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This is my biplot (produced by Matlab's functions pca and biplot, red dots are PC scores, blue lines correspond to eigenvectors; data were not standardized; first two PCs account for the ~98% of the total … If data are well clustered, how do I interpret the results of the PCA in terms of what variable has a major (or minor) influence on the system? …

I'm new in principal component analysis (PCA) and I don't really understand the biplot representation of its results, so I would really appreciate some guidance. … Therefore, I would like to know if based on the PCA biplot I could stop measuring the values for x2 if I already measured x1 values. …

I am not sure about plotting a biplot for LDA. Suppose I have a 3 feature data set with 3 classes. Then, I perform LDA to reduce the dimensionality to 2. How can I create a biplot for the LDA? … My understanding of biplot (I may be completely wrong) is simply a projection of the original features in the hyperplane (or line) of the dimensionality reduction. …

After the PCA result came out with biplot graph, I was a little bit confused to interpret this data. …

Questions: How to correctly interpret relationships between dots (respondents), colors (genders) and arrows (variables)? …

let's assume that I choose the first two components, and the biplot I get for my pca (given that I have 3 independent variables) is like the figure below (in answer 1). … first variable is the most important aspect of PC1 (with the higarhest negative coefficient), and my second variable is the most important variable of PC2. then in order to make a model that is easier to interpret …

Trying to interpret graphs like this (example below) has led to two questions. Question 1: What values is the system using for coordinates to draw the plots? … I know that when evaluating variable categories on a biplot, it's the angular distance that indicates the strength of correlation, but am not sure that works for the variables. …

I have always been confused about how to properly interpret PCA results. My data looks like this and it's a big table with more than 5 million rows and 12 columns.(the first few lines are all 0...) … And the biplot >biplot(pca) I have a few specific questions and I would really appreciate it if you can comment to help me understand the plot. …

colour=ifelse(rownames(plot_loading_correlation1)=='dependent', 'red','darkblue')) I thought about a different way to present the correlation loadings for one component only instead of using biplot … 1,1,0.2))+ theme_bw()+ theme(axis.text.x = element_text(angle=65, hjust=1, size = 8)) My question: Is it fine to present and interpret …

I could apply log-ratios to the data, but then how do I interpret the scatterplots of the log-ratios? What about biplots? …

I'd be happy to just go with by-item and by-subject analyses, but results are quite different and I'm not sure about how to interpret this. …