## ----setup, include = FALSE,eval=FALSE-----------------------------------
# knitr::opts_chunk$set(
# collapse = TRUE,
# comment = "#>"
# )
## ----packages1,eval=FALSE,message=FALSE,warning=FALSE--------------------
# library(MLML2R)
# library(minfi)
# library(GEOquery)
# library(IlluminaHumanMethylation450kmanifest)
## ----getData1,eval=FALSE-------------------------------------------------
# getGEOSuppFiles("GSE63179")
# untar("GSE63179/GSE63179_RAW.tar", exdir = "GSE63179/idat")
#
# list.files("GSE63179/idat", pattern = "idat")
# files <- list.files("GSE63179/idat", pattern = "idat.gz$", full = TRUE)
# sapply(files, gunzip, overwrite = TRUE)
## ----readData1,eval=FALSE------------------------------------------------
# rgSet <- read.metharray.exp("GSE63179/idat")
## ----eval=FALSE----------------------------------------------------------
# pData(rgSet)
## ----getPheno1,eval=FALSE------------------------------------------------
# if (!file.exists("GSE63179/GSE63179_series_matrix.txt.gz"))
# download.file(
# "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63179/matrix/GSE63179_series_matrix.txt.gz",
# "GSE63179/GSE63179_series_matrix.txt.gz")
#
# geoMat <- getGEO(filename="GSE63179/GSE63179_series_matrix.txt.gz",getGPL=FALSE)
# pD.all <- pData(geoMat)
#
# #Another option
# #geoMat <- getGEO("GSE63179")
# #pD.all <- pData(geoMat[[1]])
#
# pD <- pD.all[, c("title", "geo_accession", "characteristics_ch1.1",
# "characteristics_ch1.2","characteristics_ch1.3")]
# pD
## ----eval=FALSE----------------------------------------------------------
# sampleNames(rgSet) <- sapply(sampleNames(rgSet),function(x)
# strsplit(x,"_")[[1]][1])
# rownames(pD) <- pD$geo_accession
# pD <- pD[sampleNames(rgSet),]
# pData(rgSet) <- as(pD,"DataFrame")
# rgSet
## ----Preprocess1,eval=FALSE----------------------------------------------
# BSindex <- c(1,3,5,6)
# oxBSindex <- c(7,8,2,4)
#
# MSet.noob <- preprocessNoob(rgSet=rgSet)
## ----eval=FALSE----------------------------------------------------------
# MChannelBS <- getMeth(MSet.noob)[,BSindex]
# UChannelBS <- getUnmeth(MSet.noob)[,BSindex]
# MChannelOxBS <- getMeth(MSet.noob)[,oxBSindex]
# UChannelOxBS <- getUnmeth(MSet.noob)[,oxBSindex]
## ----MLML2Rexact1,eval=FALSE---------------------------------------------
# results_exact <- MLML(T.matrix = MChannelBS , U.matrix = UChannelBS,
# L.matrix = UChannelOxBS, M.matrix = MChannelOxBS)
#
# save(results_exact,file="results_exact_oxBS.rds")
## ----MLML2REM1,eval=FALSE------------------------------------------------
# results_em <- MLML(T.matrix = MChannelBS , U.matrix = UChannelBS,
# L.matrix = UChannelOxBS, M.matrix = MChannelOxBS,
# iterative = TRUE)
## ----echo=TRUE,eval=FALSE------------------------------------------------
# all.equal(results_exact$hmC,results_em$hmC,scale=1)
## ----plot,echo=FALSE,eval=FALSE------------------------------------------
# beta_BS <- MChannelBS/(MChannelBS+UChannelBS)
# beta_OxBS <- MChannelOxBS/(MChannelOxBS+UChannelOxBS)
# hmC_naive <- beta_BS-beta_OxBS #5-hmC naive estimate
# C_naive <- 1-beta_BS #uC naive estimate
# mC_naive <- beta_OxBS #5-mC naive estimate
#
# pdf(file="Real1_estimates.pdf",width=15,height=10)
# par(mfrow =c(2,3))
# densityPlot(results_exact$hmC,main= "5-hmC estimates - MLML2R",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion")
# densityPlot(results_exact$mC,main= "5-mC estimates - MLML2R",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion")
# densityPlot(results_exact$C,main= "uC