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Type 'q()' to quit R. > pkgname <- "varPro" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('varPro') varPro 1.0.0 Type varpro.news() to see new features, changes, and bug fixes. > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("alzheimers") > ### * alzheimers > > flush(stderr()); flush(stdout()) > > ### Name: alzheimers > ### Title: Alzheimer's Disease Dataset > ### Aliases: alzheimers > ### Keywords: datasets > > ### ** Examples > > > > cleanEx() > nameEx("clusterpro") > ### * clusterpro > > flush(stderr()); flush(stdout()) > > ### Name: clusterpro > ### Title: ClusterPro for Unsupervised Data Visualization > ### Aliases: clusterpro > ### Keywords: plot > > ### ** Examples > > > > > cleanEx() > nameEx("cv.varpro") > ### * cv.varpro > > flush(stderr()); flush(stdout()) > > ### Name: cv.varpro > ### Title: Cross-Validated Cutoff Value for Variable Priority (VarPro) > ### Aliases: cv.varpro > ### Keywords: cv.varpro > > ### ** Examples > > > > > cleanEx() > nameEx("glioma") > ### * glioma > > flush(stderr()); flush(stdout()) > > ### Name: gliomas > ### Title: Diffuse Adult Glioma > ### Aliases: glioma > ### Keywords: datasets > > ### ** Examples > > > > cleanEx() > nameEx("importance.varpro") > ### * importance.varpro > > flush(stderr()); flush(stdout()) > > ### Name: importance.varpro > ### Title: Calculate VarPro Importance > ### Aliases: importance.varpro importance > ### Keywords: varpro > > ### ** Examples > > > > ## ------------------------------------------------------------ > ## toy example - needed to pass CRAN test > ## ------------------------------------------------------------ > > ## mtcars regression > o <- varpro(mpg ~ ., mtcars, ntree = 1) Loading required package: Matrix Loaded glmnet 4.1-10 entry.c:274:90: runtime error: pointer index expression with base 0x000000000001 overflowed to 0xfffffffffffffffd #0 0x7b4386006bab in varProStrength /data/gannet/ripley/R/packages/tests-gcc-SAN/varPro/src/entry.c:274 #1 0x000000740f25 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1162 #2 0x000000750dca in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1437 #3 0x0000008cc332 in bcEval_loop /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:8114 #4 0x0000008b957b in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7497 #5 0x0000008598f2 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1167 #6 0x00000086f63a in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2389 #7 0x0000008736fa in applyClosure_core /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2302 #8 0x000000859f93 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2324 #9 0x000000859f93 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1280 #10 0x00000088838e in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:3563 #11 0x00000085a3b6 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1232 #12 0x0000009fff08 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:264 #13 0x0000009fff08 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:317 #14 0x000000a0533a in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1235 #15 0x000000a0f552 in Rf_mainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1242 #16 0x0000004131af in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29 #17 0x7f43a08115f4 in __libc_start_call_main (/lib64/libc.so.6+0x35f4) (BuildId: a1dda014206b55b07f58fe8db80121b752dc3d03) #18 0x7f43a08116a7 in __libc_start_main@@GLIBC_2.34 (/lib64/libc.so.6+0x36a7) (BuildId: a1dda014206b55b07f58fe8db80121b752dc3d03) #19 0x000000413b74 in _start (/data/gannet/ripley/R/gcc-SAN3/bin/exec/R+0x413b74) (BuildId: 2deb71c8c86ae6b6b43f870e61d3d99bf3b48538) > imp <- importance(o, local.std = FALSE) > print(imp) mean std z cyl 0.8367972 NA NA hp 0.8005710 NA NA wt 0.1690819 NA NA > > > > > cleanEx() detaching ‘package:glmnet’, ‘package:Matrix’ > nameEx("isopro") > ### * isopro > > flush(stderr()); flush(stdout()) > > ### Name: isopro > ### Title: Identify