R/datasets.R

In bigPLScox: Partial Least Squares for Cox Models with Big Matrices

#' Simulated dataset
#' 
#' This dataset provides explantory variables simulations and censoring status.
#' 
#' 
#' @name sim_data
#' @docType data
#' @format A data frame with 1000 observations on the following 11 variables.
#' \describe{ 
#' \item{status}{a binary vector} 
#' \item{X1}{a numeric vector} 
#' \item{X2}{a numeric vector} 
#' \item{X3}{a numeric vector} 
#' \item{X4}{a numeric vector} 
#' \item{X5}{a numeric vector} 
#' \item{X6}{a numeric vector} 
#' \item{X7}{a numeric vector} 
#' \item{X8}{a numeric vector} 
#' \item{X9}{a numeric vector} 
#' \item{X10}{a numeric vector} 
#' }
#' 
#' @references Maumy, M., Bertrand, F. (2023). PLS models and their extension for big data. 
#'   Joint Statistical Meetings (JSM 2023), Toronto, ON, Canada. 
#'   
#'   Maumy, M., Bertrand, F. (2023). bigPLS: Fitting and cross-validating 
#'   PLS-based Cox models to censored big data. BioC2023 — The Bioconductor 
#'   Annual Conference, Dana-Farber Cancer Institute, Boston, MA, USA. 
#'   Poster. https://doi.org/10.7490/f1000research.1119546.1  
#' 
#'   Bastien, P., Bertrand, F., Meyer, N., and Maumy-Bertrand, M.
#'   (2015). Deviance residuals-based sparse PLS and sparse kernel PLS for
#'   binary classification and survival analysis. *BMC Bioinformatics*, 16, 211.
#' 
#' @keywords datasets
#' @examples
#' 
#' \donttest{
#' data(sim_data)
#' X_sim_data_train <- sim_data[1:800,2:11]
#' C_sim_data_train <- sim_data$status[1:800]
#' X_sim_data_test <- sim_data[801:1000,2:11]
#' C_sim_data_test <- sim_data$status[801:1000]
#' rm(X_sim_data_train,C_sim_data_train,X_sim_data_test,C_sim_data_test)
#' }
#' 
NULL





#' Simulated survival dataset for Cox models
#'
#' The \code{dCox_sim} dataset contains simulated survival times, censoring
#' indicators and two binary covariates for demonstrating the Cox-related
#' procedures included in \pkg{bigPLScox}.
#'
#' @name dCox_sim
#' @docType data
#' @format A data frame with 10000 observations on the following 5 variables.
#' \describe{
#'   \item{id}{observation identifier}
#'   \item{time}{simulated survival time}
#'   \item{status}{event indicator (1 = event, 0 = censored)}
#'   \item{x.1}{first binary covariate}
#'   \item{x.2}{second binary covariate}
#' }
#' @keywords datasets
#' @examples
#' \donttest{
#' data(dCox_sim)
#' with(dCox_sim, table(status))
#' }
NULL



