Error in Principal Component Analysis (PCA) matlab

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nurin noor 2021-5-31

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https://ww2.mathworks.cn/matlabcentral/answers/844340-error-in-principal-component-analysis-pca-matlab

评论： nurin noor 2021-5-31

Error in Principal Component Analysis (PCA) matlab. I applied PCA on matlab using my variable fextracted. It is a 22 x 16 array . I only have fextracted as the information. Please help me with the steps to do PCA. Attached are my data (fextracted) and my PCA code. I dont understand what should i do first before using the PCA() function on MATLAB. Ive seen tutorials and read from matlab PCA docs but i did not manage to figure it out.

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nurin noor 2021-5-31

this is my dataset

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Answer 1

Cris LaPierre 2021-5-31

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You are getting this error because you have overwritten MATLAB's pca function with your pca.m file. Rename your file, and your call to pca should work.

See here for execution precedence.

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Cris LaPierre 2021-5-31

在 MATLAB Online 中打开

The error message suggests otherwise. Run the following code in the command window

which pca

The result should be something like this: C:\Program Files\MATLAB\R2021a\toolbox\stats\stats\pca.m

Your syntax is correct.

fextracted = rand(22,16);
[coeff,score,latent,tsquared,explained] = pca(fextracted)
coeff = 16×16
   -0.0414    0.4172    0.1205    0.2871    0.6193   -0.0735    0.1129    0.1205    0.1097    0.2141    0.0162    0.3444    0.0827   -0.1396   -0.2588    0.2025
   -0.2079    0.0292    0.1624    0.5370   -0.2892    0.0452    0.1042   -0.0634   -0.2043    0.2314    0.3456    0.2450    0.0994   -0.1214    0.4364   -0.2254
   -0.1059   -0.1846    0.2349   -0.0497   -0.1560   -0.0855    0.4983    0.3462   -0.2047    0.3313    0.0175   -0.3634   -0.0127   -0.0893   -0.1031    0.4410
    0.4788    0.0664   -0.2710    0.1164   -0.3339   -0.0306   -0.0870    0.1993    0.0728    0.0753   -0.1985    0.0602    0.4072   -0.5384   -0.0948    0.0333
    0.1765    0.3922    0.0863   -0.2889    0.0492   -0.3856    0.0676   -0.0057    0.1444   -0.2174    0.5058   -0.2350    0.2481   -0.0124    0.3428    0.1003
    0.3360   -0.2255    0.0197    0.2778   -0.0181    0.0308    0.1925   -0.3264    0.6195    0.1211   -0.0611   -0.0089   -0.1684    0.1507    0.2419    0.3193
   -0.4205    0.0812    0.1869    0.0098   -0.1779   -0.2231    0.0484    0.0416    0.5401    0.1567   -0.2317   -0.1885    0.3183    0.0822   -0.1787   -0.3881
    0.1167   -0.0461    0.3125   -0.4395   -0.0251    0.4876   -0.1637   -0.1195    0.0395    0.4076    0.1500    0.2140    0.3878    0.1460   -0.0056    0.0791
   -0.2565   -0.0794    0.1934    0.1726    0.0759    0.2987   -0.4755    0.4428    0.2267   -0.2773   -0.0164   -0.1648    0.0334   -0.1443    0.2661    0.3083
   -0.0051   -0.3974    0.0119   -0.2117   -0.1039   -0.4173   -0.1098    0.3844    0.2014    0.0472    0.2511    0.5553   -0.1852    0.0164   -0.0597   -0.0034
score = 22×16
    0.5916    0.3959   -0.0283    0.3969   -0.3703   -0.2739    0.1248   -0.1741   -0.2166   -0.3241    0.2682    0.0876    0.2363    0.0172   -0.0428   -0.0409
   -0.5745    0.3889    0.7649    0.1216    0.0371    0.1386   -0.2615   -0.2539    0.1048    0.4315   -0.0601   -0.0760    0.1336   -0.0508   -0.0344   -0.0456
   -0.5343    0.0964   -0.3965    0.1531    0.5716   -0.0529    0.1151    0.1186   -0.0198   -0.1760   -0.2102   -0.0179    0.1872    0.2434    0.0357    0.0464
    0.3658    0.1188   -0.0745   -0.0959    0.3265    0.1423   -0.1210    0.3631    0.6778   -0.0864    0.0085    0.0573   -0.0693   -0.0345   -0.0540   -0.0340
   -1.1158    0.1176    0.0473    0.0949   -0.3773   -0.3859    0.1984   -0.1899    0.1312   -0.1197    0.0049    0.0914   -0.1122   -0.0657    0.0826    0.0206
    0.2315   -0.2773    0.5269   -0.2486    0.6571   -0.0102    0.3536   -0.1562   -0.0260    0.0068    0.4174   -0.0323   -0.0993   -0.0246    0.0208    0.0606
   -0.3856    0.3658   -0.3582   -0.3215   -0.0401    0.6392    0.1356   -0.4788    0.2512   -0.0378    0.2407    0.1402    0.0733    0.0661    0.0123   -0.0255
    0.6539   -0.0443    0.4341   -0.0771    0.0258   -0.2478   -0.2313   -0.1191    0.1442   -0.2703    0.0118   -0.2130    0.0784   -0.0916   -0.0221    0.0730
   -0.2626    0.5914    0.4794    0.0879   -0.0430   -0.3025   -0.0907    0.3458    0.1498   -0.2271   -0.1490    0.0244   -0.0961    0.0940   -0.0467   -0.0369
   -0.2414   -1.2151    0.3395   -0.1321   -0.2833   -0.1413    0.4139   -0.2173    0.0041    0.0395   -0.1649    0.0482   -0.0423    0.1210   -0.1394   -0.0196
latent = 16×1
    0.2674
    0.2029
    0.1497
    0.1367
    0.1084
    0.0874
    0.0819
    0.0726
    0.0523
    0.0413
tsquared = 22×1
   16.7472
   15.6788
   16.8153
   14.5799
   12.7046
   16.2672
   15.7190
   12.3457
   11.2392
   18.7158
explained = 16×1
   20.6980
   15.7046
   11.5873
   10.5788
    8.3876
    6.7649
    6.3364
    5.6231
    4.0458
    3.1998