Anomalous Data > ### Aliases: isopro > ### Keywords: outlier > > ### ** Examples > > > > > > > cleanEx() > nameEx("ivarpro") > ### * ivarpro > > flush(stderr()); flush(stdout()) > > ### Name: ivarpro > ### Title: Individual Variable Priority (iVarPro): Case-Specific Variable > ### Importance > ### Aliases: ivarpro > ### Keywords: individual importance > > ### ** Examples > > > > cleanEx() > nameEx("outpro") > ### * outpro > > flush(stderr()); flush(stdout()) > > ### Name: outpro > ### Title: Model and subsapce aware out-of-distribution (OOD) scoring with > ### outPro > ### Aliases: outpro outpro.null > > ### ** Examples > > > > > > cleanEx() > nameEx("partialpro") > ### * partialpro > > flush(stderr()); flush(stdout()) > > ### Name: partialpro > ### Title: Partial Effects for Variable(s) > ### Aliases: partialpro > ### Keywords: plot > > ### ** Examples > > > > > > cleanEx() > nameEx("plot.partialpro") > ### * plot.partialpro > > flush(stderr()); flush(stdout()) > > ### Name: plot.partialpro > ### Title: Partial Plots for VarPro > ### Aliases: plot.partialpro > ### Keywords: plot > > ### ** Examples > > > > > cleanEx() > nameEx("predict.isopro") > ### * predict.isopro > > flush(stderr()); flush(stdout()) > > ### Name: predict.isopro > ### Title: Prediction for Isopro for Identifying Anomalous Data > ### Aliases: predict.isopro > ### Keywords: predict outlier > > ### ** Examples > > > > > cleanEx() > nameEx("predict.uvarpro") > ### * predict.uvarpro > > flush(stderr()); flush(stdout()) > > ### Name: predict.uvarpro > ### Title: Prediction on Test Data using Unsupervised VarPro > ### Aliases: predict.uvarpro > ### Keywords: predict uvarpro > > ### ** Examples > > > > > cleanEx() > nameEx("predict.varpro") > ### * predict.varpro > > flush(stderr()); flush(stdout()) > > ### Name: predict.varpro > ### Title: Prediction on Test Data using VarPro > ### Aliases: predict.varpro > ### Keywords: predict varpro > > ### ** Examples > > > ## ------------------------------------------------------------ > ## toy example - needed to pass CRAN test > ## ------------------------------------------------------------ > > ## train call > o <- varpro(mpg~., mtcars[1:20,], ntree = 1) Loading required package: Matrix Loaded glmnet 4.1-10 > > ## predict call > print(predict(o, mtcars[-(1:20),])) mpg [1,] 29.56667 [2,] 18.73750 [3,] 18.73750 [4,] 10.40000 [5,] 18.73750 [6,] 29.56667 [7,] 29.56667 [8,] 29.56667 [9,] 10.40000 [10,] 29.56667 [11,] 10.40000 [12,] 29.56667 > > > > > > cleanEx() detaching ‘package:glmnet’, ‘package:Matrix’ > nameEx("uvarpro") > ### * uvarpro > > flush(stderr()); flush(stdout()) > > ### Name: uvarpro > ### Title: Unsupervised Variable Selection using Variable Priority > ### (UVarPro) > ### Aliases: uvarpro > ### Keywords: uvarpro > > ### ** Examples > > > > ## ------------------------------------------------------------ > ## toy example - needed to pass CRAN test > ## ------------------------------------------------------------ > > ## mtcars unsupervised regression > o <- uvarpro(mtcars, ntree = 1) > > > > > cleanEx() > nameEx("varpro") > ### * varpro > > flush(stderr()); flush(stdout()) > > ### Name: varpro > ### Title: Model-Independent Variable Selection via the Rule-based Variable > ### Priority (VarPro) > ### Aliases: varpro > ### Keywords: varpro > > ### ** Examples > > > ## ------------------------------------------------------------ > ## toy example - needed to pass CRAN test > ## ------------------------------------------------------------ > > ## mtcars regression > o <- varpro(mpg ~ ., mtcars, ntree = 1) Loading required package: Matrix Loaded glmnet 4.1-10 > > > > > cleanEx() detaching ‘package:glmnet’, ‘package:Matrix’ > nameEx("varpro.strength") > ### * varpro.strength > > flush(stderr()); flush(stdout()) > > ### Name: varpro.strength > ### Title: Obtain Strength Array and Other Values from a VarPro Object > ### Aliases: varpro.strength > ### Keywords: varpro.strength > > ### ** Examples > > > > > > ### *