[Robast-commits] r396 - in pkg/RobLoxBioC: . inst/scripts man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Feb 23 13:31:49 CET 2010
Author: stamats
Date: 2010-02-23 13:31:49 +0100 (Tue, 23 Feb 2010)
New Revision: 396
Modified:
pkg/RobLoxBioC/DESCRIPTION
pkg/RobLoxBioC/inst/scripts/AffySimStudy.R
pkg/RobLoxBioC/inst/scripts/AffymetrixExample.R
pkg/RobLoxBioC/inst/scripts/IlluminaExample.R
pkg/RobLoxBioC/inst/scripts/IlluminaSimStudy.R
pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd
pkg/RobLoxBioC/man/KolmogorovMinDist.Rd
pkg/RobLoxBioC/man/SimStudies.Rd
pkg/RobLoxBioC/man/robloxbioc.Rd
Log:
minor modifications
Modified: pkg/RobLoxBioC/DESCRIPTION
===================================================================
--- pkg/RobLoxBioC/DESCRIPTION 2009-11-01 10:53:46 UTC (rev 395)
+++ pkg/RobLoxBioC/DESCRIPTION 2010-02-23 12:31:49 UTC (rev 396)
@@ -1,6 +1,6 @@
Package: RobLoxBioC
-Version: 0.7
-Date: 2009-11-01
+Version: 0.7.1
+Date: 2010-02-23
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.
Modified: pkg/RobLoxBioC/inst/scripts/AffySimStudy.R
===================================================================
--- pkg/RobLoxBioC/inst/scripts/AffySimStudy.R 2009-11-01 10:53:46 UTC (rev 395)
+++ pkg/RobLoxBioC/inst/scripts/AffySimStudy.R 2010-02-23 12:31:49 UTC (rev 396)
@@ -13,88 +13,88 @@
contD <- Norm(0, 9)
(res1 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Td(df = 3)
(res2 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Cauchy()
(res3 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(3, 1)
(res4 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(10, 1)
(res5 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(1.51)
(res6 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(1000)
(res7 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
eps <- 0.02
contD <- Norm(0, 9)
(res11 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Td(df = 3)
(res12 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Cauchy()
(res13 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(3, 1)
(res14 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(10, 1)
(res15 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(1.51)
(res16 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(1000)
(res17 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
eps <- 0.04
contD <- Norm(0, 9)
(res21 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Td(df = 3)
(res22 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Cauchy()
(res23 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(3, 1)
(res24 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(10, 1)
(res25 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(1.51)
(res26 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(1000)
(res27 <- AffySimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
Modified: pkg/RobLoxBioC/inst/scripts/AffymetrixExample.R
===================================================================
--- pkg/RobLoxBioC/inst/scripts/AffymetrixExample.R 2009-11-01 10:53:46 UTC (rev 395)
+++ pkg/RobLoxBioC/inst/scripts/AffymetrixExample.R 2010-02-23 12:31:49 UTC (rev 396)
@@ -85,9 +85,11 @@
boxplot(as.data.frame(minKD.hgu95a$dist), main = "HGU95a")
boxplot(as.data.frame(minKD.hgu133a$dist), main = "HGU133a")
-table(minKD.hgu95a$n)
-table(minKD.hgu133a$n)
+## Table 1 in Kohl and Deigner (2010)
+table(minKD.hgu95a$n)/59
+table(minKD.hgu133a$n)/42
+
###########################################################
## Comparison with normal (pseudo) random samples
###########################################################
@@ -130,39 +132,41 @@
