ANOVATest.data, shapiro.test(Guanosine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Guanosine[Condition == "Standard S5"]
## W = 0.80398, p-value = 0.08728
with(ANOVATest.data, shapiro.test(Guanosine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Guanosine[Condition == "Deficiency S4"]
## W = 0.80209, p-value = 0.08427
with(ANOVATest.data, shapiro.test(Guanosine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Guanosine[Condition == "Deficiency S5"]
## W = 0.99158, p-value = 0.9849
with(ANOVATest.data, shapiro.test(Guanosine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Guanosine[Condition == "Excess S4"]
## W = 0.96321, p-value = 0.8301
with(ANOVATest.data, shapiro.test(Guanosine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Guanosine[Condition == "Excess S5"]
## W = 0.94116, p-value = 0.6741
Hm_var <- bartlett.test(Guanosine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Guanosine by Condition
## Bartlett's K-squared = 25.942, df = 5, p-value = 9.156e-05
#### ONE WAY - ANOVA
OneWay_test <- aov(Guanosine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 340434 68087 18.48 1.56e-07 ***
## Residuals 24 88417 3684
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Guanosine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 18.52 -100.17237 137.21237 0.9963609
## Excess S4-Deficiency S4 21.94 -96.75237 140.63237 0.9920088
## Excess S5-Deficiency S4 307.10 188.40763 425.79237 0.0000004
## Standard S4-Deficiency S4 29.84 -88.85237 148.53237 0.9687849
## Standard S5-Deficiency S4 101.12 -17.57237 219.81237 0.1273162
## Excess S4-Deficiency S5 3.42 -115.27237 122.11237 0.9999991
## Excess S5-Deficiency S5 288.58 169.88763 407.27237 0.0000013
## Standard S4-Deficiency S5 11.32 -107.37237 130.01237 0.9996583
## Standard S5-Deficiency S5 82.60 -36.09237 201.29237 0.2959502
## Excess S5-Excess S4 285.16 166.46763 403.85237 0.0000016
## Standard S4-Excess S4 7.90 -110.79237 126.59237 0.9999417
## Standard S5-Excess S4 79.18 -39.51237 197.87237 0.3389447
## Standard S4-Excess S5 -277.26 -395.95237 -158.56763 0.0000025
## Standard S5-Excess S5 -205.98 -324.67237 -87.28763 0.0002158
## Standard S5-Standard S4 71.28 -47.41237 189.97237 0.4508801
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Guanosine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Uridine
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Uridine[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Uridine[Condition == "Standard S4"]
## W = 0.86488, p-value = 0.2463
with(ANOVATest.data, shapiro.test(Uridine[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Uridine[Condition == "Standard S5"]
## W = 0.82815, p-value = 0.1347
with(ANOVATest.data, shapiro.test(Uridine[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Uridine[Condition == "Deficiency S4"]
## W = 0.83352, p-value = 0.1478
with(ANOVATest.data, shapiro.test(Uridine[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Uridine[Condition == "Deficiency S5"]
## W = 0.97516, p-value = 0.9072
with(ANOVATest.data, shapiro.test(Uridine[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Uridine[Condition == "Excess S4"]
## W = 0.95553, p-value = 0.7766
with(ANOVATest.data, shapiro.test(Uridine[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Uridine[Condition == "Excess S5"]
## W = 0.93549, p-value = 0.6342
Hm_var <- bartlett.test(Uridine ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Uridine by Condition
## Bartlett's K-squared = 13.07, df = 5, p-value = 0.02273
#### ONE WAY - ANOVA
OneWay_test <- aov(Uridine ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 286169 57234 14.01 1.85e-06 ***
## Residuals 24 98034 4085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Uridine ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 23.16 -101.820346 148.140346 0.9919170
## Excess S4-Deficiency S4 36.60 -88.380346 161.580346 0.9413224
## Excess S5-Deficiency S4 282.50 157.519654 407.480346 0.0000043
## Standard S4-Deficiency S4 57.84 -67.140346 182.820346 0.7086668
## Standard S5-Deficiency S4 151.20 26.219654 276.180346 0.0114972
## Excess S4-Deficiency S5 13.44 -111.540346 138.420346 0.9993869
## Excess S5-Deficiency S5 259.34 134.359654 384.320346 0.0000168
## Standard S4-Deficiency S5 34.68 -90.300346 159.660346 0.9528482
## Standard S5-Deficiency S5 128.04 3.059654 253.020346 0.0424312
## Excess S5-Excess S4 245.90 120.919654 370.880346 0.0000373
## Standard S4-Excess S4 21.24 -103.740346 146.220346 0.9945768
## Standard S5-Excess S4 114.60 -10.380346 239.580346 0.0856085
## Standard S4-Excess S5 -224.66 -349.640346 -99.679654 0.0001345
## Standard S5-Excess S5 -131.30 -256.280346 -6.319654 0.0355344
## Standard S5-Standard S4 93.36 -31.620346 218.340346 0.2288876
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Uridine, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Glucose
