title: Plotting a linear discriminant analysis, classification tree and Naive Bayes Curve on a single ROC plot
body: <p>The data is present at the very bottom of the page and is called LDA.scores'. This is a classification task where I performed three supervised machine learning classification techniques on the data-set. All coding is supplied to show how these ROC curves were produced. I apologise for asking a loaded question but I have been trying to solve these issues using different combinations of code for almost two weeks, so if anyone can help me, then thank you. The main issue is the Naive Bayes curve shows a perfect score of 1, which is obviously wrong, and I cannot solve how to incorporate the linear discriminant analysis curve into a single ROC plot for comparison with the  coding supplied.</p>
  
  <ol>
  <li>Linear Discriminant Analysis (LDA) performed in the "MASS" package</li>
  <li>Naive Bayes (NB) in the "kLAR" package</li>
  <li>Classification Trees (CT) in the "rpart" package</li>
  </ol>
  
  <h1>Goal</h1>
  
  <ul>
  <li>A single ROC plot exhibiting a ROC curve for a comparison between each classification technique, conjunct with a legend. </li>
  <li>To calculate the area under the curve for each classification technique</li>
  </ul>
  
  <h1>PROBLEMS</h1>
  
  <ol>
  <li>Each classification technique is performed in a different R package and I     am having trouble amalgamating these ROC curves onto one plot. All the error messages are shown at the bottom of the page</li>
  <li>The ROC curves for both the LDA and NB look spurious</li>
  <li>I cannot apply a legend and experience error messages </li>
  </ol>
  
  <h1>I provided the coding for all three techniques so anyone can assess my logic in step-by-step process</h1>
  
  <h1>Linear Discriminant Analysis</h1>
  
  <pre><code>   library(MASS)
     predictors&lt;-as.matrix(LDA.scores[,2:13])
     response&lt;-as.factor(LDA.scores[,1])
  
     #Perform LDA 
  
     Family.lda&lt;-lda(response~predictors, CV=TRUE)
     predict.Family &lt;-predict(Family.lda)
     tab &lt;- table(response, Family.lda$class)
  </code></pre>
  
  <h1>Construct Confusion Matrix to predict classes</h1>
  
  <pre><code>   conCV1 &lt;- rbind(tab[1, ]/sum(tab[1, ]), tab[2, ]/sum(tab[2, ]))
     dimnames(conCV1) &lt;- list(Actual = c("No", "Yes"), "Predicted (cv)"= c("No", "Yes"))
  
     print(round(conCV1, 3))
  </code></pre>
  
  <h1>Plot discriminant scores</h1>
  
  <pre><code>    library(lattice)
      windows(width=10, height=7)
      densityplot(~predict.Family$x, groups=LDA.scores$Family) 
  </code></pre>
  
  <h1>Function to Calculate Confusion Matrices</h1>
  
  <pre><code> confusion &lt;- function(actual, predicted, names = NULL, printit = TRUE, prior = NULL) {                    
    if (is.null(names))
    names &lt;- levels(actual)
    tab &lt;- table(actual, predicted)
    acctab &lt;- t(apply(tab, 1, function(x) x/sum(x)))
    dimnames(acctab) &lt;- list(Actual = names, "Predicted (cv)" = names)
    if (is.null(prior)) {
    relnum &lt;- table(actual)
    prior &lt;- relnum/sum(relnum)
    acc &lt;- sum(tab[row(tab) == col(tab)])/sum(tab)
    }
   else {
    acc &lt;- sum(prior * diag(acctab))
    names(prior) &lt;- names
    }
    if (printit)
    print(round(c("Overall accuracy" = acc, "Prior frequency" = prior),
                  + 4))
    if (printit) {
    cat("\nConfusion matrix", "\n")
    print(round(acctab, 4))
    }
    invisible(acctab)
    }
  </code></pre>
  
  <h1>Changing the proportions to create a training and test set (70:30)</h1>
  
  <pre><code>  prior &lt;- c(0.7, 0.3)
    lda.70.30 &lt;- lda(response~predictors, CV=TRUE, prior=prior)
    confusion(response, lda.70.30$class, prior = c(0.7, 0.3))
  </code></pre>
  
