[Robast-commits] r955 - in branches/robast-1.1/pkg/RobLoxBioC: . R inst man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Jul 17 10:06:18 CEST 2018
Author: ruckdeschel
Date: 2018-07-17 10:06:17 +0200 (Tue, 17 Jul 2018)
New Revision: 955
Modified:
branches/robast-1.1/pkg/RobLoxBioC/DESCRIPTION
branches/robast-1.1/pkg/RobLoxBioC/R/AffySimStudyFunction.R
branches/robast-1.1/pkg/RobLoxBioC/R/IlluminaSimStudyFunction.R
branches/robast-1.1/pkg/RobLoxBioC/inst/NEWS
branches/robast-1.1/pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd
Log:
[RobLoxBioC] in branch 1.1 + wherever possible also use q.l internally instead of q to
provide functionality in IRKernel
Modified: branches/robast-1.1/pkg/RobLoxBioC/DESCRIPTION
===================================================================
--- branches/robast-1.1/pkg/RobLoxBioC/DESCRIPTION 2018-07-17 08:01:06 UTC (rev 954)
+++ branches/robast-1.1/pkg/RobLoxBioC/DESCRIPTION 2018-07-17 08:06:17 UTC (rev 955)
@@ -1,11 +1,12 @@
Package: RobLoxBioC
-Version: 1.0
-Date: 2015-05-03
+Version: 1.1.0
+Date: 2018-07-17
Title: Infinitesimally Robust Estimators for Preprocessing -Omics Data
Description: Functions for the determination of optimally robust influence curves and
estimators for preprocessing omics data, in particular gene expression data.
Depends: R(>= 2.14.0), methods, distr(>= 2.5.2), affy
-Imports: Biobase, BiocGenerics, beadarray, RobLox(>= 0.9.2), distrMod(>= 2.5.2), lattice, RColorBrewer
+Imports: Biobase, BiocGenerics, beadarray, RobLox(>= 0.9.2), distrMod(>= 2.5.2), lattice,
+ RColorBrewer
Suggests: affydata, hgu95av2cdf, beadarrayExampleData, illuminaHumanv3.db
Authors at R: person("Matthias", "Kohl", role=c("cre", "cph"), email="Matthias.Kohl at stamats.de")
ByteCompile: yes
@@ -14,4 +15,4 @@
Encoding: latin1
LastChangedDate: {$LastChangedDate$}
LastChangedRevision: {$LastChangedRevision$}
-SVNRevision: -Inf
+SVNRevision: 940
Modified: branches/robast-1.1/pkg/RobLoxBioC/R/AffySimStudyFunction.R
===================================================================
--- branches/robast-1.1/pkg/RobLoxBioC/R/AffySimStudyFunction.R 2018-07-17 08:01:06 UTC (rev 954)
+++ branches/robast-1.1/pkg/RobLoxBioC/R/AffySimStudyFunction.R 2018-07-17 08:06:17 UTC (rev 955)
@@ -1,120 +1,120 @@
-###############################################################################
-## Function to perform simulation study comparing Tukey's biweight with
-## rmx estimators
-###############################################################################
-AffySimStudy <- function(n, M, eps, seed = 123, eps.lower = 0, eps.upper = 0.05,
- steps = 3L, fsCor = TRUE, contD,
- plot1 = FALSE, plot2 = FALSE, plot3 = FALSE){
- stopifnot(n >= 3)
- stopifnot(eps >= 0, eps <= 0.5)
- if(plot1){
- from <- min(-6, q(contD)(1e-15))
- to <- max(6, q(contD)(1-1e-15))
- curve(pnorm, from = from, to = to, lwd = 2, n = 201,
- main = "Comparison: ideal vs. real", ylab = "cdf")
