Last updated: 2018-05-15

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Introduction

In order to investigate and compare linear regression variable selection methods, we need to construct design matrices \(X\). Here we take a look at multiple methods to simulate \(X\).

The design matrix \(X\) is simulated so that the columns have noticeable correlation structures. In our simulation, each row of \(X\) is independently drawn from a \(N(0, \Sigma)\) distribution.

Simulation Setting

Data are generated in a global null setting by \[ y_n = X_{n \times p}\beta_p + e_n \] where \[ \begin{array}{c} n = 2000 \\ p = 1000 \\ e_n \sim N(0, 1) \\ \end{array} \] and \[ \beta_p \equiv 0 \]

Empirical correlated \(N(0, 1)\) distribution

After obtaining \(p\)-values for each \(\hat\beta_j\), we transform them to \(z\)-scores. These \(z\)-scores should be correlated \(N(0, 1)\). We take a look at their empirical distribution and see how different it is from the standard normal.

\(\Sigma = I\)

\(\Sigma\): Toeplitz

\(\Sigma_{j,k} = \rho^{|j - k|}\).

\(\rho = 0.2\)

\(\rho = 0.5\)

\(\rho = 0.8\)

\(\Sigma\): high collinearity

\(\Sigma = B_{p \times d} \cdot B_{p \times d}^T + I\), where \(B_{i, j} \stackrel{\text{iid}}{\sim} N(0, 1)\). Then transform \(\Sigma\) to a correlation matrix.

\(d = 5\)

Start from \(\Sigma_{\hat\beta}\)

In the \(n > p\) setting, \(\hat\beta \sim N\left(\beta, \Sigma_{\hat\beta} = \sigma_e^2\left(X^TX\right)^{-1}\right)\). In simulation, we can first construct a desirable \(\Sigma_{\hat\beta}\), and build an \(X\) from that.

One way is to let \(\Sigma_{\hat\beta} / \sigma_e^2 = B_{p \times d} \cdot B_{p \times d}^T + I\), where \(B_{i, j} \stackrel{\text{iid}}{\sim} N(0, 1)\). Then rescale the matrix such that the mean of its diagnal \(= 1\). Generate \(X_{n \times p}\) such that \((X^TX)^{-1} = \Sigma_{\hat\beta} / \sigma_e^2\).

\(d = 1\)

\(d = 5\)

\(d = 20\)

\(d = 50\)

\(d = 100\)

Session information

sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.4

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] workflowr_1.0.1   Rcpp_0.12.16      digest_0.6.15    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.21.0      magrittr_1.5      evaluate_0.10.1  
[10] stringi_1.1.6     whisker_0.3-2     R.oo_1.21.0      
[13] R.utils_2.6.0     rmarkdown_1.9     tools_3.4.3      
[16] stringr_1.3.0     yaml_2.1.18       compiler_3.4.3   
[19] htmltools_0.3.6   knitr_1.20

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