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I have the results for a PCA with varimax rotation, but I can only figure out how to make a scree plot for the PCA without rotation (using the scree function on the original data). … Should I be trying to make a scree plot that includes the varimax rotation? Or is it typical to only generate scree plots that do not include a rotation? If this is typical, how do I do this? …

@ttnphns comments here that there exist two expositions of the Cattell scree-plot rule: If the "elbow" is the m-th eigenvalue, (1) choose to extract m components; or (2) choose to extract m-1 components … from says "The scree plot confirms the choice of three components." …

You have to determine by looking at the scree plot to see how many PC you should be using to maximize the variance while minimizing the number of PC you are going to use. …

I was suggested to look at a scree plot but am wondering how to go about plotting it in numpy. …

V1 and V2 are first and second column of V In order to plot a scree plot: I squared the S and plotted (i,i) element of S^2 with respect to 'i'. … I can rephrase the question as below : Can I take the square of diagonal elements of (S) matrix as variance so that I can use it as the y axis of the scree plot. …

I am interested in first using an average linkage algorithm, and have found that some have recommended looking for the 'elbow' in the sum of squared error (SSE) scree plot as a guideline for deciding how … I was wondering if the Gower's similarity coefficient (being non-metric and non-Euclidean) would allow me to create an SSE scree plot, or if that didn't make sense statistically. …

However, when I am plotting the scree plot of my dataset in R, then I am getting the "elbow" right after the first component. Does it mean only the first component is enough? …

Scree plot I prepared and I did the factor analysis for different number of factors from 1 to 9 with 2 different rotations: varimax and oblimin. So far so good. … Elbow method suggested 2 factors, eigenvalues on the scree plot were higher than 1 only for 1 factor. …

I always found the concept of determining the "ideal" number of components/factors for an ICA/PCA/FA via a scree plot useful and quick, but also a bit shaky. … In an effort to try to make the scree plot "elbow" more obvious (and without trying to fit a function to the scree plot data points). …

plot. … Both the Kaiser rule of eigenvalues greater than 1 and the scree plot (see Fig. 1) indicated that three-factor solution would fit the data the best and then they show a typical scree plot. …

But in the scree plot there is no elbow at all, just a decreasing line, that makes me think maybe I shouldn't be using PCA. … Could you help me to know, first if I'm using the right model if in the scree plot there is no elbow and, second how can I check how many factors I have? …

As expected, I saw four large eigenvalues and 26 small ones on the scree plot. … When I look at real test results I have on hand and do a factor analysis, the scree plots just die away basically smoothly. …

That is if group 1 has (n samples x m features) and group 2 has (n samples x k features), does it make sense to compare the slope of the scree plot/explained variance for these two groups as a measure …

For this, I can create scree plot (with residual variance) for PCA and check the elbow of the curve. Is it possible to do the same for Laplacian Eigenmap, Diffusion Map, and t-SNE? …

Ten factors in combination explained 65% of the variance, which is ok good I used varimax rotation method to extract component matrix Here are my problems: #Problem 1: Upon the inspection of scree plot …