- fun <- function(x) (1-eps)*pnorm(x) + eps*p(contD)(x)
- curve(fun, from = from, to = to, add = TRUE, col = "orange",
- lwd = 2, n = 201, ylab = "cdf")
- legend("topleft", legend = c("ideal", "real"),
- fill = c("black", "orange"))
- }
-
- set.seed(seed)
- r <- rbinom(n*M, prob = eps, size = 1)
- Mid <- rnorm(n*M)
- Mcont <- r(contD)(n*M)
- Mre <- matrix((1-r)*Mid + r*Mcont, ncol = n)
- ind <- rowSums(matrix(r, ncol = n)) >= n/2
- while(any(ind)){
- M1 <- sum(ind)
- cat("Samples to re-simulate:\t", M1, "\n")
- r <- rbinom(n*M1, prob = eps, size = 1)
- Mid <- rnorm(n*M1)
- Mcont <- r(contD)(n*M1)
- Mre[ind,] <- (1-r)*Mid + r*Mcont
- ind[ind] <- rowSums(matrix(r, ncol = n)) >= n/2
- }
- rm(Mid, Mcont, r, ind)
-
-
- if(plot2){
- ind <- if(M <= 20) 1:M else sample(1:M, 20)
- if(plot1) dev.new()
- M1 <- min(M, 20)
- print(
- stripplot(rep(1:M1, each = n) ~ as.vector(Mre[ind,]),
- ylab = "samples", xlab = "x", pch = 20,
- main = ifelse(M <= 20, "Samples", "20 randomly chosen samples"))
- )
- }
-
- ## ML-estimator: mean and sd
- Mean <- rowMeans(Mre)
- Sd <- sqrt(rowMeans((Mre-Mean)^2))
- ## Median and MAD
- Median <- rowMedians(Mre)
- Mad <- rowMedians(abs(Mre - Median))/qnorm(0.75)
- ## Tukey 1-step + MAD
- Tukey <- apply(Mre, 1, function(x) tukey.biweight(x))
- Tukey <- cbind(Tukey, Mad)
-
- ## Radius-minimax estimator
- RadMinmax <- estimate(rowRoblox(Mre, eps.lower = eps.lower,
- eps.upper = eps.upper, k = steps,
- fsCor = fsCor))
-
- if(plot3){
- Ergebnis1 <- list(Mean, Median, Tukey[,1], RadMinmax[,1])
- Ergebnis2 <- list(Sd, Mad, RadMinmax[,2])
- myCol <- brewer.pal(4, "Dark2")
- if(plot1 || plot2) dev.new()
- layout(matrix(c(1, 1, 1, 1, 3, 2, 2, 2, 2, 3), ncol = 2))
- boxplot(Ergebnis1, col = myCol, pch = 20, main = "Location")
- abline(h = 0)
- boxplot(Ergebnis2, col = myCol[c(1,2,4)], pch = 20, main = "Scale")
- abline(h = 1)
- op <- par(mar = rep(2, 4))
- plot(c(0,1), c(1, 0), type = "n", axes = FALSE)
- legend("center", c("ML", "Med/MAD", "biweight", "rmx"),
- fill = myCol, ncol = 4, cex = 1.5)
- on.exit(par(op))
- }
-
- ## ML-estimator
- MSE1.1 <- n*mean(Mean^2)
- ## Median + MAD
- MSE2.1 <- n*mean(Median^2)
- ## Tukey
- MSE3.1 <- n*mean(Tukey[,1]^2)
- ## Radius-minimax
- MSE4.1 <- n*mean(RadMinmax[,1]^2)
- empMSE <- data.frame(ML = MSE1.1, Med = MSE2.1, Tukey = MSE3.1, "rmx" = MSE4.1)
- rownames(empMSE) <- "n x empMSE (loc)"
- relMSE <- empMSE[1,]/empMSE[1,4]
- empMSE <- rbind(empMSE, relMSE)
- rownames(empMSE)[2] <- "relMSE (loc)"
-
- ## ML-estimator
- MSE1.2 <- n*mean((Sd-1)^2)
- ## Median + MAD
- MSE2.2 <- n*mean((Mad-1)^2)
- ## Tukey
- MSE3.2 <- MSE2.2
- ## Radius-minimax
- MSE4.2 <- n*mean((RadMinmax[,2]-1)^2)
- empMSE <- rbind(empMSE, c(MSE1.2, MSE2.2, MSE3.2, MSE4.2))
- rownames(empMSE)[3] <- "n x empMSE (scale)"
- relMSE <- empMSE[3,]/empMSE[3,4]
- empMSE <- rbind(empMSE, relMSE)
- rownames(empMSE)[4] <- "relMSE (scale)"
