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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...

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

Dear Sir/Madam, I have a large data set of 10,17,289 observations of 10,830 variables. I need to use PCA to reduce the dimension of dataset. I have already tried irlba, prcomp and nsprcomp packages in R but couldn't do for huge data sets. i.e pc <- prcomp_irlba(sparseYY[1:5000,], n=50, retx = TRUE, center = TRUE, scale. = FALSE) able to get only few PCs for 5000 rows only so can you