# Tag Info

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One really simple introductory statistics book is Andy Fields "Discovering Statistics using R" - also available for SPSS. It contains a lot of nice examples and is even fun to read. Less precise, though compared to other books, but with very little mathematical formulations and lots of text. I found it easy for a basic start, and am still using it from time ...

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The usual approach would be to train a classifier on the resulting partitions (if possible, first clean the data, in particular fix any errors in the clustering). There is not much to be gained from mixing clustering and classification/prediction. Use clustering to produce an initial working hypothesis, refine this hypothesis, then use prediction to ...

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Here is another intuitive explanation for PCA: A layman's introduction to principal component analysis (in 100 seconds)

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Whatever you do, it is worthwhile getting bootstrap confidence intervals on the ranks of importance of the predictors to show that you can really do this with your dataset. I am doubtful that any of the methods can reliably find the "true" predictors.

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I would say your question is a qualified question not only in cross validated but also in stack overflow, where you will be told how to implement dimension reduction in R(..etc.) to effectively help you identify which column/variable contribute the better to the variance of the whole dataset. The PCA(Principal Component Analysis) has the same functionality ...

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In PCA you want to describe the data in fewer variables. You can get the same information in fewer variables than with all the variables. For example, hours studied and test score might be correlated and we do not have to include both. In your example, let's say your objective is to measure how "good" a student/person is. Looking at all these variables, it ...

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Pages 13-20 of the tutorial you posted provide a very intuitive geometric explanation of how PCA is used for dimensionality reduction. The 13x13 matrix you mention is probably the "loading" or "rotation" matrix (I'm guessing your original data had 13 variables?) which can be interpreted in one of two (equivalent) ways: The (absolute values of the) ...

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Try using a stats package that gives you a good visualization of the PCA model - score plots will allow you to see, in the (reduced-dimension) model space, how observations relate to one another, e.g. similar observations cluster together. SPE and Hotelling's T-squared plots will help you find outliers. Loading plots will allow you to see which variables are ...

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This answer gives an intuitive and not-mathematical interpretation: The PCA will give you a set of orthogonal vectors within a high-dimensional point cloud. The order of the vectors is determined by the information conveyed aftter projecting all points onto the vectors. In different words: The first principal component vector will tell you the most about ...

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You do not need to show any at all. You should show those that are instrumental for the argument you are making/pieces of evidence you are interested in. The "var" indicates that the component plotted accounts for so-and-so many % of the variance among variables included.

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prcomp has a predict S3 method you can use to apply the same transformations to new data quickly. Pass in the data for the new month and the prcomp object like so: new.pca = predict(p, newdata=x.new) But, the fact that you are asking this suggests that you are missing something fundamental about what PCA is doing, because you can also do this with the ...

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Your question seems to be not about "two types" of PCA. The second "type" is a continuation of the explanation of the "first": the concept of loadings and how to get them, is introduced this time. Loadings are more important in factor analysis than in PCA because they are the source of interpretation of the latents (in PCA, you not often interpret the ...

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