- empMSE <- rbind(empMSE, c(MSE1.1 + MSE1.2, MSE2.1 + MSE2.2, MSE3.1 + MSE3.2,
- MSE4.1 + MSE4.2))
- rownames(empMSE)[5] <- "n x empMSE (loc + scale)"
- relMSE <- empMSE[5,]/empMSE[5,4]
- empMSE <- rbind(empMSE, relMSE)
- rownames(empMSE)[6] <- "relMSE (loc + scale)"
-
- empMSE
-}
-
-
+###############################################################################
+## Function to perform simulation study comparing Tukey's biweight with
+## rmx estimators
+###############################################################################
+AffySimStudy <- function(n, M, eps, seed = 123, eps.lower = 0, eps.upper = 0.05,
+ steps = 3L, fsCor = TRUE, contD,
+ plot1 = FALSE, plot2 = FALSE, plot3 = FALSE){
+ stopifnot(n >= 3)
+ stopifnot(eps >= 0, eps <= 0.5)
+ if(plot1){
+ from <- min(-6, q.l(contD)(1e-15))
+ to <- max(6, q.l(contD)(1-1e-15))
+ curve(pnorm, from = from, to = to, lwd = 2, n = 201,
+ main = "Comparison: ideal vs. real", ylab = "cdf")
+ fun <- function(x) (1-eps)*pnorm(x) + eps*p(contD)(x)
+ curve(fun, from = from, to = to, add = TRUE, col = "orange",
+ lwd = 2, n = 201, ylab = "cdf")
+ legend("topleft", legend = c("ideal", "real"),
+ fill = c("black", "orange"))
+ }
+
+ set.seed(seed)
+ r <- rbinom(n*M, prob = eps, size = 1)
+ Mid <- rnorm(n*M)
+ Mcont <- r(contD)(n*M)
+ Mre <- matrix((1-r)*Mid + r*Mcont, ncol = n)
+ ind <- rowSums(matrix(r, ncol = n)) >= n/2
+ while(any(ind)){
+ M1 <- sum(ind)
+ cat("Samples to re-simulate:\t", M1, "\n")
+ r <- rbinom(n*M1, prob = eps, size = 1)
+ Mid <- rnorm(n*M1)
+ Mcont <- r(contD)(n*M1)
+ Mre[ind,] <- (1-r)*Mid + r*Mcont
+ ind[ind] <- rowSums(matrix(r, ncol = n)) >= n/2
+ }
+ rm(Mid, Mcont, r, ind)
+
+
+ if(plot2){
+ ind <- if(M <= 20) 1:M else sample(1:M, 20)
+ if(plot1) dev.new()
+ M1 <- min(M, 20)
+ print(
+ stripplot(rep(1:M1, each = n) ~ as.vector(Mre[ind,]),
+ ylab = "samples", xlab = "x", pch = 20,
+ main = ifelse(M <= 20, "Samples", "20 randomly chosen samples"))
+ )
+ }
+
+ ## ML-estimator: mean and sd
+ Mean <- rowMeans(Mre)
+ Sd <- sqrt(rowMeans((Mre-Mean)^2))
+ ## Median and MAD
+ Median <- rowMedians(Mre)
+ Mad <- rowMedians(abs(Mre - Median))/qnorm(0.75)
+ ## Tukey 1-step + MAD
+ Tukey <- apply(Mre, 1, function(x) tukey.biweight(x))
+ Tukey <- cbind(Tukey, Mad)
+
+ ## Radius-minimax estimator
+ RadMinmax <- estimate(rowRoblox(Mre, eps.lower = eps.lower,
+ eps.upper = eps.upper, k = steps,
+ fsCor = fsCor))
+
+ if(plot3){
+ Ergebnis1 <- list(Mean, Median, Tukey[,1], RadMinmax[,1])
+ Ergebnis2 <- list(Sd, Mad, RadMinmax[,2])
+ myCol <- brewer.pal(4, "Dark2")
+ if(plot1 || plot2) dev.new()
+ layout(matrix(c(1, 1, 1, 1, 3, 2, 2, 2, 2, 3), ncol = 2))
+ boxplot(Ergebnis1, col = myCol, pch = 20, main = "Location")
+ abline(h = 0)
+ boxplot(Ergebnis2, col = myCol[c(1,2,4)], pch = 20, main = "Scale")
+ abline(h = 1)
+ op <- par(mar = rep(2, 4))
+ plot(c(0,1), c(1, 0), type = "n", axes = FALSE)
