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  • EM
    • E step
    • M step
  • Data
  • Session information

Last updated: 2018-08-14

library(mashr)
Loading required package: ashr
library(knitr)
library(kableExtra)
source('../code/estimate_cor.R')
source('../code/generateDataV.R')
source('../code/summary.R')

EM

We use EM algorithm to update \rho.

E step

P(ˆB,B,Z|ρ,π)=ni=1Kk=0[πkN(ˆbi;bi,V)N(bi;0,Uk)]I(zi=k)

EZ,B|ˆBlogP(ˆB,B,Z|ρ,π)=ni=1Kk=0P(zi=k|ˆbi)[logπk+EB|ˆB(logN(ˆbi;bi,V))+EB|ˆB(logN(bi;0,Uk))]

logN(ˆbi;bi,V)+logN(bi;0,Uk)=p2log2π12log|V|12(ˆbibi)TV1(ˆbibi)p2log2π12log|Uk|12bTiU1kbi=plog2π12log|Uk|12log|V|12ˆbTiV1ˆbi+ˆbTiV1bi12bTiV1bi12bTiU1kbiEbi|ˆbi[logN(ˆbi;bi,V)+logN(bi;0,Uk)]=plog2π12log|Uk|12log|V|12ˆbTiV1ˆbi+ˆbTiV1E(bi|ˆbi)12tr(V1E(bibTi|ˆbi))12tr(U1kE(bibTi|ˆbi)) V=(1ρρ1) Let μi=E(bi|ˆbi) Ebi|ˆbi[logN(ˆbi;bi,V)+logN(bi;0,Uk)]=2log2π12log|Uk|12log(1ρ2)12(1ρ2)(ˆb2i1+ˆb2i22ˆbi1μi12ˆbi2μi2+E(b2i1|ˆbi)+E(b2i2|ˆbi)2ˆbi1ˆbi2ρ+2ˆbi1μi2ρ+2ˆbi2μi1ρ2ρE(bi1bi2|ˆbi))12tr(U1kE(bibTi|ˆbi))

γZi(k)=P(zi=k|Xi)=πkN(xi;0,V+Uk)Kk=0πkN(xi;0,V+Uk)

M step

V: f(V1)=ni=1Kk=0γZi(k)[plog2π12log|Uk|12log|V|12ˆbTiV1ˆbi+ˆbTiV1E(bi|ˆbi)12tr(V1E(bibTi|ˆbi))12tr(U1kE(bibTi|ˆbi))]

f(V1)=ni=1Kk=0γZi(k)[12V12ˆbiˆbTi+E(bi|ˆbi)ˆbTi12E(bibTi|ˆbi)]=012Vn=ni=1Kk=0γZi(k)[12ˆbiˆbTiE(bi|ˆbi)ˆbTi+12E(bibTi|ˆbi)]ˆV=1nni=1[ˆbiˆbTi2E(bi|ˆbi)ˆbTi+E(bibTi|ˆbi)]

ρ: f(ρ)=ni=1Kk=1γZi(k)[2log2π12log|Uk|12log(1ρ2)12(1ρ2)(ˆb2i1+ˆb2i22ˆbi1μi12ˆbi2μi2+E(b2i1|ˆbi)+E(b2i2|ˆbi)2ˆbi1ˆbi2ρ+2ˆbi1μi2ρ+2ˆbi2μi1ρ2ρE(bi1bi2|ˆbi))12tr(U1kE(bibTi|ˆbi))]

f(ρ)=ni=1Kk=1γZi(k)[ρ1ρ2ρ(1ρ2)2(ˆb2i1+ˆb2i22ˆbi1μi12ˆbi2μi2+E(b2i1|ˆbi)+E(b2i2|ˆbi))ρ2+1(1ρ2)2(ˆbi1ˆbi2+ˆbi1μi2+ˆbi2μi1E(bi1bi2|ˆbi))]=0ρ(1ρ2)nρni=1Kk=1γZi(k)(ˆb2i1+ˆb2i22ˆbi1μi12ˆbi2μi2+E(b2i1|ˆbi)+E(b2i2|ˆbi))(ρ2+1)ni=1Kk=1γZi(k)(ˆbi1ˆbi2+ˆbi1μi2+ˆbi2μi1E(bi1bi2|ˆbi))=0nρ3ρ2ni=1(ˆbi1ˆbi2+ˆbi1μi2+ˆbi2μi1E(bi1bi2|ˆbi))ρ(ni=1(ˆb2i1+ˆb2i22ˆbi1μi12ˆbi2μi2+E(b2i1|ˆbi)+E(b2i2|ˆbi))n)ni=1(ˆbi1ˆbi2+ˆbi1μi2+ˆbi2μi1E(bi1bi2|ˆbi))=0

The polynomial has either 1 or 3 real roots in (-1, 1).

