Title: Metabolomic, Enzymatic, and Histochemical Analyzes of Cassava Roots during Postharvest Physiological Deterioration
Authors: “Uarrota VG, Maraschin M (2015)”
Supporting data and scripts used in data mining for the results reported in the manuscript
setwd("C:/Users/Virgílio/Desktop/PASTAS DO DESKTOP/ascorbato peroxidase")
require(reshape)
require(reshape2)
load("MediasTabelaFinal.RData") ##load the data "means3"
means3
## Sample Days Phenolics Flavonoids Carotenoids Anthocyanins Totalcyanide
## 20 SAN Fresh 42.57667 578.6110 3.900333 7.124667 56.86633
## 17 SAN Day3 367.29667 959.1667 4.633333 8.126667 84.65167
## 18 SAN Day5 125.16000 1408.0557 6.709000 16.253667 63.15733
## 19 SAN Day8 156.85667 1317.4997 5.625000 19.036667 60.56767
## 16 SAN Day11 103.00000 1230.8333 7.268333 6.457000 93.79167
## 15 ORI Fresh 64.01000 772.5000 3.025000 5.010000 33.04100
## 12 ORI Day3 545.22000 1299.1663 5.084667 14.249667 56.21133
## 13 ORI Day5 180.17000 781.3890 4.853333 5.511000 38.15967
## 14 ORI Day8 186.54000 1054.7223 3.661333 5.844667 64.45233
## 11 ORI Day11 125.91333 1365.2777 3.132667 10.742667 79.32000
## 10 IAC Fresh 67.73667 398.0553 4.344333 13.192000 24.20567
## 7 IAC Day3 519.27667 1373.6113 4.116333 5.844667 60.04967
## 8 IAC Day5 140.40000 1670.2780 4.379000 0.501000 22.18000
## 9 IAC Day8 198.03000 1018.0553 3.673000 6.791000 53.37767
## 6 IAC Day11 144.13000 1386.9447 4.753333 22.154000 66.61533
## 5 BRA Fresh 66.79333 509.1663 1.516000 6.345333 22.80433
## 2 BRA Day3 527.02333 2288.0553 4.216667 5.622000 80.21867
## 3 BRA Day5 153.39667 1583.6110 5.004000 8.961333 55.37300
## 4 BRA Day8 205.72667 1170.2777 5.281667 11.800333 39.02800
## 1 BRA Day11 182.07667 1445.2777 4.895667 42.916000 29.17200
## AcetoneCyano Linamarin Linamarase HydrogenPeroxide Catalase TotalSOD
## 20 3.640667 53.22533 6.135333 62.26767 14.88867 0.03933333
## 17 5.377333 79.27433 6.037000 41.24467 112.47100 0.02966667
## 18 6.961667 56.19567 5.949000 103.21467 66.50367 0.05066667
## 19 6.002000 54.56567 5.682000 117.96167 295.55567 0.01900000
## 16 4.844333 88.94733 7.134667 147.61300 167.22333 0.04000000
## 15 8.683000 24.35800 5.483000 97.41000 42.50033 0.08633333
## 12 5.880333 50.33100 6.340000 87.68300 217.89733 0.05633333
## 13 6.352333 31.80733 4.821000 112.78467 162.83100 0.13366667
## 14 8.378333 56.07400 4.869000 134.27767 117.02067 0.15300000
## 11 7.007333 72.31300 7.801000 189.34433 158.28133 0.11166667
## 10 10.054000 14.15200 8.328000 77.32867 153.41767 0.49700000
## 7 10.023333 50.02633 5.885000 103.84233 89.09500 0.12400000
## 8 8.911667 13.26800 6.153000 157.18300 97.72367 0.36200000
## 9 4.387333 48.99033 6.938000 195.46300 183.69667 0.24433333
## 6 10.922333 55.69300 6.340000 269.82633 193.10967 0.00000000
## 5 9.642667 13.16167 7.526000 120.47200 222.76100 0.05633333
## 2 6.550333 73.66867 6.974000 103.52833 115.13767 0.08800000
## 3 8.576333 46.79700 6.865667 117.49100 122.82533 0.26600000
## 4 6.900667 32.12700 5.046000 156.71267 220.72133 0.14933333
## 1 7.982667 21.18933 8.736000 180.55900 252.88267 0.11200000
## MnSOD CuZnSOD MalicAcid SuccinicAcid FumaricAcid Raffinose
## 20 0.027000000 0.014333333 0.24333333 3.136667 1.6633333 3.270000
## 17 0.006666667 0.024333333 0.24333333 3.136667 1.6633333 2.856667
## 18 0.021333333 0.028666667 0.09333333 4.550000 0.9533333 2.253333
## 19 0.001666667 0.017333333 0.11000000 5.600000 0.8700000 0.000000
## 16 0.002000000 0.037666667 0.14000000 2.840000 0.2333333 0.000000
## 15 0.000000000 0.086333333 0.07666667 2.246667 2.9700000 2.690000
## 12 0.000000000 0.056333333 0.02666667 4.200000 2.8266667 2.856667
## 13 0.000000000 0.133666667 0.08666667 2.553333 1.8533333 1.700000
## 14 0.000000000 0.153000000 0.07666667 2.320000 1.3200000 0.000000
## 11 0.000000000 0.111666667 0.28333333 2.126667 0.7966667 0.000000
## 10 0.528666667 0.003666667 0.13000000 4.403333 0.8600000 3.960000
## 7 0.073000000 0.101000000 0.12666667 5.233333 1.2300000 3.470000
## 8 0.107333333 0.254666667 0.09666667 4.400000 0.9900000 3.050000
## 9 0.071666667 0.173333333 0.03000000 4.660000 0.6300000 2.760000
## 6 0.121000000 0.000000000 0.12666667 3.926667 6.8133333 0.000000
## 5 0.011000000 0.045333333 0.52333333 4.086667 0.7733333 4.793333
## 2 0.492666667 0.000000000 1.36333333 2.883333 0.5766667 5.470000
## 3 0.048000000 0.218333333 0.57666667 3.643333 0.1700000 3.040000
## 4 0.052666667 0.137333333 0.33666667 4.463333 0.1100000 2.606667
## 1 0.131000000 0.033000000 0.16000000 4.273333 0.1233333 2.540000
## Sucrose Glucose Fructose TotalSugars Scopoletin PolyPhenol
## 20 48.850000 35.940000 26.05667 114.12 25.98333 4.573333
## 17 49.203333 29.856667 26.78000 108.69 45.40000 6.160000
## 18 13.480000 67.113333 67.94000 150.79 120.80000 5.700000
## 19 5.236667 55.596667 62.40667 123.24 125.81333 3.906667
## 16 4.076667 18.140000 18.39667 40.62 66.64333 6.500000
## 15 48.506667 15.480000 13.69000 80.37 18.59000 4.240000
## 12 58.020000 30.233333 26.07333 117.18 124.89000 3.636667
## 13 10.400000 25.220000 31.62000 68.94 48.17000 4.256667
## 14 4.633333 17.093333 23.28333 45.01 81.79667 5.103333
## 11 3.233333 9.403333 13.41000 26.05 54.93667 3.820000
## 10 92.266667 82.263333 69.87333 248.37 64.25333 3.723333
## 7 62.050000 45.740000 42.72000 153.98 81.81333 3.400000
## 8 36.773333 82.080000 84.24667 206.15 123.90000 4.543333
## 9 19.490000 91.766667 99.79667 213.81 214.00000 2.980000
## 6 5.710000 42.230000 44.65333 92.59 98.10000 5.036667
## 5 112.100000 55.910000 54.19000 226.99 91.46000 4.190000
## 2 91.483333 87.296667 81.25000 265.50 92.10667 3.213333
## 3 25.523333 117.850000 125.74000 272.16 94.99667 3.643333
## 4 26.203333 102.346667 117.27333 248.43 193.95667 3.700000
## 1 28.196667 88.156667 111.09333 229.99 223.08000 6.533333
## Ascorbic Ascorbate Guaiacol Tocopherol Proteins PPDscores
## 20 0.5400000 1.320000 0.1366667 5.5766667 60.67333 0.00000
## 17 1.9400000 25.983333 1.3633333 0.7666667 66.89333 12.62667
## 18 1.4200000 27.896667 0.5233333 4.0400000 82.82333 50.45333
## 19 3.5666667 28.243333 1.2733333 2.0566667 65.74333 66.14667
## 16 2.6733333 7.066667 3.5800000 0.4233333 36.17333 77.47000
## 15 0.6433333 12.910000 0.1933333 0.3900000 39.32333 0.00000
## 12 0.7266667 3.743333 1.3433333 0.3166667 55.82333 41.08333
## 13 0.6300000 20.200000 1.5900000 0.2266667 69.17333 88.58000
## 14 1.8233333 16.033333 5.0700000 3.6166667 21.55000 102.00000
## 11 2.5833333 118.986667 6.5933333 0.2500000 13.07333 109.02667
## 10 0.2933333 2.820000 0.1400000 0.3900000 43.60333 0.00000
## 7 1.2466667 34.323333 1.0766667 2.1133333 55.39333 16.91333
## 8 1.0533333 19.436667 0.2366667 0.2566667 110.53333 55.06333
## 9 1.0366667 7.833333 1.8400000 0.3566667 57.46333 68.92000
## 6 1.3333333 73.730000 3.4466667 0.3500000 18.24333 87.61667
## 5 0.6333333 14.013333 0.2166667 0.2133333 37.39333 0.00000
## 2 2.3600000 31.050000 0.7733333 0.2500000 56.24333 39.29667
## 3 2.4300000 44.633333 0.8266667 0.2333333 52.60333 60.92333
## 4 2.6400000 26.456667 3.1133333 0.2500000 80.03667 85.37333
## 1 3.2033333 9.043333 1.1733333 2.5600000 27.32333 95.83000
select column variables
feno<-dcast(means3[-c(4:31)],Days~Sample,fill=0) ##select phenolics
flavo<-dcast(means3[-c(3,5:31)],Days~Sample,fill=0) ##select flavonoids
caro<-dcast(means3[-c(3:4,6:31)],Days~Sample,fill=0) ##carotenoids
anto<-dcast(means3[-c(3:5,7:31)],Days~Sample,fill=0) ##anthocyanins
hcn<-dcast(means3[-c(3:6,8:31)],Days~Sample,fill=0) ##total cyanide
cn<-dcast(means3[-c(3:7,9:31)],Days~Sample,fill=0)##acetone cyanohydrin
lina<-dcast(means3[-c(3:8,10:31)],Days~Sample,fill=0) ##linamarin
linase<-dcast(means3[-c(3:9,11:31)],Days~Sample,fill=0) ##linamarase
h2o2<-dcast(means3[-c(3:10,12:31)],Days~Sample,fill=0) ##hydrogen peroxide
cata<-dcast(means3[-c(3:11,13:31)],Days~Sample,fill=0) ##catalase
totsod<-dcast(means3[-c(3:12,14:31)],Days~Sample,fill=0)##total SOD
mnsod<-dcast(means3[-c(3:13,15:31)],Days~Sample,fill=0) ##MnSOD
cuznsod<-dcast(means3[-c(3:14,16:31)],Days~Sample,fill=0) ##CuZnSOD
malic<-dcast(means3[-c(3:15,17:31)],Days~Sample,fill=0) ##Malic acid
succ<-dcast(means3[-c(3:16,18:31)],Days~Sample,fill=0) ##Succinic acid
fum<-dcast(means3[-c(3:17,19:31)],Days~Sample,fill=0) ##Fumaric acid
raf<-dcast(means3[-c(3:18,20:31)],Days~Sample,fill=0) ##Raffinose
sucro<-dcast(means3[-c(3:19,21:31)],Days~Sample,fill=0) ##Sucrose
PLOT FIGURE 1A.
windows()
par(mfrow=c(3,3))
plot_colors = c("blue","red","forestgreen","black")
plot(feno[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(feno[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=200*0:max(feno[-1]))
lines(feno[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(feno[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(feno[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(feno[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Phenolics", col.lab="black")
plot(flavo[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(flavo[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=500*0:max(flavo[-1]))
lines(flavo[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(flavo[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(flavo[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(flavo[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Flavonoids", col.lab="black")
plot(caro[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(caro[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=2*0:max(caro[-1]))
lines(caro[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(caro[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(caro[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(caro[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Carotenoids", col.lab="black")
plot(anto[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(anto[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=10*0:max(anto[-1]))
lines(anto[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(anto[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(anto[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(anto[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Anthocyanins", col.lab="black")
plot(hcn[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(hcn[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=20*0:max(hcn[-1]))
lines(hcn[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(hcn[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(hcn[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(hcn[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Total cyanide", col.lab="black")
plot(cn[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(cn[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=3*0:max(cn[-1]))
lines(cn[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(cn[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(cn[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(cn[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Acetone cyanohydrin", col.lab="black")
plot(h2o2[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(h2o2[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=100*0:max(h2o2[-1]))
lines(h2o2[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(h2o2[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(h2o2[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(h2o2[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Hydrogen peroxide", col.lab="black")
plot(lina[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(lina[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=20*0:max(lina[-1]))
lines(lina[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(lina[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(lina[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(lina[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Linamarin", col.lab="black")
plot(linase[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(linase[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=2*0:max(linase[-1]))
lines(linase[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(linase[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(linase[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(linase[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Linamarase", col.lab="black")
PLOT FIGURE 1B
windows()
par(bg="cornsilk2") ## background color
par(mfrow=c(3,3))
plot_colors = c("blue","red","forestgreen","black")
plot(cata[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(cata[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=50*0:max(cata[-1]))
lines(cata[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(cata[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(cata[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(cata[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Catalase", col.lab="black")
plot(totsod[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(totsod[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1)
lines(totsod[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(totsod[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(totsod[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(totsod[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Total SOD", col.lab="black")
plot(mnsod[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(mnsod[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1)
lines(mnsod[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(mnsod[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(mnsod[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(mnsod[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "MnSOD", col.lab="black")
plot(cuznsod[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(cuznsod[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1)
lines(cuznsod[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(cuznsod[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(cuznsod[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(cuznsod[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "CuZnSOD", col.lab="black")
plot(malic[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(malic[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1)
lines(malic[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(malic[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(malic[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(malic[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Malic acid", col.lab="black")
plot(succ[-1]$BRA, type="o", col=plot_colors[1], ylim=c(2,max(succ[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1,at=1*0:max(succ[-1]))
lines(succ[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(succ[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(succ[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(succ[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Succinic Acid", col.lab="black")
plot(fum[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(fum[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1,at=1*0:max(fum[-1]))
lines(fum[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(fum[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(fum[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(fum[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Fumaric Acid", col.lab="black")
plot(raf[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(raf[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=1*0:max(raf[-1]))
lines(raf[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(raf[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(raf[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(raf[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Raffinose", col.lab="black")
plot(sucro[-1]$BRA, type="o", col=plot_colors[1], ylim=c(0,max(sucro[-1])), axes=FALSE, ann=FALSE,lwd=2)
axis(1, at=1:5, lab=c("0","3", "5", "8", "11"))
axis(2, las=1, at=20*0:max(sucro[-1]))
lines(sucro[-1]$BRA, type="o", col=plot_colors[1], lwd=2)
lines(sucro[-1]$IAC, type="o", pch=22, lty=2,lwd=2, col=plot_colors[2])
lines(sucro[-1]$ORI, type="o", pch=23, lty=3,lwd=2, col=plot_colors[3])
lines(sucro[-1]$SAN, type="o", pch=23, lty=3, lwd=2, col=plot_colors[4])
title(xlab= "Storage days", col.lab="black")
title(ylab= "Sucrose", col.lab="black")
Prepare the data and build decision trees for each data set
###prepare the data for secMetabo (A)
set.seed(12345)
ds <- secMetabo
target <- "PPDscores"
id <- c("Sample", "Days")
ignore <- id
nobs <- nrow(ds)
form <- formula(paste(target, "~ ."))