+ legend("center", c("ML", "Med/MAD", "biweight", "rmx"),
+ fill = myCol, ncol = 4, cex = 1.5)
+ on.exit(par(op))
+ }
+
+ ## ML-estimator
+ MSE1.1 <- n*mean(Mean^2)
+ ## Median + MAD
+ MSE2.1 <- n*mean(Median^2)
+ ## Tukey
+ MSE3.1 <- n*mean(Tukey[,1]^2)
+ ## Radius-minimax
+ MSE4.1 <- n*mean(RadMinmax[,1]^2)
+ empMSE <- data.frame(ML = MSE1.1, Med = MSE2.1, Tukey = MSE3.1, "rmx" = MSE4.1)
+ rownames(empMSE) <- "n x empMSE (loc)"
+ relMSE <- empMSE[1,]/empMSE[1,4]
+ empMSE <- rbind(empMSE, relMSE)
+ rownames(empMSE)[2] <- "relMSE (loc)"
+
+ ## ML-estimator
+ MSE1.2 <- n*mean((Sd-1)^2)
+ ## Median + MAD
+ MSE2.2 <- n*mean((Mad-1)^2)
+ ## Tukey
+ MSE3.2 <- MSE2.2
+ ## Radius-minimax
+ MSE4.2 <- n*mean((RadMinmax[,2]-1)^2)
+ empMSE <- rbind(empMSE, c(MSE1.2, MSE2.2, MSE3.2, MSE4.2))
+ rownames(empMSE)[3] <- "n x empMSE (scale)"
+ relMSE <- empMSE[3,]/empMSE[3,4]
+ empMSE <- rbind(empMSE, relMSE)
+ rownames(empMSE)[4] <- "relMSE (scale)"
+ empMSE <- rbind(empMSE, c(MSE1.1 + MSE1.2, MSE2.1 + MSE2.2, MSE3.1 + MSE3.2,
+ MSE4.1 + MSE4.2))
+ rownames(empMSE)[5] <- "n x empMSE (loc + scale)"
+ relMSE <- empMSE[5,]/empMSE[5,4]
+ empMSE <- rbind(empMSE, relMSE)
+ rownames(empMSE)[6] <- "relMSE (loc + scale)"
+
+ empMSE
+}
+
+
Modified: branches/robast-1.1/pkg/RobLoxBioC/R/IlluminaSimStudyFunction.R
===================================================================
--- branches/robast-1.1/pkg/RobLoxBioC/R/IlluminaSimStudyFunction.R 2018-07-17 08:01:06 UTC (rev 954)
+++ branches/robast-1.1/pkg/RobLoxBioC/R/IlluminaSimStudyFunction.R 2018-07-17 08:06:17 UTC (rev 955)
@@ -1,128 +1,128 @@
-###############################################################################
-## Function to perform simulation study comparing Illumina's default method
-## with rmx estimators
-###############################################################################
-IlluminaSimStudy <- function(n, M, eps, seed = 123,
- eps.lower = 0, eps.upper = 0.05,
- steps = 3L, fsCor = TRUE, contD,
- plot1 = FALSE, plot2 = FALSE, plot3 = FALSE){
- stopifnot(n >= 3)
- stopifnot(eps >= 0, eps <= 0.5)
- if(plot1){
- from <- min(-6, q(contD)(1e-15))
- to <- max(6, q(contD)(1-1e-15))
- curve(pnorm, from = from, to = to, lwd = 2, n = 201,
- main = "Comparison: ideal vs. real", ylab = "cdf")
- fun <- function(x) (1-eps)*pnorm(x) + eps*p(contD)(x)
- curve(fun, from = from, to = to, add = TRUE, col = "orange",
- lwd = 2, n = 201, ylab = "cdf")
- legend("topleft", legend = c("ideal", "real"),
- fill = c("black", "orange"))
- }
-
- set.seed(seed)
- r <- rbinom(n*M, prob = eps, size = 1)
- Mid <- rnorm(n*M)
- Mcont <- r(contD)(n*M)
- Mre <- matrix((1-r)*Mid + r*Mcont, ncol = n)
- ind <- rowSums(matrix(r, ncol = n)) >= n/2
- while(any(ind)){
- M1 <- sum(ind)
- cat("Samples to re-simulate:\t", M1, "\n")
- r <- rbinom(n*M1, prob = eps, size = 1)
- Mid <- rnorm(n*M1)
- Mcont <- r(contD)(n*M1)
- Mre[ind,] <- (1-r)*Mid + r*Mcont
