我对40个变量执行PCA,并保持所有组件的最小距离。特征值1(总共5个分量)-STATA给我以下输出:
>pca *all variables*, mineigen(1)
Component | Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Comp1 | 29.0991 26.3849 0.7275 0.7275
Comp2 | 2.71421 .672651 0.0679 0.7953
Comp3 | 2.04156 .803292 0.0510 0.8464
Comp4 | 1.23826 .201987 0.0310 0.8773
Comp5 | 1.03628 .170083 0.0259 0.9032
Principal components (eigenvectors)
-----------------------------------------------------------------------------
Variable | Comp1 Comp2 Comp3 Comp4 Comp5 | Unexplained
-------------+--------------------------------------------------+------------
R_wt1_GLR~13 | 0.0036 0.2762 -0.1522 0.4151 0.3403 | .4118
R_wt7_FO14 | 0.0689 0.0353 -0.2479 -0.2684 0.3559 | .5124
std_GL~24 | 0.1825 -0.0001 0.0000 -0.0959 -0.0131 | .0191
std_GLR~6 | 0.1712 0.0201 0.1849 -0.1215 -0.0321 | .05681
std_GLR~8 | 0.1822 0.0119 -0.0235 -0.0713 -0.0102 | .02624
wt1_GL~13 | 0.0263 0.4621 0.3717 0.0753 0.1531 | .08684
wt1_GL~24 | 0.1819 0.0148 0.0268 -0.0981 -0.0225 | .0222
wt1_GLR~6 | 0.1615 0.0390 0.2516 -0.1209 -0.0765 | .08364
wt1_GLR~8 | 0.1815 0.0138 0.0119 -0.0902 -0.0269 | .02935
wt1_GLR~9 | 0.0192 0.4457 0.3554 0.1194 0.1716 | .1442
wt2_GL~24 | 0.1832 -0.0186 -0.0229 0.0034 -0.0486 | .01839
wt2_GLR~3 | 0.0133 0.4138 -0.1428 0.0898 -0.4108 | .3035
wt2_GLR~5 | 0.1828 0.0111 0.0114 -0.0629 -0.0511 | .01971
wt2_GLR~6 | 0.1758 0.0030 0.1206 -0.0245 -0.0877 | .06209
wt2_GLR~8 | 0.1826 -0.0273 -0.0410 0.0064 -0.0405 | .02255
wt3_GLR~5 | 0.1809 0.0139 0.0323 -0.0957 -0.0601 | .02963
wt3_GLR~6 | 0.1601 0.0576 0.2214 -0.1942 -0.1267 | .08216
wt4_GL~24 | 0.1801 0.0136 0.0055 -0.1630 0.0246 | .02207
wt4_GLR~5 | 0.1823 0.0107 0.0143 -0.0683 -0.0444 | .02444
wt4_GLR~6 | 0.1620 0.0340 0.1740 -0.1918 -0.0349 | .1246
wt5_GL~24 | 0.1793 -0.0337 0.0051 0.0841 -0.0665 | .04766
wt5_GLR~8 | 0.1783 -0.0447 0.0179 0.1118 -0.0840 | .04559
wt7_GL~24 | 0.1823 -0.0213 0.0006 -0.0537 0.0023 | .02847
wt7_GLR~5 | 0.1835 -0.0185 -0.0012 -0.0256 -0.0322 | .01707
wt7_GLR~8 | 0.1815 -0.0169 -0.0189 -0.0773 0.0129 | .03221
wt8_FO9 | 0.0530 0.2385 -0.4929 -0.0310 -0.2132 | .2196
wt8_GLR~8 | 0.1813 -0.0263 0.0131 0.0054 -0.0326 | .03966
wt1_FO10 | -0.0476 -0.4309 0.1862 0.0542 0.0003 | .3557
wt2_FO4 | 0.1184 0.2212 -0.3360 -0.2154 -0.0222 | .1705
wt2_LM24 | 0.1768 -0.0569 -0.0708 0.1434 0.1202 | .03092
wt2_LM8 | 0.1760 -0.0574 -0.0765 0.1076 0.0800 | .05662
wt4_LM24 | 0.1802 -0.0441 -0.0376 0.1390 0.0486 | .02063
wt4_LM6 | 0.1576 -0.0866 0.0472 0.3475 0.0284 | .1015
wt4_LM8 | 0.1803 -0.0384 -0.0418 0.0807 0.0302 | .03795
wt5_LM24 | 0.1805 -0.0226 -0.0490 0.0956 0.0570 | .03072
wt6_LM24 | 0.1773 -0.0542 -0.0484 0.2013 0.0248 | .022
wt6_LM6 | 0.1606 -0.0935 0.0132 0.3480 0.0376 | .07385
wt7_LM24 | 0.1768 -0.0383 -0.0915 0.1524 0.0798 | .03353
wt7_LM8 | 0.1769 -0.0521 -0.0889 0.1051 0.0278 | .05139
wt8_LM10 | 0.0656 0.0088 -0.0737 -0.2891 0.6266 | .3532
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我想知道为什么Comp1上所有变量的负载如此之低(基本上所有<0.2),而对于Comp2,Comp3和Comp5我有4个变量,而对于Comp4有3个变量都都> 0.3。有什么好的解释吗?
感谢您的帮助!
我不是专家,我只在openCV中使用PCA,但是对我的结果表明,变量之间存在heavy函数关系。
归一化后,第一个分量(仅第一个分量)的特征值保留了原始方差的%72的事实。
(它也比下一个特征向量方向高近11倍。)
这可能意味着其他轴只是在采样噪声,而不是您感兴趣的信号(或信号)...
您是否找到了正确的答案?