1 FRESA.CAD Benchmark

1.1 Baseline Tadpole Challenge

library(readr)

TadpoleBL <- NULL
load("TadpoleBL.RDATA")
tadnames <- as.character(t(read_csv("tadnames.csv")[,2]))
TadpoleBL <- TadpoleBL[,tadnames]
TadpoleBL <- TadpoleBL[complete.cases(TadpoleBL),]

for (i in 1:ncol(TadpoleBL))
{
  TadpoleBL[,i] <- as.numeric(TadpoleBL[,i])
}

sampleTrain <- sample(nrow(TadpoleBL),nrow(TadpoleBL)*0.20)
sampleTadpole <- TadpoleBL[sampleTrain,]




ExperimentName <- "TADPOLE_ADNI"
bswimsReps <- 20;
theData <- TadpoleBL;
theOutcome <- "ADAS13";
reps <- 38;
fraction <- 0.2;

FRESAFileName <- paste(ExperimentName,"FRESAMethod.RDATA",sep = "_")
CVFileName <- paste(ExperimentName,"CVMethod.RDATA",sep = "_")

1.2 Benchmark


BSWiMSMODEL <- BSWiMS.model(formula = paste(theOutcome," ~ 1"),data = sampleTadpole,NumberofRepeats = bswimsReps)

save(BSWiMSMODEL,file = FRESAFileName)
#load(file = FRESAFileName)

par(mfrow = c(2,2));

cp <- RegresionBenchmark(theData = theData, theOutcome = theOutcome, reps = reps, trainFraction = fraction)

par(mfrow = c(1,1));


save(cp,file = CVFileName)
#load(file = CVFileName)

1.3 Results

1.3.1 Model Selection Results


hm <- heatMaps(Outcome = "Outcome",data = cp$testPredictions,title = "Heat Map",Scale = TRUE,hCluster = "col",cexRow = 0.25,cexCol = 0.75,srtCol = 45) 


#The Times
pander::pander(cp$cpuElapsedTimes)
BSWiMS RF RPART LASSO SVM ENS
42.54 9.504 0.3061 0.9742 0.1058 53.43 

par(mfrow = c(2,1));

pr <- plot(cp,prefix = paste(ExperimentName,":"));

par(mfrow = c(1,1));
pander::pander(pr$metrics,caption = "MPG",round = 3)
MPG
  BSWiMS LASSO ENS RF SVM RPART
Spearman 0.669 0.68 0.673 0.651 0.657 0.634
MAE 5.211 5.212 5.255 5.419 5.429 5.366
Pearson 0.711 0.716 0.715 0.696 0.69 0.683
RMSE 6.757 6.759 6.809 6.977 7.059 7.041
Bias -0.05 -0.003 -0.103 -0.014 -0.346 -0.846 
pander::pander(pr$metrics_filter,caption = "MPG",round = 3)
MPG (continued below)
  LASSO BSWiMS W-Test Kendall F-Test Pearson RPART
Spearman 0.692 0.683 0.683 0.676 0.671 0.67 0.664
MAE 5.121 5.117 5.128 5.174 5.186 5.187 5.237
Pearson 0.723 0.72 0.718 0.716 0.714 0.712 0.711
RMSE 6.609 6.66 6.675 6.691 6.715 6.729 6.753
Bias -0.011 -0.123 -0.041 -0.007 -0.016 -0.037 -0.041
  mRMR RF.ref
Spearman 0.65 0.636
MAE 5.462 5.462
Pearson 0.683 0.677
RMSE 7.062 7.042
Bias -0.007 -0.06

1.3.2 Radar Plots


op <- par(no.readonly = TRUE)

library(fmsb)
par(mfrow = c(1,2),xpd = TRUE,pty = "s",mar = c(1,1,1,1))

mNames <- names(cp$cpuElapsedTimes)


classRanks <- c(pr$minMaxMetrics$Pearson[2],pr$minMaxMetrics$RMSE[1],pr$minMaxMetrics$Spearman[2],pr$minMaxMetrics$MAE[1],min(cp$cpuElapsedTimes))
classRanks <- rbind(classRanks,c(0,pr$minMaxMetrics$RMSE[2],0,pr$minMaxMetrics$MAE[2],max(cp$cpuElapsedTimes)))
classRanks <- as.data.frame(rbind(classRanks,cbind(t(pr$metrics[c("Pearson","RMSE","Spearman","MAE"),mNames]),cp$cpuElapsedTimes)))
colnames(classRanks) <- c("Pearson","RMSE","Spearman","MAE","CPU")