train <- sample(nobs, 0.70 * nobs)
(vars <- setdiff(names(ds), ignore))
## [1] "Phenolics" "Flavonoids" "Carotenoids" "Anthocyanins"
## [5] "Scopoletin" "PPDscores"
inputs <- setdiff(vars, target)
(nobs <- nrow(ds))
## [1] 60
(numerics <- intersect(inputs, names(ds)[which(sapply(ds[vars], is.numeric))]))
## [1] "Phenolics" "Flavonoids" "Carotenoids" "Anthocyanins"
(categorics <- intersect(inputs, names(ds)[which(sapply(ds[vars], is.factor))]))
## character(0)
(form <- formula(paste(target, "~ .")))
## PPDscores ~ .
length(train <- sample(nobs, 0.7*nobs))
## [1] 42
length(test <- setdiff(seq_len(nobs), train))
## [1] 18
actual <- ds[test, target]
model <- rpart(formula=form, data=ds[train, vars]) ##metabo
summary(model)
## Call:
## rpart(formula = form, data = ds[train, vars])
## n= 42
##
## CP nsplit rel error xerror xstd
## 1 0.34021722 0 1.0000000 1.0306126 0.1864732
## 2 0.19650761 1 0.6597828 0.6922332 0.1558201
## 3 0.09293521 2 0.4632752 0.5441033 0.1139205
## 4 0.01000000 3 0.3703400 0.6304867 0.1381380
##
## Variable importance
## Phenolics Flavonoids Scopoletin Carotenoids Anthocyanins
## 42 23 17 14 3
##
## Node number 1: 42 observations, complexity param=0.3402172
## mean=51.42571, MSE=2119.986
## left son=2 (9 obs) right son=3 (33 obs)
## Primary splits:
## Phenolics < 82.36 to the left, improve=0.34021720, (0 missing)
## Flavonoids < 910 to the left, improve=0.29419860, (0 missing)
## Scopoletin < 99.555 to the left, improve=0.21119000, (0 missing)
## Anthocyanins < 16.2815 to the left, improve=0.03926710, (0 missing)
## Carotenoids < 3.281 to the left, improve=0.02823829, (0 missing)
## Surrogate splits:
## Flavonoids < 744.167 to the left, agree=0.976, adj=0.889, (0 split)
## Scopoletin < 37.145 to the left, agree=0.905, adj=0.556, (0 split)
## Carotenoids < 2.257 to the left, agree=0.881, adj=0.444, (0 split)
##
## Node number 2: 9 observations
## mean=0, MSE=0
##
## Node number 3: 33 observations, complexity param=0.1965076
## mean=65.45091, MSE=1780.202
## left son=6 (12 obs) right son=7 (21 obs)
## Primary splits:
## Phenolics < 215.505 to the right, improve=0.29783680, (0 missing)
## Carotenoids < 3.97 to the right, improve=0.10768050, (0 missing)
## Flavonoids < 1355 to the right, improve=0.09484824, (0 missing)
## Scopoletin < 98.1 to the left, improve=0.08863321, (0 missing)
## Anthocyanins < 6.9305 to the left, improve=0.02635477, (0 missing)
## Surrogate splits:
## Flavonoids < 2039.166 to the right, agree=0.697, adj=0.167, (0 split)
## Carotenoids < 4.838 to the left, agree=0.697, adj=0.167, (0 split)
## Anthocyanins < 1.9205 to the left, agree=0.697, adj=0.167, (0 split)
## Scopoletin < 51.175 to the left, agree=0.697, adj=0.167, (0 split)
##
## Node number 6: 12 observations
## mean=34.99, MSE=615.4478
##
## Node number 7: 21 observations, complexity param=0.09293521
## mean=82.85714, MSE=1612.589
## left son=14 (7 obs) right son=15 (14 obs)
## Primary splits:
## Phenolics < 123.995 to the left, improve=0.24435400, (0 missing)
## Carotenoids < 4.942 to the right, improve=0.18509840, (0 missing)
## Flavonoids < 1359.166 to the right, improve=0.15249280, (0 missing)
## Anthocyanins < 10.019 to the right, improve=0.03432596, (0 missing)
## Scopoletin < 98.1 to the left, improve=0.02424582, (0 missing)
## Surrogate splits:
## Carotenoids < 5.5905 to the right, agree=0.762, adj=0.286, (0 split)
## Scopoletin < 90.265 to the left, agree=0.762, adj=0.286, (0 split)
## Flavonoids < 1571.666 to the right, agree=0.714, adj=0.143, (0 split)
## Anthocyanins < 4.9265 to the left, agree=0.714, adj=0.143, (0 split)
##
## Node number 14: 7 observations
## mean=54.78429, MSE=874.9343
##
## Node number 15: 14 observations
## mean=96.89357, MSE=1390.353
prp(model, type=2, extra=101, nn=TRUE, fallen.leaves=TRUE,
faclen=0, varlen=0, shadow.col="orange", branch.lty=2,main="(A):Decision Regression Tree using Secondary Metabolites",cex.main=0.7)
###data for cyanogenics (B)
set.seed(12345)
ds1 <- Cyanogenic
target <- "PPDscores"
id <- c("Sample", "Days")
ignore <- id
nobs <- nrow(ds1)
form <- formula(paste(target, "~ ."))
train <- sample(nobs, 0.70 * nobs)
(vars <- setdiff(names(ds1), ignore))
## [1] "Totalcyanide" "AcetoneCyano" "Linamarin" "Linamarase"
## [5] "PPDscores"
inputs <- setdiff(vars, target)
(nobs <- nrow(ds1))
## [1] 60
(numerics <- intersect(inputs, names(ds1)[which(sapply(ds1[vars], is.numeric))]))
## [1] "Totalcyanide" "AcetoneCyano" "Linamarin"
(categorics <- intersect(inputs, names(ds1)[which(sapply(ds1[vars], is.factor))]))
## character(0)
(form <- formula(paste(target, "~ .")))
## PPDscores ~ .
length(train <- sample(nobs, 0.7*nobs))
## [1] 42
length(test <- setdiff(seq_len(nobs), train))
## [1] 18
actual <- ds1[test, target]
model1 <- rpart(formula=form, data=ds1[train, vars]) ##cyanogenics
summary(model1)
## Call:
## rpart(formula = form, data = ds1[train, vars])
## n= 42
##
## CP nsplit rel error xerror xstd
## 1 0.1960632 0 1.0000000 1.030613 0.1864732
## 2 0.0100000 2 0.6078736 1.236546 0.2447902
##
## Variable importance
## AcetoneCyano Linamarin Totalcyanide Linamarase
## 50 20 17 13
##
## Node number 1: 42 observations, complexity param=0.1960632
## mean=51.42571, MSE=2119.986
## left son=2 (14 obs) right son=3 (28 obs)
## Primary splits:
## AcetoneCyano < 8.523 to the right, improve=0.16427170, (0 missing)
## Linamarin < 55.914 to the left, improve=0.13145570, (0 missing)
## Totalcyanide < 62.632 to the left, improve=0.12715540, (0 missing)
## Linamarase < 6.263 to the left, improve=0.05326872, (0 missing)
## Surrogate splits:
## Totalcyanide < 26.2775 to the left, agree=0.786, adj=0.357, (0 split)
## Linamarin < 17.2975 to the left, agree=0.786, adj=0.357, (0 split)
##
## Node number 2: 14 observations
## mean=25.03429, MSE=1309.526
##
## Node number 3: 28 observations, complexity param=0.1960632
## mean=64.62143, MSE=2002.835
## left son=6 (19 obs) right son=7 (9 obs)
## Primary splits:
## AcetoneCyano < 6.9235 to the left, improve=0.36177370, (0 missing)
## Linamarase < 7.148 to the left, improve=0.16848290, (0 missing)
## Linamarin < 49.059 to the right, improve=0.14161540, (0 missing)
## Totalcyanide < 53.766 to the right, improve=0.09665042, (0 missing)
## Surrogate splits:
## Linamarin < 47.208 to the right, agree=0.821, adj=0.444, (0 split)
## Linamarase < 7.226 to the left, agree=0.821, adj=0.444, (0 split)
## Totalcyanide < 32.333 to the right, agree=0.786, adj=0.333, (0 split)
##
## Node number 6: 19 observations
## mean=46.09526, MSE=1167.04
##
## Node number 7: 9 observations
## mean=103.7322, MSE=1513.064
printcp(model1) ##see if is a regression model
##
## Regression tree:
## rpart(formula = form, data = ds1[train, vars])
##
## Variables actually used in tree construction:
## [1] AcetoneCyano
##
## Root node error: 89039/42 = 2120
##
## n= 42
##
## CP nsplit rel error xerror xstd
## 1 0.19606 0 1.00000 1.0306 0.18647
## 2 0.01000 2 0.60787 1.2365 0.24479
prp(model1, type=2, extra=101, nn=TRUE, fallen.leaves=TRUE,
faclen=0, varlen=0, shadow.col="orange", branch.lty=2,main="(B):Decision Regression Tree using Cyanogenic Glucosides",cex.main=0.7)
###data for enzymes (C)
set.seed(12345)
ds2 <- Enzymes
target <- "PPDscores"
id <- c("Sample", "Days")
ignore <- id
nobs <- nrow(ds2)
form <- formula(paste(target, "~ ."))
train <- sample(nobs, 0.70 * nobs)
(vars <- setdiff(names(ds2), ignore))
## [1] "PolyPhenol" "Ascorbic" "Ascorbate" "Guaiacol" "Tocopherol"
## [6] "Proteins" "PPDscores"
inputs <- setdiff(vars, target)
(nobs <- nrow(ds2))
## [1] 60
(numerics <- intersect(inputs, names(ds2)[which(sapply(ds2[vars], is.numeric))]))
## [1] "PolyPhenol" "Ascorbic" "Ascorbate" "Guaiacol" "Tocopherol"
(categorics <- intersect(inputs, names(ds2)[which(sapply(ds2[vars], is.factor))]))
## character(0)
(form <- formula(paste(target, "~ .")))
## PPDscores ~ .
length(train <- sample(nobs, 0.7*nobs))
## [1] 42
length(test <- setdiff(seq_len(nobs), train))
## [1] 18
actual <- ds2[test, target]
model2 <- rpart(formula=form, data=ds2[train, vars]) ##enzymes
summary(model2)
## Call:
## rpart(formula = form, data = ds2[train, vars])
## n= 42
##
## CP nsplit rel error xerror xstd
## 1 0.38621897 0 1.0000000 1.0306126 0.1864732
## 2 0.17368229 1 0.6137810 0.9861663 0.1672450
## 3 0.06747828 2 0.4400987 0.7490577 0.1516850
## 4 0.01000000 3 0.3726205 0.7316289 0.1460642
##
## Variable importance
## Guaiacol Proteins PolyPhenol Ascorbic Ascorbate Tocopherol
## 27 26 15 15 14 3
##
## Node number 1: 42 observations, complexity param=0.386219
## mean=51.42571, MSE=2119.986
## left son=2 (31 obs) right son=3 (11 obs)
## Primary splits:
## Proteins < 36.19 to the right, improve=0.38621900, (0 missing)
## Guaiacol < 1.79 to the left, improve=0.37436460, (0 missing)
## Ascorbic < 1.015 to the left, improve=0.36556880, (0 missing)
## PolyPhenol < 4.88 to the left, improve=0.17527060, (0 missing)
## Ascorbate < 39.37 to the left, improve=0.09081982, (0 missing)
## Surrogate splits:
## Guaiacol < 3.25 to the left, agree=0.905, adj=0.636, (0 split)
## PolyPhenol < 4.88 to the left, agree=0.857, adj=0.455, (0 split)
## Ascorbic < 2.545 to the left, agree=0.833, adj=0.364, (0 split)
## Ascorbate < 58.265 to the left, agree=0.833, adj=0.364, (0 split)
##
## Node number 2: 31 observations, complexity param=0.1736823
## mean=34.38065, MSE=1303.341
## left son=4 (10 obs) right son=5 (21 obs)
## Primary splits:
## Guaiacol < 0.375 to the left, improve=0.3827525, (0 missing)
## Ascorbic < 1.015 to the left, improve=0.2965542, (0 missing)
## PolyPhenol < 3.715 to the right, improve=0.2545725, (0 missing)
## Proteins < 48.07 to the left, improve=0.1884146, (0 missing)
## Ascorbate < 19.665 to the left, improve=0.1312751, (0 missing)
## Surrogate splits:
## Ascorbic < 0.985 to the left, agree=0.871, adj=0.6, (0 split)
## Proteins < 48.07 to the left, agree=0.839, adj=0.5, (0 split)
## PolyPhenol < 3.73 to the right, agree=0.806, adj=0.4, (0 split)
## Ascorbate < 2.63 to the left, agree=0.806, adj=0.4, (0 split)
## Tocopherol < 4.685 to the right, agree=0.774, adj=0.3, (0 split)
##
## Node number 3: 11 observations
## mean=99.46182, MSE=1295.193
##
## Node number 4: 10 observations
## mean=2.014, MSE=36.50576
##
## Node number 5: 21 observations, complexity param=0.06747828
## mean=49.79333, MSE=1170.188
## left son=10 (14 obs) right son=11 (7 obs)
## Primary splits:
## Guaiacol < 1.36 to the left, improve=0.24449580, (0 missing)
## Tocopherol < 0.36 to the right, improve=0.17478930, (0 missing)
## Proteins < 56.87 to the left, improve=0.12476490, (0 missing)
## Ascorbate < 28.51 to the right, improve=0.11100170, (0 missing)
## Ascorbic < 2.27 to the left, improve=0.04312791, (0 missing)
## Surrogate splits:
## Ascorbate < 23.095 to the right, agree=0.905, adj=0.714, (0 split)
## PolyPhenol < 3.12 to the right, agree=0.810, adj=0.429, (0 split)
## Ascorbic < 1.15 to the right, agree=0.810, adj=0.429, (0 split)
## Proteins < 56.87 to the left, agree=0.714, adj=0.143, (0 split)
##
## Node number 10: 14 observations
## mean=37.83286, MSE=827.5443
##
## Node number 11: 7 observations
## mean=73.71429, MSE=997.157
prp(model2, type=2, extra=101, nn=TRUE, fallen.leaves=TRUE,
faclen=0, varlen=0, shadow.col="orange", branch.lty=2,main="(C):Decision Regression Tree using Enzymes",cex.main=0.7)
### data for Sugar and Acids (D)
set.seed(12345)
ds3 <- SugarAcids
target <- "PPDscores"
id <- c("Sample", "Days")
ignore <- id
nobs <- nrow(ds3)
form <- formula(paste(target, "~ ."))