legend("topright", legend = "minimal possible distance", fill = "orange")
#######################################
-## Figure in Kohl and Deigner (2009)
+## Figure 2 in Kohl and Deigner (2010)
#######################################
res1 <- split(as.vector(minKD.hgu95a$dist), as.vector(minKD.hgu95a$n))
res2 <- split(as.vector(minKD.hgu133a$dist), as.vector(minKD.hgu133a$n))
res3 <- lapply(as.data.frame(minKD.norm[,c(2:12,16,17)]), function(x) x)
uni.n <- c(as.integer(names(res1)), as.integer(names(res2)), as.integer(names(res3)))
-postscript(file = "minKDAffy.eps", height = 6, width = 9, paper = "special",
- horizontal = TRUE)
+#setEPS(height = 6, width = 9)
+#postscript(file = "Figure2.eps")
par(mar = c(4, 4, 3, 1))
-plot(0, 0, type = "n", ylim = c(0, 0.49), xlim = c(0.5, 37.5),
+plot(0, 0, type = "n", ylim = c(0, 0.49), xlim = c(0.5, 16.5),
panel.first = abline(h = seq(0, 0.45, by = 0.05), lty = 2, col = "grey"),
main = "Minimum Kolmogorov distance",
ylab = "minimum Kolmogorov distance",
xlab = "sample size", axes = FALSE)
-axis(1, c(1:13, 15:23, 25:37), labels = uni.n, cex.axis = 0.6)
+axis(1, c(1:5, 7:9, 11:16), labels = uni.n[c(8:12, 6,11,12,6,8:12)], cex.axis = 0.6)
axis(2, seq(0, 0.45, by = 0.05), labels = seq(0, 0.45, by = 0.05), las = 2,
cex.axis = 0.8)
box()
-boxplot(c(res1, res2, res3), at = c(1:13, 15:23, 25:37), add = TRUE, pch = 20,
- names = FALSE, axes = FALSE)
-abline(v = c(14, 24), lwd = 1.5)
-text(c(7, 19, 31), rep(0.48, 3), labels = c("HGU95A", "HGU133A", "Normal Samples"),
+boxplot(c(res1[8:12], res2[c(3,7,8)], res3[c(6,8:12)]), at = c(1:5, 7:9, 11:16), add = TRUE, pch = 20,
+ names = FALSE, axes = FALSE,
+ col = c(rep(NA, 3), grey(0.6), NA, grey(0.4), NA, NA, grey(0.4), rep(NA, 3),
+ grey(0.6), NA))
+abline(v = c(6, 10), lwd = 1.5)
+text(c(3, 8, 14), rep(0.48, 3), labels = c("HGU95A", "HGU133A", "Normal Samples"),
font = 2)
-lines(1:13, 1/(2*uni.n[1:13]), lwd = 2)
-lines(15:23, 1/(2*uni.n[14:22]), lwd = 2)
-lines(25:37, 1/(2*uni.n[23:35]), lwd = 2)
-legend("bottomleft", legend = "minimal possible distance", lty = 1,
- bg = "white", cex = 0.8)
-dev.off()
+lines(1:5, 1/(2*uni.n[8:12]), lwd = 2)
+lines(7:9, 1/(2*uni.n[c(6,8,12)]), lwd = 2)
+lines(11:16, 1/(2*uni.n[c(6,8:12)]), lwd = 2)
+legend("bottomleft", legend = "minimal possible distance", lty = 1, lwd = 2, cex = 0.8)
+abline(h = c(0.1, 0.15), lty = 2, lwd = 1.5)
+#dev.off()
## Comparison of median distances
-## Table in Kohl and Deigner (2009)
+## Table 2 in Kohl and Deigner (2010)
round(sapply(res1, quantile, prob = 0.95) - sapply(res3, quantile, prob = 0.95), 4)
round(sapply(res2, quantile, prob = 0.95) - sapply(res3, quantile, prob = 0.95)[-c(1,2,4,7)], 4)
@@ -585,13 +589,13 @@
round(tab.hgu133a.small, 4)
-## Figure in Kohl and Deigner (2009)
+## Figure 3 in Kohl and Deigner (2010)
res.sd <- assessSpikeInSD(eset.hgu133a.log2)
res.sd.133 <- assessSpikeInSD(eset.hgu133a.log2)
res.sd.mas <- assessSpikeInSD(mas5.res)
res.sd.mas.133 <- assessSpikeInSD(mas5.res.133)
-postscript(file = "AffyMeanSD.eps", height = 6, width = 9, paper = "special",
- horizontal = TRUE)
+#setEPS(width = 9, height = 6)
+#postscript(file = "Figure3.eps")
par(mfrow = c(1, 2))
plot(res.sd.mas$xsmooth, res.sd.mas$ysmooth, type = "l", xlab = "mean log expression",
ylab = "mean SD", main = "HGU95A", lwd = 2, ylim = c(0, max(res.sd.mas$ysmooth)),
@@ -606,10 +610,10 @@
panel.first = abline(h = seq(0, 1.2, by = 0.2), v = c(0, 5, 10, 15), lty = 2, col = "grey"))
lines(res.sd.mas.133$xsmooth, res.sd.mas.133$ysmooth, type = "l", lwd = 2)
legend("topright", c("biweight", "rmx"), lty = 1:2, lwd = 2, bg = "white")