classRanks$RMSE <- -classRanks$RMSE
classRanks$MAE <- -classRanks$MAE
classRanks$CPU <- -classRanks$CPU

colors_border = c( rgb(1.0,0.0,0.0,1.0), rgb(0.0,1.0,0.0,1.0) , rgb(0.0,0.0,1.0,1.0), rgb(0.2,0.2,0.0,1.0), rgb(0.0,1.0,1.0,1.0), rgb(1.0,0.0,1.0,1.0) )
colors_in = c( rgb(1.0,0.0,0.0,0.1), rgb(0.0,1.0,0.0,0.1) , rgb(0.0,0.0,1.0,0.1),rgb(1.0,1.0,0.0,0.1), rgb(0.0,1.0,1.0,0.1) , rgb(1.0,0.0,1.0,0.1) )
radarchart(classRanks,axistype = 0,maxmin = T,pcol = colors_border,pfcol = colors_in,plwd = c(6,2,2,2,2,2),plty = 1, cglcol = "grey", cglty = 1,axislabcol = "black",cglwd = 0.8, vlcex  = 0.6 ,title = "Prediction Model")

legend("topleft",legend = rownames(classRanks[-c(1,2),]),bty = "n",pch = 20,col = colors_in,text.col = colors_border,cex = 0.5,pt.cex = 2)


filnames <- c("BSWiMS","LASSO","RF.ref","F-Test","Kendall","mRMR")

filterRanks <- c(pr$minMaxMetrics$Pearson[2],pr$minMaxMetrics$RMSE[1],pr$minMaxMetrics$Spearman[2],pr$minMaxMetrics$MAE[1],max(cp$jaccard),min(cp$featsize));

filterRanks <- rbind(filterRanks,c(0,pr$minMaxMetrics$RMSE[2],0,pr$minMaxMetrics$MAE[2],0,max(cp$featsize)));

filterRanks <- as.data.frame(rbind(filterRanks,cbind(t(pr$metrics_filter[c("Pearson","RMSE","Spearman","MAE"),filnames]),cp$jaccard[filnames],cp$featsize[filnames])));
colnames(filterRanks) <- c("Pearson","RMSE","Spearman","MAE","Jaccard","SIZE")
filterRanks$RMSE <- -filterRanks$RMSE
filterRanks$MAE <- -filterRanks$MAE
filterRanks$SIZE <- -filterRanks$SIZE

colors_border = c( rgb(1.0,0.0,0.0,1.0), rgb(0.0,1.0,0.0,1.0) , rgb(0.0,0.0,1.0,1.0), rgb(0.2,0.2,0.0,1.0), rgb(0.0,1.0,1.0,1.0),rgb(1.0,0.0,1.0,1.0) )
colors_in = c( rgb(1.0,0.0,0.0,0.1), rgb(0.0,1.0,0.0,0.1) , rgb(0.0,0.0,1.0,0.1),rgb(1.0,1.0,0.0,0.1), rgb(0.0,1.0,1.0,0.1), rgb(1.0,0.0,1.0,0.1)  )
radarchart(filterRanks,axistype = 0,maxmin = T,pcol = colors_border,pfcol = colors_in,plwd = c(6,2,2,2,2,2),plty = 1, cglcol = "grey", cglty = 1,axislabcol = "black",cglwd = 0.8, vlcex  = 0.6,title = "Filter Method" )


legend("topleft",legend = rownames(filterRanks[-c(1,2),]),bty = "n",pch = 20,col = colors_in,text.col = colors_border,cex = 0.5,pt.cex = 2)


detach("package:fmsb", unload=TRUE)

par(mfrow = c(1,1))
par(op)

1.3.3 Features Analysis


pander::pander(summary(BSWiMSMODEL),caption = "Model",round = 3)

  • coefficients:

    Table continues below
      Estimate lower mean upper u.MSE
    ST12SMVO -0.295 -0.3592 -0.295 -0.2308 76.45
    Entorhinal -0.0004613 -0.0005765 -0.0004613 -0.0003461 77.06
    ST24TMT -0.8332 -1.088 -0.8332 -0.5779 65.63
    ST40CCMP -7.318 -8.802 -7.318 -4.933 75.61
    Hippocampus -0.002262 -0.002644 -0.002262 -0.001427 72.75
    ST83CMCMP -4.714 -5.581 -4.714 -2.937 65.35
    ST24SMSA 0.07946 0.04568 0.07946 0.1132 106.9
    ST74CMCMP -2.636 -3.774 -2.636 -1.498 89.61
    ST115TCV 39.33 21.87 39.33 60.23 89.48
    ST35TMSDT 28.71 17.99 28.71 41.42 108.2
    ST62CCMP 4.062 2.448 4.062 5.891 107.8
    ST74CCMP -0.8272 -1.205 -0.8272 -0.4497 93.15
    ST121MCMP 2.662 1.424 2.662 3.9 107.8
    ST85CCMP -0.9143 -1.334 -0.9143 -0.4949 89.2
    ST39SMSA 0.3318 0.2415 0.3318 0.4902 108.4
    ST40CMVO -0.2187 -0.3232 -0.2187 -0.1143 82.37
    ST90CDCMP 1.162 0.6046 1.162 1.719 104.4
    ST23CMVO 0.3414 0.1676 0.3414 0.5152 107.6
    ST62SMSA 0.3981 0.1857 0.3981 0.6104 108.4
    ST97CMCMP 0.9461 0.4373 0.9461 1.455 105.3
    ST49TMT 3.536 1.44 3.536 5.632 103.9
    ST39CMVO 0.1307 0.05783 0.1307 0.2035 104.8
    ST36TDT 2.372 0.9533 2.372 3.79 106.1
    ST97CCMP 0.2353 0.08645 0.2353 0.3842 107.4
    ST91TMTCV 4.37 1.585 4.37 7.155 82.9
    ST110CMP 0.4478 0.1235 0.4478 0.7721 105.6
    Table continues below
      r.MSE model.MSE NeRI F.pvalue t.pvalue
    ST12SMVO 61.4 52.62 0.07224 0 0.0002458
    Entorhinal 57.1 49.85 0.1274 0 1.07e-05
    ST24TMT 53.23 45.55 0.1559 7.87e-11 1.63e-07
    ST40CCMP 47.56 41.34 0.07529 7.361e-10 0.00089
    Hippocampus 46.05 41.34 0.1133 4.873e-08 0.0008486
    ST83CMCMP 45.87 41.34 0.09316 9.443e-08 0.0007036
    ST24SMSA 59.3 54.16 0.1825 1.272e-06 1.116e-05
    ST74CMCMP 48.31 44.5 0.04848 2.209e-06 0.03137
    ST115TCV 45.95 42.3 0.08202 2.584e-06 0.01006
    ST35TMSDT 44.58 41.23 0.1347 3.154e-06 0.002078
    ST62CCMP 46.6 43.27 0.06511 4.596e-06 0.01189
    ST74CCMP 41.07 38.27 0.04563 8.022e-06 0.01348
    ST121MCMP 45.75 42.6 0.06971 9.367e-06 0.01428
    ST85CCMP 40.18 37.47 0.1027 9.424e-06 0.02446
    ST39SMSA 45.27 42.4 0.07575 1.755e-05 0.04884
    ST40CMVO 53.27 49.85 0.05894 1.799e-05 0.03043
    ST90CDCMP 48.6 45.59 0.09125 2.092e-05 0.004389
    ST23CMVO 45.1 42.61 0.05323 5.626e-05 0.008258
    ST62SMSA 41.27 39.09 0.06971 7.877e-05 0.007725
    ST97CMCMP 39.42 37.47 0.07224 0.0001339 0.001499
    ST49TMT 46.01 44.08 -0.01141 0.0002168 0.2312
    ST39CMVO 47.72 45.53 0.1255 0.0002192 0.01195
    ST36TDT 42.08 40.4 0.1052 0.0005033 0.01487
    ST97CCMP 56.53 54.58 0.1027 0.0008569 0.02344
    ST91TMTCV 45.84 44.15 0.03676 0.0009024 0.05872
    ST110CMP 51.13 49.78 -0.01521 0.002084 0.2534
      Sign.pvalue Wilcox.pvalue Frequency
    ST12SMVO 0.06812 0.02038 0.15
    Entorhinal 0.01308 0.001991 0.2
    ST24TMT 0.006746 0.0001879 0.15
    ST40CCMP 0.1154 0.02095 1
    Hippocampus 0.03229 0.01224 1
    ST83CMCMP 0.06586 0.01626 1
    ST24SMSA 0.001219 0.001192 0.1
    ST74CMCMP 0.2064 0.16 0.4
    ST115TCV 0.08803 0.0811 0.7
    ST35TMSDT 0.01337 0.01124 0.95
    ST62CCMP 0.1556 0.09503 0.8
    ST74CCMP 0.1649 0.1031 0.1
    ST121MCMP 0.1254 0.08879 0.45
    ST85CCMP 0.05294 0.05829 0.1
    ST39SMSA 0.1047 0.1169 0.65
    ST40CMVO 0.1464 0.1243 0.2
    ST90CDCMP 0.07103 0.05974 0.2
    ST23CMVO 0.2105 0.08461 0.2
    ST62SMSA 0.1212 0.06946 0.3
    ST97CMCMP 0.1302 0.02257 0.1
    ST49TMT 0.4229 0.5002 0.25
    ST39CMVO 0.02413 0.07386 0.1
    ST36TDT 0.04772 0.05771 0.15
    ST97CCMP 0.03192 0.06045 0.1
    ST91TMTCV 0.232 0.27 0.15
    ST110CMP 0.5 1 0.1
  • MSE: 37.57
  • R2: 0.6562

