[Robast-commits] r1250 - in pkg/RobLoxBioC: . R inst man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat May 6 17:12:41 CEST 2023
Author: stamats
Date: 2023-05-06 17:12:41 +0200 (Sat, 06 May 2023)
New Revision: 1250
Modified:
pkg/RobLoxBioC/DESCRIPTION
pkg/RobLoxBioC/R/robloxbiocBeadLevelData.R
pkg/RobLoxBioC/inst/CITATION
pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd
pkg/RobLoxBioC/man/SimStudies.Rd
Log:
some more changes and updates
Modified: pkg/RobLoxBioC/DESCRIPTION
===================================================================
--- pkg/RobLoxBioC/DESCRIPTION 2023-05-06 09:07:22 UTC (rev 1249)
+++ pkg/RobLoxBioC/DESCRIPTION 2023-05-06 15:12:41 UTC (rev 1250)
@@ -1,6 +1,6 @@
Package: RobLoxBioC
-Version: 1.2.0
-Date: 2019-04-02
+Version: 1.2.1
+Date: 2023-05-06
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.
@@ -12,8 +12,8 @@
email="Matthias.Kohl at stamats.de")
ByteCompile: yes
License: LGPL-3
-URL: http://robast.r-forge.r-project.org/
-Encoding: latin1
+URL: https://r-forge.r-project.org/projects/robast/
+Encoding: UTF-8
LastChangedDate: {$LastChangedDate$}
LastChangedRevision: {$LastChangedRevision$}
VCS/SVNRevision: 1214
Modified: pkg/RobLoxBioC/R/robloxbiocBeadLevelData.R
===================================================================
--- pkg/RobLoxBioC/R/robloxbiocBeadLevelData.R 2023-05-06 09:07:22 UTC (rev 1249)
+++ pkg/RobLoxBioC/R/robloxbiocBeadLevelData.R 2023-05-06 15:12:41 UTC (rev 1250)
@@ -185,7 +185,7 @@
message(paste("Annotating control probes using package ", annoPkg, " Version:", annoVers, "\n", sep = ""))
mapEnv <- as.name(paste("illumina", annoName, "REPORTERGROUPNAME", sep = ""))
t <- try(eval(mapEnv), silent = TRUE)
- if (class(t) == "try-error") {
+ if (inherits(t, "try-error")) {
message(paste("Could not find a REPORTERGROUPNAME mapping in annotation package ", annoPkg,
". Perhaps it needs updating?", sep = ""))
}
Modified: pkg/RobLoxBioC/inst/CITATION
===================================================================
--- pkg/RobLoxBioC/inst/CITATION 2023-05-06 09:07:22 UTC (rev 1249)
+++ pkg/RobLoxBioC/inst/CITATION 2023-05-06 15:12:41 UTC (rev 1250)
@@ -1,19 +1,14 @@
-if(!exists("meta") || is.null(meta)) meta <- packageDescription("RobLoxBioC")
-year <- sub("-.*", "", meta$Date)
-note <- sprintf("R package version %s", meta$Version)
-
-citHeader("To cite package RobLoxBioC in publications use:")
-
-citEntry(entry="Article",
- title = "Preprocessing of gene expression data by optimally robust estimators",
- author = personList(as.person("M. Kohl"), as.person("H.P. Deigner")),
+bibentry(bibtype="Article",
+ title = "{Preprocessing of gene expression data by optimally robust estimators}",
+ author = c(as.person("M. Kohl"), as.person("H.P. Deigner")),
journal = "BMC Bioinformatics",
language = "English",
year = 2010,
Volume="11",
Pages="583",
- url = "http://www.biomedcentral.com/1471-2105/11/583/abstract",
+ url = "https://doi.org/10.1186/1471-2105-11-583",
textVersion = paste("Kohl, M. and Deigner, H.P.",
"Preprocessing of gene expression data by optimally robust estimators.",
"BMC Bioinformatics 2010, 11:583",
- "URL http://www.biomedcentral.com/1471-2105/11/583/abstract"))
+ "URL https://doi.org/10.1186/1471-2105-11-583"))
+
Modified: pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd
===================================================================
--- pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd 2023-05-06 09:07:22 UTC (rev 1249)
+++ pkg/RobLoxBioC/man/0RobLoxBioC-package.Rd 2023-05-06 15:12:41 UTC (rev 1250)
@@ -1,58 +1,61 @@
-\name{RobLoxBioC-package}
-\alias{RobLoxBioC-package}
-\alias{RobLoxBioC}
-\docType{package}
-\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.