-dev.off()
+#dev.off()
-## Table in Kohl and Deigner (2009)
+## Table 4 in Kohl and Deigner (2010)
round(tableAll(roblox.hgu95a.2, mas5.ass)[c(1:3, 8:11),], 3)
round(tableAll(roblox.hgu133a.2, mas5.ass.133)[c(1:3, 8:11),], 3)
Modified: pkg/RobLoxBioC/inst/scripts/IlluminaExample.R
===================================================================
--- pkg/RobLoxBioC/inst/scripts/IlluminaExample.R 2009-11-01 10:53:46 UTC (rev 395)
+++ pkg/RobLoxBioC/inst/scripts/IlluminaExample.R 2010-02-23 12:31:49 UTC (rev 396)
@@ -83,62 +83,63 @@
close(con)
#######################################
-## Figure in Kohl and Deigner (2009)
+## Figure 4 in Kohl and Deigner (2010)
#######################################
-res1 <- split(as.vector(minKD.Illumina$dist), as.vector(minKD.Illumina$n))[20:60]
-res2 <- split(as.vector(minKD.Illumina.log$dist), as.vector(minKD.Illumina.log$n))[20:60]
-res3 <- lapply(as.data.frame(minKD.Illumina.norm[,11:51]), function(x) x)
-uni.n <- rep(20:60, 3)
+res1 <- split(as.vector(minKD.Illumina$dist), as.vector(minKD.Illumina$n))[30:50]
+res2 <- split(as.vector(minKD.Illumina.log$dist), as.vector(minKD.Illumina.log$n))[30:50]
+res3 <- lapply(as.data.frame(minKD.Illumina.norm[,21:41]), function(x) x)
+uni.n <- rep(30:50, 3)
-postscript(file = "minKDIllumina.eps", height = 6, width = 9, paper = "special",
- horizontal = TRUE)
+#setEPS(height = 6, width = 9)
+#postscript(file = "Figure4.eps")
par(mar = c(4, 4, 3, 1))
-plot(0, 0, type = "n", ylim = c(-0.01, 0.4), xlim = c(0.5, 125.5),
+plot(0, 0, type = "n", ylim = c(-0.01, 0.4), xlim = c(0.5, 65.5),
panel.first = abline(h = seq(0, 0.35, by = 0.05), lty = 2, col = "grey"),
main = "Minimum Kolmogorov distance",
ylab = "minimum Kolmogorov distance",
xlab = "sample size", axes = FALSE)
-axis(1, c(1:41, 43:83, 85:125), labels = uni.n, cex.axis = 0.6)
+axis(1, c(1:21, 23:43, 45:65), labels = uni.n, cex.axis = 0.6)
axis(2, seq(0, 0.35, by = 0.05), labels = seq(0, 0.35, by = 0.05), las = 2,
cex.axis = 0.8)
box()
-boxplot(c(res1, res2, res3), at = c(1:41, 43:83, 85:125), add = TRUE, pch = 20,
+boxplot(c(res1, res2, res3), at = c(1:21, 23:43, 45:65), add = TRUE, pch = 20,
names = FALSE, axes = FALSE)
-abline(v = c(42, 84), lwd = 1.5)
-text(c(20, 63, 105), rep(0.38, 3), labels = c("Bead Level Data", "log Bead Level Data", "Normal Samples"),
+abline(h = c(0.055, 0.093), lty = 2, lwd = 1.5)
+abline(v = c(22, 44), lwd = 1.5)
+text(c(10, 33, 55), rep(0.38, 3), labels = c("Bead Level Data", "log Bead Level Data", "Normal Samples"),
font = 2)
-lines(1:41, 1/(2*(20:60)), lwd = 2)
-lines(43:83, 1/(2*(20:60)), lwd = 2)
-lines(85:125, 1/(2*(20:60)), lwd = 2)
+lines(1:21, 1/(2*(30:50)), lwd = 2)
+lines(23:43, 1/(2*(30:50)), lwd = 2)
+lines(45:65, 1/(2*(30:50)), lwd = 2)
legend("bottomleft", legend = "minimal possible distance", lty = 1,
bg = "white", cex = 0.8)
-dev.off()
+#dev.off()
-## Comparison of median distances
-## Figure in Kohl and Deigner (2009)
-res1 <- split(as.vector(minKD.Illumina$dist), as.vector(minKD.Illumina$n))[10:70]
-res2 <- split(as.vector(minKD.Illumina.log$dist), as.vector(minKD.Illumina.log$n))[10:70]
-res3 <- lapply(as.data.frame(minKD.Illumina.norm), function(x) x)
+## Comparison of quantiles
+## Figure 5 in Kohl and Deigner (2010)
+res1 <- split(as.vector(minKD.Illumina$dist), as.vector(minKD.Illumina$n))[15:65]
+res2 <- split(as.vector(minKD.Illumina.log$dist), as.vector(minKD.Illumina.log$n))[15:65]
+res3 <- lapply(as.data.frame(minKD.Illumina.norm), function(x) x)[6:56]
-postscript(file = "minKDIlluminaQuant.eps", height = 6, width = 9, paper = "special",