estimates - MLML2R",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion")
#
# densityPlot(hmC_naive,main= "5-hmC estimates - Naïve",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion")
# densityPlot(mC_naive,main= "5-mC estimates - Naïve",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion")
# densityPlot(C_naive,main= "uC estimates - Naïve",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion")
# dev.off()
## ----echo=FALSE,fig.width=15,fig.height=10,fig.cap="Estimated proportions of 5-hmC, 5-mC and uC for the CpGs in the dataset from Field (2015) using the MLML function with default (PAVA) options (top row) and the naïve (subtraction) method (bottom row)."----
knitr::include_graphics("Real1_estimates.pdf")
## ----packages2,eval=FALSE,message=FALSE,warning=FALSE--------------------
# library(MLML2R)
# library(minfi)
# library(GEOquery)
# library(IlluminaHumanMethylation450kmanifest)
## ----getData2,eval=FALSE-------------------------------------------------
# getGEOSuppFiles("GSE71398")
# untar("GSE71398/GSE71398_RAW.tar", exdir = "GSE71398/idat")
#
# list.files("GSE71398/idat", pattern = "idat")
# files <- list.files("GSE71398/idat", pattern = "idat.gz$", full = TRUE)
# sapply(files, gunzip, overwrite = TRUE)
## ----readData2,eval=FALSE------------------------------------------------
# rgSet <- read.metharray.exp("GSE71398/idat")
## ----eval=FALSE----------------------------------------------------------
# pData(rgSet)
## ----getPheno2,eval=FALSE------------------------------------------------
# if (!file.exists("GSE71398/GSE71398_series_matrix.txt.gz"))
# download.file(
# "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE71nnn/GSE71398/matrix/GSE71398_series_matrix.txt.gz",
# "GSE71398/GSE71398_series_matrix.txt.gz")
#
# geoMat <- getGEO(filename="GSE71398/GSE71398_series_matrix.txt.gz",getGPL=FALSE)
# pD.all <- pData(geoMat)
#
# #Another option
# #geoMat <- getGEO("GSE71398")
# #pD.all <- pData(geoMat[[1]])
#
# pD <- pD.all[, c("title", "geo_accession", "source_name_ch1",
# "tabchip or bschip:ch1","hypoxia status:ch1",
# "tumor name:ch1","batch:ch1","platform_id")]
# pD$method <- pD$`tabchip or bschip:ch1`
# pD$group <- pD$`hypoxia status:ch1`
# pD$sample <- pD$`tumor name:ch1`
# pD$batch <- pD$`batch:ch1`
## ----eval=FALSE----------------------------------------------------------
# sampleNames(rgSet) <- sapply(sampleNames(rgSet),function(x)
# strsplit(x,"_")[[1]][1])
# rownames(pD) <- as.character(pD$geo_accession)
# pD <- pD[sampleNames(rgSet),]
# pData(rgSet) <- as(pD,"DataFrame")
# rgSet
## ----eval=FALSE----------------------------------------------------------
# qcReport(rgSet, pdf= "qcReport_tab_bs.pdf")
## ----preprocess2,eval=FALSE----------------------------------------------
# BSindex <- which(pD$method=="BSchip")[-which(pD$geo_accession
# %in% c("GSM1833667","GSM1833691"))]
# TABindex <- which(pD$method=="TABchip")[-which(pD$geo_accession
# %in% c("GSM1833667","GSM1833691"))]
#
# MSet.noob <- preprocessNoob(rgSet)
#
# MChannelBS <- getMeth(MSet.noob)[,BSindex]
# UChannelBS <- getUnmeth(MSet.noob)[,BSindex]
# MChannelTAB <- getMeth(MSet.noob)[,TABindex]
# UChannelTAB <- getUnmeth(MSet.noob)[,TABindex]
## ----eval=FALSE----------------------------------------------------------
# results_exact <- MLML(T.matrix = MChannelBS , U.matrix = UChannelBS,
# G.matrix = UChannelTAB, H.matrix = MChannelTAB)
## ----MLML2REM2,eval=FALSE------------------------------------------------
# results_em <- MLML(T.matrix = MChannelBS , U.matrix = UChannelBS,
# G.matrix = UChannelTAB, H.matrix = MChannelTAB,
# iterative = TRUE)
## ----echo=TRUE,eval=FALSE------------------------------------------------
# all.equal(results_exact$hmC,results_em$hmC,scale=1)
## ----echo=TRUE,eval=FALSE------------------------------------------------
# all.equal(results_exact$mC,results_em$mC,scale=1)
## ----plot3,echo=FALSE,eval=FALSE-----------------------------------------
# beta_BS <- MChannelBS/(MChannelBS+UChannelBS)
# beta_TAB <- MChannelTAB/(MChannelTAB+UChannelTAB)
# hmC_naive <- beta_TAB #5-hmC naive estimate
# C_naive <- 1-beta_BS #uC naive estimate
# mC_naive <- beta_BS-beta_TAB #5-mC naive estimate
#
# pdf(file="Real2a_estimates.pdf",width=15,height=10)
# par(mfrow =c(2,3))
# densityPlot(results_exact$hmC,main= "5-hmC estimates - MLML2R",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=pD$group[BSindex])
# densityPlot(results_exact$mC,main= "5-mC estimates - MLML2R",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=pD$group[BSindex])
# densityPlot(results_exact$C,main= "uC estimates - MLML2R",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=pD$group[BSindex])
#
# densityPlot(hmC_naive,main= "5-hmC estimates - Naïve",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=pD$group[BSindex])
# densityPlot(mC_naive,main= "5-mC estimates - Naïve",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=pD$group[BSindex])
# densityPlot(C_naive,main= "uC estimates - Naïve",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=pD$group[BSindex])
# dev.off()
## ----echo=FALSE,fig.width=15,fig.height=10,fig.cap="Estimated proportions of 5-hmC, 5-mC and uC for the CpGs in the dataset from Thienpont et al (2016), using the MLML function with default (PAVA) options (top row) and the naïve (subtraction) method (bottom row)."----
knitr::include_graphics("Real2a_estimates.pdf")
## ----echo=TRUE,eval=FALSE------------------------------------------------
# library(GEOquery)
#
# getGEOSuppFiles("GSE70090")
# untar("GSE70090/GSE70090_RAW.tar", exdir = "GSE70090/data")
## ----echo=TRUE,eval=FALSE------------------------------------------------
# dataFiles <- list.files("GSE70090/data", pattern = "txt.gz$", full = TRUE)
# sapply(dataFiles, gunzip, overwrite = TRUE)
## ----echo=TRUE,eval=FALSE------------------------------------------------
# files <- list.files("GSE70090/data")
# filesfull <- list.files("GSE70090/data",full=TRUE)
# tissue <- sapply(files,function(x) strsplit(x,"_")[[1]][2]) # tissue
# id <- sapply(files,function(x) strsplit(x,"_")[[1]][3]) # sample id
# tmp <- sapply(files,function(x) strsplit(x,"_")[[1]][4])
# convMeth <- sapply(tmp, function(x) strsplit(x,"\\.")[[1]][1]) # DNA conversion method
# group <- ifelse(id %in% c("N1","N2","N3","N4"),"normal","tumor")
# id2 <- paste(tissue,id,sep="_")
# GSM <- sapply(files,function(x) strsplit(x,"_")[[1]][1]) # GSM
# pheno <- data.frame(GSM=GSM,tissue=tissue,id=id2,convMeth=convMeth,
# group=group,file=filesfull,stringsAsFactors = FALSE)
## ----echo=TRUE,eval=FALSE------------------------------------------------
# library(data.table)
#
# phenoLung <- pheno[pheno$tissue=="lung",]
#
# # order to have all BS samples and then all oxBS samples
# phenoLung <- phenoLung[order(phenoLung$convMeth,phenoLung$id),]
## ----echo=TRUE,eval=FALSE------------------------------------------------
# ### BS
# files <- phenoLung$file[phenoLung$convMeth=="BS"]
# C_BS <- do.call(cbind,lapply(files,function(fn)