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Glucose[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Glucose[Condition == "Standard S4"]
## W = 0.77333, p-value = 0.04825
with(ANOVATest.data, shapiro.test(Glucose[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Glucose[Condition == "Standard S5"]
## W = 0.79734, p-value = 0.07708
with(ANOVATest.data, shapiro.test(Glucose[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Glucose[Condition == "Deficiency S4"]
## W = 0.8373, p-value = 0.1576
with(ANOVATest.data, shapiro.test(Glucose[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Glucose[Condition == "Deficiency S5"]
## W = 0.91008, p-value = 0.4681
with(ANOVATest.data, shapiro.test(Glucose[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Glucose[Condition == "Excess S4"]
## W = 0.87855, p-value = 0.3028
with(ANOVATest.data, shapiro.test(Glucose[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Glucose[Condition == "Excess S5"]
## W = 0.79056, p-value = 0.06773
Hm_var <- bartlett.test(Glucose ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Glucose by Condition
## Bartlett's K-squared = 62.354, df = 5, p-value = 3.961e-12
#### ONE WAY - ANOVA
OneWay_test <- aov(Glucose ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 59096890 11819378 4.478 0.00505 **
## Residuals 24 63349883 2639578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Glucose ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 210.76 -2966.3122 3387.8322 0.9999427
## Excess S4-Deficiency S4 73.40 -3103.6722 3250.4722 0.9999997
## Excess S5-Deficiency S4 3925.80 748.7278 7102.8722 0.0095145
## Standard S4-Deficiency S4 397.46 -2779.6122 3574.5322 0.9987269
## Standard S5-Deficiency S4 190.44 -2986.6322 3367.5122 0.9999653
## Excess S4-Deficiency S5 -137.36 -3314.4322 3039.7122 0.9999932
## Excess S5-Deficiency S5 3715.04 537.9678 6892.1122 0.0154131
## Standard S4-Deficiency S5 186.70 -2990.3722 3363.7722 0.9999685
## Standard S5-Deficiency S5 -20.32 -3197.3922 3156.7522 1.0000000
## Excess S5-Excess S4 3852.40 675.3278 7029.4722 0.0112671
## Standard S4-Excess S4 324.06 -2853.0122 3501.1322 0.9995259
## Standard S5-Excess S4 117.04 -3060.0322 3294.1122 0.9999969
## Standard S4-Excess S5 -3528.34 -6705.4122 -351.2678 0.0234227
## Standard S5-Excess S5 -3735.36 -6912.4322 -558.2878 0.0147189
## Standard S5-Standard S4 -207.02 -3384.0922 2970.0522 0.9999475
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Glucose, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Fructose
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Fructose[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Fructose[Condition == "Standard S4"]
## W = 0.86996, p-value = 0.2662
with(ANOVATest.data, shapiro.test(Fructose[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Fructose[Condition == "Standard S5"]
## W = 0.83049, p-value = 0.1403
with(ANOVATest.data, shapiro.test(Fructose[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Fructose[Condition == "Deficiency S4"]
## W = 0.80506, p-value = 0.08904
with(ANOVATest.data, shapiro.test(Fructose[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Fructose[Condition == "Deficiency S5"]
## W = 0.82448, p-value = 0.1264
with(ANOVATest.data, shapiro.test(Fructose[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Fructose[Condition == "Excess S4"]
## W = 0.98204, p-value = 0.9452
with(ANOVATest.data, shapiro.test(Fructose[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Fructose[Condition == "Excess S5"]
## W = 0.75189, p-value = 0.03099
Hm_var <- bartlett.test(Fructose ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Fructose by Condition
## Bartlett's K-squared = 15.519, df = 5, p-value = 0.00836
#### ONE WAY - ANOVA
OneWay_test <- aov(Fructose ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 3057753 611551 4.204 0.00693 **
## Residuals 24 3491562 145482
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Fructose ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 573.28 -172.59154 1319.15154 0.2040969
## Excess S4-Deficiency S4 210.62 -535.25154 956.49154 0.9493514
## Excess S5-Deficiency S4 987.64 241.76846 1733.51154 0.0049368
## Standard S4-Deficiency S4 201.22 -544.65154 947.09154 0.9580205
## Standard S5-Deficiency S4 428.74 -317.13154 1174.61154 0.4978784
## Excess S4-Deficiency S5 -362.66 -1108.53154 383.21154 0.6654714
## Excess S5-Deficiency S5 414.36 -331.51154 1160.23154 0.5339887
## Standard S4-Deficiency S5 -372.06 -1117.93154 373.81154 0.6417730
## Standard S5-Deficiency S5 -144.54 -890.41154 601.33154 0.9900882
## Excess S5-Excess S4 777.02 31.14846 1522.89154 0.0377373
## Standard S4-Excess S4 -9.40 -755.27154 736.47154 1.0000000
## Standard S5-Excess S4 218.12 -527.75154 963.99154 0.9416511
## Standard S4-Excess S5 -786.42 -1532.29154 -40.54846 0.0346212
## Standard S5-Excess S5 -558.90 -1304.77154 186.97154 0.2261158
## Standard S5-Standard S4 227.52 -518.35154 973.39154 0.9309923
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Fructose, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Sucrose
