Last updated: 2017-11-05

Code version: 6639968

Introduction

This time the correlated \(z\) scores are simulated similar to the previous low rank example but with different numbers of \(k\).

n = 1e4
L = 100

\(k = 1\)

set.seed(777)
k = 1
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

\(k = 2\)

set.seed(777)
k = 2
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

\(k = 3\)

set.seed(777)
k = 3
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

\(k = 4\)

set.seed(777)
k = 4
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

\(k = 5\)

set.seed(777)
k = 5
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

\(k = 6\)

set.seed(777)
k = 6
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

\(k = 7\)

set.seed(777)
k = 7
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

\(k = 8\)

set.seed(777)
k = 8
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

\(k = 9\)

set.seed(777)
k = 9
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

\(k = 10\)

set.seed(777)
k = 10
for (j in 1 : 2) {
  z = z.sim(n, k)
  fit.gd(L, z)
}

Session information

sessionInfo()
R version 3.4.1 (2017-06-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] compiler_3.4.1  backports_1.1.1 magrittr_1.5    rprojroot_1.2  
 [5] tools_3.4.1     htmltools_0.3.6 yaml_2.1.14     Rcpp_0.12.13   
 [9] stringi_1.1.5   rmarkdown_1.6   knitr_1.17      git2r_0.19.0   
[13] stringr_1.2.0   digest_0.6.12   evaluate_0.10.1

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