  <h1>A function to create a ROC Curve</h1>
  
  <pre><code> truepos &lt;- numeric(19)
   falsepos &lt;- numeric(19)
   p1 &lt;- (1:19)/20
   for (i in 1:19) {
   p &lt;- p1[i]
   Family.ROC &lt;- lda(response~predictors, CV = TRUE, prior = c(p,  1 - p))
  
   confmat &lt;- confusion(LDA.scores$Family, Family.ROC$class, printit = FALSE)
   falsepos[i] &lt;- confmat[1, 2]
   truepos[i] &lt;- confmat[2, 2]
   }
  </code></pre>
  
  <h1>Plot ROC curve</h1>
  
  <pre><code> windows(width=10, height=7)
   LDA.ROC&lt;-plot(truepos~falsepos, type = "l", lwd=2, 
                 xlab = "False     positive    rate (Specificity)", 
                 ylab = "True positive rate (Sensitivity)" col ="green")
                 abline(a=0,b=1, col="red")
  </code></pre>
  
  <h1>Figure 1</h1>
  
  <p><a href="https://i.stack.imgur.com/roCb2.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/roCb2.jpg" alt="enter image description here"></a></p>
  
  <h1>Classification trees</h1>
  
  <h1>Generate a testing and training set 70:30</h1>
  
  <pre><code>  index&lt;-1:nrow(LDA.scores) 
    trainindex.LDA=sample(index, trunc(length(index)*0.70), replace=FALSE) 
    LDA.70.trainset&lt;-LDA.scores[trainindex,]
    LDA.30.testset&lt;-LDA.scores[-trainindex,]
  </code></pre>
  
  <h1>Grow Trees with 70 % training set</h1>
  
  <pre><code>#Grow Tree the tree with the 70 % training set
  
    library(rpart)
    tree.split3&lt;-rpart(Family~., data=LDA.70.trainset3, method="class")
    summary(tree.split3)
    print(tree.split3)
    plot(tree.split3)
    text(tree.split3,use.n=T,digits=0)
    printcp(tree.split3)
  </code></pre>
  
  <h1>Make Classification Tree Predictions using the test and training set (70:30)</h1>
  
  <pre><code> res3=predict(tree.split3,newdata=LDA.30.testset3)
   res4=as.data.frame(res3)
  </code></pre>
  
  <h2>Create a binary system (0 or 1) for a binomial distribution for the categorical grouping factor</h2>
  
  <pre><code> res4$actual2 = NA
   res4$actual2[res4$actual=="G8"]= 1
   res4$actual2[res4$actual=="V4"]= 0
  </code></pre>
  
  <h1>Plotting the ROC curve</h1>
  
  <pre><code> roc_pred &lt;- prediction(re4$Predicted.prob, res4$actual2)
   perf &lt;- performance(roc_pred, "tpr", "fpr")
   plot(perf, col="blue", lwd=2)
   abline(0,1,col="grey")
  </code></pre>
  
  <p>Figure 2</p>
  
  <p><a href="https://i.stack.imgur.com/AMvsk.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/AMvsk.jpg" alt="enter image description here"></a></p>
  
  <h1>Naive Bayes</h1>
  
  <pre><code> library(klaR)
   library(caret)
  </code></pre>
  
  <h1>Generate the test and training set 70:30</h1>
  
  <pre><code> trainIndex &lt;- createDataPartition(LDA.scores$Family, p=0.70, list=FALSE)
   sig.train=LDA.scores[trainIndex,]
   sig.test=LDA.scores[-trainIndex,]
  </code></pre>
  
  <h1>Build the NB model and make predictions on the test set</h1>
  
  <pre><code> sig.train$Family&lt;-as.factor(sig.train$Family)
   sig.test$Family&lt;-as.factor(sig.test$Family)
   nbmodel&lt;-NaiveBayes(Family~., data=sig.train)
   prediction&lt;-predict(nbmodel, sig.test[2:13])
   NB&lt;-as.data.frame(prediction)
   colnames(NB)&lt;-c("Family", "Actual", "Predicted")
  </code></pre>
  
  <h1>Create a binary system (0 or 1) for a binomial distribution for the categorical factor</h1>
  