# fread(fn,data.table=FALSE,select=c("methylated_read_count"))))
# TotalBS <- do.call(cbind,lapply(files,function(fn)
# fread(fn,data.table=FALSE,select=c("total_read_count"))))
# T_BS <- TotalBS - C_BS
#
#
# ### oxBS
# files <- phenoLung$file[phenoLung$convMeth=="oxBS"]
# C_OxBS <- do.call(cbind,lapply(files,function(fn)
# fread(fn,data.table=FALSE,select=c("methylated_read_count"))))
# TotalOxBS <- do.call(cbind,lapply(files,function(fn)
# fread(fn,data.table=FALSE,select=c("total_read_count"))))
# T_OxBS <- TotalOxBS - C_OxBS
#
# # since rownames and colnames are the same across files:
# tmp <- fread(files[1], data.table=FALSE, select=c("chr","position"))
# CpG <- paste(tmp[,1],tmp[,2],sep="-")
#
# rownames(C_BS) <- CpG
# rownames(T_BS) <- CpG
#
# colnames(C_BS) <- phenoLung$id[phenoLung$convMeth=="BS"]
# colnames(T_BS) <- phenoLung$id[phenoLung$convMeth=="BS"]
#
# rownames(C_OxBS) <- CpG
# rownames(T_OxBS) <- CpG
#
# colnames(C_OxBS) <- phenoLung$id[phenoLung$convMeth=="oxBS"]
# colnames(T_OxBS) <- phenoLung$id[phenoLung$convMeth=="oxBS"]
#
# Tm = as.matrix(C_BS)
# Um = as.matrix(T_BS)
# Lm = as.matrix(T_OxBS)
# Mm = as.matrix(C_OxBS)
## ----echo=TRUE,eval=FALSE------------------------------------------------
# TotalBS <- Tm+Um
# TotalOxBS <- Lm+Mm
#
# library(matrixStats)
#
# tmp1 <- rowMins(TotalBS,na.rm=TRUE) # minimum coverage across samples from BS for each CpG
# tmp2 <- rowMins(TotalOxBS,na.rm=TRUE) # minimum coverage across samples from oxBS for each CpG
#
# aa <-which(tmp1>=10 & tmp2>=10)
# # CpGs with coverage at least 10 across all samples for both methods (BS and oxBS)
# length(aa)
## ----echo=TRUE,eval=FALSE------------------------------------------------
# library(MLML2R)
#
# results_exact <- MLML(T.matrix = Tm[aa,],
# U.matrix = Um[aa,],
# L.matrix = Lm[aa,],
# M.matrix = Mm[aa,])
#
# results_em <- MLML(T.matrix = Tm[aa,],
# U.matrix = Um[aa,],
# L.matrix = Lm[aa,],
# M.matrix = Mm[aa,],
# iterative = TRUE)
## ----echo=TRUE,eval=FALSE------------------------------------------------
# all.equal(results_exact$hmC,results_em$hmC,scale=1)
## ----echo=TRUE,eval=FALSE------------------------------------------------
# all.equal(results_exact$mC,results_em$mC,scale=1)
## ----echo=FALSE,eval=FALSE-----------------------------------------------
# beta_BS <- Tm/TotalBS
# beta_OxBS <- Mm/TotalOxBS
# hmC_naive <- beta_BS-beta_OxBS
# C_naive <- 1-beta_BS
# mC_naive <- beta_OxBS
#
# library(minfi)
# pdf(file="Real3a_estimates.pdf",width=15,height=10)
# par(mfrow =c(2,3))
# densityPlot(results_exact$hmC,main= "5-hmC estimates - MLML2R",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=phenoLung$group[1:6])
# densityPlot(results_exact$mC,main= "5-mC estimates - MLML2R",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=phenoLung$group[1:6])
# densityPlot(results_exact$C,main= "uC estimates - MLML2R",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=phenoLung$group[1:6])
#
# densityPlot(hmC_naive[aa,],main= "5-hmC estimates - Naïve",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=phenoLung$group[1:6])
# densityPlot(mC_naive[aa,],main= "5-mC estimates - Naïve",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=phenoLung$group[1:6])
# densityPlot(C_naive[aa,],main= "uC estimates - Naïve",cex.axis=1.4,cex.main=1.5,cex.lab=1.4,xlab="Proportion",sampGroups=phenoLung$group[1:6])
# dev.off()
## ----echo=FALSE,fig.width=15,fig.height=10,fig.cap="Estimated proportions of 5-hmC, 5-mC and uC for the CpGs in the dataset from Li et al (2016) using the MLML function with default options (top row) and the naïve method (bottom row)."----
knitr::include_graphics("Real3a_estimates.pdf")
