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Shotgun Stochastic Search (SSS) from Hans, Dobra and West (http://pubs.amstat.org/doi/abs/10.1198/016214507000000121) - wrappers written to integrate as an R package to call platform-specific executable.
Sage-Bionetworks/sss
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README DOCUMENT FOR THE sss PACKAGE AUTHOR: BRIAN M. BOT ATTRIBUTION: Hans C, Dobra A, and West M. Shotgun Stochastic Search for "Large p" Regression. Journal of the American Statistical Association 2007. Wang, Q. for the source code This current version uses pre-compiled binaries. If one's system architecture is not represented by one of these, the package will not build. A new version which will allow dynamic compilation is forthcoming. ####################################################################### # sssVignette.R # AUTHORS # Erich S. Huang & Brian Bot # Sage Bionetworks # Seattle, Washington # SOURCE # https://github.com/Sage-Bionetworks/sss # NOTES # A vignette for using the new "sss" package which provides R integration for # Shotgun Stochastic Search by Chris Hans, Adrian Dobra and Mike West # INSTALL SSS # Alternative #1 # Download from Github at https://github.com/Sage-Bionetworks/sss and # "R CMD INSTALL sss" from the parent directory # Alternative #2 # Install "devtools" from CRAN require(devtools) install_github("sss", "Sage-Bionetworks") ## START MODELING require(sss) require(Biobase) # from Bioconductor require(breastCancerTRANSBIG) # from Bioconductor data(transbig) expressData <- exprs(transbig)[1:500, ] # Subset the data for convenience pheno <- phenoData(transbig) # CREATE TRAINING AND VALIDATION COHORTS set.seed(031512) randVec <- rbinom(dim(transbig)[2], size = 1, prob = 0.5) trainExpress <- expressData[ , randVec == 0] validExpress <- expressData[ , randVec == 1] trainScore <- as.numeric(pheno@data$er[randVec == 0]) validScore <- as.numeric(pheno@data$er[randVec == 1]) # BINARY MODEL OF 'ER Status' USING SSS sssERFit <- sss(trainScore ~ t(trainExpress)) print(sssERFit) # print output below: # Type of model fit: "sssBinaryModel" # # Call: sss(formula = trainScore ~ t(trainExpress)) # Number of features searched : 500 # Number of training samples : 85 # To test this predictive model against a validation set, pass a new feature matrix to: # predict(object, newdata=newFeatureMatrix) # # ---------------------- # Contains slots (class) # ---------------------- # standScore (numeric) # postMargProb (numeric) # trainPredictionSummary (numeric) # testPredictionSummary (numeric) # model (sssBinaryModel) # nBestFits (list) # p # score # indices # pmode # pvar # trainPrediction # testPrediction # PREDICT ON THE VALIDATION SET validScoreHat <- predict(sssERFit, newdata = t(validExpress))
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Shotgun Stochastic Search (SSS) from Hans, Dobra and West (http://pubs.amstat.org/doi/abs/10.1198/016214507000000121) - wrappers written to integrate as an R package to call platform-specific executable.
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