  • bootstrap:


topFeat <- min(ncol(BSWiMSMODEL$bagging$formulaNetwork),30);
shortformulaNetwork <- BSWiMSMODEL$bagging$formulaNetwork[1:topFeat,1:topFeat]
validf <- diag(shortformulaNetwork) > 0.1
gplots::heatmap.2(shortformulaNetwork[validf,validf],trace="none",mar = c(10,10),main = "B:SWiMS Formula Network")



rm <- rowMeans(cp$featureSelectionFrequency[,c("BSWiMS","LASSO","RPART","RF.ref","W-Test","Kendall","mRMR")])
selFrequency <- cp$featureSelectionFrequency[rm > 0.10,]


gplots::heatmap.2(selFrequency,trace = "none",mar = c(10,10),main = "Features",cexRow = 0.6)

hm <- heatMaps(Outcome = theOutcome,data = theData[,c(theOutcome,rownames(selFrequency))],title = "Heat Map",Scale = TRUE,hCluster = "col",cexRow = 0.25,cexCol = 0.65,srtCol = 45)


vlist <- rownames(selFrequency)
vlist <- cbind(vlist,vlist)
univ <- univariateRankVariables(variableList = vlist,formula = paste(theOutcome,"~1"),Outcome = theOutcome,data = theData,type = "LM",rankingTest = "Ztest",uniType = "Regression")[,c("cohortMean","cohortStd","kendall.r","kendall.p")]