Algorithm:

Input: X, Ulist, init_rho
Compute loglikelihood
delta = 1
while delta > tol
  Given rho, Estimate pi using convex method (current mash method)
  M step: update rho: find all roots of polynomial, if it has three real roots, choose the one with higher loglikelihood.
  Compute loglikelihood
  Update delta

Data

ˆβ|βN2(ˆβ;β,(10.50.51))

β14δ0+14N2(0,(1000))+14N2(0,(0001))+14N2(0,(1111))

n = 4000

set.seed(1)
n = 4000; p = 2
Sigma = matrix(c(1,0.5,0.5,1),p,p)
U0 = matrix(0,2,2)
U1 = U0; U1[1,1] = 1
U2 = U0; U2[2,2] = 1
U3 = matrix(1,2,2)
Utrue = list(U0=U0, U1=U1, U2=U2, U3=U3)
data = generate_data(n, p, Sigma, Utrue)
m.data = mash_set_data(data$Bhat, data$Shat)
U.c = cov_canonical(m.data)
grid = mashr:::autoselect_grid(m.data, sqrt(2))
Ulist = mashr:::normalize_Ulist(U.c)
xUlist = mashr:::expand_cov(Ulist,grid,usepointmass =  TRUE)
result <- mixture.EM2.times(data$Bhat, xUlist, init_rho = c(-0.7,0,0.7), grid=1)
plot(result$result$log_liks)

The estimated ρ is 0.5576293.

m.data.em = mash_set_data(data$Bhat, data$Shat, V = matrix(c(1,result[[1]]$rho,result[[1]]$rho,1),2,2))
U.c = cov_canonical(m.data.em)
m.em = mash(m.data.em, U.c, verbose= FALSE)
null.ind = which(apply(data$B,1,sum) == 0)

The log likelihood is -1.23021110^{4}. There are 37 significant samples, 1 false positives. The RRMSE is 0.5871084.

The estimated pi is

barplot(get_estimated_pi(m.em), las=2, cex.names = 0.7, main='OverEst', ylim=c(0,0.8))

The ROC curve:

m.data.correct = mash_set_data(data$Bhat, data$Shat, V=Sigma)
m.correct = mash(m.data.correct, U.c, verbose = FALSE)
m.correct.seq = ROC.table(data$B, m.correct)
m.em.seq = ROC.table(data$B, m.em)

Session information

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] kableExtra_0.9.0 knitr_1.20       mashr_0.2-11     ashr_2.2-10     

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.18      compiler_3.5.1    pillar_1.3.0     
 [4] plyr_1.8.4        iterators_1.0.10  tools_3.5.1      
 [7] digest_0.6.15     viridisLite_0.3.0 evaluate_0.11    
[10] tibble_1.4.2      lattice_0.20-35   pkgconfig_2.0.1  
[13] rlang_0.2.1       Matrix_1.2-14     foreach_1.4.4    
[16] rstudioapi_0.7    yaml_2.2.0        parallel_3.5.1   
[19] mvtnorm_1.0-8     xml2_1.2.0        httr_1.3.1       
[22] stringr_1.3.1     REBayes_1.3       hms_0.4.2        
[25] rprojroot_1.3-2   grid_3.5.1        R6_2.2.2         
[28] rmarkdown_1.10    rmeta_3.0         readr_1.1.1      
[31] magrittr_1.5      scales_0.5.0      backports_1.1.2  
[34] codetools_0.2-15  htmltools_0.3.6   MASS_7.3-50      
[37] rvest_0.3.2       assertthat_0.2.0  colorspace_1.3-2 
[40] stringi_1.2.4     Rmosek_8.0.69     munsell_0.5.0    
[43] pscl_1.5.2        doParallel_1.0.11 truncnorm_1.0-8  
[46] SQUAREM_2017.10-1 crayon_1.3.4     

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