- ind[ind] <- rowSums(matrix(r, ncol = n)) >= n/2
- }
- rm(Mid, Mcont, r, ind)
-
-
- if(plot2){
- ind <- if(M <= 20) 1:M else sample(1:M, 20)
- if(plot1) dev.new()
- M1 <- min(M, 20)
- print(
- stripplot(rep(1:M1, each = n) ~ as.vector(Mre[ind,]),
- ylab = "samples", xlab = "x", pch = 20,
- main = ifelse(M <= 20, "Samples", "20 randomly chosen samples"))
- )
- }
-
- ## ML-estimator: mean and sd
- Mean <- rowMeans(Mre)
- Sd <- sqrt(rowMeans((Mre-Mean)^2))
- ## Median and MAD
- Median <- rowMedians(Mre)
- Mad <- rowMedians(abs(Mre - Median))/qnorm(0.75)
- ## Illumina method
- ind <- (Mre < (Median - 3*Mad)) | (Mre > (Median + 3*Mad))
- x.ill <- Mre
- x.ill[ind] <- NA
- Illum.mean <- rowMeans(x.ill, na.rm = TRUE)
- n.ill <- rowSums(!is.na(x.ill))
- n.ill[n.ill < 1] <- NA
- Illum.sd <- sqrt(rowSums((x.ill - Illum.mean)^2, na.rm = TRUE)/(n.ill-1))
- Illum <- cbind(Illum.mean, Illum.sd)
-
- ## Radius-minimax estimator
- RadMinmax <- estimate(rowRoblox(Mre, eps.lower = eps.lower,
- eps.upper = eps.upper, k = steps,
- fsCor = fsCor))
-
- if(plot3){
- Ergebnis1 <- list(Mean, Median, Illum[,1], RadMinmax[,1])
- Ergebnis2 <- list(Sd, Mad, Illum[,2], RadMinmax[,2])
- myCol <- brewer.pal(4, "Dark2")
- if(plot1 || plot2) dev.new()
- layout(matrix(c(1, 1, 1, 1, 3, 2, 2, 2, 2, 3), ncol = 2))
- boxplot(Ergebnis1, col = myCol, pch = 20, main = "Location")
- abline(h = 0)
- boxplot(Ergebnis2, col = myCol, pch = 20, main = "Scale")
- abline(h = 1)
- op <- par(mar = rep(2, 4))
- plot(c(0,1), c(1, 0), type = "n", axes = FALSE)
- legend("center", c("ML", "Med/MAD", "Illumina", "rmx"),
- fill = myCol, ncol = 4, cex = 1.5)
- op$cin <- op$cra <- op$csi <- op$cxy <- op$din <- NULL
- on.exit(par(op))
- }
-
- ## ML-estimator
- MSE1.1 <- n*mean(Mean^2)
- ## Median + MAD
- MSE2.1 <- n*mean(Median^2)
- ## Illumina's default method
- MSE3.1 <- n*mean(Illum[,1]^2)
- ## Radius-minimax
- MSE4.1 <- n*mean(RadMinmax[,1]^2)
- empMSE <- data.frame(ML = MSE1.1, Med = MSE2.1, Illumina = MSE3.1, "rmx" = MSE4.1)
- rownames(empMSE) <- "n x empMSE (loc)"
- relMSE <- empMSE[1,]/empMSE[1,4]
- empMSE <- rbind(empMSE, relMSE)
- rownames(empMSE)[2] <- "relMSE (loc)"
-
- ## ML-estimator
- MSE1.2 <- n*mean((Sd-1)^2)
- ## Median + MAD
- MSE2.2 <- n*mean((Mad-1)^2)
- ## Illumina's default method
- MSE3.2 <- n*mean((Illum[,2]-1)^2)
- ## Radius-minimax
- MSE4.2 <- n*mean((RadMinmax[,2]-1)^2)
- empMSE <- rbind(empMSE, c(MSE1.2, MSE2.2, MSE3.2, MSE4.2))
- rownames(empMSE)[3] <- "n x empMSE (scale)"
- relMSE <- empMSE[3,]/empMSE[3,4]
- empMSE <- rbind(empMSE, relMSE)
- rownames(empMSE)[4] <- "relMSE (scale)"
- empMSE <- rbind(empMSE, c(MSE1.1 + MSE1.2, MSE2.1 + MSE2.2, MSE3.1 + MSE3.2,
- MSE4.1 + MSE4.2))
- rownames(empMSE)[5] <- "n x empMSE (loc + scale)"
- relMSE <- empMSE[5,]/empMSE[5,4]
- empMSE <- rbind(empMSE, relMSE)
- rownames(empMSE)[6] <- "relMSE (loc + scale)"
-
- empMSE
-}
-
-
+###############################################################################