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Sucrose[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Sucrose[Condition == "Standard S4"]
## W = 0.76906, p-value = 0.04426
with(ANOVATest.data, shapiro.test(Sucrose[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Sucrose[Condition == "Standard S5"]
## W = 0.84379, p-value = 0.1757
with(ANOVATest.data, shapiro.test(Sucrose[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Sucrose[Condition == "Deficiency S4"]
## W = 0.85761, p-value = 0.2198
with(ANOVATest.data, shapiro.test(Sucrose[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Sucrose[Condition == "Deficiency S5"]
## W = 0.79791, p-value = 0.07791
with(ANOVATest.data, shapiro.test(Sucrose[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Sucrose[Condition == "Excess S4"]
## W = 0.79919, p-value = 0.07981
with(ANOVATest.data, shapiro.test(Sucrose[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Sucrose[Condition == "Excess S5"]
## W = 0.90838, p-value = 0.4579
Hm_var <- bartlett.test(Sucrose ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Sucrose by Condition
## Bartlett's K-squared = 8.9954, df = 5, p-value = 0.1092
#### ONE WAY - ANOVA
OneWay_test <- aov(Sucrose ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 4438472 887694 5.001 0.00281 **
## Residuals 24 4260217 177509
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Sucrose ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 812.90 -10.99154 1636.791539 0.0546232
## Excess S4-Deficiency S4 -437.90 -1261.79154 385.991539 0.5796267
## Excess S5-Deficiency S4 327.44 -496.45154 1151.331539 0.8186012
## Standard S4-Deficiency S4 -1.24 -825.13154 822.651539 1.0000000
## Standard S5-Deficiency S4 -37.24 -861.13154 786.651539 0.9999915
## Excess S4-Deficiency S5 -1250.80 -2074.69154 -426.908461 0.0011355
## Excess S5-Deficiency S5 -485.46 -1309.35154 338.431539 0.4713541
## Standard S4-Deficiency S5 -814.14 -1638.03154 9.751539 0.0540830
## Standard S5-Deficiency S5 -850.14 -1674.03154 -26.248461 0.0403650
## Excess S5-Excess S4 765.34 -58.55154 1589.231539 0.0793718
## Standard S4-Excess S4 436.66 -387.23154 1260.551539 0.5824954
## Standard S5-Excess S4 400.66 -423.23154 1224.551539 0.6653242
## Standard S4-Excess S5 -328.68 -1152.57154 495.211539 0.8163063
## Standard S5-Excess S5 -364.68 -1188.57154 459.211539 0.7445403
## Standard S5-Standard S4 -36.00 -859.89154 787.891539 0.9999928
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Sucrose, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Citrate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Citrate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Citrate[Condition == "Standard S4"]
## W = 0.94196, p-value = 0.6798
with(ANOVATest.data, shapiro.test(Citrate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Citrate[Condition == "Standard S5"]
## W = 0.94918, p-value = 0.7313
with(ANOVATest.data, shapiro.test(Citrate[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Citrate[Condition == "Deficiency S4"]
## W = 0.96714, p-value = 0.8566
with(ANOVATest.data, shapiro.test(Citrate[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Citrate[Condition == "Deficiency S5"]
## W = 0.95944, p-value = 0.8041
with(ANOVATest.data, shapiro.test(Citrate[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Citrate[Condition == "Excess S4"]
## W = 0.93515, p-value = 0.6319
with(ANOVATest.data, shapiro.test(Citrate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Citrate[Condition == "Excess S5"]
## W = 0.97157, p-value = 0.8852
Hm_var <- bartlett.test(Citrate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Citrate by Condition
## Bartlett's K-squared = 27.587, df = 5, p-value = 4.382e-05
#### ONE WAY - ANOVA
OneWay_test <- aov(Citrate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 20123902 4024780 13.16 3.16e-06 ***
## Residuals 24 7342364 305932
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Citrate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 1925.42 843.8064 3007.0336 0.0001535