#' Microsat features and survival times
#' 
#' This dataset provides Microsat specifications and survival times.
#' 
#' 
#' @name micro.censure
#' @docType data
#' @format A data frame with 117 observations on the following 43 variables.
#' \describe{ \item{numpat}{a factor with levels \code{B1006}
#' \code{B1017} \code{B1028} \code{B1031} \code{B1046} \code{B1059}
#' \code{B1068} \code{B1071} \code{B1102} \code{B1115} \code{B1124}
#' \code{B1139} \code{B1157} \code{B1161} \code{B1164} \code{B1188}
#' \code{B1190} \code{B1192} \code{B1203} \code{B1211} \code{B1221}
#' \code{B1225} \code{B1226} \code{B1227} \code{B1237} \code{B1251}
#' \code{B1258} \code{B1266} \code{B1271} \code{B1282} \code{B1284}
#' \code{B1285} \code{B1286} \code{B1287} \code{B1290} \code{B1292}
#' \code{B1298} \code{B1302} \code{B1304} \code{B1310} \code{B1319}
#' \code{B1327} \code{B1353} \code{B1357} \code{B1363} \code{B1368}
#' \code{B1372} \code{B1373} \code{B1379} \code{B1388} \code{B1392}
#' \code{B1397} \code{B1403} \code{B1418} \code{B1421t1} \code{B1421t2}
#' \code{B1448} \code{B1451} \code{B1455} \code{B1460} \code{B1462}
#' \code{B1466} \code{B1469} \code{B1493} \code{B1500} \code{B1502}
#' \code{B1519} \code{B1523} \code{B1529} \code{B1530} \code{B1544}
#' \code{B1548} \code{B500} \code{B532} \code{B550} \code{B558} \code{B563}
#' \code{B582} \code{B605} \code{B609} \code{B634} \code{B652} \code{B667}
#' \code{B679} \code{B701} \code{B722} \code{B728} \code{B731} \code{B736}
#' \code{B739} \code{B744} \code{B766} \code{B771} \code{B777} \code{B788}
#' \code{B800} \code{B836} \code{B838} \code{B841} \code{B848} \code{B871}
#' \code{B873} \code{B883} \code{B889} \code{B912} \code{B924} \code{B925}
#' \code{B927} \code{B938} \code{B952} \code{B954} \code{B955} \code{B968}
#' \code{B972} \code{B976} \code{B982} \code{B984}} \item{D18S61}{a
#' numeric vector} \item{D17S794}{a numeric vector}
#' \item{D13S173}{a numeric vector} \item{D20S107}{a numeric
#' vector} \item{TP53}{a numeric vector} \item{D9S171}{a
#' numeric vector} \item{D8S264}{a numeric vector}
#' \item{D5S346}{a numeric vector} \item{D22S928}{a numeric
#' vector} \item{D18S53}{a numeric vector} \item{D1S225}{a
#' numeric vector} \item{D3S1282}{a numeric vector}
#' \item{D15S127}{a numeric vector} \item{D1S305}{a numeric
#' vector} \item{D1S207}{a numeric vector} \item{D2S138}{a
#' numeric vector} \item{D16S422}{a numeric vector}
#' \item{D9S179}{a numeric vector} \item{D10S191}{a numeric
#' vector} \item{D4S394}{a numeric vector} \item{D1S197}{a
#' numeric vector} \item{D6S264}{a numeric vector}
#' \item{D14S65}{a numeric vector} \item{D17S790}{a numeric
#' vector} \item{D5S430}{a numeric vector} \item{D3S1283}{a
#' numeric vector} \item{D4S414}{a numeric vector}
#' \item{D8S283}{a numeric vector} \item{D11S916}{a numeric
#' vector} \item{D2S159}{a numeric vector} \item{D16S408}{a
#' numeric vector} \item{D6S275}{a numeric vector}
#' \item{D10S192}{a numeric vector} \item{sexe}{a numeric
#' vector} \item{Agediag}{a numeric vector} \item{Siege}{a
#' numeric vector} \item{T}{a numeric vector} \item{N}{a
#' numeric vector} \item{M}{a numeric vector} \item{STADE}{a
#' factor with levels \code{0} \code{1} \code{2} \code{3} \code{4}}
#' \item{survyear}{a numeric vector} \item{DC}{a numeric
#' vector} }
#' @references plsRcox, Cox-Models in a high dimensional setting in R, Frederic
#' Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014).
#' Proceedings of User2014!, Los Angeles, page 152.\cr
#' 
#' Deviance residuals-based sparse PLS and sparse kernel PLS regression for
#' censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam
#' Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404,
#' doi:10.1093/bioinformatics/btu660.
#' @source Allelotyping identification of genomic alterations in rectal
#' chromosomally unstable tumors without preoperative treatment,
#' #' Benoît Romain, Agnès Neuville, Nicolas Meyer,
#' Cécile Brigand, Serge Rohr, Anne Schneider, Marie-Pierre Gaub
#' and Dominique Guenot, \emph{BMC Cancer 2010}, 10:561,
#' doi:10.1186/1471-2407-10-561.
#' @keywords datasets
#' @examples
#' 
#' \donttest{
#' data(micro.censure)
#' Y_train_micro <- micro.censure$survyear[1:80]
#' C_train_micro <- micro.censure$DC[1:80]
#' Y_test_micro <- micro.censure$survyear[81:117]
#' C_test_micro <- micro.censure$DC[81:117]
#' rm(Y_train_micro,C_train_micro,Y_test_micro,C_test_micro)
#' }
#' 
NULL