  <pre><code> NB$actual2 = NA
   NB$actual2[NB$Family=="V4"]=1
   NB$actual2[NB$Family=="G8"]=0
   NB2&lt;-as.data.frame(NB)
  </code></pre>
  
  <h1>PLOT ROC CURVE - THIS CURVE LOOKS SUSPECT</h1>
  
  <pre><code> library(ROCR)
   windows(width=10, height=7)
   roc_pred.NB&lt;- prediction(NB2$Predicted, NB2$actual2)
   perf.NB &lt;- performance(roc_pred.NB, "tpr", "fpr")
   plot(perf.NB, col="orange", lwd=2)
   abline(0,1,col="grey")
  </code></pre>
  
  <p>Figure 3: This ROC CURVE IS OBVIOUSLY WRONG</p>
  
  <p><a href="https://i.stack.imgur.com/CcEbL.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/CcEbL.jpg" alt="enter image description here"></a></p>
  
  <h1>Plotting all ROC curves onto a single plot</h1>
  
  <pre><code> windows(width=10, height=7)
   plot(fit.perf, col="blue", lwd=2); #CT
   plot(LDA.ROC, col="green", lwd=2, add=T); #LDA
   plot(perf.NB,lwd=2,col="orange", lwd=2, add=T);NB
   abline(0,1,col="red", lwd=2)
  </code></pre>
  
  <h1>Error messages</h1>
  
  <pre><code> Warning in min(x) : no non-missing arguments to min; returning Inf
   Warning in max(x) : no non-missing arguments to max; returning -Inf
   Warning in min(x) : no non-missing arguments to min; returning Inf
   Warning in max(x) : no non-missing arguments to max; returning -Inf
   Warning in plot.window(...) : "add" is not a graphical parameter
   Error in plot.window(...) : need finite 'xlim' values
   plot(fit.NB,lwd=2,col="orange", lwd=2, add=T); #NB
   Error in plot(fit.NB, lwd = 2, col = "orange", lwd = 2, add = T) : 
   error in evaluating the argument 'x' in selecting a method for function          
   Warning in plot.window(...) : "add" is not a graphical parameter
   Error in plot.window(...) : need finite 'xlim' values
  </code></pre>
  
  <h1>Area under the curve</h1>
  
  <pre><code>  auc1&lt;-performance(fit.pred,"auc")#CT
    auc2&lt;-performance(fit.NB, "auc")#NB
  </code></pre>
  
  <h1>I AM UNSURE HOW TO CALCULATE THE AREA UNDER THE CURVE FOR THE LDA CODE SUPPLIED</h1>
  
  <h1>Production of the legend</h1>
  
  <h1>This code produces error messages</h1>
  
  <pre><code>legend(c('fit.pred',fit.NB','LDA.ROC'), col=c('blue',orange','green'),lwd=3)
  </code></pre>
  