## Excess S4-Deficiency S4 727.44 -354.1736 1809.0536 0.3305401
## Excess S5-Deficiency S4 -201.34 -1282.9536 880.2736 0.9917483
## Standard S4-Deficiency S4 -565.74 -1647.3536 515.8736 0.5957361
## Standard S5-Deficiency S4 -31.26 -1112.8736 1050.3536 0.9999991
## Excess S4-Deficiency S5 -1197.98 -2279.5936 -116.3664 0.0239185
## Excess S5-Deficiency S5 -2126.76 -3208.3736 -1045.1464 0.0000377
## Standard S4-Deficiency S5 -2491.16 -3572.7736 -1409.5464 0.0000032
## Standard S5-Deficiency S5 -1956.68 -3038.2936 -875.0664 0.0001233
## Excess S5-Excess S4 -928.78 -2010.3936 152.8336 0.1223103
## Standard S4-Excess S4 -1293.18 -2374.7936 -211.5664 0.0127470
## Standard S5-Excess S4 -758.70 -1840.3136 322.9136 0.2881102
## Standard S4-Excess S5 -364.40 -1446.0136 717.2136 0.8989448
## Standard S5-Excess S5 170.08 -911.5336 1251.6936 0.9962261
## Standard S5-Standard S4 534.48 -547.1336 1616.0936 0.6505930
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Citrate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Malate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Malate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Malate[Condition == "Standard S4"]
## W = 0.69262, p-value = 0.008004
with(ANOVATest.data, shapiro.test(Malate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Malate[Condition == "Standard S5"]
## W = 0.94179, p-value = 0.6786
with(ANOVATest.data, shapiro.test(Malate[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Malate[Condition == "Deficiency S4"]
## W = 0.85884, p-value = 0.2241
with(ANOVATest.data, shapiro.test(Malate[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Malate[Condition == "Deficiency S5"]
## W = 0.94898, p-value = 0.7299
with(ANOVATest.data, shapiro.test(Malate[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Malate[Condition == "Excess S4"]
## W = 0.94965, p-value = 0.7347
with(ANOVATest.data, shapiro.test(Malate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Malate[Condition == "Excess S5"]
## W = 0.67618, p-value = 0.005306
Hm_var <- bartlett.test(Malate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Malate by Condition
## Bartlett's K-squared = 12.851, df = 5, p-value = 0.02481
#### ONE WAY - ANOVA
OneWay_test <- aov(Malate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 10330744 2066149 14.99 1.03e-06 ***
## Residuals 24 3307604 137817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Malate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 -110.86 -836.8171 615.0971 0.9967115
## Excess S4-Deficiency S4 -1199.28 -1925.2371 -473.3229 0.0004081
## Excess S5-Deficiency S4 -1343.78 -2069.7371 -617.8229 0.0000897
## Standard S4-Deficiency S4 -1196.36 -1922.3171 -470.4029 0.0004208
## Standard S5-Deficiency S4 -1412.08 -2138.0371 -686.1229 0.0000441
## Excess S4-Deficiency S5 -1088.42 -1814.3771 -362.4629 0.0013115
## Excess S5-Deficiency S5 -1232.92 -1958.8771 -506.9629 0.0002864
## Standard S4-Deficiency S5 -1085.50 -1811.4571 -359.5429 0.0013523
## Standard S5-Deficiency S5 -1301.22 -2027.1771 -575.2629 0.0001398
## Excess S5-Excess S4 -144.50 -870.4571 581.4571 0.9888099
## Standard S4-Excess S4 2.92 -723.0371 728.8771 1.0000000
## Standard S5-Excess S4 -212.80 -938.7571 513.1571 0.9410936
## Standard S4-Excess S5 147.42 -578.5371 873.3771 0.9877539
## Standard S5-Excess S5 -68.30 -794.2571 657.6571 0.9996803
## Standard S5-Standard S4 -215.72 -941.6771 510.2371 0.9377948
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Malate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Succinate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Succinate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Succinate[Condition == "Standard S4"]
## W = 0.83193, p-value = 0.1438
with(ANOVATest.data, shapiro.test(Succinate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Succinate[Condition == "Standard S5"]
## W = 0.94656, p-value = 0.7126
with(ANOVATest.data, shapiro.test(Succinate[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Succinate[Condition == "Deficiency S4"]
## W = 0.88992, p-value = 0.3567
with(ANOVATest.data, shapiro.test(Succinate[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Succinate[Condition == "Deficiency S5"]
## W = 0.92396, p-value = 0.5558
with(ANOVATest.data, shapiro.test(Succinate[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Succinate[Condition == "Excess S4"]
## W = 0.82521, p-value = 0.128
with(ANOVATest.data, shapiro.test(Succinate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Succinate[Condition == "Excess S5"]
## W = 0.92434, p-value = 0.5583
Hm_var <- bartlett.test(Succinate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Succinate by Condition
## Bartlett's K-squared = 10.027, df = 5, p-value = 0.07449
#### ONE WAY - ANOVA
OneWay_test <- aov(Succinate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 16286 3257 3.479 0.0166 *
