Last updated: 2018-05-15

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    rmd c6211ab Lei Sun 2018-04-03 knockoff vs ash

Introduction

The true \(\beta\) are simulated as \(\beta \sim \pi_0\delta_0 + (1 - \pi_0)N(0, \sigma_\beta^2)\).

varbvs.get.lfsr <- function (fit) {

# For each variable, and each hyperparameter setting, get the
# posterior probability that the regression coefficient is exactly
# zero.
p0 <- 1 - fit$alpha

# For each variable, and for each hyperparameter setting, get the
# posterior probability that the regression coefficient is negative.
pn <- with(fit,alpha * pnorm(0,mu,sqrt(s)))

# For each variable, and for each hyperparameter setting, ompute the
# local false sign rate (LFSR) following the formula given in
# Matthew's Biostatistics paper, "False discovery rates: a new deal".
p        <- nrow(fit$alpha)
k          <- ncol(fit$alpha)
lfsr     <- matrix(0,p,k)
b        <- pn > 0.5*(1 - p0)
lfsr[b]  <- 1 - pn[b]
lfsr[!b] <- p0[!b] + pn[!b]

# Average the average LFSR over the hyperparameter settings, weighted
# by the probability of each hyperparameter setting.
lfsr <-    c(lfsr %*% fit$w)

return(lfsr)
}

\(n > p\)

n <- 2000
p <- 1000
k <- 200
m <- 100
q <- 0.1

Independent design

\(X_{n \times p}\) has independent columns simulated from \(N(0, (1/\sqrt n)^2)\) so they are roughly normalized.

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Local correlation / AR model for \(X\)

\(X_{n \times p}\) has correlation \(\Sigma_{ij} = \rho^{|i - j|}\). Each row is independently \(N(0, \frac1n\Sigma)\).

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Factor Model for \(X\)

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Factor Model for \(\hat\beta\)

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Observation

  1. Model-\(X\) knockoff is very powerful.
  2. Using estimated distribution of \(X\) rather than the true distribution hurts the power of Model-\(X\) knockoff.
  3. The power of Model-\(X\) knockoff using estimated distributio of \(X\) is on par with that of ASH and BH, probably because the presence of small signals makes knockoff less powerful.
  4. Sometimes equi is better than SDP when generating knockoffs, as shown in previous simulations using factor model for \(X\).

\(n < p\)

n <- 300
p <- 1000
k <- 200
m <- 100
q <- 0.1

Independent design

Cov.X <- diag(1 / n, p)

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Local correlation design

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Factor Model for \(X\)

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Factor Model for \(\hat\beta\)

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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

This reproducible R Markdown analysis was created with workflowr 1.0.1