  <h1>Data named LDA.scores</h1>
  
  <pre><code>       Family    Swimming Not.Swimming      Running  Not.Running
      1      v4 -0.48055680 -0.086292700 -0.157157188 -0.438809944
      2      v4  0.12600625 -0.074481895  0.057316151 -0.539013927
      3      v4  0.06823834 -0.056765686  0.064711783 -0.539013927
      4      v4  0.67480139 -0.050860283  0.153459372 -0.539013927
      5      v4  0.64591744 -0.050860283  0.072107416 -0.472211271
      6      v4  0.21265812 -0.068576492  0.057316151 -0.071395338
      7      v4 -0.01841352 -0.068576492 -0.053618335 -0.071395338
      8      v4  0.12600625  0.055436970  0.012942357  0.296019267
      9      v4 -0.22060120  0.114491000 -0.038827070  0.563229889
      10     v4  0.27042603 -0.021333268  0.049920519 -0.037994010
      11     v4  0.03935439 -0.044954880  0.012942357  0.195815284
      12     v4 -0.45167284  0.008193747 -0.075805232 -0.171599321
      13     v4 -0.04729748 -0.056765686  0.035129254 -0.305204632
      14     v4 -0.10506539  0.008193747 -0.046222702  0.062209973
      15     v4  0.09712230  0.037720761  0.109085578 -0.104796666
      16     v4 -0.07618143  0.014099150 -0.038827070  0.095611301
      17     v4  0.29930998  0.108585597  0.057316151  0.028808645
      18     v4  0.01047043 -0.074481895  0.020337989 -0.071395338
      19     v4 -0.24948516  0.002288344  0.035129254  0.329420595
      20     v4 -0.04729748  0.049531567  0.057316151  0.296019267
      21     v4 -0.01841352  0.043626164  0.005546724 -0.171599321
      22     v4 -0.19171725  0.049531567 -0.016640173 -0.071395338
      23     v4 -0.48055680  0.020004552 -0.142365923  0.596631217
      24     v4  0.01047043  0.008193747  0.220020063  0.062209973
      25     v4 -0.42278889  0.025909955 -0.149761556  0.028808645
      26     v4 -0.45167284  0.031815358 -0.134970291 -0.138197994
      27     v4 -0.30725307  0.049531567  0.042524886  0.095611301
      28     v4  0.24154207 -0.039049477  0.072107416 -0.104796666
      29     v4  1.45466817 -0.003617059  0.064711783  0.296019267
      30     v4 -0.01841352  0.002288344  0.020337989  0.028808645
      31     G8  0.38596185  0.084963985  0.049920519 -0.037994010
      32     G8  0.15489021 -0.080387298  0.020337989 -0.338605960
      33     G8 -0.04729748  0.067247776  0.138668107  0.129012629
      34     G8  0.27042603  0.031815358  0.049920519  0.195815284
      35     G8 -0.07618143  0.037720761  0.020337989 -0.037994010
      36     G8 -0.10506539  0.025909955 -0.083200864  0.396223251
      37     G8 -0.01841352  0.126301805 -0.024035805  0.362821923
      38     G8  0.01047043  0.031815358 -0.016640173 -0.138197994
      39     G8  0.06823834  0.037720761 -0.038827070  0.262617940
      40     G8 -0.16283329 -0.050860283 -0.038827070 -0.405408616
      41     G8 -0.01841352 -0.039049477  0.005546724 -0.205000649
      42     G8 -0.39390493 -0.003617059 -0.090596497  0.129012629
      43     G8 -0.04729748  0.008193747 -0.009244540  0.195815284
      44     G8  0.01047043 -0.039049477 -0.016640173 -0.205000649
      45     G8  0.01047043 -0.003617059 -0.075805232 -0.004592683
      46     G8  0.06823834  0.008193747 -0.090596497 -0.205000649
      47     G8 -0.04729748  0.014099150  0.012942357 -0.071395338
      48     G8 -0.22060120 -0.015427865 -0.075805232 -0.171599321
      49     G8 -0.16283329  0.020004552 -0.061013967 -0.104796666
      50     G8 -0.07618143  0.031815358 -0.038827070 -0.138197994
      51     G8 -0.22060120  0.020004552 -0.112783394 -0.104796666
      52     G8 -0.19171725 -0.033144074 -0.068409599 -0.071395338
      53     G8 -0.16283329 -0.039049477 -0.090596497 -0.104796666
      54     G8 -0.22060120 -0.009522462 -0.053618335 -0.037994010
      55     G8 -0.13394934 -0.003617059 -0.075805232 -0.004592683
      56     G8 -0.27836911 -0.044954880 -0.090596497 -0.238401977
      57     G8 -0.04729748 -0.050860283  0.064711783  0.028808645
      58     G8  0.01047043 -0.044954880  0.012942357 -0.305204632
      59     G8  0.12600625 -0.068576492  0.042524886 -0.305204632
      60     G8  0.06823834 -0.033144074 -0.061013967 -0.271803305