## Residuals 24 22472 936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Succinate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 34.94 -24.89824 94.7782422 0.4810926
## Excess S4-Deficiency S4 2.16 -57.67824 61.9982422 0.9999972
## Excess S5-Deficiency S4 -24.70 -84.53824 35.1382422 0.7946033
## Standard S4-Deficiency S4 8.20 -51.63824 68.0382422 0.9980313
## Standard S5-Deficiency S4 45.68 -14.15824 105.5182422 0.2098852
## Excess S4-Deficiency S5 -32.78 -92.61824 27.0582422 0.5486098
## Excess S5-Deficiency S5 -59.64 -119.47824 0.1982422 0.0511137
## Standard S4-Deficiency S5 -26.74 -86.57824 33.0982422 0.7371161
## Standard S5-Deficiency S5 10.74 -49.09824 70.5782422 0.9930195
## Excess S5-Excess S4 -26.86 -86.69824 32.9782422 0.7335812
## Standard S4-Excess S4 6.04 -53.79824 65.8782422 0.9995494
## Standard S5-Excess S4 43.52 -16.31824 103.3582422 0.2533450
## Standard S4-Excess S5 32.90 -26.93824 92.7382422 0.5448093
## Standard S5-Excess S5 70.38 10.54176 130.2182422 0.0146715
## Standard S5-Standard S4 37.48 -22.35824 97.3182422 0.4055930
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Succinate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Fumarate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Fumarate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Fumarate[Condition == "Standard S4"]
## W = 0.85621, p-value = 0.2149
with(ANOVATest.data, shapiro.test(Fumarate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Fumarate[Condition == "Standard S5"]
## W = 0.96352, p-value = 0.8322
with(ANOVATest.data, shapiro.test(Fumarate[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Fumarate[Condition == "Deficiency S4"]
## W = 0.90382, p-value = 0.4314
with(ANOVATest.data, shapiro.test(Fumarate[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Fumarate[Condition == "Deficiency S5"]
## W = 0.83663, p-value = 0.1558
with(ANOVATest.data, shapiro.test(Fumarate[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Fumarate[Condition == "Excess S4"]
## W = 0.97483, p-value = 0.9052
with(ANOVATest.data, shapiro.test(Fumarate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Fumarate[Condition == "Excess S5"]
## W = 0.82653, p-value = 0.131
Hm_var <- bartlett.test(Fumarate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Fumarate by Condition
## Bartlett's K-squared = 10.313, df = 5, p-value = 0.06684
#### ONE WAY - ANOVA
OneWay_test <- aov(Fumarate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 169390 33878 2.919 0.0339 *
## Residuals 24 278574 11607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Fumarate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 -55.68 -266.3606 155.000608 0.9614660
## Excess S4-Deficiency S4 -163.64 -374.3206 47.040608 0.1953256
## Excess S5-Deficiency S4 -188.20 -398.8806 22.480608 0.0991713
## Standard S4-Deficiency S4 -134.56 -345.2406 76.120608 0.3847419
## Standard S5-Deficiency S4 -213.74 -424.4206 -3.059392 0.0453742
## Excess S4-Deficiency S5 -107.96 -318.6406 102.720608 0.6159606
## Excess S5-Deficiency S5 -132.52 -343.2006 78.160608 0.4010616
## Standard S4-Deficiency S5 -78.88 -289.5606 131.800608 0.8520074
## Standard S5-Deficiency S5 -158.06 -368.7406 52.620608 0.2250382
## Excess S5-Excess S4 -24.56 -235.2406 186.120608 0.9990939
## Standard S4-Excess S4 29.08 -181.6006 239.760608 0.9979625
## Standard S5-Excess S4 -50.10 -260.7806 160.580608 0.9754110
## Standard S4-Excess S5 53.64 -157.0406 264.320608 0.9670657
## Standard S5-Excess S5 -25.54 -236.2206 185.140608 0.9989057
## Standard S5-Standard S4 -79.18 -289.8606 131.500608 0.8500379
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Fumarate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Formate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Formate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Formate[Condition == "Standard S4"]
## W = 0.75349, p-value = 0.03205
with(ANOVATest.data, shapiro.test(Formate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Formate[Condition == "Standard S5"]
## W = 0.80602, p-value = 0.09064
with(ANOVATest.data, shapiro.test(Formate[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Formate[Condition == "Deficiency S4"]
## W = 0.91602, p-value = 0.5046
with(ANOVATest.data, shapiro.test(Formate[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Formate[Condition == "Deficiency S5"]
## W = 0.96304, p-value = 0.829
with(ANOVATest.data, shapiro.test(Formate[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Formate[Condition == "Excess S4"]
## W = 0.91495, p-value = 0.4979
with(ANOVATest.data, shapiro.test(Formate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Formate[Condition == "Excess S5"]
## W = 0.88154, p-value = 0.3164
Hm_var <- bartlett.test(Formate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Formate by Condition