train <- sample(nobs, 0.70 * nobs)
(vars <- setdiff(names(ds3), ignore))
## [1] "MalicAcid" "SuccinicAcid" "FumaricAcid" "Raffinose"
## [5] "Sucrose" "Glucose" "Fructose" "TotalSugars"
## [9] "PPDscores"
inputs <- setdiff(vars, target)
(nobs <- nrow(ds3))
## [1] 60
(numerics <- intersect(inputs, names(ds3)[which(sapply(ds3[vars], is.numeric))]))
## [1] "MalicAcid" "SuccinicAcid" "FumaricAcid" "Raffinose"
## [5] "Sucrose" "Glucose" "Fructose"
(categorics <- intersect(inputs, names(ds3)[which(sapply(ds3[vars], is.factor))]))
## character(0)
(form <- formula(paste(target, "~ .")))
## PPDscores ~ .
length(train <- sample(nobs, 0.7*nobs))
## [1] 42
length(test <- setdiff(seq_len(nobs), train))
## [1] 18
actual <- ds3[test, target]
model3 <- rpart(formula=form, data=ds3[train, vars]) ## sugar and acids
summary(model3)
## Call:
## rpart(formula = form, data = ds3[train, vars])
## n= 42
##
## CP nsplit rel error xerror xstd
## 1 0.52182910 0 1.0000000 1.0306126 0.1864732
## 2 0.04402899 1 0.4781709 0.5134705 0.1138624
## 3 0.01000000 2 0.4341419 0.7210448 0.1609335
##
## Variable importance
## Sucrose Raffinose Fructose FumaricAcid Glucose
## 33 23 12 12 9
## MalicAcid SuccinicAcid TotalSugars
## 7 3 2
##
## Node number 1: 42 observations, complexity param=0.5218291
## mean=51.42571, MSE=2119.986
## left son=2 (19 obs) right son=3 (23 obs)
## Primary splits:
## Sucrose < 33.845 to the right, improve=0.5218291, (0 missing)
## Raffinose < 2.765 to the right, improve=0.3942982, (0 missing)
## FumaricAcid < 0.81 to the right, improve=0.2041970, (0 missing)
## TotalSugars < 56.975 to the right, improve=0.1969301, (0 missing)
## Fructose < 92.71 to the left, improve=0.1273802, (0 missing)
## Surrogate splits:
## Raffinose < 3.075 to the right, agree=0.857, adj=0.684, (0 split)
## FumaricAcid < 0.465 to the right, agree=0.714, adj=0.368, (0 split)
## Fructose < 92.71 to the left, agree=0.690, adj=0.316, (0 split)
## MalicAcid < 0.085 to the right, agree=0.643, adj=0.211, (0 split)
## Glucose < 87.73 to the left, agree=0.643, adj=0.211, (0 split)
##
## Node number 2: 19 observations
## mean=14.83105, MSE=483.5404
##
## Node number 3: 23 observations, complexity param=0.04402899
## mean=81.65609, MSE=1451.686
## left son=6 (16 obs) right son=7 (7 obs)
## Primary splits:
## SuccinicAcid < 2.895 to the right, improve=0.11741410, (0 missing)
## Raffinose < 2.58 to the right, improve=0.09735484, (0 missing)
## Glucose < 42.23 to the right, improve=0.08458517, (0 missing)
## Fructose < 44.655 to the right, improve=0.08458517, (0 missing)
## Sucrose < 5.19 to the right, improve=0.07201502, (0 missing)
## Surrogate splits:
## Glucose < 18.53 to the right, agree=0.957, adj=0.857, (0 split)
## Fructose < 37.765 to the right, agree=0.957, adj=0.857, (0 split)
## TotalSugars < 80.765 to the right, agree=0.957, adj=0.857, (0 split)
## Sucrose < 5.19 to the right, agree=0.913, adj=0.714, (0 split)
## Raffinose < 1.91 to the right, agree=0.826, adj=0.429, (0 split)
##
## Node number 6: 16 observations
## mean=73.02062, MSE=1217.297
##
## Node number 7: 7 observations
## mean=101.3943, MSE=1427.387
prp(model3, type=2, extra=101, nn=TRUE, fallen.leaves=TRUE,
faclen=0, varlen=0, shadow.col="orange", branch.lty=2,main="(D):Decision Regression Tree using Sugars and Organic Acids",cex.main=0.7)
### Scavenging Reative oxygen species (E)
set.seed(12345)
ds4 <- ROS
target <- "PPDscores"
id <- c("Sample", "Days")
ignore <- id
nobs <- nrow(ds4)
form <- formula(paste(target, "~ ."))
train <- sample(nobs, 0.70 * nobs)
(vars <- setdiff(names(ds4), ignore))
## [1] "HydrogenPeroxide" "Catalase" "TotalSOD"
## [4] "MnSOD" "CuZnSOD" "PPDscores"
inputs <- setdiff(vars, target)
(nobs <- nrow(ds4))
## [1] 60
(numerics <- intersect(inputs, names(ds4)[which(sapply(ds4[vars], is.numeric))]))
## [1] "HydrogenPeroxide" "Catalase" "TotalSOD"
## [4] "MnSOD"
(categorics <- intersect(inputs, names(ds4)[which(sapply(ds4[vars], is.factor))]))
## character(0)
(form <- formula(paste(target, "~ .")))
## PPDscores ~ .
length(train <- sample(nobs, 0.7*nobs))
## [1] 42
length(test <- setdiff(seq_len(nobs), train))
## [1] 18
actual <- ds4[test, target]
model4 <- rpart(formula=form, data=ds4[train, vars]) ## SCAVENGING Reactive species
summary(model4)
## Call:
## rpart(formula = form, data = ds4[train, vars])
## n= 42
##
## CP nsplit rel error xerror xstd
## 1 0.42045949 0 1.0000000 1.0306126 0.1864732
## 2 0.04284409 1 0.5795405 0.6715438 0.1216672
## 3 0.01000000 2 0.5366964 0.7626752 0.1237643
##
## Variable importance
## HydrogenPeroxide Catalase CuZnSOD MnSOD
## 57 21 9 7
## TotalSOD
## 6
##
## Node number 1: 42 observations, complexity param=0.4204595
## mean=51.42571, MSE=2119.986
## left son=2 (25 obs) right son=3 (17 obs)
## Primary splits:
## HydrogenPeroxide < 127.6885 to the left, improve=0.42045950, (0 missing)
## Catalase < 98.508 to the left, improve=0.18286030, (0 missing)
## CuZnSOD < 0.086 to the left, improve=0.14167750, (0 missing)
## TotalSOD < 0.1045 to the left, improve=0.10560780, (0 missing)
## MnSOD < 0.0185 to the right, improve=0.05389475, (0 missing)
## Surrogate splits:
## Catalase < 121.57 to the left, agree=0.738, adj=0.353, (0 split)
## CuZnSOD < 0.086 to the left, agree=0.667, adj=0.176, (0 split)
## TotalSOD < 0.081 to the left, agree=0.643, adj=0.118, (0 split)
## MnSOD < 0.0745 to the left, agree=0.643, adj=0.118, (0 split)
##
## Node number 2: 25 observations, complexity param=0.04284409
## mean=26.806, MSE=941.7524
## left son=4 (9 obs) right son=5 (16 obs)
## Primary splits:
## HydrogenPeroxide < 99.92 to the left, improve=0.16203040, (0 missing)
## TotalSOD < 0.0425 to the left, improve=0.13758140, (0 missing)
## CuZnSOD < 0.0065 to the left, improve=0.07977996, (0 missing)
## Catalase < 89.801 to the left, improve=0.06416749, (0 missing)
## MnSOD < 0.0185 to the right, improve=0.02020611, (0 missing)
## Surrogate splits:
## Catalase < 52.384 to the left, agree=0.84, adj=0.556, (0 split)
## MnSOD < 0.0025 to the left, agree=0.68, adj=0.111, (0 split)
##
## Node number 3: 17 observations
## mean=87.63118, MSE=1650.478
##
## Node number 4: 9 observations
## mean=10.33556, MSE=507.2742
##
## Node number 5: 16 observations
## mean=36.07062, MSE=947.7207
prp(model4, type=2, extra=101, nn=TRUE, fallen.leaves=TRUE,
faclen=0, varlen=0, shadow.col="orange", branch.lty=2,main="(E):Decision Regression Tree using Reactive Oxygen Species",cex.main=0.7)
## all data set (F)
set.seed(12345)
ds5 <- alldata
target <- "PPDscores"
id <- c("Sample", "Days")
ignore <- id
nobs <- nrow(ds5)
form <- formula(paste(target, "~ ."))
train <- sample(nobs, 0.70 * nobs)
(vars <- setdiff(names(ds5), ignore))
## [1] "Phenolics" "Flavonoids" "Carotenoids"
## [4] "Anthocyanins" "Totalcyanide" "AcetoneCyano"
## [7] "Linamarin" "Linamarase" "HydrogenPeroxide"
## [10] "Catalase" "TotalSOD" "MnSOD"
## [13] "CuZnSOD" "MalicAcid" "SuccinicAcid"
## [16] "FumaricAcid" "Raffinose" "Sucrose"
## [19] "Glucose" "Fructose" "TotalSugars"
## [22] "Scopoletin" "PolyPhenol" "Ascorbic"
## [25] "Ascorbate" "Guaiacol" "Tocopherol"
## [28] "Proteins" "PPDscores"
inputs <- setdiff(vars, target)
(nobs <- nrow(ds5))
## [1] 60
(numerics <- intersect(inputs, names(ds5)[which(sapply(ds5[vars], is.numeric))]))
## [1] "Phenolics" "Flavonoids" "Carotenoids"
## [4] "Anthocyanins" "Totalcyanide" "AcetoneCyano"
## [7] "Linamarin" "Linamarase" "HydrogenPeroxide"
## [10] "Catalase" "TotalSOD" "MnSOD"
## [13] "CuZnSOD" "MalicAcid" "SuccinicAcid"
## [16] "FumaricAcid" "Raffinose" "Sucrose"
## [19] "Glucose" "Fructose" "TotalSugars"
## [22] "Scopoletin" "PolyPhenol" "Ascorbic"
## [25] "Ascorbate" "Guaiacol" "Tocopherol"
(categorics <- intersect(inputs, names(ds5)[which(sapply(ds5[vars], is.factor))]))
## character(0)
(form <- formula(paste(target, "~ .")))
