# principal component

Hi there guys i'm facing a statistics Problem and hope you might be able to help me out. (sorry for my english it isn't that good) I want to analyse the Price of electricity in germany and have already bulid a dataset with 70 Variables (Solarproduction, Windproduction, Nuclearproduction and so...
2. ### Prior knowledge of Confirmatory Factor Analysis required for Principal Component Analysis (PCA)?

Can someone please tell me the prerequisites to learn PCA? Is prior knowledge of CFA needed? I am new to the topic. Thanks in advance!
3. ### Does it make sense to conduct a Hierarchical Principal Compomnent Analysis (PCA)?

So I have twelve variables that theoretically belong to the same dimension and I want do reduce them to one single item. A hierarchical factor analysis gives really low loadings at the second level. However, if I do PCA at both levels I get good results, but does it make sense to do a...
4. ### PCA or clustering on binary data?

Hello, I have a dataset consisting of around 30 species of plants (rows) and around 50 variables (my columns) that are medicinal properties of the plant species, and which take on binary values; for instance, if a plant species has anti-fungal properties (one of the variables) it would have...
5. ### Principal Component Analysis in R- Data rotation

I ran prcomp on my data (7000 observations, 48 variables), and I biplot I got seems to have thousands of points. My guess is that R is treating each of the 7000 observations as variables. Is there a way to "rotate" my data so that I get 48 points on my PCA graph instead of 7000? Each column...
6. ### Principal Component Analysis in R Help

I am a beginner to R. I have read several guides, but still am stuck on this: I have data in an excel csv file, on which I want to run PCA. I'm not sure how the prcomp formula works. The help page states: prcomp(x, retx = TRUE, center = TRUE, scale. = FALSE, tol = NULL, ...)...
7. ### Correct Procedure for analysis of Drivers

Hello, I have some survey data which shows an effort score for interacting with a business. I am working in a programme of work tasked to reduce that effort. I want to understand (from what's available in the survey data set) what are the drivers of that effort score. This will help us...
8. ### Factor Analyzing Price Time Series

Hi I have a question regarding using factor analysis for price time series (specifically, online advertising prices of various versions of ads). To build the correlation matrix for FA, would I use the raw price series, or should I be using the interval changes of that series? Thanks
9. ### principal component analysis - right choice & interpretation

Hi everyone, I'm kinda stuck with a problem. I am building a scoring model with 14 different factors. Here my first question: as it's not a regression, can I use a principal component analysis to narrow that down? If I can, I am also struggling with interpreting the results. I have 4...
10. ### How to create graph showing PCA with groups from Cluster analysis?

Yet again I have run into another wall. I am doing a project for uni and have came across a problem, I am looking to build a graph with PCA scores (2 components, so pca1 VS pca2). I also need to label the scores with what group they belong to, after having done a cluster analysis. Is this even...
11. ### Once completing a PCA on a dataset how do I go about adding the PCA scores to data?

I am only new to STATA and was wondering once I have completed a PCA on my dataset, how do I see the scores on the dataset? (The data tab at the top beside the graphics tab) Also while I am here, after completing a cluster analysis and dendrogram how do I reduce it down to 6 groups (6 groups is...
12. ### First PCA component versus mean

Hello, I have a question regarding PCA. I have 10 time series (highly correlated in low frequencies) and run standard PCA. I am curious about the reasons why the first principal component has almost 100% correlation with mean of these 10 series. Can anyone explain it to me please? Thank you!
13. ### testing different models using the same group of longitudinal data

Description of my research Longitudinal growth data are fitted using parametric models giving growthcurves. Differentiating these growthcurves gives velocity curves. From every curve biological parameters can be determinated. Every parameteric model has function parameters. The same...
14. ### Variable reduction- which method to use?

I have a panel data set of 121 countries across 10 years and about 43 variables. In theory all the variables should be attempting to measure the same unobservable factor so certain groups of the variables are closely correlated. I am hoping to be able to drop some variables as they are...