## Bartlett's K-squared = 19.273, df = 5, p-value = 0.00171
#### ONE WAY - ANOVA
OneWay_test <- aov(Formate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 279962 55992 8.98 6.47e-05 ***
## Residuals 24 149647 6235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Formate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 237.38 82.96534 391.79466 0.0009812
## Excess S4-Deficiency S4 0.36 -154.05466 154.77466 1.0000000
## Excess S5-Deficiency S4 176.08 21.66534 330.49466 0.0189735
## Standard S4-Deficiency S4 -7.18 -161.59466 147.23466 0.9999902
## Standard S5-Deficiency S4 125.94 -28.47466 280.35466 0.1572817
## Excess S4-Deficiency S5 -237.02 -391.43466 -82.60534 0.0009988
## Excess S5-Deficiency S5 -61.30 -215.71466 93.11466 0.8192816
## Standard S4-Deficiency S5 -244.56 -398.97466 -90.14534 0.0006876
## Standard S5-Deficiency S5 -111.44 -265.85466 42.97466 0.2606215
## Excess S5-Excess S4 175.72 21.30534 330.13466 0.0192911
## Standard S4-Excess S4 -7.54 -161.95466 146.87466 0.9999875
## Standard S5-Excess S4 125.58 -28.83466 279.99466 0.1593832
## Standard S4-Excess S5 -183.26 -337.67466 -28.84534 0.0135864
## Standard S5-Excess S5 -50.14 -204.55466 104.27466 0.9120744
## Standard S5-Standard S4 133.12 -21.29466 287.53466 0.1198541
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Formate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
p.Cumarate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(p.Cumarate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: p.Cumarate[Condition == "Standard S4"]
## W = 0.96971, p-value = 0.8734
with(ANOVATest.data, shapiro.test(p.Cumarate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: p.Cumarate[Condition == "Standard S5"]
## W = 0.90604, p-value = 0.4441
with(ANOVATest.data, shapiro.test(p.Cumarate[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: p.Cumarate[Condition == "Deficiency S4"]
## W = 0.91435, p-value = 0.4941
with(ANOVATest.data, shapiro.test(p.Cumarate[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: p.Cumarate[Condition == "Deficiency S5"]
## W = 0.91301, p-value = 0.4859
with(ANOVATest.data, shapiro.test(p.Cumarate[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: p.Cumarate[Condition == "Excess S4"]
## W = 0.91646, p-value = 0.5074
with(ANOVATest.data, shapiro.test(p.Cumarate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: p.Cumarate[Condition == "Excess S5"]
## W = 0.83933, p-value = 0.1631
Hm_var <- bartlett.test(p.Cumarate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: p.Cumarate by Condition
## Bartlett's K-squared = 16.223, df = 5, p-value = 0.006236
#### ONE WAY - ANOVA
OneWay_test <- aov(p.Cumarate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 5678 1135.6 7.913 0.00016 ***
## Residuals 24 3444 143.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = p.Cumarate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 11.04 -12.386284 34.466284 0.6932045
## Excess S4-Deficiency S4 3.12 -20.306284 26.546284 0.9982817
## Excess S5-Deficiency S4 36.28 12.853716 59.706284 0.0008993
## Standard S4-Deficiency S4 3.58 -19.846284 27.006284 0.9967002
## Standard S5-Deficiency S4 28.42 4.993716 51.846284 0.0112175
## Excess S4-Deficiency S5 -7.92 -31.346284 15.506284 0.8976201
## Excess S5-Deficiency S5 25.24 1.813716 48.666284 0.0295288
## Standard S4-Deficiency S5 -7.46 -30.886284 15.966284 0.9184024
## Standard S5-Deficiency S5 17.38 -6.046284 40.806284 0.2350428
## Excess S5-Excess S4 33.16 9.733716 56.586284 0.0024804
## Standard S4-Excess S4 0.46 -22.966284 23.886284 0.9999999
## Standard S5-Excess S4 25.30 1.873716 48.726284 0.0290084
## Standard S4-Excess S5 -32.70 -56.126284 -9.273716 0.0028779
## Standard S5-Excess S5 -7.86 -31.286284 15.566284 0.9004882
## Standard S5-Standard S4 24.84 1.413716 48.266284 0.0332266
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = p.Cumarate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Ferulate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Ferulate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Ferulate[Condition == "Standard S4"]
## W = 0.9665, p-value = 0.8523
with(ANOVATest.data, shapiro.test(Ferulate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Ferulate[Condition == "Standard S5"]
## W = 0.84164, p-value = 0.1695
with(ANOVATest.data, shapiro.test(Ferulate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Ferulate[Condition == "Excess S5"]
## W = 0.76978, p-value = 0.04491
Hm_var <- bartlett.test(Ferulate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Ferulate by Condition
## Bartlett's K-squared = Inf, df = 5, p-value < 2.2e-16
#### ONE WAY - ANOVA
OneWay_test <- aov(Ferulate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 23233 4647 14.76 1.18e-06 ***