## PPDscores ~ .
length(train <- sample(nobs, 0.7*nobs))
## [1] 42
length(test <- setdiff(seq_len(nobs), train))
## [1] 18
actual <- ds5[test, target]
model5 <- rpart(formula=form, data=ds5[train, vars]) ## all dataset
summary(model5)
## Call:
## rpart(formula = form, data = ds5[train, vars])
## n= 42
##
## CP nsplit rel error xerror xstd
## 1 0.52182910 0 1.0000000 1.0306126 0.1864732
## 2 0.08156605 1 0.4781709 0.5134705 0.1138624
## 3 0.01000000 2 0.3966049 0.7678115 0.1697925
##
## Variable importance
## Sucrose HydrogenPeroxide Raffinose Guaiacol
## 22 15 15 14
## Ascorbic Phenolics Carotenoids SuccinicAcid
## 14 10 3 2
## Flavonoids Proteins Scopoletin
## 2 2 2
##
## Node number 1: 42 observations, complexity param=0.5218291
## mean=51.42571, MSE=2119.986
## left son=2 (19 obs) right son=3 (23 obs)
## Primary splits:
## Sucrose < 33.845 to the right, improve=0.5218291, (0 missing)
## HydrogenPeroxide < 127.6885 to the left, improve=0.4204595, (0 missing)
## Raffinose < 2.765 to the right, improve=0.3942982, (0 missing)
## Proteins < 36.19 to the right, improve=0.3862190, (0 missing)
## Guaiacol < 1.79 to the left, improve=0.3743646, (0 missing)
## Surrogate splits:
## HydrogenPeroxide < 109.333 to the left, agree=0.857, adj=0.684, (0 split)
## Raffinose < 3.075 to the right, agree=0.857, adj=0.684, (0 split)
## Ascorbic < 0.985 to the left, agree=0.833, adj=0.632, (0 split)
## Guaiacol < 0.81 to the left, agree=0.810, adj=0.579, (0 split)
## Phenolics < 82.36 to the left, agree=0.762, adj=0.474, (0 split)
##
## Node number 2: 19 observations
## mean=14.83105, MSE=483.5404
##
## Node number 3: 23 observations, complexity param=0.08156605
## mean=81.65609, MSE=1451.686
## left son=6 (14 obs) right son=7 (9 obs)
## Primary splits:
## Carotenoids < 4.653 to the right, improve=0.2175159, (0 missing)
## Proteins < 27.34 to the right, improve=0.2066228, (0 missing)
## HydrogenPeroxide < 126.747 to the left, improve=0.1635979, (0 missing)
## Guaiacol < 1.79 to the left, improve=0.1539508, (0 missing)
## MnSOD < 0.003 to the right, improve=0.1379522, (0 missing)
## Surrogate splits:
## SuccinicAcid < 2.5 to the right, agree=0.826, adj=0.556, (0 split)
## Flavonoids < 1112.5 to the right, agree=0.783, adj=0.444, (0 split)
## Scopoletin < 63.06 to the right, agree=0.783, adj=0.444, (0 split)
## Guaiacol < 3.495 to the left, agree=0.783, adj=0.444, (0 split)
## Proteins < 24.47 to the right, agree=0.783, adj=0.444, (0 split)
##
## Node number 6: 14 observations
## mean=67.40857, MSE=1066.812
##
## Node number 7: 9 observations
## mean=103.8189, MSE=1243.425
prp(model5, type=2, extra=101, nn=TRUE, fallen.leaves=TRUE,
faclen=0, varlen=0, shadow.col="orange", branch.lty=2,main="(F):Decision Regression Tree using all dataset",cex.main=0.7)
FIGURE 3 (RIGHT)
setwd("C:/Users/Virgílio/Desktop/PASTAS DO DESKTOP/ascorbato peroxidase")
load("newdata.RData")
newdata_melt <- melt(newdata, id = c("Sample", "Days"))
means<-dcast(newdata_melt, Sample + Days ~ variable, mean) ## all means
sd<-dcast(newdata_melt, Sample + Days ~ variable, sd) ##all standard deviations
ppd<-means[-c(3:6)] ##call means ppd
ppd2=matrix(ppd$asinPPD,5,4) ##transform to matrix
rownames(ppd2) = levels(ppd$Days)
colnames(ppd2) = levels(ppd$Sample)
ppd2
## BRA IAC ORI SAN
## Fresh 0.00000 0.00000 0.00000 0.00000
## Day3 39.29667 16.91333 41.08333 12.62667
## Day5 60.92333 55.06333 88.58000 50.45333
## Day8 85.37333 68.92000 102.00000 66.14667
## Day11 95.83000 87.61667 109.02667 77.47000
SD<-sd[-c(3:6)] ##call standard deviation of tocopherol
SD2=matrix(SD$asinPPD,5,4)
rownames(SD2) = levels(SD$Days)
colnames(SD2) = levels(SD$Sample)
SD2
## BRA IAC ORI SAN
## Fresh 0.00000 0.00000 0.00000 0.00000
## Day3 26.12894 16.99559 31.48827 14.11173
## Day5 41.27605 30.65746 60.76923 27.26702
## Day8 46.85692 33.61286 49.77227 34.61916
## Day11 37.65194 31.26715 43.31654 35.35903
mydata<-ppd2[-1,] ##delete first row
SD3<-SD2[-1,]
#### Function to plot "one end" error bar
superpose.eb <- function (x, y, ebl, ebu = ebl, length = 0.08, ...)
arrows(x, y + ebu, x, y - ebl, angle = 90, code = 3,
length = length, ...)
require(TeachingDemos)
fillcolours = c("aliceblue","antiquewhite4","aquamarine4","chartreuse4")
x.abscis <- barplot(
mydata, beside=TRUE,
col=fillcolours, # makes sense in the context of width and space parameters
space=c(0.4,2), # spacing between bars in the same group, and then between groups
ylim=c(0,250),
xlab="Cassava cultivars",axis.lty=1, # enable tick marks on the X axis
font.lab=2, # bold for axis labels
ylab="PPD Scores (Arcsin PPD)")
superpose.eb(x.abscis, mydata, ebl=0, ebu=SD3)
legend(x=2, y=225, box.lty=0, legend=c(rownames(mydata)), fill=fillcolours)
TABLE 1
setwd("C:/Users/Virgílio/Desktop/PASTAS DO DESKTOP/ascorbato peroxidase")
load("TabelaFinalTese2014.RData") ##load data
TabelaFinalTese2014
## Sample Days Phenolics Flavonoids Carotenoids Anthocyanins Totalcyanide
## 1 SAN Fresh 41.38 577.500 1.435 1.837 60.096
## 2 SAN Fresh 44.21 562.500 5.312 8.182 55.297
## 3 SAN Fresh 42.14 595.833 4.954 11.355 55.206
## 4 ORI Fresh 61.97 739.167 1.343 5.845 36.194
## 5 ORI Fresh 54.97 760.833 2.894 4.008 32.219
## 6 ORI Fresh 75.09 817.500 4.838 5.177 30.710
## 7 IAC Fresh 71.57 385.833 4.306 8.516 24.632
## 8 IAC Fresh 65.22 380.833 4.190 15.196 24.221
## 9 IAC Fresh 66.42 427.500 4.537 15.864 23.764
## 10 BRA Fresh 78.74 495.833 1.597 9.518 22.850
## 11 BRA Fresh 66.04 490.833 1.493 7.681 22.850
## 12 BRA Fresh 55.60 540.833 1.458 1.837 22.713
## 13 SAN Day3 299.81 1049.167 4.745 9.518 86.967
## 14 SAN Day3 338.93 932.500 4.525 4.342 84.956
## 15 SAN Day3 463.15 895.833 4.630 10.520 82.032
## 16 ORI Day3 558.99 1460.833 4.641 9.518 58.679
## 17 ORI Day3 545.60 1465.833 5.567 16.866 55.206
## 18 ORI Day3 531.07 970.833 5.046 16.365 54.749
## 19 IAC Day3 524.97 1514.167 5.046 1.169 59.227
## 20 IAC Day3 539.43 1359.167 4.132 5.511 60.324
## 21 IAC Day3 493.43 1247.500 3.171 10.854 60.598
## 22 BRA Day3 538.62 2942.500 3.414 0.000 80.889
## 23 BRA Day3 543.33 2410.833 4.757 0.668 80.249
## 24 BRA Day3 499.12 1510.833 4.479 16.198 79.518
## 25 SAN Day5 95.79 1407.500 7.951 16.365 66.265
## 26 SAN Day5 167.93 1394.167 6.227 15.864 64.254
## 27 SAN Day5 111.76 1422.500 5.949 16.532 58.953
## 28 ORI Day5 143.84 859.167 4.977 5.177 39.942
## 29 ORI Day5 171.64 735.833 5.382 6.179 39.576
## 30 ORI Day5 225.03 749.167 4.201 5.177 34.961
## 31 IAC Day5 141.82 1609.167 4.722 0.668 22.622
## 32 IAC Day5 116.42 1667.500 4.630 0.000 22.165
## 33 IAC Day5 162.96 1734.167 3.785 0.835 21.753
## 34 BRA Day5 131.13 1555.833 4.919 8.015 56.668
## 35 BRA Day5 118.43 1587.500 5.000 8.683 55.388
## 36 BRA Day5 210.63 1607.500 5.093 10.186 54.063
## 37 SAN Day8 114.09 1295.833 6.146 11.689 61.695
## 38 SAN Day8 190.57 1340.833 6.215 20.373 60.781
## 39 SAN Day8 165.91 1315.833 4.514 25.048 59.227
## 40 ORI Day8 164.53 1052.500 3.391 2.672 66.265
## 41 ORI Day8 225.72 1052.500 3.808 8.683 64.483
## 42 ORI Day8 169.37 1059.167 3.785 6.179 62.609
## 43 IAC Day8 209.43 1025.833 2.917 8.015 53.469
## 44 IAC Day8 220.38 1025.833 4.676 6.680 53.469
## 45 IAC Day8 164.28 1002.500 3.426 5.678 53.195
## 46 BRA Day8 187.30 1172.500 5.208 9.852 39.165
## 47 BRA Day8 224.09 1145.833 5.706 10.520 39.074
## 48 BRA Day8 205.79 1192.500 4.931 15.029 38.845
## 49 SAN Day11 93.46 1189.167 7.708 4.175 93.228
## 50 SAN Day11 85.98 1262.500 5.741 8.015 93.685
## 51 SAN Day11 129.56 1240.833 8.356 7.181 94.462
## 52 ORI Day11 158.30 1350.833 0.984 8.850 79.061
## 53 ORI Day11 88.43 1367.500 4.236 10.520 79.472
## 54 ORI Day11 131.01 1377.500 4.178 12.858 79.427
## 55 IAC Day11 133.02 1334.167 4.630 22.877 68.687
## 56 IAC Day11 179.81 1404.167 5.440 21.375 66.265
## 57 IAC Day11 119.56 1422.500 4.190 22.210 64.894
## 58 BRA Day11 203.02 1512.500 4.595 43.083 29.705
## 59 BRA Day11 191.07 1415.833 4.965 41.580 29.020
## 60 BRA Day11 152.14 1407.500 5.127 44.085 28.791
## AcetoneCyano Linamarin Linamarase HydrogenPeroxide Catalase TotalSOD
## 1 4.387 55.708 6.055 61.797 17.085 0.029
## 2 3.290 52.007 6.157 62.268 13.320 0.041
## 3 3.245 51.961 6.194 62.738 14.261 0.048
## 4 8.729 27.466 5.477 97.567 37.794 0.021
## 5 8.683 23.535 5.483 97.567 42.500 0.087
## 6 8.637 22.073 5.489 97.096 47.207 0.151
## 7 10.100 14.533 8.312 75.917 126.747 0.461
## 8 10.054 14.167 8.330 76.858 150.280 0.533
## 9 10.008 13.756 8.342 79.211 183.226 0.497
## 10 9.688 13.162 7.518 119.217 203.935 0.000
## 11 9.643 13.207 7.524 121.570 228.879 0.096
## 12 9.597 13.116 7.536 120.629 235.469 0.073
## 13 5.438 81.529 6.019 40.617 112.628 0.016
## 14 5.393 79.564 6.037 41.088 108.392 0.033
## 15 5.301 76.730 6.055 42.029 116.393 0.040
## 16 5.941 52.738 6.332 86.271 202.993 0.044
## 17 5.850 49.356 6.338 88.154 214.760 0.068
## 18 5.850 48.899 6.350 88.624 235.939 0.057
## 19 10.145 49.082 5.881 103.685 87.683 0.344
## 20 10.008 50.316 5.887 103.215 89.095 0.028
## 21 9.917 50.681 5.887 104.627 90.507 0.000
## 22 6.627 74.263 6.976 102.744 107.921 0.249
## 23 6.535 73.714 6.970 102.273 117.805 0.000
## 24 6.489 73.029 6.976 105.568 119.687 0.015
## 25 7.038 59.227 5.947 102.273 70.269 0.057
## 26 6.992 57.262 5.947 104.627 71.681 0.040
## 27 6.855 52.098 