-}
-\details{
-\tabular{ll}{
-Package: \tab RobLoxBioC \cr
-Version: \tab 1.2.0 \cr
-Date: \tab 2019-04-02 \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
-VCS/SVNRevision: \tab 1214 \cr
-Encoding: \tab latin1 \cr
-}
-}
-\author{
-Matthias Kohl \email{Matthias.Kohl at stamats.de}\cr%
-Maintainer: Matthias Kohl \email{matthias.kohl at stamats.de}}
-\references{
- Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
- Bayreuth: Dissertation.
- Kohl M. and Deigner H.P. (2010). Preprocessing of gene expression data by optimally
- robust estimators. \emph{BMC Bioinformatics}, 11:583.
- M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation
- in General Smoothly Parametrized Models. \emph{Statistical Methods and Application},
- \bold{19}(3):333-354.
-
- Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
- Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing
- the Radius. \emph{Statistical Methods and Applications} \bold{17}(1) 13-40.
- Extended version: \url{http://r-kurs.de/RRlong.pdf}
-}
-\seealso{
-\code{\link[RobLox]{roblox}}, \code{\link[RobLox]{rowRoblox}}
-}
-\section{Package versions}{
-Note: The first two numbers of package versions do not necessarily reflect
- package-individual development, but rather are chosen for the
- RobAStXXX family as a whole in order to ease updating "depends"
- information.
-}
-\examples{
-library(RobLoxBioC)
-}
-\keyword{package}
+\name{RobLoxBioC-package}
+\alias{RobLoxBioC-package}
+\alias{RobLoxBioC}
+\docType{package}
+\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.
+}
+\details{
+\tabular{ll}{
+Package: \tab RobLoxBioC \cr
+Version: \tab 1.2.1 \cr
+Date: \tab 2023-05-06 \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 https://r-forge.r-project.org/projects/robast/\cr
+VCS/SVNRevision: \tab 1214 \cr
+Encoding: \tab latin1 \cr
+}
+}
+\author{
+Matthias Kohl \email{Matthias.Kohl at stamats.de}\cr%
+Maintainer: Matthias Kohl \email{matthias.kohl at stamats.de}}
+\references{
+ Kohl, M. (2005) \emph{Numerical Contributions to the Asymptotic Theory of Robustness}.
+ Bayreuth: Dissertation.
+
+ Kohl M. and Deigner H.P. (2010). Preprocessing of gene expression data by optimally
+ robust estimators. \emph{BMC Bioinformatics}, 11:583.
+
+ M. Kohl, P. Ruckdeschel, and H. Rieder (2010). Infinitesimally Robust Estimation
+ in General Smoothly Parametrized Models. \emph{Statistical Methods and Application},
+ \bold{19}(3):333-354.
+
+ Rieder, H. (1994) \emph{Robust Asymptotic Statistics}. New York: Springer.
+
+ Rieder, H., Kohl, M. and Ruckdeschel, P. (2008) The Costs of not Knowing
+ the Radius. \emph{Statistical Methods and Applications} \bold{17}(1) 13-40.
+ Extended version: \url{http://r-kurs.de/RRlong.pdf}
+}
+\seealso{
+\code{\link[RobLox]{roblox}}, \code{\link[RobLox]{rowRoblox}}
+}
+\section{Package versions}{
+Note: The first two numbers of package versions do not necessarily reflect
+ package-individual development, but rather are chosen for the
+ RobAStXXX family as a whole in order to ease updating "depends"
+ information.
