pca

  1. D

    Can iterative PCA be applied to grouped data?

    My data set consist of 156 individuals with fifteen variables. The variables consist of one body mass (dependent) variable and fourteen (independent) variables of different bird bone dimensions (of one type of bone). The 156 individuals can be divided over 30 bird species, where some groups of...
  2. T

    PCA pre-treatment: Centering or Standardizing?

    Hello all, I have a matrix X that contains industrial data, where each column is a variable. Since PCA is done by the correlation matrix, should I use: . original X; . X after Centering (subtracting each column of X by the mean of the respective column); .X after Standardizing? (subtracting...
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    Interpreting principal components

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    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!
  5. J

    how to determine whether the point is outside/within the ellipse in PCA

    Dear all, Happy New Year. Wish you a very successful year! I am creating PCA plots in R in an automated way and need a script to define whether the sample (red dot on the figure) is outside/within the circle. Does anyone have a good solution for it? So far I've been using mahalanobis distance...
  6. E

    Merge two EOFs (principal components)

    Hi, I'm doing an EOF analysis to my data, and I make a decision on how many EOFs to retain. I also used the North's rule of thumb (North et al. 1982, Sampling errors in the estimation of empirical orthogonal functions) to see which EOFs can be "separated". The attached file shows how my...
  7. S

    Can i use the results from a PCA if the matrix is 'not positive definite'

    Hi, I have a 'not positive definite' correlation matrix having done a principal component analysis (PCA) on SPSS. The data i have used is from a questionnaire i did using a 7 point likert type scale. There were 36 questions (36 variables) i got 16 responses (n=16). The questionnaire was very...
  8. L

    Principal component and clustering

    Hi! I have a question concerning principal component analysis and how to pursue. First of all, this is my case: I have a lot of data points containing a calculated Q and a measured T. Q is calculated by the sum of different q's. I would like to make the following diagonal clusters...
  9. A

    Factor Analysis

    Hello, Recently I conducted a pilot study. The sample size was 36 and the Questionnaire contains 41 items. I ran PCA but unfortunately the out put showed that the correlation matrix is not positive definite. The one reason I understand is the number of cases X variables. Is there any other...
  10. F

    PCA for Compositional Data using R

    Good evening! As the title suggest, I'm approaching to PCA for compositional Data. The dataset is about 5200 proteins for which is recorded a value for the chromatography in 30 intervals (obviously, the sum of every row is equal to 1). According to Aitchison's theory, I scaled data with...
  11. N

    PCA analysis - identifying extremes of each factor

    I am analysing the size and shape of a particular population of people using 25 different body measurements. I completed a PCA/factor analysis in SPSS which suggested 3 factors. The next step is to identify "boundary manikins" that represent the extremes of each factor so I can then...
  12. D

    how can i perfom K-means clustering via Principal component analysis

    can anyone please explain to me how to run a cluster analysis( K-means) after the principal component analysis? immediate help will be very much appreciated. thanks
  13. L

    PCA Analysis - Factor Analysis gave only 1 column?! Need to do a scatterplot!

    I am conducting a PCA on my dataset which has four scale variables (duration, %, rate 1, and rate 2) to see if they contribute to a single component. I conducted a factor analysis and it created only one column. When I have conducted PCA before, I have been given two columns by which I was able...
  14. C

    One response variable and multiple explanatory variables (numeric and factor values)

    Hello everyone, I am a master student in Biology. I am now trying to analyse my thesis data, but I am facing a problem: I do not understand which test I should use for my large dataset containing one response variable and multiple explanatory variables (factor and numeric values!). Please...
  15. S

    Trouble with 3D geometric morphometrics/PCAs

    Hi all, I've been doing some 3D shape analyses using landmarks and analysed the resulting data using PCAs. I'm not very familiar with all this so I was wondering if there's a way to superimpose the landmarks on my PCA plots. I'm guessing that I'd have to start with figuring out which raw...
  16. 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...
  17. 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...
  18. 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...
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    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...
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    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, ...)...