This list is about R programming, not statistics, although they do often intersect. Nevertheless, this discussion seems to be all about the latter, not the former, so I think you would do better bringing it to a statistics list like stats.stackexchange.com rather than here.
Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Sep 15, 2017 at 5:12 AM, Ismail SEZEN <sezenism...@gmail.com> wrote: > First, see the example at https://isezen.github.io/PCA/ > > > On 15 Sep 2017, at 13:43, Shylashree U.R <shylashivash...@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 > > 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. > > > 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. > > isezen > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.