5.953 102.744 57.561 0.055
## 28 6.398 33.544 4.797 112.628 150.751 0.133
## 29 6.352 33.224 4.815 112.628 162.517 0.132
## 30 6.307 28.654 4.851 113.098 175.225 0.136
## 31 8.957 13.664 6.151 158.281 89.095 0.356
## 32 8.912 13.253 6.151 157.340 95.684 0.395
## 33 8.866 12.887 6.157 155.928 108.392 0.335
## 34 8.683 47.985 6.621 115.452 119.217 0.258
## 35 8.546 46.843 6.687 117.334 132.395 0.275
## 36 8.500 45.563 7.289 119.687 116.864 0.265
## 37 6.078 55.617 5.676 119.217 299.007 0.020
## 38 5.987 54.794 5.682 117.334 286.770 0.018
## 39 5.941 53.286 5.688 117.334 300.890 0.019
## 40 8.455 57.810 4.839 133.807 101.332 0.155
## 41 8.363 56.120 4.869 133.807 123.453 0.155
## 42 8.317 54.292 4.899 135.219 126.277 0.149
## 43 4.433 49.036 6.934 194.992 192.639 0.285
## 44 4.387 49.082 6.940 195.463 176.166 0.359
## 45 4.342 48.853 6.940 195.934 182.285 0.089
## 46 6.946 32.218 5.044 154.987 210.053 0.405
## 47 6.901 32.173 5.044 157.811 204.405 0.043
## 48 6.855 31.990 5.050 157.340 247.706 0.000
## 49 4.890 88.338 7.108 147.456 160.634 0.045
## 50 4.844 88.841 7.133 148.868 169.106 0.054
## 51 4.799 89.663 7.163 146.515 171.930 0.021
## 52 7.266 71.795 7.789 188.874 162.517 0.113
## 53 6.901 72.572 7.807 189.815 154.987 0.126
## 54 6.855 72.572 7.807 189.344 157.340 0.096
## 55 11.014 57.673 6.338 270.297 153.104 0.000
## 56 10.922 55.343 6.338 269.356 214.760 0.000
## 57 10.831 54.063 6.344 269.826 211.465 0.000
## 58 7.998 21.707 8.728 178.990 245.352 0.177
## 59 7.998 21.022 8.740 180.873 258.060 0.126
## 60 7.952 20.839 8.740 181.814 255.236 0.033
## MnSOD CuZnSOD MalicAcid SuccinicAcid FumaricAcid Raffinose Sucrose
## 1 0.027 0.002 0.28 3.18 1.66 3.14 46.62
## 2 0.000 0.041 0.31 3.11 1.78 3.40 51.08
## 3 0.054 0.000 0.14 3.12 1.55 3.27 48.85
## 4 0.000 0.021 0.06 2.12 2.74 2.56 45.60
## 5 0.000 0.087 0.06 2.40 3.06 2.82 51.41
## 6 0.000 0.151 0.11 2.22 3.11 2.69 48.51
## 7 0.528 0.000 0.13 4.34 0.81 3.83 89.98
## 8 0.522 0.011 0.14 4.48 0.84 4.09 94.55
## 9 0.536 0.000 0.12 4.39 0.93 3.96 92.27
## 10 0.000 0.000 0.48 3.87 0.82 4.65 108.00
## 11 0.033 0.063 0.52 4.20 0.90 4.94 116.20
## 12 0.000 0.073 0.57 4.19 0.60 4.79 112.10
## 13 0.020 0.000 0.28 3.18 1.66 2.77 47.49
## 14 0.000 0.033 0.31 3.11 1.78 2.94 50.92
## 15 0.000 0.040 0.14 3.12 1.55 2.86 49.20
## 16 0.000 0.044 0.03 4.20 2.78 2.74 54.54
## 17 0.000 0.068 0.02 4.56 3.06 2.97 61.50
## 18 0.000 0.057 0.03 3.84 2.64 2.86 58.02
## 19 0.041 0.303 0.09 5.39 1.27 3.40 60.43
## 20 0.071 0.000 0.10 5.47 1.31 3.54 63.67
## 21 0.107 0.000 0.19 4.84 1.11 3.47 62.05
## 22 0.480 0.000 1.36 2.88 0.64 5.40 90.64
## 23 0.453 0.000 1.46 2.99 0.59 5.54 92.33
## 24 0.545 0.000 1.27 2.78 0.50 5.47 91.48
## 25 0.017 0.039 0.14 4.56 0.94 2.12 12.85
## 26 0.003 0.036 0.12 4.86 0.99 2.39 14.11
## 27 0.044 0.011 0.02 4.23 0.93 2.25 13.48
## 28 0.000 0.133 0.02 2.81 1.89 1.74 10.60
## 29 0.000 0.132 0.03 2.59 1.92 1.66 10.20
## 30 0.000 0.136 0.21 2.26 1.75 1.70 10.40
## 31 0.119 0.237 0.08 4.42 0.96 2.99 35.55
## 32 0.099 0.296 0.09 4.75 1.02 3.11 38.00
## 33 0.104 0.231 0.12 4.03 0.99 3.05 36.77
## 34 0.000 0.258 0.71 3.98 0.04 3.11 26.50
## 35 0.038 0.237 0.72 3.90 0.04 2.97 24.55
## 36 0.106 0.160 0.30 3.05 0.43 3.04 25.52
## 37 0.000 0.020 0.08 5.43 0.80 0.00 5.04
## 38 0.005 0.013 0.08 5.61 0.86 0.00 5.43
## 39 0.000 0.019 0.17 5.76 0.95 0.00 5.24
## 40 0.000 0.155 0.03 2.15 1.35 0.00 4.95
## 41 0.000 0.155 0.03 2.35 1.18 0.00 4.32
## 42 0.000 0.149 0.17 2.46 1.43 0.00 4.63
## 43 0.013 0.273 0.03 4.80 0.80 2.74 19.43
## 44 0.114 0.245 0.03 4.93 0.83 2.78 19.55
## 45 0.088 0.002 0.03 4.25 0.26 2.76 19.49
## 46 0.013 0.391 0.43 4.67 0.05 2.57 26.10
## 47 0.022 0.021 0.46 4.87 0.05 2.64 26.31
## 48 0.123 0.000 0.12 3.85 0.23 2.61 26.20
## 49 0.002 0.043 0.17 2.74 0.30 0.00 3.85
## 50 0.004 0.049 0.18 3.13 0.32 0.00 4.30
## 51 0.000 0.021 0.07 2.65 0.08 0.00 4.08
## 52 0.000 0.113 0.27 2.03 0.76 0.00 3.12
## 53 0.000 0.126 0.31 2.02 0.78 0.00 3.35
## 54 0.000 0.096 0.27 2.33 0.85 0.00 3.23
## 55 0.104 0.000 0.08 3.94 6.46 0.00 5.57
## 56 0.148 0.000 0.08 3.91 7.14 0.00 5.85
## 57 0.111 0.000 0.22 3.93 6.84 0.00 5.71
## 58 0.078 0.099 0.08 3.72 0.04 2.49 26.70
## 59 0.171 0.000 0.09 4.11 0.04 2.59 29.69
## 60 0.144 0.000 0.31 4.99 0.29 2.54 28.20
## Glucose Fructose TotalSugars Scopoletin PolyPhenol Ascorbic Ascorbate
## 1 34.17 24.70 114.12 28.12 4.49 0.53 1.80
## 2 37.71 27.41 114.12 23.85 4.52 0.50 1.24
## 3 35.94 26.06 114.12 25.98 4.71 0.59 0.92
## 4 14.94 13.23 80.37 15.91 4.53 0.69 13.53
## 5 16.02 14.15 80.37 21.27 4.15 0.42 14.18
## 6 15.48 13.69 80.37 18.59 4.04 0.82 11.02
## 7 77.81 65.59 248.37 64.95 3.66 0.25 2.70
## 8 86.72 74.16 248.37 63.56 3.77 0.25 3.42
## 9 82.26 69.87 248.37 64.25 3.74 0.38 2.34
## 10 53.83 52.14 226.99 83.91 4.44 0.97 13.71
## 11 57.99 56.24 226.99 99.01 3.76 0.91 14.05
## 12 55.91 54.19 226.99 91.46 4.37 0.02 14.28
## 13 29.02 26.10 108.69 46.17 6.28 0.33 26.06
## 14 30.69 27.46 108.69 44.63 6.10 3.27 25.80
## 15 29.86 26.78 108.69 45.40 6.10 2.22 26.09
## 16 29.46 25.72 117.18 116.53 3.59 0.80 2.92
## 17 31.01 26.43 117.18 133.25 3.72 0.50 4.51
## 18 30.23 26.07 117.18 124.89 3.60 0.88 3.80
## 19 44.95 41.82 153.98 78.13 3.32 1.27 34.37
## 20 46.53 43.62 153.98 85.50 3.50 1.25 34.34
## 21 45.74 42.72 153.98 81.81 3.38 1.22 34.26
## 22 86.10 80.18 265.50 95.65 3.22 2.30 31.09
## 23 88.49 82.32 265.50 88.56 3.24 2.24 31.56
## 24 87.30 81.25 265.50 92.11 3.18 2.54 30.50
## 25 65.98 66.75 150.79 122.25 5.76 1.55 27.77
## 26 68.25 69.13 150.79 119.35 5.64 1.44 28.25
## 27 67.11 67.94 150.79 120.80 5.70 1.27 27.67
## 28 24.98 31.22 68.94 47.21 4.29 0.97 19.94
## 29 25.46 32.02 68.94 49.13 4.17 0.72 20.53
## 30 25.22 31.62 68.94 48.17 4.31 0.20 20.13
## 31 79.24 81.15 206.15 121.66 4.56 1.02 19.55
## 32 84.92 87.34 206.15 126.14 4.52 0.97 19.20
## 33 82.08 84.25 206.15 123.90 4.55 1.17 19.56
## 34 118.94 126.30 272.16 104.46 3.58 2.44 44.82
## 35 116.76 125.18 272.16 85.53 3.64 2.71 44.37
## 36 117.85 125.74 272.16 95.00 3.71 2.14 44.71
## 37 53.35 59.80 123.24 126.56 3.99 3.82 27.73
## 38 57.84 65.01 123.24 125.07 3.96 3.80 29.25
## 39 55.60 62.41 123.24 125.81 3.77 3.08 27.75
## 40 17.89 24.44 45.01 102.16 5.12 1.77 16.45
## 41 16.30 22.13 45.01 61.43 5.15 1.50 15.50
## 42 17.09 23.28 45.01 81.80 5.04 2.20 16.15
## 43 90.05 98.08 213.81 214.29 2.93 1.08 7.74
## 44 93.48 101.51 213.81 213.71 3.06 1.00 8.19
## 45 91.77 99.80 213.81 214.00 2.95 1.03 7.57
## 46 100.38 114.99 248.43 175.80 3.69 2.47 27.74
## 47 104.31 119.56 248.43 212.11 3.74 2.66 25.82
## 48 102.35 117.27 248.43 193.96 3.67 2.79 25.81
## 49 17.36 17.64 40.62 68.60 6.36 2.60 6.67
## 50 18.92 19.15 40.62 64.69 6.68 2.58 6.82
## 51 18.14 18.40 40.62 66.64 6.46 2.84 7.71
## 52 9.26 13.21 26.05 54.18 3.80 2.55 119.18
## 53 9.55 13.61 26.05 55.69 3.82 2.60 119.18
## 54 9.40 13.41 26.05 54.94 3.84 2.60 118.60
## 55 41.34 43.91 92.59 100.10 5.05 1.58 71.71
## 56 43.12 45.40 92.59 96.10 5.11 1.50 74.24
## 57 42.23 44.65 92.59 98.10 4.95 0.92 75.24
## 58 83.17 104.93 229.99 218.10 6.44 3.02 8.91
## 59 93.14 117.26 229.99 228.06 6.50 3.05 8.48
## 60 88.16 111.09 229.99 223.08 6.66 3.54 9.74
## Guaiacol Tocopherol Proteins PPDscores factor
## 1 0.15 5.80 60.64 0.00 SAN
## 2 0.16 5.26 60.74 0.00 SAN
## 3 0.10 5.67 60.64 0.00 SAN
## 4 0.15 0.29 39.29 0.00 SAN3
## 5 0.23 0.54 39.39 0.00 SAN3
## 6 0.20 0.34 39.29 0.00 SAN3
## 7 0.15 0.48 43.57 0.00 SAN5
## 8 0.13 0.28 43.67 0.00 SAN5
## 9 0.14 0.41 43.57 0.00 SAN5
## 10 0.23 0.22 37.36 0.00 SAN8
## 11 0.21 0.20 37.46 0.00 SAN8
## 12 0.21 0.22 37.36 0.00 SAN8
## 13 1.33 0.52 66.86 10.02 SAN11
## 14 1.33 0.69 66.96 0.00 SAN11
## 15 1.43 1.09 66.86 27.86 SAN11
## 16 1.39 0.34 55.79 72.98 ORI
## 17 1.27 0.37 55.89 10.02 ORI
## 18 1.37 0.24 55.79 40.25 ORI
## 19 1.08 1.89 55.36 16.75 ORI3
## 20 1.17 1.64 55.46 0.00 ORI3
## 21 0.98 2.81 55.36 33.99 ORI3
## 22 0.72 0.15 56.21 60.25 ORI5
## 23 0.80 0.22 56.31 10.02 ORI5
## 24 0.80 0.38 56.21 47.62 ORI5
## 25 0.51 4.11 82.79 72.98 ORI8
## 26 0.54 4.51 82.89 20.14 ORI8
## 27 0.52 3.50 82.79 58.24 ORI8
## 28 1.61 0.23 69.14 157.08 ORI11
## 29 1.58 0.23 69.24 41.15 ORI11
## 30 1.58 0.22 69.14 67.51 ORI11
## 31 0.24 0.26 110.50 77.54 IAC
## 32 0.24 0.20 110.60 20.14 IAC
## 33 0.23 0.31 110.50 67.51 IAC
## 34 0.84 0.23 52.57 98.51 IAC3
## 35 0.82 0.21 52.67 16.75 IAC3
## 36 0.82 0.26 52.57 67.51 IAC3
## 37 1.29 2.22 65.71 92.73 IAC5
## 38 1.25 1.52 65.81 27.00 IAC5
## 39 1.28 2.43 65.71 78.71 IAC5
## 40 5.07 3.53 21.50 157.08 IAC8
## 41 5.07 3.64 21.65 60.25 IAC8
## 42 5.07 3.68 21.50 88.67 IAC8
## 43 1.94 0.35 57.43 92.73 IAC11
## 44 1.77 0.30 57.53 30.47 IAC11
## 45 1.81 0.42 57.43 83.56 IAC11
## 46 3.16 0.21 80.00 131.20 BRA
## 47 3.13 0.25 80.11 37.55 BRA
## 48 3.05 0.29 80.00 87.37 BRA
## 49 3.34 0.47 36.14 104.85 BRA3
## 50 3.45 0.50 36.24 37.55 BRA3
## 51 3.95 0.30 36.14 90.01 BRA3
## 52 6.42 0.20 13.00 157.08 BRA5
## 53 6.58 0.27 13.22 72.98 BRA5
## 54 6.78 0.28 13.00 97.02 BRA5
## 55 3.54 0.42 18.21 111.98 BRA8
## 56 3.39 0.31 18.31 52.36 BRA8
## 57 3.41 0.32 18.21 98.51 BRA8
## 58 1.12 3.28 27.29 131.20 BRA11
## 59 1.24 1.90 27.39 56.25 BRA11
## 60 1.16 2.50 27.29 100.04 BRA11
Tabela1<-TabelaFinalTese2014[-c(3:20,31:32)]