## ----simulation,echo=TRUE,eval=FALSE-------------------------------------
# load("results_exact_oxBS.rds") # load estimates from previous example
#
# set.seed(112017)
#
# index <- sample(1:dim(results_exact$mC)[1],1000,replace=FALSE) # 1000 CpGs
#
# Coverage <- round(MChannelBS+UChannelBS)[index,1:2] # considering 2 samples
#
# temp1 <- data.frame(n=as.vector(Coverage),
# p_m=c(results_exact$mC[index,1],
# results_exact$mC[index,1]),
# p_h=c(results_exact$hmC[index,1],
# results_exact$hmC[index,1]))
#
# MChannelBS_temp <- c()
# for (i in 1:dim(temp1)[1])
# {
# MChannelBS_temp[i] <- rbinom(n=1, size=temp1$n[i],
# prob=(temp1$p_m[i]+temp1$p_h[i]))
# }
#
#
# UChannelBS_sim2 <- matrix(Coverage - MChannelBS_temp,ncol=2)
# MChannelBS_sim2 <- matrix(MChannelBS_temp,ncol=2)
#
#
# MChannelOxBS_temp <- c()
# for (i in 1:dim(temp1)[1])
# {
# MChannelOxBS_temp[i] <- rbinom(n=1, size=temp1$n[i], prob=temp1$p_m[i])
# }
#
# UChannelOxBS_sim2 <- matrix(Coverage - MChannelOxBS_temp,ncol=2)
# MChannelOxBS_sim2 <- matrix(MChannelOxBS_temp,ncol=2)
#
#
# MChannelTAB_temp <- c()
# for (i in 1:dim(temp1)[1])
# {
# MChannelTAB_temp[i] <- rbinom(n=1, size=temp1$n[i], prob=temp1$p_h[i])
# }
#
#
# UChannelTAB_sim2 <- matrix(Coverage - MChannelTAB_temp,ncol=2)
# MChannelTAB_sim2 <- matrix(MChannelTAB_temp,ncol=2)
#
# true_parameters_sim2 <- data.frame(p_m=results_exact$mC[index,1],
# p_h=results_exact$hmC[index,1])
# true_parameters_sim2$p_u <- 1-true_parameters_sim2$p_m-true_parameters_sim2$p_h
## ----eval=FALSE,echo=FALSE-----------------------------------------------
# save(true_parameters_sim2,MChannelBS_sim2,UChannelBS_sim2,MChannelOxBS_sim2,UChannelOxBS_sim2,MChannelTAB_sim2,UChannelTAB_sim2,file="Data_sim2.rds")
## ----plot2,echo=FALSE,eval=FALSE-----------------------------------------
# pdf(file="True_parameters.pdf",width=15,height=5)
# par(mfrow =c(1,3))
# plot(density(results_exact$hmC[index,1]),main= "True 5-hmC",xlab="Proportions",xlim=c(0,1),ylim=c(0,10),cex.axis=1.5,cex.main=1.5,cex.lab=1.5)
# plot(density(results_exact$mC[index,1]),main= "True 5-mC",xlab="Proportions",xlim=c(0,1),ylim=c(0,10),cex.axis=1.5,cex.main=1.5,cex.lab=1.5)
# plot(density(results_exact$C[index,1]),main= "True uC",ylim=c(0,10),xlab="Proportions",xlim=c(0,1),cex.axis=1.5,cex.main=1.5,cex.lab=1.5)
# dev.off()
## ----echo=FALSE,fig.width=15,fig.height=5,fig.cap="True proportions of hydroxymethylation, methylation and unmethylation for the CpGs used to generate the datasets."----
knitr::include_graphics("True_parameters.pdf")
## ----echo=FALSE,eval=TRUE------------------------------------------------
load("Data_sim2.rds")
## ------------------------------------------------------------------------
library(MLML2R)
results_exactBO1 <- MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2)
## ------------------------------------------------------------------------
results_emBO1 <- MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
iterative=TRUE)
## ------------------------------------------------------------------------
all.equal(results_emBO1$hmC,results_exactBO1$hmC,scale=1)
## ------------------------------------------------------------------------
library(microbenchmark)
mbmBO1 = microbenchmark(
EXACT = MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2),
EM = MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
