*** Discriminant Analysis ***

	*** Discriminant Analysis ***

Call:
discrim(structure(.Data = clase ~ Sepal.L. + Sepal.W. + Petal.L. + Petal.W., class = "formula"), data = irises,
	family = Classical(cov.structure = "homoscedastic"), na.action = na.omit, prior = "proportional")

Group means:
   Sepal.L. Sepal.W. Petal.L. Petal.W.  N  Priors 
G1    6.588    2.974    5.552    2.026 50 0.33333
G2    5.006    3.428    1.462    0.246 50 0.33333
G3    5.936    2.770    4.260    1.326 50 0.33333

Covariance Structure: homoscedastic 
         Sepal.L. Sepal.W. Petal.L. Petal.W. 
Sepal.L.  0.26501  0.09272  0.16751 0.038401
Sepal.W.           0.11539  0.05524 0.032710
Petal.L.                    0.18519 0.042665
Petal.W.                            0.041882

Constants:
      G1      G2      G3 
 -104.37 -86.308 -72.853

Linear Coefficients:
             G1      G2     G3 
Sepal.L. 12.446  23.544 15.698
Sepal.W.  3.685  23.588  7.073
Petal.L. 12.767 -16.431  5.211
Petal.W. 21.079 -17.398  6.434

Tests for Homogeneity of Covariances:
      Statistic df Pr 
Box.M    146.66 20  0
adj.M    140.94 20  0

Tests for the Equality of Means:
Group Variable: clase 
                       Statistics      F df1 df2 Pr 
          Wilks Lambda      0.023  199.1   8 288  0
          Pillai Trace      1.192   53.5   8 290  0
Hotelling-Lawley Trace     32.477  580.5   8 286  0
     Roy Greatest Root     32.192 1167.0   4 145  0
* Tests assume covariance homoscedasticity.
  F Statistic for Wilks' Lambda is exact.
  F Statistic for Roy's Greatest Root is an upper bound.

Hotelling's T Squared for Differences in Means Between Each Group:
           F df1 df2 Pr 
G1-G2 1098.3   4 144  0
G1-G3  105.3   4 144  0
G2-G3  550.2   4 144  0

95% Simultaneous Confidence Intervals Using the Sidak Method:
                        Estimate Std.Error Lower Bound Upper Bound      
G1.Sepal.L.-G2.Sepal.L.    1.580    0.1030       1.320       1.840 ****
G1.Sepal.W.-G2.Sepal.W.   -0.454    0.0679      -0.625      -0.283 ****
G1.Petal.L.-G2.Petal.L.    4.090    0.0861       3.870       4.310 ****
G1.Petal.W.-G2.Petal.W.    1.780    0.0409       1.680       1.880 ****
(critical point: 2.5212 )
                        Estimate Std.Error Lower Bound Upper Bound      
G1.Sepal.L.-G3.Sepal.L.    0.652    0.1030      0.3920       0.912 ****
G1.Sepal.W.-G3.Sepal.W.    0.204    0.0679      0.0327       0.375 ****
G1.Petal.L.-G3.Petal.L.    1.290    0.0861      1.0800       1.510 ****
G1.Petal.W.-G3.Petal.W.    0.700    0.0409      0.5970       0.803 ****
(critical point: 2.5212 )
                        Estimate Std.Error Lower Bound Upper Bound      
G2.Sepal.L.-G3.Sepal.L.   -0.930    0.1030      -1.190      -0.670 ****
G2.Sepal.W.-G3.Sepal.W.    0.658    0.0679       0.487       0.829 ****
G2.Petal.L.-G3.Petal.L.   -2.800    0.0861      -3.010      -2.580 ****
G2.Petal.W.-G3.Petal.W.   -1.080    0.0409      -1.180      -0.977 ****
(critical point: 2.5212 )
* Intervals excluding 0 are flagged by '****'

Mahalanobis Distance:
       G1     G2     G3 
G1   0.00 179.38 17.201
G2          0.00 89.864
G3                0.000

Kolmogorov-Smirnov Test for Normality:
         Statistic Probability 
Sepal.L.   0.03520     0.99240
Sepal.W.   0.07973     0.29604
Petal.L.   0.04854     0.87143
Petal.W.   0.04786     0.88207

Plug-in classification table:
        G1 G2 G3 Error Posterior.Error 
     G1 49  0  1  0.02         0.01022
     G2  0 50  0  0.00         0.00000
     G3  2  0 48  0.04         0.03832
Overall           0.02         0.01618
(from=rows,to=columns)

Rule Mean Square Error: 0.028328 
(conditioned on the training data)

Cross-validation table:
        G1 G2 G3 Error Posterior.Error 
     G1 49  0  1  0.02         0.01451
     G2  0 50  0  0.00         0.00000
     G3  2  0 48  0.04         0.03933
Overall           0.02         0.01795
(from=rows,to=columns)