head(Tabela1,5)
## Sample Days Glucose Fructose TotalSugars Scopoletin PolyPhenol Ascorbic
## 1 SAN Fresh 34.17 24.70 114.12 28.12 4.49 0.53
## 2 SAN Fresh 37.71 27.41 114.12 23.85 4.52 0.50
## 3 SAN Fresh 35.94 26.06 114.12 25.98 4.71 0.59
## 4 ORI Fresh 14.94 13.23 80.37 15.91 4.53 0.69
## 5 ORI Fresh 16.02 14.15 80.37 21.27 4.15 0.42
## Ascorbate Guaiacol Tocopherol Proteins
## 1 1.80 0.15 5.80 60.64
## 2 1.24 0.16 5.26 60.74
## 3 0.92 0.10 5.67 60.64
## 4 13.53 0.15 0.29 39.29
## 5 14.18 0.23 0.54 39.39
###two-way anova of the variables
gluco<-Tabela1[1:3] ##glucose
frut<-Tabela1[-c(3,5:12)] ##frutose
tsug<-Tabela1[-c(3:4,6:12)] ##total sugars
scopo<-Tabela1[-c(3:5,7:12)] ##scopoletin
poli<-Tabela1[-c(3:6,8:12)] ##poliphenol oxidase
abic<-Tabela1[-c(3:7, 9:12)] ##ascorbic acid
abate<-Tabela1[-c(3:8,10:12)] ##ascorbate peroxidase
gua<-Tabela1[-c(3:9, 11:12)] ##guaiacol
toco<-Tabela1[-c(3:10,12)] ##tocopherol
pro<-Tabela1[-c(3:11)] ##proteins
require(easyanova)
gluco1<-ea2(gluco, design=1)
frut1<-ea2(frut, design=1)
tsug1<-ea2(tsug, design=1)
scopo1<-ea2(scopo, design=1)
poli1<-ea2(poli, design=1)
abic1<-ea2(abic, design=1)
abate1<-ea2(abate, design=1)
gua1<-ea2(gua, design=1)
toco1<-ea2(toco, design=1)
pro1<-ea2(pro, design=1)
gluco1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 9 BRA.Day5 117.8500 1.1568 a a a a a
## 13 BRA.Day8 102.3467 1.1568 b b b b b
## 1 BRA.Day11 88.1567 1.1568 c c c c c
## 5 BRA.Day3 87.2967 1.1568 c c c c c
## 17 BRA.Fresh 55.9100 1.1568 d d d d d
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 14 IAC.Day8 91.7667 1.1568 a a a a a
## 18 IAC.Fresh 82.2633 1.1568 b b b b b
## 10 IAC.Day5 82.0800 1.1568 b b b b b
## 6 IAC.Day3 45.7400 1.1568 c c c c c
## 2 IAC.Day11 42.2300 1.1568 c d d d d
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 7 ORI.Day3 30.2333 1.1568 a a a a a
## 11 ORI.Day5 25.2200 1.1568 b b b b b
## 15 ORI.Day8 17.0933 1.1568 c c c c c
## 19 ORI.Fresh 15.4800 1.1568 c c c c c
## 3 ORI.Day11 9.4033 1.1568 d d d d d
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 12 SAN.Day5 67.1133 1.1568 a a a a a
## 16 SAN.Day8 55.5967 1.1568 b b b b b
## 20 SAN.Fresh 35.9400 1.1568 c c c c c
## 8 SAN.Day3 29.8567 1.1568 d d d d d
## 4 SAN.Day11 18.1400 1.1568 e e e e e
frut1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 9 BRA.Day5 125.7400 1.2522 a a a a a
## 13 BRA.Day8 117.2733 1.2522 b b b b b
## 1 BRA.Day11 111.0933 1.2522 c c c c c
## 5 BRA.Day3 81.2500 1.2522 d d d d d
## 17 BRA.Fresh 54.1900 1.2522 e e e e e
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 14 IAC.Day8 99.7967 1.2522 a a a a a
## 10 IAC.Day5 84.2467 1.2522 b b b b b
## 18 IAC.Fresh 69.8733 1.2522 c c c c c
## 2 IAC.Day11 44.6533 1.2522 d d d d d
## 6 IAC.Day3 42.7200 1.2522 d d d d d
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 11 ORI.Day5 31.6200 1.2522 a a a a a
## 7 ORI.Day3 26.0733 1.2522 b b b b b
## 15 ORI.Day8 23.2833 1.2522 b b b b b
## 19 ORI.Fresh 13.6900 1.2522 c c c c c
## 3 ORI.Day11 13.4100 1.2522 c c c c c
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 12 SAN.Day5 67.9400 1.2522 a a a a a
## 16 SAN.Day8 62.4067 1.2522 b b b b b
## 8 SAN.Day3 26.7800 1.2522 c c c c c
## 20 SAN.Fresh 26.0567 1.2522 c c c c c
## 4 SAN.Day11 18.3967 1.2522 d d d d d
tsug1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 9 BRA.Day5 272.16 0 a a a a a
## 5 BRA.Day3 265.50 0 b b b b b
## 13 BRA.Day8 248.43 0 c c c c c
## 1 BRA.Day11 229.99 0 d d d d d
## 17 BRA.Fresh 226.99 0 e e e e e
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 18 IAC.Fresh 248.37 0 a a a a a
## 14 IAC.Day8 213.81 0 b b b b b
## 10 IAC.Day5 206.15 0 c c c c c
## 6 IAC.Day3 153.98 0 d d d d d
## 2 IAC.Day11 92.59 0 e e e e e
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 7 ORI.Day3 117.18 0 a a a a a
## 19 ORI.Fresh 80.37 0 b b b b b
## 11 ORI.Day5 68.94 0 c c c c c
## 15 ORI.Day8 45.01 0 d d d d d
## 3 ORI.Day11 26.05 0 e e e e e
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 12 SAN.Day5 150.79 0 a a a a a
## 16 SAN.Day8 123.24 0 b b b b b
## 20 SAN.Fresh 114.12 0 c c c c c
## 8 SAN.Day3 108.69 0 d d d d d
## 4 SAN.Day11 40.62 0 e e e e e
scopo1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 1 BRA.Day11 223.0800 4.1666 a a a a a
## 13 BRA.Day8 193.9567 4.1666 b b b b b
## 9 BRA.Day5 94.9967 4.1666 c c c c c
## 5 BRA.Day3 92.1067 4.1666 c c c c c
## 17 BRA.Fresh 91.4600 4.1666 c c c c c
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 14 IAC.Day8 214.0000 4.1666 a a a a a
## 10 IAC.Day5 123.9000 4.1666 b b b b b
## 2 IAC.Day11 98.1000 4.1666 c c c c c
## 6 IAC.Day3 81.8133 4.1666 c d d d d
## 18 IAC.Fresh 64.2533 4.1666 d e e e e
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 7 ORI.Day3 124.8900 4.1666 a a a a a
## 15 ORI.Day8 81.7967 4.1666 b b b b b
## 3 ORI.Day11 54.9367 4.1666 c c c c c
## 11 ORI.Day5 48.1700 4.1666 c c c c c
## 19 ORI.Fresh 18.5900 4.1666 d d d d d
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 16 SAN.Day8 125.8133 4.1666 a a a a a
## 12 SAN.Day5 120.8000 4.1666 a a a a a
## 4 SAN.Day11 66.6433 4.1666 b b b b b
## 8 SAN.Day3 45.4000 4.1666 c c c c c
## 20 SAN.Fresh 25.9833 4.1666 d d d d d
poli1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 1 BRA.Day11 6.5333 0.0745 a a a a a
## 17 BRA.Fresh 4.1900 0.0745 b b b b b
## 13 BRA.Day8 3.7000 0.0745 c c c c c
## 9 BRA.Day5 3.6433 0.0745 c c c c c
## 5 BRA.Day3 3.2133 0.0745 d d d d d
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 2 IAC.Day11 5.0367 0.0745 a a a a a
## 10 IAC.Day5 4.5433 0.0745 b b b b b
## 18 IAC.Fresh 3.7233 0.0745 c c c c c
## 6 IAC.Day3 3.4000 0.0745 d d d d d
## 14 IAC.Day8 2.9800 0.0745 e e e e e
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 15 ORI.Day8 5.1033 0.0745 a a a a a
## 11 ORI.Day5 4.2567 0.0745 b b b b b
## 19 ORI.Fresh 4.2400 0.0745 b b b b b
## 3 ORI.Day11 3.8200 0.0745 c c c c c
## 7 ORI.Day3 3.6367 0.0745 c c c c c
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 4 SAN.Day11 6.5000 0.0745 a a a a a
## 8 SAN.Day3 6.1600 0.0745 b b b b b
## 12 SAN.Day5 5.7000 0.0745 c c c c c
## 20 SAN.Fresh 4.5733 0.0745 d d d d d
## 16 SAN.Day8 3.9067 0.0745 e e e e e
abic1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 1 BRA.Day11 3.2033 0.2398 a a a a a
## 13 BRA.Day8 2.6400 0.2398 a a ab ab b
## 9 BRA.Day5 2.4300 0.2398 a a b b b
## 5 BRA.Day3 2.3600 0.2398 a a b b b
## 17 BRA.Fresh 0.6333 0.2398 b b c c c
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 2 IAC.Day11 1.3333 0.2398 a a a a a
## 6 IAC.Day3 1.2467 0.2398 ab a a a a
## 10 IAC.Day5 1.0533 0.2398 ab ab a a a
## 14 IAC.Day8 1.0367 0.2398 ab a a a a
## 18 IAC.Fresh 0.2933 0.2398 b b b b b
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 3 ORI.Day11 2.5833 0.2398 a a a a a
## 15 ORI.Day8 1.8233 0.2398 a b b b b
## 7 ORI.Day3 0.7267 0.2398 b c c c c
## 19 ORI.Fresh 0.6433 0.2398 b c c c c
## 11 ORI.Day5 0.6300 0.2398 b c c c c
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 16 SAN.Day8 3.5667 0.2398 a a a a a
## 4 SAN.Day11 2.6733 0.2398 ab b b b b
## 8 SAN.Day3 1.9400 0.2398 bc c c c c
## 12 SAN.Day5 1.4200 0.2398 cd c c c c
## 20 SAN.Fresh 0.5400 0.2398 d d d d d
abate1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 9 BRA.Day5 44.6333 0.4297 a a a a a
## 5 BRA.Day3 31.0500 0.4297 b b b b b
## 13 BRA.Day8 26.4567 0.4297 c c c c c
## 17 BRA.Fresh 14.0133 0.4297 d d d d d
## 1 BRA.Day11 9.0433 0.4297 e e e e e
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 2 IAC.Day11 73.7300 0.4297 a a a a a
## 6 IAC.Day3 34.3233 0.4297 b b b b b
## 10 IAC.Day5 19.4367 0.4297 c c c c c
## 14 IAC.Day8 7.8333 0.4297 d d d d d
## 18 IAC.Fresh 2.8200 0.4297 e e e e e
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 3 ORI.Day11 118.9867 0.4297 a a a a a
## 11 ORI.Day5 20.2000 0.4297 b b b b b
## 15 ORI.Day8 16.0333 0.4297 c c c c c
## 19 ORI.Fresh 12.9100 0.4297 d d d d d
## 7 ORI.Day3 3.7433 0.4297 e e e e e
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 16 SAN.Day8 28.2433 0.4297 a a a a a
## 12 SAN.Day5 27.8967 0.4297 a a a a a
## 8 SAN.Day3 25.9833 0.4297 b b b b b
## 4 SAN.Day11 7.0667 0.4297 c c c c c
## 20 SAN.Fresh 1.3200 0.4297 d d d d d
gua1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 13 BRA.Day8 3.1133 0.0551 a a a a a
## 1 BRA.Day11 1.1733 0.0551 b b b b b
## 9 BRA.Day5 0.8267 0.0551 c c c c c
## 5 BRA.Day3 0.7733 0.0551 c c c c c
## 17 BRA.Fresh 0.2167 0.0551 d d d d d
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 2 IAC.Day11 3.4467 0.0551 a a a a a
## 14 IAC.Day8 1.8400 0.0551 b b b b b
## 6 IAC.Day3 1.0767 0.0551 c c c c c
## 10 IAC.Day5 0.2367 0.0551 d d d d d
## 18 IAC.Fresh 0.1400 0.0551 d d d d d
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 3 ORI.Day11 6.5933 0.0551 a a a a a
## 15 ORI.Day8 5.0700 0.0551 b b b b b
## 11 ORI.Day5 1.5900 0.0551 c c c c c
## 7 ORI.Day3 1.3433 0.0551 d d d d d
## 19 ORI.Fresh 0.1933 0.0551 e e e e e
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 4 SAN.Day11 3.5800 0.0551 a a a a a
## 8 SAN.Day3 1.3633 0.0551 b b b b b
## 16 SAN.Day8 1.2733 0.0551 b b b b b
## 12 SAN.Day5 0.5233 0.0551 c c c c c
## 20 SAN.Fresh 0.1367 0.0551 d d d d d
toco1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 1 BRA.Day11 2.5600 0.1627 a a a a a
