Dear Sir/Madam,
I am trying to do PCA analysis with "iris" dataset and trying to interpret
the result. Dataset contains 150 obs of 5 variables
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4
0.2 setosa
2 4.9 3.0 1.4
0.2 setosa
.....
.....
150 5.9 3.0 5.1 18
verginica
now I used 'prcomp' function on dataset and got result as following:
>print(pc)
Standard deviations (1, .., p=4):
[1] 1.7083611 0.9560494 0.3830886 0.1439265
Rotation (n x k) = (4 x 4):
PC1 PC2 PC3 PC4
Sepal.Length 0.5210659 -0.37741762 0.7195664 0.2612863
Sepal.Width -0.2693474 -0.92329566 -0.2443818 -0.1235096
Petal.Length 0.5804131 -0.02449161 -0.1421264 -0.8014492
Petal.Width 0.5648565 -0.06694199 -0.6342727 0.5235971
I'm planning to use PCA as feature selection process and remove variables
which are corelated in my project, I have interpreted the PCA result, but
not sure is my interpretation is correct or wrong.
If you can correct me it will be of great help.
If i notice the PCs result, I found both positive and negative data.
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