Last updated: 2018-05-15

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    rmd 85b0795 LSun 2017-05-09 normalization

Introduction

When fitting Gaussian derivatives, normalization could potentially increase the parity in the magnitude of the coefficients and thus make the results more accurate.

data.list = readRDS("../output/z_null_liver_777_select.RDS")
zscore = data.list[[3]]
sel.num = length(zscore)
data.list.index = readRDS("../output/z_null_liver_777_select_index.RDS")
ord = data.list.index[[3]]$gd.ord
source("../code/gdash.R")
library(ashr)
library(PolynomF)
x <- polynom()
H <- polylist(x, - 1 + x^2)
for(n in 2 : 19)
  H[[n+1]] <- x * H[[n]] - n * H[[n-1]]

Correlated null

Fitted w: -0.03642797 0.2788315 0.02632224 -0.106871 
Time Cost in Seconds: 0.492 0.022 0.493 

Fitted w: 0.03361434 1.037918 -0.3782364 0.9186735 -0.6135029 0.6083007 -0.5077214 0.2384846 -0.2000794 
Time Cost in Seconds: 0.522 0.013 0.46 

Fitted w: 0.02273596 1.301977 0.05448238 0.8574552 -0.1466924 0.09679719 -0.3403967 -0.1284122 -0.1752019 
Time Cost in Seconds: 0.527 0.01 0.437 

Fitted w: 0.04544396 -0.1800044 0.02158272 0.04857491 
Time Cost in Seconds: 0.491 0.009 0.371 

Fitted w: 0.006084177 0.5623754 -0.02229827 0.1278911 
Time Cost in Seconds: 0.357 0.006 0.377

Signal \(+\) correlated error

Converged: TRUE 
Number of Iteration: 21 
Time Cost in Seconds: 38.654 2.382 38.811 

Converged: TRUE 
Number of Iteration: 24 
Time Cost in Seconds: 65.451 4.351 58.666 

Converged: TRUE 
Number of Iteration: 21 
Time Cost in Seconds: 53.677 3.877 53.751 

Converged: TRUE 
Number of Iteration: 27 
Time Cost in Seconds: 59.707 3.056 38.881 

Converged: TRUE 
Number of Iteration: 12 
Time Cost in Seconds: 30.248 1.343 23.311

Conclusion

It appears normalization indeed increases the accuracy, although the computation seems slowing down a little bit? Not sure.

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