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Version 1.7.0  (2003-04-16)

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> library(MASS);
> test <- read.table("madham1.txt", sep="");
> train <- read.table("disputed1.txt", sep="");
> cl <- factor(c(rep("m",14), rep("h",18)))
> 
> test;
        V1      V2
1  206.943  95.794
2  212.915 113.243
3  202.583  88.930
4  211.161  88.386
5  236.943  76.805
6  216.064  91.271
7  204.622 104.284
8  211.095  97.983
9  218.177  91.756
10 203.793 121.724
11 201.891  90.780
12 200.383 101.533
13 209.506  85.558
14 210.016  97.469
15 224.116 100.379
16 189.962  96.623
17 215.364 116.341
18 212.991  98.187
19 219.352 100.304
20 212.911 109.864
21 215.019 106.343
22 238.305  97.964
23 241.800  93.776
24 232.937 100.170
25 232.227 114.797
26 231.380 110.725
27 232.432  85.135
28 216.651  94.196
29 230.453  85.832
30 226.647  92.429
31 235.176 109.548
32 231.627  96.300
> train;
        V1      V2
1  226.277 107.664
2  205.268  96.276
3  225.536  88.435
4  217.273 114.612
5  208.796  82.407
6  232.581  96.241
7  210.914 117.011
8  192.638 101.840
9  226.509 107.127
10 207.393  98.896
11 220.076  91.138
12 213.391  92.348
> cl;
 [1] m m m m m m m m m m m m m m h h h h h h h h h h h h h h h h h h
Levels: h m
> 
> z <- lda(test, cl);
> 
> predict(z, train);
$class
 [1] h m h h m h h m h m h m
Levels: h m

$posterior
           h         m
1  0.8967995 0.1032005
2  0.1827444 0.8172556
3  0.6256962 0.3743038
4  0.8246339 0.1753661
5  0.1037051 0.8962949
6  0.8872417 0.1127583
7  0.7134061 0.2865939
8  0.0631667 0.9368333
9  0.8955841 0.1044159
10 0.2671380 0.7328620
11 0.5048856 0.4951144
12 0.3197294 0.6802706

$x
    
             LD1
  1  -1.20283057
  2   1.27770108
  3  -0.08567565
  4  -0.78663622
  5   1.72423138
  6  -1.13553985
  7  -0.35554198
  8   2.09021569
  9  -1.19397672
  10  0.94651020
  11  0.24929868
  12  0.77424579

>