iterative=TRUE),
times=10)
mbmBO1
## ------------------------------------------------------------------------
all.equal(true_parameters_sim2$p_h,results_exactBO1$hmC[,1],scale=1)
## ------------------------------------------------------------------------
all.equal(true_parameters_sim2$p_h,results_emBO1$hmC[,1],scale=1)
## ------------------------------------------------------------------------
results_exactBT1 <- MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2)
## ------------------------------------------------------------------------
results_emBT1 <- MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2,
iterative=TRUE)
## ------------------------------------------------------------------------
all.equal(results_emBT1$hmC,results_exactBT1$hmC,scale=1)
## ------------------------------------------------------------------------
mbmBT1 = microbenchmark(
EXACT = MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2),
EM = MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2,
iterative=TRUE),
times=10)
mbmBT1
## ------------------------------------------------------------------------
all.equal(true_parameters_sim2$p_h,results_exactBT1$hmC[,1],scale=1)
## ------------------------------------------------------------------------
all.equal(true_parameters_sim2$p_h,results_emBT1$hmC[,1],scale=1)
## ------------------------------------------------------------------------
results_exactOT1 <- MLML(L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2)
## ------------------------------------------------------------------------
results_emOT1 <- MLML(L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2,
iterative=TRUE)
## ------------------------------------------------------------------------
all.equal(results_emOT1$hmC,results_exactOT1$hmC,scale=1)
## ------------------------------------------------------------------------
mbmOT1 = microbenchmark(
EXACT = MLML(L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2),
EM = MLML(L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2,
iterative=TRUE),
times=10)
mbmOT1
## ------------------------------------------------------------------------
all.equal(true_parameters_sim2$p_h,results_exactOT1$hmC[,1],scale=1)
## ------------------------------------------------------------------------
all.equal(true_parameters_sim2$p_h,results_emOT1$hmC[,1],scale=1)
## ------------------------------------------------------------------------
results_exactBOT1 <- MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2)
## ------------------------------------------------------------------------
results_emBOT1 <- MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2,iterative=TRUE)
## ------------------------------------------------------------------------
all.equal(results_emBOT1$hmC,results_exactBOT1$hmC,scale=1)
## ----computationCost-----------------------------------------------------
mbmBOT1 = microbenchmark(
EXACT = MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2),
EM = MLML(T.matrix = MChannelBS_sim2,
U.matrix = UChannelBS_sim2,
L.matrix = UChannelOxBS_sim2,
M.matrix = MChannelOxBS_sim2,
G.matrix = UChannelTAB_sim2,
H.matrix = MChannelTAB_sim2,
iterative=TRUE),
times=10)
mbmBOT1
## ------------------------------------------------------------------------
all.equal(true_parameters_sim2$p_h,results_exactBOT1$hmC[,1],scale=1)
## ------------------------------------------------------------------------
all.equal(true_parameters_sim2$p_h,results_emBOT1$hmC[,1],scale=1)