- horizontal = TRUE)
+#setEPS(height = 6, width = 9)
+#postscript(file = "Figure5.eps")
par(mar = c(4, 4, 3, 1))
-plot(10:70, sapply(res3, quantile, prob = 0.99), type = "l", lwd = 2, xlab = "sample size",
+plot(15:65, sapply(res3, quantile, prob = 0.99), type = "l", lwd = 2, xlab = "sample size",
ylab = "quantile of mimimum Kolmogorov distances",
main = "50% and 99% quantiles of minimum Kolmogorov distances",
ylim = c(0.05, 0.23),
- panel.first = abline(h = c(0.05, 0.1, 0.15, 0.2), v = seq(10, 70, by = 10),
+ panel.first = abline(h = c(0.05, 0.1, 0.15, 0.2), v = seq(15, 65, by = 5),
lty = 2, col = "grey"))
-lines(10:70, sapply(res1, quantile, prob = 0.99), lwd = 2, lty = 2)
-lines(10:70, sapply(res2, quantile, prob = 0.99), lwd = 2, lty = 3)
-lines(10:70, sapply(res3, quantile, prob = 0.5), lwd = 2, lty = 1)
-lines(10:70, sapply(res1, quantile, prob = 0.5), lwd = 2, lty = 2)
-lines(10:70, sapply(res2, quantile, prob = 0.5), lwd = 2, lty = 3)
+lines(15:65, sapply(res1, quantile, prob = 0.99), lwd = 2, lty = 2)
+lines(15:65, sapply(res2, quantile, prob = 0.99), lwd = 2, lty = 3)
+lines(15:65, sapply(res3, quantile, prob = 0.5), lwd = 2, lty = 1)
+lines(15:65, sapply(res1, quantile, prob = 0.5), lwd = 2, lty = 2)
+lines(15:65, sapply(res2, quantile, prob = 0.5), lwd = 2, lty = 3)
text(22, 0.18, "99% quantiles", font = 2)
text(22, 0.115, "50% quantiles", font = 2)
legend("topright", legend = c("normal samples", "bead level data", "log bead level data"),
lty = 1:3, lwd = 2, bg = "white")
-dev.off()
+#dev.off()
#load(file = "spikeInData.RData")
@@ -165,9 +166,9 @@
ill.SD <- assessSpikeInSD(res.ill, genenames = genenames, method.name = "Illumina")
rmx.SD <- assessSpikeInSD(res.rmx, genenames = genenames, method.name = "rmx estimator")
-## Figure in Kohl and Deigner (2009)
-postscript(file = "IllMeanSD.eps", height = 6, width = 9, paper = "special",
- horizontal = TRUE)
+## Figure 6 in Kohl and Deigner (2010)
+setEPS(height = 6, width = 9)
+postscript(file = "Figure6.eps")
plot(ill.SD$xsmooth, ill.SD$ysmooth, type = "l", xlab = "mean log expression",
ylab = "mean SD", main = "Spike-in data of Dunning et al. (2008)", lwd = 2,
panel.first = abline(h = c(0.1, 0.12, 0.14, 0.16), v = seq(6, 16, 2), lty = 2, col = "grey"))
@@ -176,7 +177,7 @@
dev.off()
-## Table in Kohl and Deigner (2009)
+## Table 6 in Kohl and Deigner (2009)
quantile(ill.SD$y, prob = c(0.25, 0.5, 0.75, 0.99))
quantile(rmx.SD$y, prob = c(0.25, 0.5, 0.75, 0.99))
Modified: pkg/RobLoxBioC/inst/scripts/IlluminaSimStudy.R
===================================================================
--- pkg/RobLoxBioC/inst/scripts/IlluminaSimStudy.R 2009-11-01 10:53:46 UTC (rev 395)
+++ pkg/RobLoxBioC/inst/scripts/IlluminaSimStudy.R 2010-02-23 12:31:49 UTC (rev 396)
@@ -15,88 +15,88 @@
contD <- Norm(0, 9)
(res1 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Td(df = 3)
(res2 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Cauchy()
(res3 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(3, 1)
(res4 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(10, 1)
(res5 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(3)
(res6 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(1000)
(res7 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
eps <- 0.02
contD <- Norm(0, 9)
(res11 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Td(df = 3)
(res12 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Cauchy()
(res13 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(3, 1)
(res14 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(10, 1)