      61     G8  0.06823834 -0.027238671 -0.061013967 -0.037994010
      62     G8  0.32819394 -0.068576492  0.064711783 -0.372007288
      63     G8  0.32819394  0.014099150  0.175646269  0.095611301
      64     G8 -0.27836911  0.002288344 -0.068409599  0.195815284
      65     G8  0.18377416  0.025909955  0.027733621  0.162413956
      66     G8  0.55926557 -0.009522462  0.042524886  0.229216612
      67     G8 -0.19171725 -0.009522462 -0.038827070  0.229216612
      68     G8 -0.19171725  0.025909955 -0.009244540  0.396223251
      69     G8  0.01047043  0.155828820  0.027733621  0.630032545
      70     G8 -0.19171725  0.002288344 -0.031431438  0.463025906
      71     G8 -0.01841352 -0.044954880 -0.046222702  0.496427234
      72     G8 -0.07618143 -0.015427865 -0.031431438  0.062209973
      73     G8 -0.13394934  0.008193747 -0.068409599 -0.071395338
      74     G8 -0.39390493  0.037720761 -0.120179026  0.229216612
      75     G8 -0.04729748  0.008193747  0.035129254 -0.071395338
      76     G8 -0.27836911 -0.015427865 -0.061013967 -0.071395338
      77     G8  0.70368535 -0.056765686  0.397515240 -0.205000649
      78     G8  0.29930998  0.079058582  0.138668107  0.229216612
      79     G8 -0.13394934 -0.056765686  0.020337989 -0.305204632
      80     G8  0.21265812  0.025909955  0.035129254  0.396223251
         Family    Fighting Not.Fighting        Resting  Not.Resting
      1      v4 -0.67708172 -0.097624192  0.01081204879 -0.770462870
      2      v4 -0.58224128 -0.160103675 -0.03398160805  0.773856776
      3      v4 -0.11436177 -0.092996082  0.05710879700 -2.593072768
      4      v4 -0.34830152 -0.234153433 -0.04063432116 -2.837675606
      5      v4 -0.84568695 -0.136963126 -0.13084281035 -1.680828329
      6      v4 -0.32933343 -0.157789620 -0.02997847693 -0.947623773
      7      v4  0.35984044 -0.157789620  0.12732080268 -0.947623773
      8      v4 -0.32511830 -0.023574435 -0.10281705810 -2.607366431
      9      v4  1.51478626  0.001880170  0.08155320398 -0.637055341
      10     v4  0.11114773 -0.224897213 -0.17932134171 -1.818396455
      11     v4  0.27975296 -0.109194467 -0.14338902206  2.170944974
      12     v4 -0.89626852 -0.069855533 -0.02058415581 -0.658126752
      13     v4  0.12379312 -0.123078796 -0.11528274705 -0.808243774
      14     v4  0.66965255 -0.111508522 -0.11764091337  2.377766908
      15     v4  1.56536783 -0.143905291  0.04389156236  2.111220276
      16     v4  0.56427428 -0.099938247  0.01399844913 -0.322326312
      17     v4 -0.71291033 -0.118450687 -0.05755560242  2.218858946
      18     v4 -0.75927677  1.519900201  0.04711630687  3.920878638
      19     v4 -0.75295407  0.177748344  0.01584280360 -0.304945754
      20     v4 -1.00164679  0.108326696  0.09348590900  1.038591535
      21     v4 -1.03958296  0.652129604  0.09677967302  1.752268128
      22     v4  0.82139726  0.638245274  0.02053612974  0.907465624
      23     v4 -1.07541157 -0.072169588 -0.03608286844  1.137774798
      24     v4 -1.03115270  0.087500202  0.07805238146 -3.663486997
      25     v4 -0.98900139 -0.180930170 -0.00009686695  2.350924346
      26     v4 -1.06908888 -0.146219346 -0.02285413055  0.067293462
      27     v4 -1.20186549 -0.049029039 -0.00424187149 -1.898454393
      28     v4  0.58324237 -0.125392851  0.01446241356 -2.497647463
      29     v4 -0.97003330 -0.134649071  0.03187450017 -4.471716512
      30     v4  0.22917139 -0.060599313  0.11323315542 -1.465081244
      31     G8  0.41042201 -0.086053918 -0.01171898422 -0.232806371
      32     G8 -1.11545531 -0.197128554 -0.06499053655 -3.043893581
      33     G8 -0.19023412 -0.083739863 -0.07758659568 -2.323908986
      34     G8  0.25446217 -0.092996082 -0.07399758157  1.437404886
      35     G8 -0.05324237  0.844196163 -0.11503350996  1.079056696
      36     G8  0.09007207  0.055103433  0.02167111711  1.110865131
      37     G8  1.21129685  1.971140911  0.01904454162  1.404724068
      38     G8  0.62539368 -0.111508522  0.05768779393 -1.706664294
      39     G8  1.32932051 -0.224897213  0.05555202379  0.736746935