## Residuals 24 7555 315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Ferulate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 -2.131628e-14 -34.696087 34.696087 1.0000000
## Excess S4-Deficiency S4 -1.776357e-14 -34.696087 34.696087 1.0000000
## Excess S5-Deficiency S4 6.912000e+01 34.423913 103.816087 0.0000310
## Standard S4-Deficiency S4 2.862000e+01 -6.076087 63.316087 0.1491359
## Standard S5-Deficiency S4 5.350000e+01 18.803913 88.196087 0.0009468
## Excess S4-Deficiency S5 3.552714e-15 -34.696087 34.696087 1.0000000
## Excess S5-Deficiency S5 6.912000e+01 34.423913 103.816087 0.0000310
## Standard S4-Deficiency S5 2.862000e+01 -6.076087 63.316087 0.1491359
## Standard S5-Deficiency S5 5.350000e+01 18.803913 88.196087 0.0009468
## Excess S5-Excess S4 6.912000e+01 34.423913 103.816087 0.0000310
## Standard S4-Excess S4 2.862000e+01 -6.076087 63.316087 0.1491359
## Standard S5-Excess S4 5.350000e+01 18.803913 88.196087 0.0009468
## Standard S4-Excess S5 -4.050000e+01 -75.196087 -5.803913 0.0156420
## Standard S5-Excess S5 -1.562000e+01 -50.316087 19.076087 0.7312498
## Standard S5-Standard S4 2.488000e+01 -9.816087 59.576087 0.2667173
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Ferulate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Glycerol
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Glycerol[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Glycerol[Condition == "Standard S4"]
## W = 0.85661, p-value = 0.2163
with(ANOVATest.data, shapiro.test(Glycerol[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Glycerol[Condition == "Standard S5"]
## W = 0.82942, p-value = 0.1377
with(ANOVATest.data, shapiro.test(Glycerol[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Glycerol[Condition == "Deficiency S4"]
## W = 0.94195, p-value = 0.6797
with(ANOVATest.data, shapiro.test(Glycerol[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Glycerol[Condition == "Deficiency S5"]
## W = 0.73977, p-value = 0.02386
with(ANOVATest.data, shapiro.test(Glycerol[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Glycerol[Condition == "Excess S4"]
## W = 0.86468, p-value = 0.2456
with(ANOVATest.data, shapiro.test(Glycerol[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Glycerol[Condition == "Excess S5"]
## W = 0.89802, p-value = 0.399
Hm_var <- bartlett.test(Glycerol ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Glycerol by Condition
## Bartlett's K-squared = 13.098, df = 5, p-value = 0.02248
#### ONE WAY - ANOVA
OneWay_test <- aov(Glycerol ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 442201 88440 6.885 0.00041 ***
## Residuals 24 308303 12846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Glycerol ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 163.46 -58.17753 385.09753 0.2404421
## Excess S4-Deficiency S4 -7.04 -228.67753 214.59753 0.9999985
## Excess S5-Deficiency S4 256.42 34.78247 478.05753 0.0168480
## Standard S4-Deficiency S4 -0.62 -222.25753 221.01753 1.0000000
## Standard S5-Deficiency S4 271.88 50.24247 493.51753 0.0101619
## Excess S4-Deficiency S5 -170.50 -392.13753 51.13753 0.2033620
## Excess S5-Deficiency S5 92.96 -128.67753 314.59753 0.7838252
## Standard S4-Deficiency S5 -164.08 -385.71753 57.55753 0.2369924
## Standard S5-Deficiency S5 108.42 -113.21753 330.05753 0.6599399
## Excess S5-Excess S4 263.46 41.82247 485.09753 0.0134014
## Standard S4-Excess S4 6.42 -215.21753 228.05753 0.9999991
## Standard S5-Excess S4 278.92 57.28247 500.55753 0.0080453
## Standard S4-Excess S5 -257.04 -478.67753 -35.40247 0.0165134
## Standard S5-Excess S5 15.46 -206.17753 237.09753 0.9999265
## Standard S5-Standard S4 272.50 50.86247 494.13753 0.0099558
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Glycerol, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Ascorbate
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Ascorbate[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Ascorbate[Condition == "Standard S4"]
## W = 0.86961, p-value = 0.2648
with(ANOVATest.data, shapiro.test(Ascorbate[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Ascorbate[Condition == "Standard S5"]
## W = 0.9339, p-value = 0.6232
with(ANOVATest.data, shapiro.test(Ascorbate[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Ascorbate[Condition == "Deficiency S4"]
## W = 0.79018, p-value = 0.06724
with(ANOVATest.data, shapiro.test(Ascorbate[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Ascorbate[Condition == "Deficiency S5"]
## W = 0.89318, p-value = 0.3733
with(ANOVATest.data, shapiro.test(Ascorbate[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Ascorbate[Condition == "Excess S4"]
