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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  206.943 194.927
2  212.915 194.665
3  202.583 190.775
4  211.161 195.173
5  236.943 206.221
6  216.064 202.788
7  204.622 200.676
8  211.095 198.127
9  218.177 193.999
10 203.793 201.034
11 201.891 213.712
12 200.383 206.130
13 209.506 221.207
14 210.016 189.553
15 224.116 196.338
16 189.962 197.467
17 215.364 174.067
18 212.991 176.234
19 219.352 176.292
20 212.911 182.438
21 215.019 173.041
22 238.305 179.417
23 241.800 183.768
24 232.937 186.078
25 232.227 183.254
26 231.380 189.176
27 232.432 192.793
28 216.651 200.851
29 230.453 201.646
30 226.647 201.082
31 235.176 187.940
32 231.627 180.943
> train;
        V1      V2
1  226.277 203.163
2  205.268 181.653
3  225.536 182.627
4  217.273 183.053
5  208.796 198.148
6  232.581 184.962
7  210.914 184.366
8  192.638 196.933
9  226.509 207.898
10 207.393 187.710
11 220.076 186.476
12 213.391 194.591
> 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] m h h h m h h m m h h m
Levels: h m

$posterior
            h          m
1  0.43306157 0.56693843
2  0.69634742 0.30365258
3  0.94711811 0.05288189
4  0.87188448 0.12811552
5  0.20030848 0.79969152
6  0.96391965 0.03608035
7  0.73455164 0.26544836
8  0.04955353 0.95044647
9  0.27089311 0.72910689
10 0.52678907 0.47321093
11 0.84342044 0.15657956
12 0.41990075 0.58009925

$x
    
            LD1
  1   0.3909138
  2  -0.1806083
  3  -1.2491684
  4  -0.7461128
  5   0.9705780
  6  -1.4570665
  7  -0.2782810
  8   1.7865350
  9   0.7656043
  10  0.1951116
  11 -0.6245528
  12  0.4188886

>