+}
+\examples{
+library(RobLoxBioC)
+}
+\keyword{package}
Modified: pkg/RobLoxBioC/man/SimStudies.Rd
===================================================================
--- pkg/RobLoxBioC/man/SimStudies.Rd 2023-05-06 09:07:22 UTC (rev 1249)
+++ pkg/RobLoxBioC/man/SimStudies.Rd 2023-05-06 15:12:41 UTC (rev 1250)
@@ -1,70 +1,69 @@
-\name{SimStudies}
-\Rdversion{1.1}
-\alias{AffySimStudy}
-\alias{IlluminaSimStudy}
-\title{Perform Monte-Carlo Study.}
-\description{
- The function \code{AffySimStudy} can be used to perform Monte-Carlo studies
- comparing Tukey's biweight and rmx estimators for normal location and scale.
- The function \code{IlluminaSimStudy} can be used to perform Monte-Carlo studies
- comparing Illumina's default method - a Huber-type skipped mean and sd
- (cf. Hampel (1985)) - and rmx estimators for normal location and scale.
- In addition, maximum likelihood (ML) estimators (mean and sd) and median and
- MAD are computed. The comparison is based on the empirical MSE.
-}
-\usage{
-AffySimStudy(n, M, eps, seed = 123, eps.lower = 0, eps.upper = 0.05,
- steps = 3L, fsCor = TRUE, contD, plot1 = FALSE,
- plot2 = FALSE, plot3 = FALSE)
-IlluminaSimStudy(n, M, eps, seed = 123, eps.lower = 0, eps.upper = 0.05,
- steps = 3L, fsCor = TRUE, contD, plot1 = FALSE,
- plot2 = FALSE, plot3 = FALSE)
-}
-%- maybe also 'usage' for other objects documented here.
-\arguments{
- \item{n}{integer; sample size, should be at least 3.}
- \item{M}{integer; Monte-Carlo replications.}
- \item{eps}{amount of contamination in [0, 0.5].}
- \item{seed}{random seed.}
- \item{eps.lower}{used by rmx estimator.}
- \item{eps.upper}{used by rmx estimator.}
- \item{steps}{integer; steps used for estimator construction.}
- \item{fsCor}{logical; use finite-sample correction.}
- \item{contD}{object of class \code{"UnivariateDistribution"}; contaminating distribution.}
- \item{plot1}{logical; plot cdf of ideal and real distribution.}
- \item{plot2}{logical; plot 20 (or M if M < 20) randomly selected samples.}
- \item{plot3}{logical; generate boxplots of the results.}
-}
-\details{
-Normal location and scale with mean = 0 and sd = 1 is used as ideal model (without
-restriction due to equivariance).
-
-Since there is no estimator which yields reliable results if 50 percent or more of the
-observations are contaminated, we use a modification where we re-simulate all samples
-including at least 50 percent contaminated data.
-
-We use funtion \code{\link[RobLox]{rowRoblox}} for the computation of the rmx estimator.
-}
-\value{Data.frame including empirical MSE (standardized by sample size n) and
-relMSE with respect to the rmx estimator.
-}
-\references{
- Affymetrix, Inc. (2002). \emph{Statistical Algorithms Description Document}.
- Affymetrix, Santa Clara.
-
- Hampel F.R. (1985). The breakdown points of the mean combined with some rejection
- rules. Technometrics, 27(2):95-107.