+## Function to perform simulation study comparing Illumina's default method
+## with rmx estimators
+###############################################################################
+IlluminaSimStudy <- function(n, M, eps, seed = 123,
+ eps.lower = 0, eps.upper = 0.05,
+ steps = 3L, fsCor = TRUE, contD,
+ plot1 = FALSE, plot2 = FALSE, plot3 = FALSE){
+ stopifnot(n >= 3)
+ stopifnot(eps >= 0, eps <= 0.5)
+ if(plot1){
+ from <- min(-6, q.l(contD)(1e-15))
+ to <- max(6, q.l(contD)(1-1e-15))
+ curve(pnorm, from = from, to = to, lwd = 2, n = 201,
+ main = "Comparison: ideal vs. real", ylab = "cdf")
+ fun <- function(x) (1-eps)*pnorm(x) + eps*p(contD)(x)
+ curve(fun, from = from, to = to, add = TRUE, col = "orange",
+ lwd = 2, n = 201, ylab = "cdf")
+ legend("topleft", legend = c("ideal", "real"),
+ fill = c("black", "orange"))
+ }
+
+ set.seed(seed)
+ r <- rbinom(n*M, prob = eps, size = 1)
+ Mid <- rnorm(n*M)
+ Mcont <- r(contD)(n*M)
+ Mre <- matrix((1-r)*Mid + r*Mcont, ncol = n)
+ ind <- rowSums(matrix(r, ncol = n)) >= n/2
+ while(any(ind)){
+ M1 <- sum(ind)
+ cat("Samples to re-simulate:\t", M1, "\n")
+ r <- rbinom(n*M1, prob = eps, size = 1)
+ Mid <- rnorm(n*M1)
+ Mcont <- r(contD)(n*M1)
+ Mre[ind,] <- (1-r)*Mid + r*Mcont
+ ind[ind] <- rowSums(matrix(r, ncol = n)) >= n/2
+ }
+ rm(Mid, Mcont, r, ind)
+
+
+ if(plot2){
+ ind <- if(M <= 20) 1:M else sample(1:M, 20)
+ if(plot1) dev.new()
+ M1 <- min(M, 20)
+ print(
+ stripplot(rep(1:M1, each = n) ~ as.vector(Mre[ind,]),
+ ylab = "samples", xlab = "x", pch = 20,
+ main = ifelse(M <= 20, "Samples", "20 randomly chosen samples"))
+ )
+ }
+
+ ## ML-estimator: mean and sd
+ Mean <- rowMeans(Mre)
+ Sd <- sqrt(rowMeans((Mre-Mean)^2))
+ ## Median and MAD
+ Median <- rowMedians(Mre)
+ Mad <- rowMedians(abs(Mre - Median))/qnorm(0.75)
+ ## Illumina method
+ ind <- (Mre < (Median - 3*Mad)) | (Mre > (Median + 3*Mad))
+ x.ill <- Mre
+ x.ill[ind] <- NA
+ Illum.mean <- rowMeans(x.ill, na.rm = TRUE)
+ n.ill <- rowSums(!is.na(x.ill))
+ n.ill[n.ill < 1] <- NA
+ Illum.sd <- sqrt(rowSums((x.ill - Illum.mean)^2, na.rm = TRUE)/(n.ill-1))
+ Illum <- cbind(Illum.mean, Illum.sd)
+
+ ## Radius-minimax estimator
+ RadMinmax <- estimate(rowRoblox(Mre, eps.lower = eps.lower,
+ eps.upper = eps.upper, k = steps,
+ fsCor = fsCor))
+
+ if(plot3){
+ Ergebnis1 <- list(Mean, Median, Illum[,1], RadMinmax[,1])
+ Ergebnis2 <- list(Sd, Mad, Illum[,2], RadMinmax[,2])
+ myCol <- brewer.pal(4, "Dark2")
+ if(plot1 || plot2) dev.new()
+ layout(matrix(c(1, 1, 1, 1, 3, 2, 2, 2, 2, 3), ncol = 2))
+ boxplot(Ergebnis1, col = myCol, pch = 20, main = "Location")
+ abline(h = 0)
+ boxplot(Ergebnis2, col = myCol, pch = 20, main = "Scale")
+ abline(h = 1)
+ op <- par(mar = rep(2, 4))
+ plot(c(0,1), c(1, 0), type = "n", axes = FALSE)
+ legend("center", c("ML", "Med/MAD", "Illumina", "rmx"),
+ fill = myCol, ncol = 4, cex = 1.5)
