[R] Regarding Principal Component Analysis result Interpretation
sezenismail at gmail.com
Fri Sep 15 14:12:32 CEST 2017
First, see the example at https://isezen.github.io/PCA/
> On 15 Sep 2017, at 13:43, Shylashree U.R <shylashivashree at gmail.com> wrote:
> 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
> now I used 'prcomp' function on dataset and got result as following:
> Standard deviations (1, .., p=4):
>  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.
You want to “remove variables which are correlated”. Correlated among themselves? If so, why don’t you create a pearson correlation matrix (see ?cor) and define a threshold and remove variables which are correlated according to this threshold? Perhaps I did not understand you correctly, excuse me.
for iris dataset, each component will be as much as correlated with PC1 and remaining part will be correlated PC2 and so on. Hence, you can identify which variables are similar in terms of VARIANCE. You can understand it if you examine the example that I gave above.
In PCA, you can also calculate the correlations between variables and PCs but this shows you how PCs are affected by this variables. I don’t know how you plan to accomplish feature selection process so I hope this helps you. Also note that resources part at the end of example.
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