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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software ModuleRscript (source code is shown below)
Title produced by softwareR console
Date of computationTue, 25 May 2010 12:54:31 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/May/25/t1274792072iv1ziqxftwtzpso.htm/, Retrieved Thu, 02 May 2024 12:24:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=76382, Retrieved Thu, 02 May 2024 12:24:13 +0000
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Original text written by user:Warning: you must have the RWeka R package installed to reproduce this computation. The RWeka package requires Sun Java on your machine!
IsPrivate?No (this computation is public)
User-defined keywordsICEL 2010, peer review, regression tree, evaluation, social constructivism, reproducible computing, educational technology, e-learning
Estimated Impact192

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [R console] [Reviewing Peer Re...] [2010-05-25 12:54:31] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
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Dataset

Tables (Output of Computation)

> {
+     library(RWeka)
+     con <- url('https://automated.biganalytics.eu/download/ICEL2010')
+     load(con)
+     close(con)
+     y <- y[!is.na(y$X1), ]
+    .... [TRUNCATED] 
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   3.000   3.000   3.306   4.000   5.000 
J48 pruned tree
------------------

x$WORDSPA <= 2093
|   x$WORDSPA <= 1678: Fail (17.0)
|   x$WORDSPA > 1678
|   |   x$Pop <= 0
|   |   |   x$WORDSPA <= 1962: Fail (8.0)
|   |   |   x$WORDSPA > 1962: Pass (4.0/1.0)
|   |   x$Pop > 0: Pass (9.0/1.0)
x$WORDSPA > 2093: Pass (227.0/13.0)

Number of Leaves  : 	5

Size of the tree : 	9


=== Summary ===

Correctly Classified Instances         250               94.3396 %
Incorrectly Classified Instances        15                5.6604 %
Kappa statistic                          0.7389
Mean absolute error                      0.1049
Root mean squared error                  0.229 
Relative absolute error                 40.622  %
Root relative squared error             63.9602 %
Coverage of cases (0.95 level)         100      %
Mean rel. region size (0.95 level)      95.283  %
Total Number of Instances              265     

=== Confusion Matrix ===

   a   b   <-- classified as
  25  15 |   a = Fail
   0 225 |   b = Pass
=== 10 Fold Cross Validation ===

=== Summary ===

Correctly Classified Instances         241               90.9434 %
Incorrectly Classified Instances        24                9.0566 %
Kappa statistic                          0.6234
Mean absolute error                      0.115 
Root mean squared error                  0.2975
Relative absolute error                 44.5037 %
Root relative squared error             83.0932 %
Coverage of cases (0.95 level)          92.8302 %
Mean rel. region size (0.95 level)      60      %
Total Number of Instances              265     

=== Confusion Matrix ===

   a   b   <-- classified as
  25  15 |   a = Fail
   9 216 |   b = Pass

Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (body of R function):
{
    library(RWeka)
    con <- url("https://automated.biganalytics.eu/download/ICEL2010")
    load(con)
    close(con)
    y <- y[!is.na(y$X1), ]
    x <- y
    x <- x[!is.na(x$PSCORE), ]
    x <- cbind(x[, 1:3], x[, 5], x[, 8:9], x[, 11:22], x[, 188])
    colnames(x)[4] <- "name2"
    colnames(x)[19] <- "REV"
    x <- x[complete.cases(x), ]
    xx <- as.numeric(x$PSCORE)
    print(summary(xx))
    n <- length(xx)
    myy <- array("Fail", dim = c(n, 1))
    for (i in 1:n) {
        if (xx[i] > 2.7) 
            myy[i] <- "Pass"
    }
    m1 <- J48(as.factor(myy) ~ x$NNZFG + x$MWH + x$Year + x$WORDSPA + 
        x$MRT + x$AFL + x$LPM + x$LPC + x$BC + x$Gender + x$Pop)
    print(m1)
    print(summary(m1))
    print(e <- evaluate_Weka_classifier(m1, numFolds = 10))
    
    plot(m1)
    
}