## 5 BRA.Day3 0.2500 0.1627 b b b b b
## 13 BRA.Day8 0.2500 0.1627 b b b b b
## 9 BRA.Day5 0.2333 0.1627 b b b b b
## 17 BRA.Fresh 0.2133 0.1627 b b b b b
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 6 IAC.Day3 2.1133 0.1627 a a a a a
## 18 IAC.Fresh 0.3900 0.1627 b b b b b
## 14 IAC.Day8 0.3567 0.1627 b b b b b
## 2 IAC.Day11 0.3500 0.1627 b b b b b
## 10 IAC.Day5 0.2567 0.1627 b b b b b
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 15 ORI.Day8 3.6167 0.1627 a a a a a
## 19 ORI.Fresh 0.3900 0.1627 b b b b b
## 7 ORI.Day3 0.3167 0.1627 b b b b b
## 3 ORI.Day11 0.2500 0.1627 b b b b b
## 11 ORI.Day5 0.2267 0.1627 b b b b b
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 20 SAN.Fresh 5.5767 0.1627 a a a a a
## 12 SAN.Day5 4.0400 0.1627 b b b b b
## 16 SAN.Day8 2.0567 0.1627 c c c c c
## 8 SAN.Day3 0.7667 0.1627 d d d d d
## 4 SAN.Day11 0.4233 0.1627 d d d d d
pro1$"Adjusted means (factor 2 in levels of factor 1)"
## $`factor_2 in BRA`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 13 BRA.Day8 80.0367 0.0375 a a a a a
## 5 BRA.Day3 56.2433 0.0375 b b b b b
## 9 BRA.Day5 52.6033 0.0375 c c c c c
## 17 BRA.Fresh 37.3933 0.0375 d d d d d
## 1 BRA.Day11 27.3233 0.0375 e e e e e
##
## $`factor_2 in IAC`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 10 IAC.Day5 110.5333 0.0375 a a a a a
## 14 IAC.Day8 57.4633 0.0375 b b b b b
## 6 IAC.Day3 55.3933 0.0375 c c c c c
## 18 IAC.Fresh 43.6033 0.0375 d d d d d
## 2 IAC.Day11 18.2433 0.0375 e e e e e
##
## $`factor_2 in ORI`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 11 ORI.Day5 69.1733 0.0375 a a a a a
## 7 ORI.Day3 55.8233 0.0375 b b b b b
## 19 ORI.Fresh 39.3233 0.0375 c c c c c
## 15 ORI.Day8 21.5500 0.0375 d d d d d
## 3 ORI.Day11 13.0733 0.0375 e e e e e
##
## $`factor_2 in SAN`
## treatment adjusted.mean standard.error tukey snk duncan t scott_knott
## 12 SAN.Day5 82.8233 0.0375 a a a a a
## 8 SAN.Day3 66.8933 0.0375 b b b b b
## 16 SAN.Day8 65.7433 0.0375 c c c c c
## 20 SAN.Fresh 60.6733 0.0375 d d d d d
## 4 SAN.Day11 36.1733 0.0375 e e e e e
OLS REGRESSION MODELS
setwd("C:/Users/Virgílio/Desktop/PASTAS DO DESKTOP/ascorbato peroxidase")
require(lme4)
require(arm)
data1<-TabelaFinalTese2014[3:31]
data2<-TabelaFinalTese2014[2:31] ##include days
data3<-TabelaFinalTese2014[1:31] ##include cultivars
Model1 <- glm(PPDscores ~ ., data = data1) ##usando todas variaveis (menos factors)
display(Model1)
## glm(formula = PPDscores ~ ., data = data1)
## coef.est coef.se
## (Intercept) -191.09 221.92
## Phenolics -0.13 0.12
## Flavonoids 0.01 0.03
## Carotenoids -1.86 5.05
## Anthocyanins 0.88 1.55
## Totalcyanide 20536.50 11608.23
## AcetoneCyano -20516.50 11607.22
## Linamarin -20535.11 11608.23
## Linamarase 12.63 24.72
## HydrogenPeroxide -0.13 0.39
## Catalase 0.40 0.24
## TotalSOD 100.81 154.48
## MnSOD -28.89 118.24
## CuZnSOD -50.24 177.59
## MalicAcid 20.47 53.67
## SuccinicAcid -20.66 13.98
## FumaricAcid 4.80 12.96
## Raffinose 40.17 35.45
## Sucrose -4.08 1.79
## Glucose -2.64 5.27
## Fructose -2.17 5.12
## TotalSugars 1.83 0.98
## Scopoletin 0.35 0.39
## PolyPhenol -18.88 20.79
## Ascorbic 8.41 11.72
## Ascorbate -0.70 0.87
## Guaiacol 11.72 14.70
## Tocopherol 5.05 6.44
## Proteins 1.01 1.31
## ---
## n = 60, k = 29
## residual deviance = 31747.9, null deviance = 122979.3 (difference = 91231.5)
## overdispersion parameter = 1024.1
## residual sd is sqrt(overdispersion) = 32.00
AIC(Model1) ## 606.5 ##akaike information criterion (quality of statistical model= must minor in testing our model, relative to information lost)
## [1] 606.5468
Model2 <- glm(PPDscores ~ ., data = data2)
display(Model2)
## glm(formula = PPDscores ~ ., data = data2)
## coef.est coef.se
## (Intercept) 1221.55 487.82
## DaysDay3 -335.27 249.60
## DaysDay5 -207.14 153.34
## DaysDay8 -168.17 66.25
## DaysFresh -559.66 292.38
## Phenolics -0.30 0.15
## Flavonoids 0.01 0.03
## Carotenoids -7.82 5.16
## Anthocyanins 0.11 1.49
## Totalcyanide 25857.43 11279.45
## AcetoneCyano -25870.54 11282.13
## Linamarin -25858.80 11279.72
## Linamarase -57.77 33.91
## HydrogenPeroxide -1.29 1.02
## Catalase -0.22 0.32
## TotalSOD 64.63 141.90
## MnSOD -23.94 141.42
## CuZnSOD -25.75 166.41
## MalicAcid -66.72 55.25
## SuccinicAcid -16.95 14.30
## FumaricAcid 6.36 18.04
## Raffinose -2.34 49.62
## Sucrose -0.19 2.13
## Glucose 3.05 6.07
## Fructose -8.89 5.93
## TotalSugars 3.00 0.97
## Scopoletin 0.79 0.44
## PolyPhenol -21.59 21.63
## Ascorbic 12.06 11.40
## Ascorbate 0.70 0.90
## Guaiacol -18.51 17.02
## Tocopherol -1.06 6.33
## Proteins -2.36 1.63
## ---
## n = 60, k = 33
## residual deviance = 22275.3, null deviance = 122979.3 (difference = 100704.0)
## overdispersion parameter = 825.0
## residual sd is sqrt(overdispersion) = 28.72
AIC(Model2) ## 593.3 something was improved
## [1] 593.2861
Model3 <- glm(PPDscores ~ ., data = data3)
display(Model3)
## glm(formula = PPDscores ~ ., data = data3)
## coef.est coef.se
## (Intercept) 1234.64 511.13
## SampleIAC 1.24 159.89
## SampleORI -90.11 240.62
## SampleSAN -93.52 271.83
## DaysDay3 -293.09 328.20
## DaysDay5 -128.66 211.58
## DaysDay8 -134.81 145.57
## DaysFresh -424.97 368.41
## Phenolics -0.28 0.17
## Flavonoids 0.03 0.04
## Carotenoids -7.71 5.61
## Anthocyanins 0.89 1.85
## Totalcyanide 27608.13 12095.61
## AcetoneCyano -27622.91 12099.96
## Linamarin -27608.98 12095.58
## Linamarase -53.52 56.65
## HydrogenPeroxide -1.85 1.43
## Catalase -0.14 0.36
## TotalSOD 26.60 165.57
## MnSOD 14.84 174.10
## CuZnSOD 32.73 198.99
## MalicAcid -50.75 62.84
## SuccinicAcid -20.31 16.98
## FumaricAcid 7.36 19.46
## Raffinose -7.73 68.66
## Sucrose -0.74 4.24
## Glucose 3.03 9.85
## Fructose -9.31 9.62
## TotalSugars 2.58 1.97
## Scopoletin 1.33 0.86
## PolyPhenol -17.72 28.31
## Ascorbic 15.01 12.51
## Ascorbate 0.86 1.22
## Guaiacol -14.74 31.11
## Tocopherol -4.34 10.25
## Proteins -2.22 2.12
## ---
## n = 60, k = 36
## residual deviance = 21703.6, null deviance = 122979.3 (difference = 101275.7)
## overdispersion parameter = 904.3
## residual sd is sqrt(overdispersion) = 30.07
AIC(Model3) ## Nothing was improved
## [1] 597.726
anova(Model1,Model2, test="F") ## there is difference at 5%
## Analysis of Deviance Table
##
## Model 1: PPDscores ~ Phenolics + Flavonoids + Carotenoids + Anthocyanins +
## Totalcyanide + AcetoneCyano + Linamarin + Linamarase + HydrogenPeroxide +
## Catalase + TotalSOD + MnSOD + CuZnSOD + MalicAcid + SuccinicAcid +
## FumaricAcid + Raffinose + Sucrose + Glucose + Fructose +
## TotalSugars + Scopoletin + PolyPhenol + Ascorbic + Ascorbate +
## Guaiacol + Tocopherol + Proteins
## Model 2: PPDscores ~ Days + Phenolics + Flavonoids + Carotenoids + Anthocyanins +
## Totalcyanide + AcetoneCyano + Linamarin + Linamarase + HydrogenPeroxide +
## Catalase + TotalSOD + MnSOD + CuZnSOD + MalicAcid + SuccinicAcid +
## FumaricAcid + Raffinose + Sucrose + Glucose + Fructose +
## TotalSugars + Scopoletin + PolyPhenol + Ascorbic + Ascorbate +
## Guaiacol + Tocopherol + Proteins
## Resid. Df Resid. Dev Df Deviance F Pr(>F)
## 1 31 31748
## 2 27 22275 4 9472.5 2.8704 0.04206 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(Model1, Model3, test="F")## there is no difference
## Analysis of Deviance Table
##
## Model 1: PPDscores ~ Phenolics + Flavonoids + Carotenoids + Anthocyanins +
## Totalcyanide + AcetoneCyano + Linamarin + Linamarase + HydrogenPeroxide +
## Catalase + TotalSOD + MnSOD + CuZnSOD + MalicAcid + SuccinicAcid +
## FumaricAcid + Raffinose + Sucrose + Glucose + Fructose +
## TotalSugars + Scopoletin + PolyPhenol + Ascorbic + Ascorbate +
## Guaiacol + Tocopherol + Proteins
## Model 2: PPDscores ~ Sample + Days + Phenolics + Flavonoids + Carotenoids +
## Anthocyanins + Totalcyanide + AcetoneCyano + Linamarin +
## Linamarase + HydrogenPeroxide + Catalase + TotalSOD + MnSOD +
## CuZnSOD + MalicAcid + SuccinicAcid + FumaricAcid + Raffinose +
## Sucrose + Glucose + Fructose + TotalSugars + Scopoletin +
## PolyPhenol + Ascorbic + Ascorbate + Guaiacol + Tocopherol +
## Proteins
## Resid. Df Resid. Dev Df Deviance F Pr(>F)
## 1 31 31748
## 2 24 21704 7 10044 1.5867 0.1873
anova(Model2,Model3,test="F") ##THERE IS NO DIFF.
## Analysis of Deviance Table
##
## Model 1: PPDscores ~ Days + Phenolics + Flavonoids + Carotenoids + Anthocyanins +
## Totalcyanide + AcetoneCyano + Linamarin + Linamarase + HydrogenPeroxide +
## Catalase + TotalSOD + MnSOD + CuZnSOD + MalicAcid + SuccinicAcid +
## FumaricAcid + Raffinose + Sucrose + Glucose + Fructose +
## TotalSugars + Scopoletin + PolyPhenol + Ascorbic + Ascorbate +
## Guaiacol + Tocopherol + Proteins
## Model 2: PPDscores ~ Sample + Days + Phenolics + Flavonoids + Carotenoids +
## Anthocyanins + Totalcyanide + AcetoneCyano + Linamarin +
## Linamarase + HydrogenPeroxide + Catalase + TotalSOD + MnSOD +
## CuZnSOD + MalicAcid + SuccinicAcid + FumaricAcid + Raffinose +
## Sucrose + Glucose + Fructose + TotalSugars + Scopoletin +
## PolyPhenol + Ascorbic + Ascorbate + Guaiacol + Tocopherol +
## Proteins
## Resid. Df Resid. Dev Df Deviance F Pr(>F)
## 1 27 22275
## 2 24 21704 3 571.71 0.2107 0.888
anova(Model1, Model2, Model3, test="F") ##second model is the best (MINOR AIC)
