Last updated: 2019-01-22
workflowr checks: (Click a bullet for more information) ✔ R Markdown file: up-to-date
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
✔ Environment: empty
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
✔ Seed:
set.seed(20180501)
The command set.seed(20180501)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
✔ Session information: recorded
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
✔ Repository version: b5029e2
wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: data/.DS_Store
Untracked files:
Untracked: analysis/chipexoeg.Rmd
Untracked: analysis/efsd.Rmd
Untracked: analysis/talk1011.Rmd
Untracked: data/chipexo_examples/
Untracked: data/chipseq_examples/
Untracked: talk.Rmd
Untracked: talk.pdf
Unstaged changes:
Modified: analysis/binomial.Rmd
Modified: analysis/fda.Rmd
Modified: analysis/index.Rmd
Modified: analysis/sigma.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
For the method used in these examples, see here
R function for shrinking adjusted R squared:
#'@param y,X: response and design matrix.
#'@output shrinked ajusted R squared
library(ashr)
R2Shrink=function(y,X,output='SR2'){
if(is.null(dim(X))){p=1}else{p=ncol(X)}
n=length(y)
mod=lm(y~X,data=data.frame(y=y,X=X))
mst=sum((y-mean(y))^2)/(n-1)
mse=sum((y-fitted(mod))^2)/(n-p-1)
mset=mse/mst
fash=ashr::ash(log(mset),1,lik=ashr::lik_logF(df1=n-p-1,df2=n-1))
R2s=1-exp(fash$result$PosteriorMean)
if(output=='SR2'){
return(R2s)
}
if(output=='all'){
mod.sy=summary(mod)
R2=mod.sy$r.squared
R2a=mod.sy$adj.r.squared
return(list(R2s=R2s,R2a=R2a,R2=R2))
}
}
Let \(y=\mu+x_1\beta_1+x_2\beta_2+x_3\beta_3+N(0,I_n)\). Sample size \(n=100\).
For each of settings below, fit three models:
Compare shrinked, adjusted and original \(R^2\).
set.seed(12345)
n=100
p=3
X=matrix(rnorm(n*(p)),n,p)
beta=2*c(1,1,1)
y=X%*%beta+rnorm(n)
ra=R2Shrink(y,X[,1],output = 'all')
rb=R2Shrink(y,X[,1:2],output = 'all')
rc=R2Shrink(y,X,output = 'all')
rlt=rbind(unlist(ra),unlist(rb),unlist(rc))
colnames(rlt)=c('Shrinked R2','Adjusted R2','R2')
rownames(rlt)=c('a','b','c')
round(rlt,3)
Shrinked R2 Adjusted R2 R2
a 0.314 0.465 0.470
b 0.740 0.742 0.747
c 0.935 0.935 0.937
set.seed(12345)
n=100
p=3
X=matrix(rnorm(n*(p)),n,p)
beta=2*c(1,1,0)
y=X%*%beta+rnorm(n)
ra=R2Shrink(y,X[,1],output = 'all')
rb=R2Shrink(y,X[,1:2],output = 'all')
rc=R2Shrink(y,X,output = 'all')
rlt=rbind(unlist(ra),unlist(rb),unlist(rc))
colnames(rlt)=c('Shrinked R2','Adjusted R2','R2')
rownames(rlt)=c('a','b','c')
round(rlt,3)
Shrinked R2 Adjusted R2 R2
a 0.456 0.542 0.547
b 0.924 0.924 0.925
c 0.929 0.929 0.931
set.seed(12345)
n=100
p=3
X=matrix(rnorm(n*(p)),n,p)
beta=2*c(1,0,0)
y=X%*%beta+rnorm(n)
ra=R2Shrink(y,X[,1],output = 'all')
rb=R2Shrink(y,X[,1:2],output = 'all')
rc=R2Shrink(y,X,output = 'all')
rlt=rbind(unlist(ra),unlist(rb),unlist(rc))
colnames(rlt)=c('Shrinked R2','Adjusted R2','R2')
rownames(rlt)=c('a','b','c')
round(rlt,3)
Shrinked R2 Adjusted R2 R2
a 0.855 0.857 0.858
b 0.857 0.858 0.861
c 0.867 0.868 0.872
set.seed(12345)
n=100
p=3
X=matrix(rnorm(n*(p)),n,p)
beta=c(0,0,0)
y=X%*%beta+rnorm(n)
ra=R2Shrink(y,X[,1],output = 'all')
rb=R2Shrink(y,X[,1:2],output = 'all')
rc=R2Shrink(y,X,output = 'all')
rlt=rbind(unlist(ra),unlist(rb),unlist(rc))
colnames(rlt)=c('Shrinked R2','Adjusted R2','R2')
rownames(rlt)=c('a','b','c')
round(rlt,3)
Shrinked R2 Adjusted R2 R2
a 0 0.010 0.020
b 0 0.019 0.039
c 0 0.084 0.112
sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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
other attached packages:
[1] ashr_2.2-7
loaded via a namespace (and not attached):
[1] Rcpp_0.12.18 knitr_1.20 whisker_0.3-2
[4] magrittr_1.5 workflowr_1.1.1 REBayes_1.3
[7] MASS_7.3-51.1 pscl_1.5.2 doParallel_1.0.14
[10] SQUAREM_2017.10-1 lattice_0.20-35 foreach_1.4.4
[13] stringr_1.3.1 tools_3.5.1 parallel_3.5.1
[16] grid_3.5.1 R.oo_1.22.0 git2r_0.23.0
[19] htmltools_0.3.6 iterators_1.0.10 assertthat_0.2.0
[22] yaml_2.2.0 rprojroot_1.3-2 digest_0.6.17
[25] Matrix_1.2-14 codetools_0.2-15 R.utils_2.7.0
[28] evaluate_0.11 rmarkdown_1.10 stringi_1.2.4
[31] compiler_3.5.1 Rmosek_8.0.69 backports_1.1.2
[34] R.methodsS3_1.7.1 truncnorm_1.0-8
This reproducible R Markdown analysis was created with workflowr 1.1.1