Sparse PCA: reproduction of the synthetic example

 1 library(elasticnet)
 2 
 3 ## sample version of SPCA
 4 n = 1000
 5 v1 = rnorm(n,0,sqrt(290))
 6 v2 = rnorm(n,0,sqrt(300))
 7 v3 = -.3*v1 + 0.925*v2 + rnorm(n)
 8 x1 = v1 + rnorm(n)
 9 x2 = v1 + rnorm(n)
10 x3 = v1 + rnorm(n)
11 x4 = v1 + rnorm(n)
12 
13 x5 = v2 + rnorm(n)
14 x6 = v2 + rnorm(n)
15 x7 = v2 + rnorm(n)
16 x8 = v2 + rnorm(n)
17 
18 x9 = v3 + rnorm(n)
19 x10 = v3 + rnorm(n)
20 
21 x = cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10)
22 x.cov = t(x) %*% x/n; head(x.cov)
23 a = spca(x, 2, type=‘predictor‘, sparse=‘varnum‘, para=c(4,4), lambda=0)
24 a

25 ## population version of SPCA
26 g1 = matrix(290, 4, 4)
27 diag(g1) = 291
28 
29 g2 = matrix(300, 4, 4)
30 diag(g2) = 301
31 
32 g3 = matrix(283.7875, 2, 2)
33 diag(g3) = diag(g3)+1
34 
35 
36 g1g3 = matrix(-87, 4, 2)
37 g2g3 = matrix(277.5, 4, 2)
38 
39 # construct the exact covariance matrix
40 x.cov = matrix(0, 10, 10)
41 x.cov[1:4,1:4] = g1
42 x.cov[5:8,5:8] = g2
43 x.cov[9:10,9:10] = g3
44 x.cov[1:4,9:10] = g1g3
45 x.cov[9:10,1:4] = t(g1g3)
46 x.cov[5:8,9:10] = g2g3
47 x.cov[9:10,5:8] = t(g2g3)
48   
49 
50 b = spca(x.cov, 2, type=‘Gram‘, sparse=‘varnum‘, para=c(4,4), lambda=0)
51 b

> b

Call:
spca(x = x.cov, K = 2, para = c(4, 4), type = "Gram", sparse = "varnum", 
    lambda = 0)

2 sparse PCs 
Pct. of exp. var. : 40.9 39.5 
Num. of non-zero loadings :  4 4 
Sparse loadings 
      PC1 PC2
 [1,] 0.0 0.5
 [2,] 0.0 0.5
 [3,] 0.0 0.5
 [4,] 0.0 0.5
 [5,] 0.5 0.0
 [6,] 0.5 0.0
 [7,] 0.5 0.0
 [8,] 0.5 0.0
 [9,] 0.0 0.0
[10,] 0.0 0.0

> a

Call:
spca(x = x, K = 2, para = c(4, 4), type = "predictor", sparse = "varnum", 
    lambda = 0)

2 sparse PCs 
Pct. of exp. var. : 37.9 37.6 
Num. of non-zero loadings :  4 4 
Sparse loadings 
       PC1    PC2
x1   0.000 -0.303
x2   0.000 -0.533
x3   0.000 -0.576
x4   0.000 -0.540
x5  -0.492  0.000
x6  -0.287  0.000
x7  -0.481  0.000
x8  -0.666  0.000
x9   0.000  0.000
x10  0.000  0.000