## Analysis of Deviance Table
##
## Model 1: PPDscores ~ Phenolics + Flavonoids + Carotenoids + Anthocyanins +
## Totalcyanide + AcetoneCyano + Linamarin + Linamarase + HydrogenPeroxide +
## Catalase + TotalSOD + MnSOD + CuZnSOD + MalicAcid + SuccinicAcid +
## FumaricAcid + Raffinose + Sucrose + Glucose + Fructose +
## TotalSugars + Scopoletin + PolyPhenol + Ascorbic + Ascorbate +
## Guaiacol + Tocopherol + Proteins
## Model 2: PPDscores ~ Days + Phenolics + Flavonoids + Carotenoids + Anthocyanins +
## Totalcyanide + AcetoneCyano + Linamarin + Linamarase + HydrogenPeroxide +
## Catalase + TotalSOD + MnSOD + CuZnSOD + MalicAcid + SuccinicAcid +
## FumaricAcid + Raffinose + Sucrose + Glucose + Fructose +
## TotalSugars + Scopoletin + PolyPhenol + Ascorbic + Ascorbate +
## Guaiacol + Tocopherol + Proteins
## Model 3: PPDscores ~ Sample + Days + Phenolics + Flavonoids + Carotenoids +
## Anthocyanins + Totalcyanide + AcetoneCyano + Linamarin +
## Linamarase + HydrogenPeroxide + Catalase + TotalSOD + MnSOD +
## CuZnSOD + MalicAcid + SuccinicAcid + FumaricAcid + Raffinose +
## Sucrose + Glucose + Fructose + TotalSugars + Scopoletin +
## PolyPhenol + Ascorbic + Ascorbate + Guaiacol + Tocopherol +
## Proteins
## Resid. Df Resid. Dev Df Deviance F Pr(>F)
## 1 31 31748
## 2 27 22275 4 9472.5 2.6187 0.0602 .
## 3 24 21704 3 571.7 0.2107 0.8880
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
setwd("C:/Users/Virgílio/Desktop/PASTAS DO DESKTOP/ascorbato peroxidase")
secMetabo<-TabelaFinalTese2014[-c(7:23,25:30,32)]
Cyanogenic<-TabelaFinalTese2014[-c(3:6,11:30,32)]
Enzymes<-TabelaFinalTese2014[-c(3:24,32)]
SugarAcids<-TabelaFinalTese2014[-c(3:15,24:30,32)]
ROS<-TabelaFinalTese2014[-c(3:10,16:30,32)]
require(plyr)
setwd("C:/Users/Virgílio/Desktop/PASTAS DO DESKTOP/ascorbato peroxidase")
modellist <- dlply(TabelaFinalTese2014[1:31], .(Sample, Days), function(x) glm(PPDscores ~ ., data = TabelaFinalTese2014[1:31]))
display(modellist[[1]])
## glm(formula = PPDscores ~ ., data = TabelaFinalTese2014[1:31])
## coef.est coef.se
## (Intercept) 1234.64 511.13
## SampleIAC 1.24 159.89
## SampleORI -90.11 240.62
## SampleSAN -93.52 271.83
## DaysDay3 -293.09 328.20
## DaysDay5 -128.66 211.58
## DaysDay8 -134.81 145.57
## DaysFresh -424.97 368.41
## Phenolics -0.28 0.17
## Flavonoids 0.03 0.04
## Carotenoids -7.71 5.61
## Anthocyanins 0.89 1.85
## Totalcyanide 27608.13 12095.61
## AcetoneCyano -27622.91 12099.96
## Linamarin -27608.98 12095.58
## Linamarase -53.52 56.65
## HydrogenPeroxide -1.85 1.43
## Catalase -0.14 0.36
## TotalSOD 26.60 165.57
## MnSOD 14.84 174.10
## CuZnSOD 32.73 198.99
## MalicAcid -50.75 62.84
## SuccinicAcid -20.31 16.98
## FumaricAcid 7.36 19.46
## Raffinose -7.73 68.66
## Sucrose -0.74 4.24
## Glucose 3.03 9.85
## Fructose -9.31 9.62
## TotalSugars 2.58 1.97
## Scopoletin 1.33 0.86
## PolyPhenol -17.72 28.31
## Ascorbic 15.01 12.51
## Ascorbate 0.86 1.22
## Guaiacol -14.74 31.11
## Tocopherol -4.34 10.25
## Proteins -2.22 2.12
## ---
## n = 60, k = 36
## residual deviance = 21703.6, null deviance = 122979.3 (difference = 101275.7)
## overdispersion parameter = 904.3
## residual sd is sqrt(overdispersion) = 30.07
display(modellist[[2]])
## glm(formula = PPDscores ~ ., data = TabelaFinalTese2014[1:31])
## coef.est coef.se
## (Intercept) 1234.64 511.13
## SampleIAC 1.24 159.89
## SampleORI -90.11 240.62
## SampleSAN -93.52 271.83
## DaysDay3 -293.09 328.20
## DaysDay5 -128.66 211.58
## DaysDay8 -134.81 145.57
## DaysFresh -424.97 368.41
## Phenolics -0.28 0.17
## Flavonoids 0.03 0.04
## Carotenoids -7.71 5.61
## Anthocyanins 0.89 1.85
## Totalcyanide 27608.13 12095.61
## AcetoneCyano -27622.91 12099.96
## Linamarin -27608.98 12095.58
## Linamarase -53.52 56.65
## HydrogenPeroxide -1.85 1.43
## Catalase -0.14 0.36
## TotalSOD 26.60 165.57
## MnSOD 14.84 174.10
## CuZnSOD 32.73 198.99
## MalicAcid -50.75 62.84
## SuccinicAcid -20.31 16.98
## FumaricAcid 7.36 19.46
## Raffinose -7.73 68.66
## Sucrose -0.74 4.24
## Glucose 3.03 9.85
## Fructose -9.31 9.62
## TotalSugars 2.58 1.97
## Scopoletin 1.33 0.86
## PolyPhenol -17.72 28.31
## Ascorbic 15.01 12.51
## Ascorbate 0.86 1.22
## Guaiacol -14.74 31.11
## Tocopherol -4.34 10.25
## Proteins -2.22 2.12
## ---
## n = 60, k = 36
## residual deviance = 21703.6, null deviance = 122979.3 (difference = 101275.7)
## overdispersion parameter = 904.3
## residual sd is sqrt(overdispersion) = 30.07
display(modellist[[3]])
## glm(formula = PPDscores ~ ., data = TabelaFinalTese2014[1:31])
## coef.est coef.se
## (Intercept) 1234.64 511.13
## SampleIAC 1.24 159.89
## SampleORI -90.11 240.62
## SampleSAN -93.52 271.83
## DaysDay3 -293.09 328.20
## DaysDay5 -128.66 211.58
## DaysDay8 -134.81 145.57
## DaysFresh -424.97 368.41
## Phenolics -0.28 0.17
## Flavonoids 0.03 0.04
## Carotenoids -7.71 5.61
## Anthocyanins 0.89 1.85
## Totalcyanide 27608.13 12095.61
## AcetoneCyano -27622.91 12099.96
## Linamarin -27608.98 12095.58
## Linamarase -53.52 56.65
## HydrogenPeroxide -1.85 1.43
## Catalase -0.14 0.36
## TotalSOD 26.60 165.57
## MnSOD 14.84 174.10
## CuZnSOD 32.73 198.99
## MalicAcid -50.75 62.84
## SuccinicAcid -20.31 16.98
## FumaricAcid 7.36 19.46
## Raffinose -7.73 68.66
## Sucrose -0.74 4.24
## Glucose 3.03 9.85
## Fructose -9.31 9.62
## TotalSugars 2.58 1.97
## Scopoletin 1.33 0.86
## PolyPhenol -17.72 28.31
## Ascorbic 15.01 12.51
## Ascorbate 0.86 1.22
## Guaiacol -14.74 31.11
## Tocopherol -4.34 10.25
## Proteins -2.22 2.12
## ---
## n = 60, k = 36
## residual deviance = 21703.6, null deviance = 122979.3 (difference = 101275.7)
## overdispersion parameter = 904.3
## residual sd is sqrt(overdispersion) = 30.07
Model4 <- glm(PPDscores ~ ., data = secMetabo[-c(1:2)])
Model5 <- glm(PPDscores ~ ., data = Cyanogenic[-c(1:2)])
Model6 <- glm(PPDscores ~ ., data = Enzymes[-c(1:2)])
Model7 <- glm(PPDscores ~ ., data = SugarAcids[-c(1:2)])
Model8 <- glm(PPDscores ~ ., data = ROS[-c(1:2)])
display(Model4)
## glm(formula = PPDscores ~ ., data = secMetabo[-c(1:2)])
## coef.est coef.se
## (Intercept) 2.21 20.49
## Phenolics -0.08 0.04
## Flavonoids 0.04 0.01
## Carotenoids 0.30 3.91
## Anthocyanins 0.39 0.67
## Scopoletin 0.18 0.12
## ---
## n = 60, k = 6
## residual deviance = 93563.9, null deviance = 122979.3 (difference = 29415.5)
## overdispersion parameter = 1732.7
## residual sd is sqrt(overdispersion) = 41.63
AIC(Model4)
## [1] 625.3959
AIC(Model5)
## [1] 635.8171
AIC(Model6)
## [1] 610.141
AIC(Model7)
## [1] 593.961
AIC(Model8)
## [1] 612.7231
###using stargazer
require(stargazer)
summary(Model4)
##
## Call:
## glm(formula = PPDscores ~ ., data = secMetabo[-c(1:2)])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -58.725 -31.577 -7.118 23.195 122.773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.20652 20.49181 0.108 0.9147
## Phenolics -0.08297 0.03985 -2.082 0.0421 *
## Flavonoids 0.03714 0.01449 2.563 0.0132 *
## Carotenoids 0.30345 3.91011 0.078 0.9384
## Anthocyanins 0.38901 0.67454 0.577 0.5665
## Scopoletin 0.18222 0.11597 1.571 0.1220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1732.664)
##
## Null deviance: 122979 on 59 degrees of freedom
## Residual deviance: 93564 on 54 degrees of freedom
## AIC: 625.4
##
## Number of Fisher Scoring iterations: 2
summary(Model5)
##
## Call:
## glm(formula = PPDscores ~ ., data = Cyanogenic[-c(1:2)])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -68.364 -39.798 -5.052 29.789 112.683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.388e+01 5.167e+01 0.269 0.789
## Totalcyanide 1.672e+04 1.449e+04 1.154 0.253
## AcetoneCyano -1.672e+04 1.449e+04 -1.154 0.253
## Linamarin -1.672e+04 1.449e+04 -1.154 0.253
## Linamarase -2.528e-01 5.758e+00 -0.044 0.965
##
## (Dispersion parameter for gaussian family taken to be 2092.441)
##
## Null deviance: 122979 on 59 degrees of freedom
## Residual deviance: 115084 on 55 degrees of freedom
## AIC: 635.82
##
## Number of Fisher Scoring iterations: 2
summary(Model6)
##
## Call:
## glm(formula = PPDscores ~ ., data = Enzymes[-c(1:2)])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -70.49 -20.61 -10.76 25.66 106.78
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -13.15560 28.26173 -0.465 0.643487
## PolyPhenol 1.48337 5.06397 0.293 0.770723
## Ascorbic 10.16273 5.24657 1.937 0.058081 .
## Ascorbate -0.06942 0.23650 -0.294 0.770282
## Guaiacol 16.22768 4.03651 4.020 0.000185 ***
## Tocopherol -0.45231 3.25737 -0.139 0.890090
## Proteins 0.32678 0.24160 1.353 0.181941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1324.15)
##
## Null deviance: 122979 on 59 degrees of freedom
## Residual deviance: 70180 on 53 degrees of freedom
## AIC: 610.14
##
## Number of Fisher Scoring iterations: 2
summary(Model7)
##
## Call:
## glm(formula = PPDscores ~ ., data = SugarAcids[-c(1:2)])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -72.528 -21.402 -0.419 19.336 88.438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 92.8606 19.1779 4.842 1.24e-05 ***
## MalicAcid 18.7041 18.1690 1.029 0.30813
## SuccinicAcid -6.9382 5.7054 -1.216 0.22955
## FumaricAcid -0.0505 3.1209 -0.016 0.98715
## Raffinose -15.6627 7.4786 -2.094 0.04122 *
## Sucrose -2.4380 0.9690 -2.516 0.01506 *
## Glucose -4.0160 1.2804 -3.136 0.00284 **
## Fructose -0.3196 1.1682 -0.274 0.78553
## TotalSugars 2.2975 0.9126 2.517 0.01500 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 983.0493)
##
## Null deviance: 122979 on 59 degrees of freedom
## Residual deviance: 50136 on 51 degrees of freedom
## AIC: 593.96
##
## Number of Fisher Scoring iterations: 2
summary(Model8)
##
## Call:
## glm(formula = PPDscores ~ ., data = ROS[-c(1:2)])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -61.365 -25.507 -1.259 19.075 102.891
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -16.78133 15.43900 -1.087 0.281890
## HydrogenPeroxide 0.38830 0.10912 3.558 0.000787 ***
## Catalase 0.10429 0.07686 1.357 0.180462
## TotalSOD -56.66691 70.33544 -0.806 0.423969
## MnSOD -3.56664 53.58815 -0.067 0.947181
## CuZnSOD 143.25047 98.43518 1.455 0.151382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1402.767)
##
## Null deviance: 122979 on 59 degrees of freedom
## Residual deviance: 75749 on 54 degrees of freedom
## AIC: 612.72
##
## Number of Fisher Scoring iterations: 2
Table1B<-stargazer(Model4,Model5, Model6, Model7, Model8,Model1, type="text",Align=TRUE,
title="Table 1B: Results of Ordinary Least Square (OLS) Regression models",
intercept.bottom = FALSE,notes.label = "Significance levels:",
dep.var.caption = "OLS REGRESSION MODELS",
dep.var.labels = "DEPENDENT VARIABLE:PPD Scores",column.labels = c("Metabolites", "Cyanogenics","Enzymes","Sugar+Acids","ROS","All data"),
model.numbers= FALSE,digits=1,cex=0.7)
##
## Table 1B: Results of Ordinary Least Square (OLS) Regression models
## ==================================================================================
## OLS REGRESSION MODELS
## -------------------------------------------------------------
## DEPENDENT VARIABLE:PPD Scores
## Metabolites Cyanogenics Enzymes Sugar+Acids ROS All data
## ----------------------------------------------------------------------------------
## Constant 2.2 13.9 -13.2 92.9*** -16.8 -191.1
## (20.5) (51.7) (28.3) (19.2) (15.4) (221.9)
##
## Phenolics -0.1** -0.1
## (0.04) (0.1)
##
## Flavonoids 0.04** 0.01
## (0.01) (0.03)
##
## Carotenoids 0.3 -1.9
## (3.9) (5.1)
##
## Anthocyanins 0.4 0.9
## (0.7) (1.5)
##
## Scopoletin 0.2 0.4
## (0.1) (0.4)
##
## Totalcyanide 16,723.7 20,536.5*
## (14,490.4) (11,608.2)
##
## AcetoneCyano -16,721.6 -20,516.5*
## (14,489.8) (11,607.2)
##
## Linamarin -16,723.2 -20,535.1*
## (14,490.3) (11,608.2)
##
## Linamarase -0.3 12.6
## (5.8) (24.7)
##
## PolyPhenol 1.5 -18.9
## (5.1) (20.8)
##
## Ascorbic 10.2* 8.4
## (5.2) (11.7)
##
## Ascorbate -0.1 -0.7
## (0.2) (0.9)
##
## Guaiacol 16.2*** 11.7
## (4.0) (14.7)
##
## Tocopherol -0.5 5.1
## (3.3) (6.4)
##
## Proteins 0.3 1.0
## (0.2) (1.3)
##
## MalicAcid 18.7 20.5
## (18.2) (53.7)
##
## SuccinicAcid -6.9 -20.7
## (5.7) (14.0)
##
## FumaricAcid -0.1 4.8
## (3.1) (13.0)
##
## Raffinose -15.7** 40.2
## (7.5) (35.5)
##
## Sucrose -2.4** -4.1**
## (1.0) (1.8)
##
## Glucose -4.0*** -2.6
## (1.3) (5.3)
##
## Fructose -0.3 -2.2
## (1.2) (5.1)
##
## TotalSugars 2.3** 1.8*
## (0.9) (1.0)
##
## HydrogenPeroxide 0.4*** -0.1
## (0.1) (0.4)
##
## Catalase 0.1 0.4
## (0.1) (0.2)
##
## TotalSOD -56.7 100.8
## (70.3) (154.5)
##
## MnSOD -3.6 -28.9
## (53.6) (118.2)
##
## CuZnSOD 143.3 -50.2
## (98.4) (177.6)
##
## ----------------------------------------------------------------------------------
## Observations 60 60 60 60 60 60
## Log Likelihood -306.7 -312.9 -298.1 -288.0 -300.4 -274.3
## Akaike Inf. Crit. 625.4 635.8 610.1 594.0 612.7 606.5
## ==================================================================================
## Significance levels: *p<0.1; **p<0.05; ***p<0.01
##
## Table 1B: Results of Ordinary Least Square (OLS) Regression models
## ====
## TRUE
## ----
##
## Table 1B: Results of Ordinary Least Square (OLS) Regression models
## ===
## 0.7
## ---
correlation between variables with PPD are also provided as html document
This document was produced by authors in R SOFTWARE to facilitate the reproduction of the experiment.