pca analysis

  1. Y

    Could you tell me what it means that PC1 is 100% from PCA analysis?

    I am completely new to PCA analysis. I followed a script but my result showed 100% PC1 and 0% PC2. I am afraid that my data input was not right or something...
  2. B

    results of factor analysis (pca, varimax)

    Hey there, I need your help in understanding the results of a factor analysis I did. Base level: data come from a nation wide survey (n = 1000). We asked people about meanings they assign to 7 different (typical) landscape types (a,b,c,d,e,f,g). Therefore we developed a set of 20...
  3. M

    Interpreting principal components

  4. 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...
  5. M

    how to put labels on ggplot2 graph

    I have a data and I did PCA on that data I used the following code i.pca=prcomp(tmydf) scores <- as.data.frame(i.pca$x) qplot(x = PC1, y = PC2, data = scores, geom = "point",col=race) I need to put the rownames of tmydf as labels on the graph how can I do that ?
  6. S

    Principal component analysis to group items for two different countries

    Hi guys, We are two master thesis students doing our thesis about craft beer. We are performing a principal component analysis to group items from a survey we carried out in relation to a theoretical concept "Country of Origin" and how that affects attitudes towards craft beer from two...
  7. W

    comparison of multivariate (4 DV and 1 IV) datasets (data matrices)

    I want to compare 06 different Multivariate datasets (data matrices). Each dataset consists of SAME number of columns but different number of rows. Number of columns represent the variables (4 dependent variables and 1 independent variable). All variables are quantitative in nature. Independent...
  8. M

    Multivariate Statistic: Linear or unimodal relationship?

    Hi, if I want to analyze a multivariate dataset, I have to choose between methods assuming a linear relationship between the outcome and underlying gradients (e.g. in PCA or RDA) or an unimodal relationship (e.g. in CA or CCA). Does somebody know if there are appropriate test statistics...
  9. M

    what is the objective of yeast dataset?

    Recently, l started working on yeast data (http://archive.ics.uci.edu/ml/datasets/Yeast). My goal is to do an exploratory statistics upon these data, l applied PCA (principal component analysis ) as a tool of analysis. However l'm not aware of the objective of yeast data , l finally found some...
  10. M

    Generating factor analysis variable output for a new sample

    There may indeed be a better way to do this, but here's my current thinking. I have a survey of 500 students at a university and have performed principal component analysis. Two factors are relevant for my research question, and I have output a score for each student so that there are now 2 new...
  11. H

    ANOVA vs. principal component analysis for nonlinear analysis

    First, let me say that I am not a trained statistician by any means. Here's what I'm working on. I have a dataset with 8 factors (sample attached). Each factor is a measured variable that may or may not affect the response. I need to determine which factors are important and then find a...
  12. R

    What does component signifies in Principal component analysis?

    Structure Matrix ......................................Component ...................................1 ..... 2 .....3 ......4 Eff_class.................. .893 .139 .236 .143 Prod_features.......... .889 -.194 .209 .219 offer......................... .912 .006 .486 .278 Brand_recognition...
  13. J

    Distinguishing noise (batch variation) from true differences

    Hello, I carried out a screen for 400 different genes. This was done in batches of 20 genes. Each batch includes 2 replicas of an internal control: a gene that works as a negative control. There is batch to batch variation and the differences between the batch means for the parameter being...
  14. G

    PCA and symmetric distribution.

    Hello, I hope you can clarify my doubts. I'm an Italian student of business administration. For my thesis I study the application of PCA method for the reduction of the original variables, and my data have skewed distribution. I have read that the PCA works better when the original data have...