#' Imputed Microsat features
#' 
#' This dataset provides imputed microsat specifications. Imputations were
#' computed using Multivariate Imputation by Chained Equations (MICE) using
#' predictive mean matching for the numeric columns, logistic regression
#' imputation for the binary data or the factors with 2 levels and polytomous
#' regression imputation for categorical data i.e. factors with three or more
#' levels.
#' 
#' 
#' @name Xmicro.censure_compl_imp
#' @docType data
#' @format A data frame with 117 observations on the following 40 variables.
#' \describe{ \item{D18S61}{a numeric vector} \item{D17S794}{a
#' numeric vector} \item{D13S173}{a numeric vector}
#' \item{D20S107}{a numeric vector} \item{TP53}{a numeric
#' vector} \item{D9S171}{a numeric vector} \item{D8S264}{a
#' numeric vector} \item{D5S346}{a numeric vector}
#' \item{D22S928}{a numeric vector} \item{D18S53}{a numeric
#' vector} \item{D1S225}{a numeric vector} \item{D3S1282}{a
#' numeric vector} \item{D15S127}{a numeric vector}
#' \item{D1S305}{a numeric vector} \item{D1S207}{a numeric
#' vector} \item{D2S138}{a numeric vector} \item{D16S422}{a
#' numeric vector} \item{D9S179}{a numeric vector}
#' \item{D10S191}{a numeric vector} \item{D4S394}{a numeric
#' vector} \item{D1S197}{a numeric vector} \item{D6S264}{a
#' numeric vector} \item{D14S65}{a numeric vector}
#' \item{D17S790}{a numeric vector} \item{D5S430}{a numeric
#' vector} \item{D3S1283}{a numeric vector} \item{D4S414}{a
#' numeric vector} \item{D8S283}{a numeric vector}
#' \item{D11S916}{a numeric vector} \item{D2S159}{a numeric
#' vector} \item{D16S408}{a numeric vector} \item{D6S275}{a
#' numeric vector} \item{D10S192}{a numeric vector}
#' \item{sexe}{a numeric vector} \item{Agediag}{a numeric
#' vector} \item{Siege}{a numeric vector} \item{T}{a numeric
#' vector} \item{N}{a numeric vector} \item{M}{a numeric
#' vector} \item{STADE}{a factor with levels \code{0} \code{1} \code{2}
#' \code{3} \code{4}} }
#' @references plsRcox, Cox-Models in a high dimensional setting in R, Frederic
#' Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014).
#' Proceedings of User2014!, Los Angeles, page 152.\cr
#' 
#' Deviance residuals-based sparse PLS and sparse kernel PLS regression for
#' censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam
#' Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404,
#' doi:10.1093/bioinformatics/btu660.
#' @source Allelotyping identification of genomic alterations in rectal
#' chromosomally unstable tumors without preoperative treatment,
#' Benoît Romain, Agnès Neuville, Nicolas Meyer,
#' Cécile Brigand, Serge Rohr, Anne Schneider, Marie-Pierre Gaub
#' and Dominique Guenot, \emph{BMC Cancer 2010}, 10:561,
#' doi:10.1186/1471-2407-10-561.
#' @keywords datasets
#' @examples
#' 
#' \donttest{
#' data(Xmicro.censure_compl_imp)
#' X_train_micro <- Xmicro.censure_compl_imp[1:80,]
#' X_test_micro <- Xmicro.censure_compl_imp[81:117,]
#' rm(X_train_micro,X_test_micro)
#' }
#' 
NULL