      40     G8  0.40199175 -0.187872334 -0.01031175326 -0.005516985
      41     G8  0.44625062 -0.160103675 -0.00458313459  1.727170333
      42     G8  0.60221046 -0.194814499  0.17430774591  1.685228831
      43     G8  0.33665722 -0.053657149  0.00481502094  1.836016918
      44     G8 -0.63493041 -0.206384774 -0.00928412956  0.466173920
      45     G8 -0.28296700  0.108326696  0.09047589183  1.697173771
      46     G8 -0.32722587 -0.164731785  0.08917985896  1.057314221
      47     G8 -0.11646933  0.187004564 -0.05671203072  0.933704227
      48     G8 -0.10171637  0.025020719 -0.05333390954  0.482480775
      49     G8  0.13643851  0.057417488  0.08541446168  0.680713089
      50     G8 -0.57802615  0.434608441  0.10140397965  0.090780703
      51     G8  0.05002833  0.057417488 -0.02509342995  0.680713089
      52     G8 -0.16072820  0.073615872 -0.03698779080 -0.982921741
      53     G8 -0.29139726 -0.035144709  0.04609635201 -2.281900378
      54     G8  0.13222338 -0.051343094  0.06524159499  0.972089090
      55     G8 -0.41152848 -0.134649071  0.08459773090  0.027767791
      56     G8  0.68229794 -0.185558279 -0.03239032508 -0.162881500
      57     G8 -0.24292325  0.013450444 -0.03208740616 -0.530221948
      58     G8 -0.11646933 -0.134649071  0.06264952925 -0.385741863
      59     G8 -0.21341734 -0.215640993  0.05241547086 -0.972251823
      60     G8 -0.24292325 -0.185558279 -0.03437271856  0.002267358
      61     G8 -0.24292325 -0.005061995 -0.03437271856 -1.134447998
      62     G8  0.09007207 -0.238781543 -0.06747523863  0.626424009
      63     G8 -0.34197883 -0.099938247 -0.01270059491 -0.722750217
      64     G8 -0.30825778 -0.167045840  0.10014629095 -0.382722075
      65     G8 -0.08696342 -0.208698829 -0.02872845706 -0.356550578
      66     G8 -0.81196590  0.048161268 -0.00950652573 -1.851614124
      67     G8  0.49683219  0.048161268  0.04867308008 -1.851614124
      68     G8 -0.13754498 -0.037458764  0.02486518629  1.731465143
      69     G8 -0.48318570  0.161549960 -0.05951115497  0.254319006
      70     G8  0.39988418  0.031962884 -0.02353665674  2.043778341
      71     G8  0.90148474 -0.102252302 -0.01967923345 -0.289913920
      72     G8  0.28396809 -0.123078796 -0.10148651548  1.386940871
      73     G8  1.05322945 -0.139277181 -0.00480936518  0.054207713
      74     G8  1.24923303 -0.208698829 -0.00098261723  0.594212936
      75     G8  0.47154141 -0.118450687 -0.13970798195  1.551821303
      76     G8  1.27873894 -0.072169588 -0.00286148145  3.100704184
      77     G8  0.05002833 -0.044400929 -0.05492902692  0.327263666
      78     G8  1.54218461 -0.030516599  0.10732815358 -1.055195336
      79     G8  0.74763247 -0.132335016  0.11660744219 -1.134447998
      80     G8  0.11747042 -0.037458764 -0.02016620439  1.730726972
         Family    Fighting      Hunting Not.Hunting     Grooming
      1      v4 -0.67708172  0.114961983   0.2644238  0.105443109
      2      v4 -0.58224128  0.556326739  -1.9467488 -0.249016133
      3      v4 -0.11436177  0.326951992   2.1597867 -0.563247851
      4      v4 -0.34830152  0.795734469   2.1698228 -0.611969290
      5      v4 -0.84568695  0.770046573   0.2554708 -0.230476117
      6      v4 -0.32933343  0.736574466   0.1225477 -0.270401826
      7      v4  0.35984044  0.215724268   0.1225477  1.057451389
      8      v4 -0.32511830 -0.200731013   0.2593696 -0.260830004
      9      v4  1.51478626 -2.160535836   0.8687508  1.030589923
      10     v4  0.11114773  0.660462182   1.7955299 -0.809959417
      11     v4  0.27975296 -0.293709087  -0.8377330 -0.292132450
      12     v4 -0.89626852  0.565754284   1.3339454 -0.573854465
      13     v4  0.12379312 -0.499644710  -0.5100101 -0.372285683
      14     v4  0.66965255  0.080624964  -2.6852985 -0.470590886
      15     v4  1.56536783 -4.076143639  -0.8432925  1.657328707
      16     v4  0.56427428 -0.127040484  -0.8662526 -0.161145079
      17     v4 -0.71291033  0.661240603  -2.1990933 -0.381900622
      18     v4 -0.75927677  0.294950237  -3.5062302 -0.121909231