## W = 0.97341, p-value = 0.8966
with(ANOVATest.data, shapiro.test(Ascorbate[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Ascorbate[Condition == "Excess S5"]
## W = 0.87077, p-value = 0.2695
Hm_var <- bartlett.test(Ascorbate ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Ascorbate by Condition
## Bartlett's K-squared = 67.095, df = 5, p-value = 4.118e-13
#### ONE WAY - ANOVA
OneWay_test <- aov(Ascorbate ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 12669624 2533925 37.59 1.37e-10 ***
## Residuals 24 1617784 67408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Ascorbate ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 28.10 -479.6085 535.80847 0.9999766
## Excess S4-Deficiency S4 123.04 -384.6685 630.74847 0.9733200
## Excess S5-Deficiency S4 1598.46 1090.7515 2106.16847 0.0000000
## Standard S4-Deficiency S4 -23.82 -531.5285 483.88847 0.9999897
## Standard S5-Deficiency S4 1163.46 655.7515 1671.16847 0.0000035
## Excess S4-Deficiency S5 94.94 -412.7685 602.64847 0.9915747
## Excess S5-Deficiency S5 1570.36 1062.6515 2078.06847 0.0000000
## Standard S4-Deficiency S5 -51.92 -559.6285 455.78847 0.9995199
## Standard S5-Deficiency S5 1135.36 627.6515 1643.06847 0.0000052
## Excess S5-Excess S4 1475.42 967.7115 1983.12847 0.0000001
## Standard S4-Excess S4 -146.86 -654.5685 360.84847 0.9441595
## Standard S5-Excess S4 1040.42 532.7115 1548.12847 0.0000203
## Standard S4-Excess S5 -1622.28 -2129.9885 -1114.57153 0.0000000
## Standard S5-Excess S5 -435.00 -942.7085 72.70847 0.1237084
## Standard S5-Standard S4 1187.28 679.5715 1694.98847 0.0000025
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Ascorbate, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
Choline
#### NORMALITY's TEST AND HOMOGENEITY OF VARIANCE
with(ANOVATest.data, shapiro.test(Choline[Condition == "Standard S4"]))
##
## Shapiro-Wilk normality test
##
## data: Choline[Condition == "Standard S4"]
## W = 0.88797, p-value = 0.347
with(ANOVATest.data, shapiro.test(Choline[Condition == "Standard S5"]))
##
## Shapiro-Wilk normality test
##
## data: Choline[Condition == "Standard S5"]
## W = 0.88619, p-value = 0.3383
with(ANOVATest.data, shapiro.test(Choline[Condition == "Deficiency S4"]))
##
## Shapiro-Wilk normality test
##
## data: Choline[Condition == "Deficiency S4"]
## W = 0.96271, p-value = 0.8267
with(ANOVATest.data, shapiro.test(Choline[Condition == "Deficiency S5"]))
##
## Shapiro-Wilk normality test
##
## data: Choline[Condition == "Deficiency S5"]
## W = 0.89765, p-value = 0.397
with(ANOVATest.data, shapiro.test(Choline[Condition == "Excess S4"]))
##
## Shapiro-Wilk normality test
##
## data: Choline[Condition == "Excess S4"]
## W = 0.73884, p-value = 0.02338
with(ANOVATest.data, shapiro.test(Choline[Condition == "Excess S5"]))
##
## Shapiro-Wilk normality test
##
## data: Choline[Condition == "Excess S5"]
## W = 0.95671, p-value = 0.7849
Hm_var <- bartlett.test(Choline ~ Condition, data =ANOVATest.data)
Hm_var
##
## Bartlett test of homogeneity of variances
##
## data: Choline by Condition
## Bartlett's K-squared = 16.403, df = 5, p-value = 0.005783
#### ONE WAY - ANOVA
OneWay_test <- aov(Choline ~ Condition, data =ANOVATest.data)
summary(OneWay_test)
## Df Sum Sq Mean Sq F value Pr(>F)
## Condition 5 675161 135032 17.31 2.85e-07 ***
## Residuals 24 187241 7802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### POST-HOC TUKEY's Test
TukeyHSD(OneWay_test)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Choline ~ Condition, data = ANOVATest.data)
##
## $Condition
## diff lwr upr p adj
## Deficiency S5-Deficiency S4 10.80 -161.92459 183.52459 0.9999572
## Excess S4-Deficiency S4 -46.26 -218.98459 126.46459 0.9592686
## Excess S5-Deficiency S4 347.64 174.91541 520.36459 0.0000266
## Standard S4-Deficiency S4 1.08 -171.64459 173.80459 1.0000000
## Standard S5-Deficiency S4 257.22 84.49541 429.94459 0.0014164
## Excess S4-Deficiency S5 -57.06 -229.78459 115.66459 0.9061321
## Excess S5-Deficiency S5 336.84 164.11541 509.56459 0.0000425
## Standard S4-Deficiency S5 -9.72 -182.44459 163.00459 0.9999746
## Standard S5-Deficiency S5 246.42 73.69541 419.14459 0.0022791
## Excess S5-Excess S4 393.90 221.17541 566.62459 0.0000038
## Standard S4-Excess S4 47.34 -125.38459 220.06459 0.9551836
## Standard S5-Excess S4 303.48 130.75541 476.20459 0.0001831
## Standard S4-Excess S5 -346.56 -519.28459 -173.83541 0.0000279
## Standard S5-Excess S5 -90.42 -263.14459 82.30459 0.5948963
## Standard S5-Standard S4 256.14 83.41541 428.86459 0.0014856
#### BOXPLOT
ggplot(ANOVATest.data, aes(x = Condition, y = Choline, fill = Condition)) +
geom_boxplot() +
geom_jitter (shape = 15,
color = "steelblue",
position = position_jitter(0.21)) +
theme_classic()
