Re: [R] Note on PCA (not directly with R)
See if you can track down Thurstone's box problem dataset. It comes (I believe) on a CD with Loehlin's book 'latent variable models', but I'd be surprised if you couldn't find it elsewhere. Thurstone measured boxes, and using EFA (rather than PCA, but they might be similar enough to start off with) found that they were three dimensional. Jeremy On 28 June 2010 02:27, Christofer Bogaso wrote: > Dear all, I am looking for some interactive study materials on Principal > component analysis. Basically I would like to know what we are actually > doing with PCA? What is happening within the dataset at the time of doing > PCA. > > Probably a 3-dimensional interactive explanation would be best for me. > > I have gone through some online materials specially Wikipedia etc, however > what I need a "movable explanation" to understand that. > > Any suggestion please? > > Thanks for your time > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list > 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. > -- Jeremy Miles Psychology Research Methods Wiki: www.researchmethodsinpsychology.com __ R-help@r-project.org mailing list 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.
Re: [R] Note on PCA (not directly with R)
Christofer Bogaso wrote: > Dear all, I am looking for some interactive study materials on Principal > component analysis. Basically I would like to know what we are actually > doing with PCA? Having in mind the eigenvalue decomposition and a bivariate data set, the sum- it-all in a few sentences I think is: - PCA rotates (and scales) the data set in such a way that aligns the thransformed axes (the principal components) with the direction(s) of maximum variance - eigen values are proportional to the lentgh of the axes of variation - eigen (or characteristic) vectors define the rotation > What is happening within the dataset at the time of doing > PCA. The algorithm (classically): - mean-centers the data matrix - calculates the covariance matrix (non-standardised PCA) or the correlation matrix (standartised PCA, a step also known as scaling) - calculates the the eigenvalue decomposition (EVD) (the eigenvectors and eigenvalues) of a data variance-covariance (non-standardised) or the correlation matrix (standardised) - sorts the variances (i.e. the eigenvalues) in decreasing order and finally projects the original dataset signals into what is named Principal Components or scores, by multiplying them with the eigenvectors which act as weighting coefficients. The algorithm does actually three (or more?) things: - minimises the mean square error of approximating the original data set, - keeps the maximum possible variance(s) of the original data set, - gives decorrelated variables > Probably a 3-dimensional interactive explanation would be best for me. > I have gone through some online materials specially Wikipedia etc, however > what I need a "movable explanation" to understand that. > > Any suggestion please? For what is worth, I think a 2-dimensional example is better to start with. You can have a look at the plotpc() package. It really is educational. Good luck, Nikos __ R-help@r-project.org mailing list 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.
[R] Note on PCA (not directly with R)
Dear all, I am looking for some interactive study materials on Principal component analysis. Basically I would like to know what we are actually doing with PCA? What is happening within the dataset at the time of doing PCA. Probably a 3-dimensional interactive explanation would be best for me. I have gone through some online materials specially Wikipedia etc, however what I need a "movable explanation" to understand that. Any suggestion please? Thanks for your time [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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.