(res15 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(3)
(res16 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(1000)
(res17 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
eps <- 0.04
contD <- Norm(0, 9)
(res21 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Td(df = 3)
(res22 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Cauchy()
(res23 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(3, 1)
(res24 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Norm(10, 1)
(res25 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(3)
(res26 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
contD <- Dirac(1000)
(res27 <- IlluminaSimStudy(n = n, M = M, eps = eps, seed = seed,
eps.lower = eps.lower, eps.upper = eps.upper,
- contD = contD, plot1 = FALSE, plot2 = FALSE, plot3 = FALSE))
+ contD = contD))
Modified: pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd
===================================================================
--- pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd 2009-11-01 10:53:46 UTC (rev 395)
+++ pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd 2010-02-23 12:31:49 UTC (rev 396)
@@ -12,8 +12,8 @@
\details{
\tabular{ll}{
Package: \tab RobLoxBioC \cr
-Version: \tab 0.7 \cr
-Date: \tab 2009-11-01 \cr
+Version: \tab 0.7.1 \cr
+Date: \tab 2010-02-23 \cr
Depends: \tab R(>= 2.8.1), methods, Biobase, affy, beadarray, distr, RobLox, lattice, RColorBrewer \cr
LazyLoad: \tab yes \cr
License: \tab LGPL-3 \cr
@@ -28,9 +28,6 @@
Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
Bayreuth: Dissertation.
- Kohl M. and Deigner H.P. (2009). Using infinitesimally robust estimators for
- preprocessing gene expression data. In preparation.
-
Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing
Modified: pkg/RobLoxBioC/man/KolmogorovMinDist.Rd
===================================================================
--- pkg/RobLoxBioC/man/KolmogorovMinDist.Rd 2009-11-01 10:53:46 UTC (rev 395)
+++ pkg/RobLoxBioC/man/KolmogorovMinDist.Rd 2010-02-23 12:31:49 UTC (rev 396)
@@ -55,9 +55,6 @@
\references{
Huber, P.J. (1981) \emph{Robust Statistics}. New York: Wiley.
- Kohl M. and Deigner H.P. (2009). Using infinitesimally robust estimators for
- preprocessing gene expression data. In preparation.
-
Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
}
\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
Modified: pkg/RobLoxBioC/man/SimStudies.Rd
===================================================================
--- pkg/RobLoxBioC/man/SimStudies.Rd 2009-11-01 10:53:46 UTC (rev 395)
+++ pkg/RobLoxBioC/man/SimStudies.Rd 2010-02-23 12:31:49 UTC (rev 396)
@@ -54,9 +54,6 @@
Hampel F.R. (1985). The breakdown points of the mean combined with some rejection
rules. Technometrics, 27(2):95-107.
-
- Kohl M. and Deigner H.P. (2009). Using infinitesimally robust estimators for
- preprocessing gene expression data. In preparation.
}
\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
%\note{}
Modified: pkg/RobLoxBioC/man/robloxbioc.Rd
===================================================================
--- pkg/RobLoxBioC/man/robloxbioc.Rd 2009-11-01 10:53:46 UTC (rev 395)
+++ pkg/RobLoxBioC/man/robloxbioc.Rd 2010-02-23 12:31:49 UTC (rev 396)
@@ -108,9 +108,6 @@
Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
Bayreuth: Dissertation.
- Kohl M. and Deigner H.P. (2009). Using infinitesimally robust estimators for
- preprocessing gene expression data. In preparation.
-
Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing
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