      19     v4 -0.75295407  0.548369546  -1.3326746 -0.338568723
      20     v4 -1.00164679  0.137622686  -1.7580862 -0.312742050
      21     v4 -1.03958296  0.019302681  -2.2730277  0.708985315
      22     v4  0.82139726 -0.043057497  -3.1829838 -0.378408200
      23     v4 -1.07541157  0.351515502  -0.3762928 -0.304161903
      24     v4 -1.03115270 -0.007163636   1.3605877 -0.431053223
      25     v4 -0.98900139  0.253780410  -1.1388134 -0.554883286
      26     v4 -1.06908888  0.700680605   0.6629041  0.113074697
      27     v4 -1.20186549  0.340704098   0.9979915 -0.693545361
      28     v4  0.58324237 -1.727041782   1.5589254  0.180163686
      29     v4 -0.97003330  0.209410408   1.7613786 -0.258156792
      30     v4  0.22917139 -2.441026901   1.3929340  0.276959818
      31     G8  0.41042201  0.383257784  -0.5374467  0.165978418
      32     G8 -1.11545531 -1.098682982   2.9654839  0.148947473
      33     G8 -0.19023412  0.873144122   2.5120581 -0.846910101
      34     G8  0.25446217  0.968889915  -0.4130434 -0.938661624
      35     G8 -0.05324237  0.936455703  -2.5993065 -0.949914982
      36     G8  0.09007207 -0.467815937  -1.0766479  1.474170593
      37     G8  1.21129685 -1.239490708  -4.1335895  1.357023559
      38     G8  0.62539368  0.177235670   2.4989896  1.393241265
      39     G8  1.32932051 -4.736158229  -0.5718146  2.467225606
      40     G8  0.40199175  0.342693397   0.5675981  0.648320657
      41     G8  0.44625062  0.488950070  -1.6998195  0.709588943
      42     G8  0.60221046 -0.415575233  -1.4313741  0.728473890
      43     G8  0.33665722  0.353937257  -2.2985148  0.379706002
      44     G8 -0.63493041  0.262083568   0.2245685 -0.367629121
      45     G8 -0.28296700  0.574316915  -1.0020637  0.280710938
      46     G8 -0.32722587  0.323665326  -1.1559252  0.119455912
      47     G8 -0.11646933  0.786566398   0.1746772 -0.858206576
      48     G8 -0.10171637  0.718065343  -0.2673407 -0.552555005
      49     G8  0.13643851  0.584868846  -0.1203383 -0.335378116
      50     G8 -0.57802615 -0.053955393   0.6359729  0.057885811
      51     G8  0.05002833  0.738563765  -0.1203383 -0.188308359
      52     G8 -0.16072820  0.778263240   2.1906890 -0.545138998
      53     G8 -0.29139726  0.751018502   1.6039070  0.198100074
      54     G8  0.13222338  0.297804447  -0.5217068 -0.514310832
      55     G8 -0.41152848  0.102161281   0.3866610 -0.036323341
      56     G8  0.68229794  0.371667959   1.6179863 -0.176365139
      57     G8 -0.24292325  0.631574111   1.4206594 -0.269668849
      58     G8 -0.11646933 -0.004568899   1.6827511  0.003731717
      59     G8 -0.21341734  0.214080935   1.0590019  0.036586351
      60     G8 -0.24292325  0.796339908   1.2727184 -0.615289246
      61     G8 -0.24292325  0.796339908   2.6745838 -0.615289246
      62     G8  0.09007207 -0.396720145   0.2644238  0.290800156
      63     G8 -0.34197883  0.441985331   1.4545220 -0.520648930
      64     G8 -0.30825778 -2.489721464   1.3587105  1.711267220
      65     G8 -0.08696342  0.407907785   0.8136610 -0.273333736
      66     G8 -0.81196590  0.554423932   1.3666527 -0.594420949
      67     G8  0.49683219  0.697912886   1.3666527 -0.446661330
      68     G8 -0.13754498  0.491198842  -1.3307974 -0.333825929
      69     G8 -0.48318570  0.604848320  -0.1305910 -0.601492025
      70     G8  0.39988418  0.773938679  -0.5078441 -0.712559657
      71     G8  0.90148474  0.734412186  -0.1166561 -0.548803885
      72     G8  0.28396809  1.145505011  -1.3062489 -0.921846260
      73     G8  1.05322945  0.616784110   0.9039851 -0.165629176
      74     G8  1.24923303  0.329287256   0.3647117  0.111867440
      75     G8  0.47154141 -0.016764163  -1.1586689 -0.476713403
      76     G8  1.27873894  0.007799347  -3.0386529  0.215087903
      77     G8  0.05002833  0.209496900  -1.5080522  0.324560232
      78     G8  1.54218461 -5.031179821   1.6811626  2.366893936
      79     G8  0.74763247 -0.325105405   1.6851337  1.351590903
      80     G8  0.11747042 -0.756350687  -1.3315194  0.375911766
  