100 : ST26TDT 200 : ST72CDCMP



cnames <- colnames(univ);
univ <- cbind(univ,rm[rownames(univ)])
colnames(univ) <- c(cnames,"Frequency")
univ <- univ[order(-univ[,5]),]
pander::pander(univ,caption = "Features",round = 4)
Features
  cohortMean cohortStd kendall.r kendall.p Frequency
ST24TMT 3.262 0.481 -0.3938 0 0.8421
ST30SMVO 9.304 2.375 0.3465 0 0.7632
Hippocampus 6801 1185 -0.3828 0 0.7406
ST83CMCMP 5.768 0.5308 -0.3807 0 0.688
APOE4 0.5709 0.6697 0.2572 0 0.6729
meanCOMPD 0.2147 0.0421 0.2224 0 0.6128
ST12SMVO 10.87 0.7131 -0.318 0 0.5977
ST24CMVO 11.94 0.9453 -0.3418 0 0.5865
ST83CCMP 5.841 0.5852 -0.3403 0 0.5301
ST24CCMP 5.696 0.5774 -0.3455 0 0.5
ST29SMVO 14.98 0.8995 -0.3819 0 0.5
ST40CCMP 6.719 0.3984 -0.3074 0 0.5
ST34TMT 2.357 0.2192 -0.2464 0 0.5
StdACOMP 0.8301 0.0542 -0.2666 0 0.485
Entorhinal 3485 781.2 -0.3386 0 0.4812
StdevT 0.4284 0.0608 -0.2417 0 0.4699
Ventricles 39348 22085 0.2264 0 0.4624
ST32TMT 2.682 0.2115 -0.327 0 0.4549
ST60TMT 3.508 0.3639 -0.2888 0 0.4511
ST21SMVO 12.9 0.8893 0.1944 0 0.4323
MeanATD 0.141 0.0294 0.1584 0 0.4286
ST40TMT 2.683 0.2137 -0.341 0 0.4248
ST83TMTCV 0.2663 0.0686 0.3499 0 0.4211
ST13TMT 2.307 0.2065 -0.2897 0 0.4173
ST129MT 2.862 0.1889 -0.2354 0 0.4135
ST29SDVO 0.3446 0.3226 0.1522 0 0.4135
ST72TMTCV 0.2313 0.0382 0.2741 0 0.4098
ST99CDCMP 0.2522 0.2037 0.1493 0 0.4023
ST26TMT 2.548 0.211 -0.3322 0 0.3947
stdCOMPD 0.1807 0.0451 0.1718 0 0.3947
ST32CCMP 6.704 0.3768 -0.2739 0 0.391
MeanT 2.373 0.1513 -0.3442 0 0.3872
ST59CCMP 6.241 0.3316 -0.2748 0 0.3835
ST119MTCV 0.2035 0.049 0.2499 0 0.3835
ST130MTCV 0.3232 0.0339 0.2559 0 0.3797
ST44CMVO 12.47 0.6953 -0.2346 0 0.3797
ST55TMT 2.155 0.1508 -0.2306 0 0.3684
ST24TMSDT 0.8344 0.12 0.1654 0 0.3647
MeanTCV 0.2789 0.022 0.3021 0 0.3534
ST44TMT 2.547 0.3473 -0.2629 0 0.3534
ST11SMVO 7.702 0.4802 -0.1912 0 0.3534
ST31TMT 2.244 0.2006 -0.3135 0 0.3496
ST85TMTCV 0.2914 0.0338 0.2798 0 0.3496
ST91CMCMP 6.719 0.353 -0.2886 0 0.3459
ST93CMCMP 4.907 0.2468 -0.2448 0 0.3459
ST50TMT 2.401 0.1746 -0.2048 0 0.3421
ST24TTCV 0.2722 0.0755 0.3059 0 0.3383
ST91TMTCV 0.2845 0.0305 0.264 0 0.3383
ST60CMVO 12.62 0.7659 -0.2067 0 0.3383
ST36TMT 2.472 0.1681 -0.1886 0 0.3383
ST38TMT 1.835 0.1478 -0.2241 0 0.3346
ST39TMT 2.274 0.1734 -0.1833 0 0.3346
PTEDUCAT 15.92 2.85 -0.1234 0 0.3346
ST31CMVO 22.49 1.27 -0.2209 0 0.3308
ST46TMT 2.533 0.2134 -0.1897 0 0.3308
ST91CDCMP 0.2297 0.1909 0.1186 0 0.3195
ST56TMT 2.493 0.177 -0.2703 0 0.3158
ST35TMT 2.047 0.1764 -0.2298 0 0.3158
ST103MTCV 0.3214 0.0516 0.2515 0 0.3045
ST32TDT 0.1381 0.1148 0.1102 0 0.3045
