Table 14.2, page 380.
NOTE: The values shown in the text are listed as eigenvectors in the Stata output.
use https://stats.idre.ucla.edu/stat/stata/examples/cama4/depress, clear
factor c1-c20, factors(5) pc
(obs=294)
(principal components; 5 components retained)
Component Eigenvalue Difference Proportion Cumulative
------------------------------------------------------------------
1 7.05542 5.56985 0.3528 0.3528
2 1.48557 0.25406 0.0743 0.4270
3 1.23151 0.16582 0.0616 0.4886
4 1.06569 0.05305 0.0533 0.5419
5 1.01263 0.04517 0.0506 0.5925
6 0.96746 0.02064 0.0484 0.6409
7 0.94682 0.17758 0.0473 0.6883
8 0.76924 0.07460 0.0385 0.7267
9 0.69464 0.03451 0.0347 0.7614
10 0.66013 0.05249 0.0330 0.7945
11 0.60764 0.05830 0.0304 0.8248
12 0.54934 0.01270 0.0275 0.8523
13 0.53663 0.02792 0.0268 0.8791
14 0.50872 0.05781 0.0254 0.9046
15 0.45090 0.07572 0.0225 0.9271
16 0.37518 0.05400 0.0188 0.9459
17 0.32118 0.02662 0.0161 0.9619
18 0.29456 0.02619 0.0147 0.9767
19 0.26837 0.07000 0.0134 0.9901
20 0.19838 . 0.0099 1.0000
Eigenvectors
Variable | 1 2 3 4 5
-------------+------------------------------------------------------
c1 | 0.27744 -0.14498 -0.05770 0.00272 -0.08827
c2 | 0.31318 0.02714 -0.03163 -0.24781 -0.02440
c3 | 0.26780 -0.15472 -0.03459 -0.24725 0.21831
c4 | 0.24355 -0.31940 -0.17694 0.07155 0.17293
c5 | 0.28678 -0.04972 -0.13839 -0.27935 0.04111
c6 | 0.22057 0.05340 -0.22421 -0.18229 0.33987
c7 | 0.28437 -0.16436 0.01896 0.07606 0.08700
c8 | 0.10810 -0.30452 -0.11033 0.55670 0.09761
c9 | 0.17578 -0.16900 0.39623 0.01463 -0.53548
c10 | 0.27662 -0.04542 0.08346 -0.00842 -0.36505
c11 | 0.24327 -0.10482 0.13140 -0.04138 -0.24193
c12 | 0.17902 0.22998 -0.16343 -0.14506 -0.03684
c13 | 0.12591 0.21263 -0.26453 0.54002 -0.09529
c14 | 0.18025 0.40148 0.10141 0.24611 0.08471
c15 | 0.20036 0.20978 -0.27032 -0.03121 -0.08341
c16 | 0.19243 0.41745 0.18501 0.04674 0.03993
c17 | 0.20968 0.39048 0.08602 0.06840 0.04991
c18 | 0.17171 0.01533 -0.20193 0.06286 -0.27522
c19 | 0.13149 0.05687 0.63261 0.02318 0.33492
c20 | 0.23570 -0.22826 0.19325 0.24043 0.29094
Figure 14.5, page 381.
Panel A:
matrix E1 = get(Ev) matrix E2 = E1' svmat E2, name(col) gen eigen = _n if r1 != . gen c = sum(r1) if r1 != . gen cumlative = (c/20)*100 graph twoway scatter cumlative eigen, ylabel(0(10)100)
Panel B:
NOTE: The following graph can easily be reproduced using the greigen command, however, the greigen command only uses the first 13 eigenvalues.
graph twoway scatter r1 eigen, ylabel(0(1)7)