-}
-\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
-%\note{}
-\seealso{\code{\link[RobLox]{rowRoblox}}}
-\examples{
-set.seed(123) # to have reproducible results for package checking
-
-AffySimStudy(n = 11, M = 100, eps = 0.02, contD = Norm(mean = 0, sd = 3),
- plot1 = TRUE, plot2 = TRUE, plot3 = TRUE)
-IlluminaSimStudy(n = 30, M = 100, eps = 0.02, contD = Norm(mean = 0, sd = 3),
- plot1 = TRUE, plot2 = TRUE, plot3 = TRUE)
-}
-\concept{Monte-Carlo study}
-\keyword{robust}
+\name{SimStudies}
+\alias{AffySimStudy}
+\alias{IlluminaSimStudy}
+\title{Perform Monte-Carlo Study.}
+\description{
+ The function \code{AffySimStudy} can be used to perform Monte-Carlo studies
+ comparing Tukey's biweight and rmx estimators for normal location and scale.
+ The function \code{IlluminaSimStudy} can be used to perform Monte-Carlo studies
+ comparing Illumina's default method - a Huber-type skipped mean and sd
+ (cf. Hampel (1985)) - and rmx estimators for normal location and scale.
+ In addition, maximum likelihood (ML) estimators (mean and sd) and median and
+ MAD are computed. The comparison is based on the empirical MSE.
+}
+\usage{
+AffySimStudy(n, M, eps, seed = 123, eps.lower = 0, eps.upper = 0.05,
+ steps = 3L, fsCor = TRUE, contD, plot1 = FALSE,
+ plot2 = FALSE, plot3 = FALSE)
+IlluminaSimStudy(n, M, eps, seed = 123, eps.lower = 0, eps.upper = 0.05,
+ steps = 3L, fsCor = TRUE, contD, plot1 = FALSE,
+ plot2 = FALSE, plot3 = FALSE)
+}
+%- maybe also 'usage' for other objects documented here.
+\arguments{
+ \item{n}{integer; sample size, should be at least 3.}
+ \item{M}{integer; Monte-Carlo replications.}
+ \item{eps}{amount of contamination in [0, 0.5].}
+ \item{seed}{random seed.}
+ \item{eps.lower}{used by rmx estimator.}
+ \item{eps.upper}{used by rmx estimator.}
+ \item{steps}{integer; steps used for estimator construction.}
+ \item{fsCor}{logical; use finite-sample correction.}
+ \item{contD}{object of class \code{"UnivariateDistribution"}; contaminating distribution.}
+ \item{plot1}{logical; plot cdf of ideal and real distribution.}
+ \item{plot2}{logical; plot 20 (or M if M < 20) randomly selected samples.}
+ \item{plot3}{logical; generate boxplots of the results.}
+}
+\details{
+Normal location and scale with mean = 0 and sd = 1 is used as ideal model (without
+restriction due to equivariance).
+
+Since there is no estimator which yields reliable results if 50 percent or more of the
+observations are contaminated, we use a modification where we re-simulate all samples
+including at least 50 percent contaminated data.
+
+We use funtion \code{\link[RobLox]{rowRoblox}} for the computation of the rmx estimator.
+}
+\value{Data.frame including empirical MSE (standardized by sample size n) and
+relMSE with respect to the rmx estimator.
+}
+\references{
+ Affymetrix, Inc. (2002). \emph{Statistical Algorithms Description Document}.
+ Affymetrix, Santa Clara.
+
+ Hampel F.R. (1985). The breakdown points of the mean combined with some rejection
+ rules. Technometrics, 27(2):95-107.
+}
+\author{Matthias Kohl \email{Matthias.Kohl at stamats.de}}
+%\note{}
+\seealso{\code{\link[RobLox]{rowRoblox}}}
+\examples{
+set.seed(123) # to have reproducible results for package checking
+
+AffySimStudy(n = 11, M = 100, eps = 0.02, contD = Norm(mean = 0, sd = 3),
+ plot1 = TRUE, plot2 = TRUE, plot3 = TRUE)
+IlluminaSimStudy(n = 30, M = 100, eps = 0.02, contD = Norm(mean = 0, sd = 3),
+ plot1 = TRUE, plot2 = TRUE, plot3 = TRUE)
+}
+\concept{Monte-Carlo study}
+\keyword{robust}
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