ST99TMTCV 0.2597 0.0315 0.2576 0 0.297
ST40TTCV 0.2643 0.0339 0.2465 0 0.297
ST111DCMP 0.1271 0.1002 0.101 0 0.2932
MidTemp 19427 3071 -0.2511 0 0.2857
ST40TDT 0.1269 0.1074 0.1334 0 0.2857
ST36TTCV 0.2945 0.0316 0.1552 0 0.2857
ST57TDT 0.0725 0.0602 0.0841 0 0.2857
StdCVD 0.601 0.1159 0.1269 0 0.282
ST60TTCV 0.2086 0.0559 0.1991 0 0.282
ST85CMCMP 6.445 0.3363 -0.2867 0 0.2782
ST40CMVO 21.25 1.157 -0.2525 0 0.2782
ST119CMP 6.067 0.4657 -0.2078 0 0.2744
ST45TMT 2.369 0.1722 -0.2228 0 0.2744
ST31TDT 0.0987 0.0779 0.0857 0 0.2744
ST90CDCMP 0.2743 0.1952 0.0534 0.004 0.2744
ST15CCMP 5.698 0.3381 -0.2195 0 0.2707
MeanCVD 0.7516 0.1251 0.1324 0 0.2669
ST83TTCV 0.2604 0.0778 0.3107 0 0.2632
ST31CCMP 6.287 0.4026 -0.2779 0 0.2632
ST40CDVO 0.9946 0.7319 0.1078 0 0.2632
ST99CMCMP 6.804 0.3662 -0.3087 0 0.2594
ST121MTCV 0.211 0.0364 0.1997 0 0.2594
ST32TTCV 0.286 0.0339 0.238 0 0.2556
ST13CCMP 4.7 0.3057 -0.2374 0 0.2556
ST119TCV 0.1984 0.0591 0.2176 0 0.2556
ST47TMT 2.243 0.1748 -0.1877 0 0.2556
ST15TMT 2.327 0.1945 -0.2663 0 0.2519
ST49TMT 1.837 0.1658 -0.1979 0 0.2481
ST109MCMP 5.178 0.217 -0.2001 0 0.2481
ST55CCMP 6.48 0.312 -0.218 0 0.2444
ST62TMSDT 0.4472 0.0607 0.1223 0 0.2444
ST91TTCV 0.283 0.0337 0.2418 0 0.2331
ST93CCMP 4.866 0.2791 -0.2224 0 0.2293
ST26TTCV 0.2919 0.0367 0.2538 0 0.2293
ST34CMVO 12.93 0.7596 -0.1605 0 0.2293
ST83CDCMP 0.3776 0.3214 0.124 0 0.2293
ST85CCMP 6.419 0.3639 -0.2727 0 0.2256
ST51TMT 2.249 0.2294 -0.2032 0 0.2218
ST58TMT 2.509 0.2142 -0.3146 0 0.2218
ST32CMVO 21.03 1.216 -0.2257 0 0.2218
ST129TCV 0.3247 0.0388 0.2439 0 0.2218
ST62TMT 2.151 0.2547 -0.149 0 0.218
ST85CDCMP 0.2098 0.171 0.0776 0 0.218
PTGENDER 720 599 -0.1134 0 0.218
ST103MCMP 5.088 0.3646 -0.2528 0 0.2143
ST13TTCV 0.239 0.0432 0.2399 0 0.2143
ST44TTCV 0.3196 0.0557 0.2423 0 0.2143
ST130TCV 0.3217 0.0358 0.206 0 0.2143
ST58CMVO 21.41 1.028 -0.1915 0 0.2143
ST105CMP 5.281 0.2905 -0.1791 0 0.2105
ST56TDT 0.0678 0.0537 0.042 0.0241 0.2068
ST48TMT 1.452 0.1317 -0.1638 0 0.203
ST13TMSDT 0.5269 0.0633 0.1431 0 0.203
ST72TTCV 0.2237 0.044 0.2382 0 0.1992
ST13CMVO 12.85 0.7489 -0.19 0 0.1992
ST105MCMP 5.213 0.2596 -0.1937 0 0.1992
ST90TTCV 0.2741 0.0315 0.191 0 0.1992
ST54TTCV 0.2812 0.0513 0.1455 0 0.1992
ST116DCMP 0.1415 0.1193 0.0739 1e-04 0.1992
ST109MTCV 0.2849 0.0337 0.1625 0 0.1955
ST103CMP 5.028 0.3706 -0.2334 0 0.1955
ST15TTCV 0.2557 0.0369 0.2019 0 0.1955
ST58TDT 0.1069 0.0898 0.0904 0 0.1955
ST37SMVO 25.68 4.694 0.2143 0 0.1917
ST44CCMP 5.148 0.417 -0.2352 0 0.188
ST117DCMP 0.1742 0.1464 0.1109 0 0.188
ST85TTCV 0.2909 0.0372 0.2572 0 0.1842
