pca

  1. R

    PCA (correlation) Biplot - correlation and angles

    Hi, I have a question regarding a PCA correlation Biplot. As far as I understand, angles between lines are (approx.) correlations between corresponding variables. Especially, if the angle is about 90° between two lines, these two variables are uncorrelated. But how does the plot look like if...
  2. J

    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...
  3. V

    Principal Component Analysis - global R2

    Hi, I've extracted the principal components from a set of k studentized variables. I can compute the cumulative R2.i.e the percentage of variation explained by the first r principal components by taking the ratio of the sum of the first r eigenvalues (i.e the largest r eigenvalues) over the...
  4. I

    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...
  5. I

    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, ...)...
  6. R

    PCA & predict

    Hey, I've started my first R project a few days ago. Currently I'm wondering about some results. Hopefully someone can give me feedback wether the following code is correct or not. Some Information: The project: I try to design a stress testing model. Input: 12 variables, 29 observations...
  7. A

    Best way to impute NAs before PCA in R

    Hey, I have a dataset with approximately 4000 rows and 150 columns. I want to predict the values of a single column (= target). The data is on cities (demography, social, economic, ... indicators). A lot of these are highly correlated, so I want to do a PCA - Principal Component Analysis...
  8. J

    Statistica - marking catagorised case states by colour on a PCA projection

    Hi, I have used statistica to run a PCA analysis, and would like to colour code the 'projection of the cases' output. I have used the 'Case states' - 'Color or mark by category' tool, by right clicking on the case states in the spread sheet, to colour code my variables (I would like to...
  9. D

    Can the PCA scores be used as variables in multiple linear regression

    Hello, I used PCA to reduce the dimensions of a data set of 10 variables into 2 principal components (describing 82% of variance in data set of 10 variables) and calculated the scores for both PCs for all the samples (n=85). Can I use these PCs (Scores) (separately or together) as...
  10. D

    Using principle component as explanatory variable in multiple regression

    Hello, I used PCA to reduce the dimensions of a data set of 10 variables into 2 principal components (describing 82% of variance in data set of 10 variables) and calculated the scores for both PCs for all the samples (n=85). Can I use these PCs (Scores) (separately or together) as...
  11. T

    Factor Analysis / PCA with longitudinal data

    Hello, I have a problem which I'm trying to get around... I am trying to conduct a principal component analysis which compares changes in labour market regulation in the EU pre and post financial crisis. The first problem I faced was a low sample size as my sample size initially concerned just...
  12. J

    Polychoric Principal Component Analysis

    Hi everyone: I am working with a questionnaire to evaluate socioeconomic position in a sample. It was basically counting a list of items to assess living standards. These items were home ownership, number of bathroom, ownership of household items (cars, bicycles, etc)... I want to use...
  13. purifyz

    covariance matrix vs correlation matrix

    Dear all, I am now studying PCA/FA and stumbled upon following problem: if variables are measured on different scales it's reasonably obvious that we should use correlation matrix. But, when one should use covariance matrix if variables have the same scale? Jolliffe wrote that there might be...
  14. S

    principal components analyses PCA forced through zero

    Hi, I am trying to proceed with an unconventional PCA analyses on two variables. I would like the first principal component forced through the intercept (0,0). Anyone got an R code that would allow me to do that ? cheers
  15. J

    Moving Window Principal Component Analysis

    Hi, I'm finishing my thesis we're I'm forming currency-hedge investment portfolios out of the PCA on the currencies. I need to do a PCA using a "moving-window" of the previous 60 months of data, throughout my entire data-set. If you want the "pseudo-code" is: -Run PCA using previous...
  16. M

    Choosing factors for principle components analysis

    Hello, I am running a PCA on a dataset that includes 43 water wells, each with up to five descriptive factors associated with it (temp, distance to significant location, depth, number of taxa, volume of flow). I am using XLSTAT to perform the analysis. Running the PCA with the whole...
  17. H

    Create a Biplot with the result of FactoMineR

    As I have some NA's in my data, and FactoMineR handles those exeptionally well, and I have arranged myself with FactoMineR quite well which is why I want to use the results of FactoMineR. But it occured to me that there is no "official" way to do a biplot with the results of FactoMineR. I...
  18. T

    I invite you to help me interpret this Biplot from PCA analysis!

    I would like to delete this thread as nobody answer it.
  19. P

    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!
  20. A

    Comparing PCA to multiple regression.

    Dear all, I would appreciate any help on this question. I have been asked to compare the results of PCA to multiple regression. I ve been given 10 variables. For the PCA, i am expected to regress the 3 components that result after the dimension reduction process, and for the multiple...