         Family Not.Grooming
      1      v4  0.019502286
      2      v4 -0.290451956
      3      v4  0.359948884
      4      v4  0.557840914
      5      v4  0.117453376
      6      v4  0.126645924
      7      v4  0.126645924
      8      v4  0.196486873
      9      v4  0.152780228
      10     v4  0.354469789
      11     v4 -0.261430968
      12     v4  0.176448238
      13     v4 -0.007374708
      14     v4 -0.557848621
      15     v4 -0.213674557
      16     v4 -0.005819262
      17     v4 -0.470070992
      18     v4 -0.786078864
      19     v4  0.006063789
      20     v4 -0.271842650
      21     v4 -0.349418792
      22     v4 -0.338096262
      23     v4 -0.165119403
      24     v4  0.346566439
      25     v4 -0.344191931
      26     v4  0.074321265
      27     v4  0.179825379
      28     v4  0.278407054
      29     v4  0.593125727
      30     v4  0.199177375
      31     G8 -0.058900625
      32     G8  0.633875622
      33     G8  0.428150308
      34     G8 -0.206023441
      35     G8 -0.436958199
      36     G8 -0.291839246
      37     G8 -0.907641911
      38     G8  0.448567295
      39     G8 -0.127186127
      40     G8  0.024715134
      41     G8 -0.416345030
      42     G8 -0.330697382
      43     G8 -0.469720666
      44     G8 -0.047494017
      45     G8 -0.301732446
      46     G8 -0.138901021
      47     G8  0.098101379
      48     G8 -0.002063769
      49     G8 -0.028324190
      50     G8  0.071630763
      51     G8 -0.028324190
      52     G8  0.295110588
      53     G8  0.347112947
      54     G8 -0.083577573
      55     G8 -0.036886152
      56     G8  0.189045953
      57     G8  0.467596992
      58     G8  0.303378276
      59     G8  0.218879697
      60     G8  0.092005711
      61     G8  0.270111340
      62     G8 -0.012909856
      63     G8  0.262292068
      64     G8  0.107125772
      65     G8  0.123422927
      66     G8  0.299426602
      67     G8  0.299426602
      68     G8 -0.326871824
      69     G8 -0.022088391
      70     G8 -0.428508341
      71     G8 -0.014675497
      72     G8 -0.114462294
      73     G8  0.087227267
      74     G8 -0.031519161
      75     G8 -0.159318008
      76     G8 -0.397875854
      77     G8  0.101520559
      78     G8  0.244481505
      79     G8  0.529968994
      80     G8 -0.326619590                
  </code></pre>