ST115TCV 0.2646 0.0293 0.2301 0 0.1842
ST74TMTCV 0.2568 0.0336 0.2164 0 0.1842
ST111MTCV 0.2902 0.0281 0.1879 0 0.1842
ST57CMVO 22.36 1.148 -0.167 0 0.1842
ST43TTCV 0.2847 0.0389 0.1512 0 0.1842
ST113MTCV 0.2771 0.0423 0.1398 0 0.1842
MaxCVD 2.461 0.601 0.1013 0 0.1842
ST12SDVO 0.3804 0.3071 0.0922 0 0.1842
ST129DT 0.1217 0.0936 0.0545 0.0034 0.1842
ST58CCMP 6.563 0.3591 -0.2725 0 0.1805
ST91CCMP 6.733 0.3892 -0.251 0 0.1805
ST34CCMP 4.948 0.2653 -0.2206 0 0.1805
ST60CCMP 6.129 0.4544 -0.1771 0 0.1805
ST52TMT 2.143 0.1922 -0.284 0 0.1767
ST59CMVO 20.77 1.013 -0.1722 0 0.1767
ST14TMSDT 0.7722 0.1182 0.1533 0 0.1767
ST119DCMP 0.3219 0.2729 0.0896 0 0.1767
ST58TTCV 0.284 0.0376 0.2487 0 0.1729
ST56TTCV 0.2625 0.0318 0.2418 0 0.1729
ST57TTCV 0.2924 0.0319 0.1651 0 0.1729
ST50CMVO 14.25 0.6442 -0.1286 0 0.1729
ST32CDVO 0.8107 0.6642 0.0889 0 0.1729
ST82TMTCV 0.2703 0.0289 0.1052 0 0.1729
ST115MTCV 0.2635 0.0293 0.2473 0 0.1692
ST50CCMP 5.178 0.2503 -0.1857 0 0.1692
ST73TMTCV 0.3005 0.049 0.1769 0 0.1692
ST55TTCV 0.2841 0.0293 0.1577 0 0.1692
ST105MTCV 0.2651 0.0339 0.1428 0 0.1692
ST35TDT 0.0983 0.0739 0.0394 0.0342 0.1692
ST47TTCV 0.2597 0.0364 0.1712 0 0.1654
ST90TMTCV 0.2748 0.0289 0.1941 0 0.1617
ST129MSDT 0.9219 0.0858 0.1078 0 0.1617
ST117MTCV 0.2779 0.0351 0.2607 0 0.1579
ST52CDVO 0.5006 0.3966 0.0637 6e-04 0.1579
ST31TTCV 0.2755 0.031 0.1698 0 0.1541
ST53SMVO 16.68 0.745 -0.1206 0 0.1541
meanSVD 0.5376 0.168 0.0845 0 0.1541
ST74CMCMP 5.675 0.318 -0.2223 0 0.1504
ST129CMP 6.07 0.255 -0.1955 0 0.1504
ST102MTCV 0.2846 0.0348 0.1659 0 0.1504
ST73TTCV 0.3115 0.0529 0.1296 0 0.1504
ST130DTCV 0.0251 0.0189 0.0718 1e-04 0.1504
ST99CCMP 6.889 0.3842 -0.2601 0 0.1466
ST106DTCV 0.0254 0.0193 -0.0447 0.017 0.1466
ST52CMVO 20.09 1.041 -0.1906 0 0.1429
ST59TTCV 0.2646 0.0337 0.2095 0 0.1429
ST47CCMP 5.11 0.2973 -0.1661 0 0.1429
ST43TMT 2.16 0.2043 -0.1805 0 0.1429
ST14TTCV 0.2895 0.0641 0.1639 0 0.1429
StdATD 0.1351 0.039 0.1077 0 0.1429
ST25CCMP 4.433 0.3344 -0.1276 0 0.1429
ST26TDT 0.1207 0.1011 0.0634 6e-04 0.1429
ST52TDT 0.0814 0.0641 0.0501 0.0071 0.1429
ST26CDVO 0.7306 0.5664 0.0601 0.0012 0.1429
ST83TDTCV 0.0548 0.0426 0.0726 1e-04 0.1429
ST90CMCMP 6.403 0.3761 -0.2878 0 0.1391
ST26CCMP 6.472 0.3592 -0.2504 0 0.1391
ST56CCMP 7.305 0.311 -0.236 0 0.1391
ST52TTCV 0.2896 0.0296 0.1753 0 0.1391
ST60TMSDT 0.697 0.1169 0.1538 0 0.1391
maxCOMPD 0.7655 0.2475 0.131 0 0.1391
ST24CDVO 0.743 0.598 0.0802 0 0.1391
ST24TDT 0.335 0.2645 0.0814 0 0.1353
StdCOM 0.8364 0.0521 -0.2463 0 0.1316
ST119MCMP 6.098 0.41 -0.2177 0 0.1316
