I used PCA on 9-D data. I want to plot PCs direction

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farshad jahangiri
farshad jahangiri 2022 年 9 月 28 日
回答済み: the cyclist 2022 年 9 月 29 日
Hi , I extract 2 PC and know I want to plot direction of PCs but I don't know how.
I attached data and code
clc;
clear;
data=importdata('data.txt');
n_data=zscore(data);
[coeff,scores,latent] = pca(n_data,'Algorithm','eig');
p=scatter(scores(:,1),scores(:,2),'filled')
p =
Scatter with properties: Marker: 'o' MarkerEdgeColor: 'none' MarkerFaceColor: 'flat' LineWidth: 0.5000 XData: [-2.6987 -2.9086 -2.6022 -2.7088 -2.5972 -2.6443 -2.4184 -2.6105 -2.6442 -2.6342 -2.7498 -2.8929 -3.0329 -2.9947 -2.7199 -2.6996 -2.6480 -2.6630 -2.5890 -2.4588 -2.3512 -2.6149 -2.6046 -2.6306 -2.7813 -2.9008 -2.8532 -2.5646 … ] YData: [-1.1820 -0.3770 -1.9835 -1.6290 -1.4321 -0.6428 -1.4788 -1.7337 -1.9207 -2.0610 -2.2508 -1.8076 0.1766 -0.5802 -1.0807 -1.6126 -1.1661 -1.0277 -2.4950 -1.5415 -1.4948 -1.3786 -0.6466 0.3495 0.0197 -0.0436 0.5450 0.6005 0.2341 … ] SizeData: 36 CData: [0 0.4470 0.7410] Show all properties
xlabel('Principal Component 1');
ylabel('Principal Component 2');
  3 件のコメント
farshad jahangiri
farshad jahangiri 2022 年 9 月 29 日
Moved: the cyclist 2022 年 9 月 29 日
@the cyclist Hi . I am confused about this. in some researches like this picture they showed direction of principal components. I want to show when covariance matrix changes, the PCs direction will be change.
I think we just can show this direction in not transformed data and in original axis, not transformed data (scores).
In this case How can I show the direction of PCs in original data and what data should be used instead of scores? I am confused because the scores have all 9 dimensions and can be shown in one dimension, but the original data cannot

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the cyclist
the cyclist 2022 年 9 月 29 日
Your comment was helpful. I think the biplot function may do what you need.
The biplot illustrates what the original axes look like in (two or three dimensions of) PC space.
I think that making a biplot of the inverse of coeff would illustrate what the PC vectors look like in the original space. (I am not certain of this last statement. I'd need to think about it more, and experiment a bit.)

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