+ op$cin <- op$cra <- op$csi <- op$cxy <- op$din <- NULL
+ on.exit(par(op))
+ }
+
+ ## ML-estimator
+ MSE1.1 <- n*mean(Mean^2)
+ ## Median + MAD
+ MSE2.1 <- n*mean(Median^2)
+ ## Illumina's default method
+ MSE3.1 <- n*mean(Illum[,1]^2)
+ ## Radius-minimax
+ MSE4.1 <- n*mean(RadMinmax[,1]^2)
+ empMSE <- data.frame(ML = MSE1.1, Med = MSE2.1, Illumina = MSE3.1, "rmx" = MSE4.1)
+ rownames(empMSE) <- "n x empMSE (loc)"
+ relMSE <- empMSE[1,]/empMSE[1,4]
+ empMSE <- rbind(empMSE, relMSE)
+ rownames(empMSE)[2] <- "relMSE (loc)"
+
+ ## ML-estimator
+ MSE1.2 <- n*mean((Sd-1)^2)
+ ## Median + MAD
+ MSE2.2 <- n*mean((Mad-1)^2)
+ ## Illumina's default method
+ MSE3.2 <- n*mean((Illum[,2]-1)^2)
+ ## Radius-minimax
+ MSE4.2 <- n*mean((RadMinmax[,2]-1)^2)
+ empMSE <- rbind(empMSE, c(MSE1.2, MSE2.2, MSE3.2, MSE4.2))
+ rownames(empMSE)[3] <- "n x empMSE (scale)"
+ relMSE <- empMSE[3,]/empMSE[3,4]
+ empMSE <- rbind(empMSE, relMSE)
+ rownames(empMSE)[4] <- "relMSE (scale)"
+ empMSE <- rbind(empMSE, c(MSE1.1 + MSE1.2, MSE2.1 + MSE2.2, MSE3.1 + MSE3.2,
+ MSE4.1 + MSE4.2))
+ rownames(empMSE)[5] <- "n x empMSE (loc + scale)"
+ relMSE <- empMSE[5,]/empMSE[5,4]
+ empMSE <- rbind(empMSE, relMSE)
+ rownames(empMSE)[6] <- "relMSE (loc + scale)"
+
+ empMSE
+}
+
+
Modified: branches/robast-1.1/pkg/RobLoxBioC/inst/NEWS
===================================================================
--- branches/robast-1.1/pkg/RobLoxBioC/inst/NEWS 2018-07-17 08:01:06 UTC (rev 954)
+++ branches/robast-1.1/pkg/RobLoxBioC/inst/NEWS 2018-07-17 08:06:17 UTC (rev 955)
@@ -8,6 +8,14 @@
information)
#######################################
+version 1.1
+#######################################
+
+under the hood:
++ wherever possible also use q.l internally instead of q to
+ provide functionality in IRKernel
+
+#######################################
version 1.0
#######################################
Modified: branches/robast-1.1/pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd
===================================================================
--- branches/robast-1.1/pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd 2018-07-17 08:01:06 UTC (rev 954)
+++ branches/robast-1.1/pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd 2018-07-17 08:06:17 UTC (rev 955)
@@ -12,15 +12,15 @@
\details{
\tabular{ll}{
Package: \tab RobLoxBioC \cr
-Version: \tab 1.0 \cr
-Date: \tab 2015-05-03 \cr
+Version: \tab 1.1.0 \cr
+Date: \tab 2018-07-17 \cr
Depends:\tab R(>= 2.14.0), methods, distr(>= 2.5.2), affy \cr
Imports:\tab Biobase, BiocGenerics, beadarray, RobLox(>= 0.9.2), distrMod(>= 2.5.2), lattice, RColorBrewer \cr
Suggests:\tab affydata, hgu95av2cdf, beadarrayExampleData, illuminaHumanv3.db \cr
ByteCompile: \tab yes \cr
License: \tab LGPL-3 \cr
URL: \tab http://robast.r-forge.r-project.org/\cr
-SVNRevision: \tab -Inf \cr
+SVNRevision: \tab 940 \cr
Encoding: \tab latin1 \cr
}
}
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