ST99TTCV 0.2551 0.035 0.2232 0 0.1316
ST130MCMP 6.086 0.2479 -0.205 0 0.1316
ST72CCMP 4.722 0.2789 -0.1908 0 0.1316
ST130DCMP 0.1205 0.0973 0.0697 2e-04 0.1316
ST25TMT 2.583 0.2416 -0.1171 0 0.1316
ST115DCMP 0.1321 0.1045 0.0513 0.0058 0.1316
ST31TDSDT 0.0404 0.0335 0.0239 0.1997 0.1316
ST47TDSDT 0.0543 0.0408 -0.0393 0.035 0.1316
ST38TMSDT 0.5891 0.0553 -0.1316 0 0.1278
ST23TTCV 0.2656 0.0369 0.1056 0 0.1278
ST34TTCV 0.3213 0.0435 0.0639 6e-04 0.1278
ST72CDCMP 0.2235 0.175 0.0578 0.0019 0.1278
ST121TCV 0.2062 0.0421 0.1768 0 0.1241
ST94TTCV 0.2964 0.0305 0.1374 0 0.1241
ST109TCV 0.2887 0.0389 0.1118 0 0.1241
ST109DCMP 0.1748 0.1324 0.0446 0.0162 0.1241
ST93TMTCV 0.3259 0.0385 0.096 0 0.1203
ST95TDTCV 0.0273 0.0203 -0.0448 0.0165 0.1203
ST26CMVO 20.44 1.098 -0.2138 0 0.1203
ST117TCV 0.2717 0.0378 0.2334 0 0.1203
ST36CCMP 5.982 0.2357 -0.158 0 0.1203
ST46CMVO 12.65 0.6027 -0.1373 0 0.1203
ST85TDTCV 0.0236 0.0184 0.0573 0.0022 0.1203
ST98CMCMP 5.455 0.281 -0.1765 0 0.1165
ST130CMP 6.102 0.2634 -0.1955 0 0.1165
ST109CMP 5.178 0.2359 -0.1686 0 0.1165
ST110MTCV 0.2881 0.0415 0.1822 0 0.1165
ST51TTCV 0.2866 0.0443 0.1781 0 0.1165
ST116MTCV 0.2929 0.0294 0.1715 0 0.1165
ST60TDT 0.2724 0.2374 0.0735 1e-04 0.1165
ST62TDT 0.1901 0.1433 0.0475 0.0105 0.1165
ST91TDTCV 0.0231 0.018 0.0212 0.2583 0.1165
ST43TDT 0.1036 0.0817 0.0654 4e-04 0.1128
ST15TDSDT 0.0459 0.0369 0.0358 0.0546 0.1128
Fusiform 17263 2729 -0.2115 0 0.1128
ST45TTCV 0.2363 0.0378 0.2064 0 0.1128
ST116TCV 0.2933 0.031 0.1532 0 0.1128
ST54TMT 2.816 0.2278 -0.0891 0 0.1128
ST82TTCV 0.2749 0.0367 0.0586 0.0017 0.1128
ST46TDSDT 0.0811 0.0618 -0.042 0.0239 0.1128
ST72CMCMP 4.711 0.2561 -0.2455 0 0.109
MeanCV 16.94 0.6701 -0.2072 0 0.109
ST95CMCMP 5.965 0.2196 -0.1667 0 0.109
ST104MCMP 5.429 0.257 -0.1622 0 0.109
ST15CMVO 17.38 1.048 -0.132 0 0.109
ST50TTCV 0.2811 0.0413 0.1582 0 0.109
ST23TMT 1.728 0.1445 -0.139 0 0.109
maxTCVD 0.1135 0.0367 0.0783 0 0.109
ST99TDTCV 0.023 0.0182 0.0562 0.0027 0.109
ST117DTCV 0.0238 0.0186 0.0493 0.0085 0.109
ST74TDTCV 0.0229 0.0185 0.054 0.0039 0.109
ST94CDCMP 0.1759 0.1316 0.0316 0.0889 0.109
ST16SDVO 0.2793 0.2227 0.044 0.0179 0.1053
ST111TCV 0.2908 0.0309 0.1725 0 0.1053
ST62CCMP 4.197 0.32 -0.1247 0 0.1053
ST114DTCV 0.0181 0.0151 -0.0689 2e-04 0.1053
ST26SDSA 2.439 1.938 0.0291 0.1169 0.1053
ST39CCMP 5.444 0.3039 -0.1683 0 0.1015
ST98CCMP 5.466 0.3004 -0.1594 0 0.1015
stdTS 0.1286 0.0179 0.1227 0 0.1015
ST34TMSDT 0.7629 0.0826 -0.1065 0 0.1015
ST18SDVO 0.3561 0.3879 6e-04 0.9723 0.1015
ST14TDSDT 0.1222 0.0954 -0.0125 0.5001 0.1015