principal components

  1. H

    How to compare two PCA vectors - classification and clustering problem

    Dear all, Assume I have one reference PCA vector and two sample PCA vectors, all of the same length. I want to know which of the two sample PCA's is more similar to the reference vector. Do I just subtract one sample from the reference and the other sample from the reference and compare...
  2. A

    How to comment a projection of data in Principal Composant Analysis?

    From: A=\begin{bmatrix} 1 & 0 & 0\\ 0 & 0 & 1\\ 0 & 1 & 2\\ 2 & 2 & 1\\ 1 & 0 & 0\\ 2 & 3 & 2\\ \end{bmatrix} I first had to calculate the gravity point g (1,1,1), Y the centered data matrix Y=\begin{bmatrix} -1 & -1 & -1\\ -1 & -1 & 0\\ -1 & 0 & 1\\ 1 & 1 & 0\\ 0 & -1 & -1\\ 1 & 2 & 1\\...
  3. S

    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...
  4. S

    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
  5. 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...
  6. 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...
  7. 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
  8. A


    Hi, I am a bit confused as to which variables should I include in the factor analysis. I have a huge questionnaire which i know i definitely should include to find the factors but im not sure whether I should include the demographics? Thank you in advance! Astero
  9. O

    Between-Group and Within-Group PCA

    Hi all, I'm running a PCA with two species (one fossil and one extant) divided into multiple geographic groups. The program I'm using is called PAST (PAlaeontological STatistics). Anyhow, I'm a little confused about within-group and between-group PCAs and how they differ from a PCA...
  10. LyeTweek

    Bimodality of a factor

    Hi people, I am planning a project looking at socially desirable response types and have a sneaking suspicion, based on previous work, that I am going to get items loading onto the same factor both negatively and positively. I may have to justify this as not being a statistical artifact of...
  11. F

    Adjusted principal component analysis

    Hi, I was wondering if it is possible to perform an age adjusted principal component analysis just like it is done for linear regression (finding out the principal components accounting for age), and whether this was possible in R. Thank you, francy
  12. Y

    Principal Component Analysis: What determines sign of variable loading?

    Hi, I have a question about how the signs of variable loadings on a component (factor) are determined in the PCA. I am studying anxiety/fear response in animals. I tested a group of animals by showing a negative stimulus and measuring their behavioural responses to the stimulus. The...
  13. N

    Reduce number of predictor variables ((multi)collinearity): PCA not helpful

    Howdy, The attached spreadsheet contains the correlation matrix of predictor variables and response variable. Correlation exists between them. Additionaly included are the results from a Principal Components Analysis on my (scaled) predictor variables. I used the prcomp command in R with the...