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Type 'q()' to quit R. > pkgname <- "CovRegRF" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('CovRegRF') > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("covregrf") > ### * covregrf > > flush(stderr()); flush(stdout()) > > ### Name: covregrf > ### Title: Covariance Regression with Random Forests > ### Aliases: covregrf > > ### ** Examples > > options(rf.cores=2, mc.cores=2) > > ## load generated example data > data(data, package = "CovRegRF") > xvar.names <- colnames(data$X) > yvar.names <- colnames(data$Y) > data1 <- data.frame(data$X, data$Y) > > ## define train/test split > set.seed(2345) > smp <- sample(1:nrow(data1), size = round(nrow(data1)*0.6), replace = FALSE) > traindata <- data1[smp,,drop=FALSE] > testdata <- data1[-smp, xvar.names, drop=FALSE] > > ## formula object > formula <- as.formula(paste(paste(yvar.names, collapse="+"), ".", sep=" ~ ")) > > ## train covregrf > covregrf.obj <- covregrf(formula, traindata, params.rfsrc = list(ntree = 50), + importance = TRUE) randomForestSRC.c:34434:56: runtime error: pointer index expression with base 0x000000000001 overflowed to 0xfffffffffffffff9 #0 0x7febda3f4f26 in rfsrcGrow /data/gannet/ripley/R/packages/tests-gcc-SAN/CovRegRF/src/randomForestSRC.c:34434 #1 0x7276e1 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1062 #2 0x73aff2 in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1437 #3 0x8a7243 in bcEval_loop /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:8122 #4 0x87840f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7505 #5 0x83f152 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1167 #6 0x84a122 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2393 #7 0x83daca in applyClosure_core /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2306 #8 0x83f7d6 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2328 #9 0x83f7d6 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1280 #10 0x60131e in do_docall /data/gannet/ripley/R/svn/R-devel/src/main/coerce.c:2764 #11 0x8a7243 in bcEval_loop /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:8122 #12 0x87840f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7505 #13 0x83f152 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1167 #14 0x84a122 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2393 #15 0x83daca in applyClosure_core /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2306 #16 0x83f7d6 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2328 #17 0x83f7d6 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1280 #18 0x8632c6 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:3571 #19 0x83fc06 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1232 #20 0x9c9699 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:265 #21 0x9c9699 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:317 #22 0x9cab9b in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1219 #23 0x9d5112 in Rf_mainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1226 #24 0x4293ff in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29 #25 0x7febee82950f in __libc_start_call_main (/lib64/libc.so.6+0x2950f) (BuildId: 8257ee907646e9b057197533d1e4ac8ede7a9c5c) #26 0x7febee8295c8 in __libc_start_main_alias_2 (/lib64/libc.so.6+0x295c8) (BuildId: 8257ee907646e9b057197533d1e4ac8ede7a9c5c) #27 0x429de4 in _start (/data/gannet/ripley/R/gcc-SAN3/bin/exec/R+0x429de4) (BuildId: 5e148b08f50883e4fe61db372b4722d6e52a85b3) randomForestSRC.c:27574:21: runtime error: pointer index expression with base 0x000000000001 overflowed to 0xfffffffffffffffd #0 0x7febda370553 in stackForestObjectsOutput /data/gannet/ripley/R/packages/tests-gcc-SAN/CovRegRF/src/randomForestSRC.c:27574 #1 0x7febda370553 in stackForestObjectsOutput /data/gannet/ripley/R/packages/tests-gcc-SAN/CovRegRF/src/randomForestSRC.c:27484 #2 0x7febda1dc3c7 in rfsrc /data/gannet/ripley/R/packages/tests-gcc-SAN/CovRegRF/src/randomForestSRC.c:10653 #3 0x7febda3f3adf in rfsrcGrow /data/gannet/ripley/R/packages/tests-gcc-SAN/CovRegRF/src/randomForestSRC.c:34599 #4 0x7276e1 in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1062 #5 0x73aff2 in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1437 #6 0x8a7243 in bcEval_loop /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:8122 #7 0x87840f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7505 #8 0x83f152 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1167 #9 0x84a122 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2393 #10 0x83daca in applyClosure_core /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2306 #11 0x83f7d6 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2328 #12 0x83f7d6 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1280 #13 0x60131e in do_docall /data/gannet/ripley/R/svn/R-devel/src/main/coerce.c:2764 #14 0x8a7243 in bcEval_loop /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:8122 #15 0x87840f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7505 #16 0x83f152 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1167 #17 0x84a122 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2393 #18 0x83daca in applyClosure_core /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2306 #19 0x83f7d6 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2328 #20 0x83f7d6 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1280 #21 0x8632c6 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:3571 #22 0x83fc06 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1232 #23 0x9c9699 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:265 #24 0x9c9699 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:317 #25 0x9cab9b in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1219 #26 0x9d5112 in Rf_mainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1226 #27 0x4293ff in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29 #28 0x7febee82950f in __libc_start_call_main (/lib64/libc.so.6+0x2950f) (BuildId: 8257ee907646e9b057197533d1e4ac8ede7a9c5c) #29 0x7febee8295c8 in __libc_start_main_alias_2 (/lib64/libc.so.6+0x295c8) (BuildId: 8257ee907646e9b057197533d1e4ac8ede7a9c5c) #30 0x429de4 in _start (/data/gannet/ripley/R/gcc-SAN3/bin/exec/R+0x429de4) (BuildId: 5e148b08f50883e4fe61db372b4722d6e52a85b3) > > ## get the OOB predictions > pred.oob <- covregrf.obj$predicted.oob > > ## predict with new test data > pred.obj <- predict(covregrf.obj, newdata = testdata) randomForestSRC.c:34912:90: runtime error: pointer index expression with base 0x000000000001 overflowed to 0xfffffffffffffffd #0 0x7febda407e42 in rfsrcPredict /data/gannet/ripley/R/packages/tests-gcc-SAN/CovRegRF/src/randomForestSRC.c:34912 #1 0x71149d in R_doDotCall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1336 #2 0x73aff2 in do_dotcall /data/gannet/ripley/R/svn/R-devel/src/main/dotcode.c:1437 #3 0x8a7243 in bcEval_loop /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:8122 #4 0x87840f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7505 #5 0x83f152 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1167 #6 0x84a122 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2393 #7 0x83daca in applyClosure_core /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2306 #8 0xa5391b in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2328 #9 0xa5391b in applyMethod /data/gannet/ripley/R/svn/R-devel/src/main/objects.c:120 #10 0xa57ad7 in dispatchMethod /data/gannet/ripley/R/svn/R-devel/src/main/objects.c:473 #11 0xf4f2dd in Rf_usemethod.isra.0 /data/gannet/ripley/R/svn/R-devel/src/main/objects.c:513 #12 0xa59746 in do_usemethod /data/gannet/ripley/R/svn/R-devel/src/main/objects.c:579 #13 0x8b42bd in bcEval_loop /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:8142 #14 0x87840f in bcEval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:7505 #15 0x83f152 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1167 #16 0x84a122 in R_execClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2393 #17 0x83daca in applyClosure_core /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2306 #18 0x83f7d6 in Rf_applyClosure /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:2328 #19 0x83f7d6 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1280 #20 0x8632c6 in do_set /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:3571 #21 0x83fc06 in Rf_eval /data/gannet/ripley/R/svn/R-devel/src/main/eval.c:1232 #22 0x9c9699 in Rf_ReplIteration /data/gannet/ripley/R/svn/R-devel/src/main/main.c:265 #23 0x9c9699 in R_ReplConsole /data/gannet/ripley/R/svn/R-devel/src/main/main.c:317 #24 0x9cab9b in run_Rmainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1219 #25 0x9d5112 in Rf_mainloop /data/gannet/ripley/R/svn/R-devel/src/main/main.c:1226 #26 0x4293ff in main /data/gannet/ripley/R/svn/R-devel/src/main/Rmain.c:29 #27 0x7febee82950f in __libc_start_call_main (/lib64/libc.so.6+0x2950f) (BuildId: 8257ee907646e9b057197533d1e4ac8ede7a9c5c) #28 0x7febee8295c8 in __libc_start_main_alias_2 (/lib64/libc.so.6+0x295c8) (BuildId: 8257ee907646e9b057197533d1e4ac8ede7a9c5c) #29 0x429de4 in _start (/data/gannet/ripley/R/gcc-SAN3/bin/exec/R+0x429de4) (BuildId: 5e148b08f50883e4fe61db372b4722d6e52a85b3) > pred <- pred.obj$predicted > > ## get the variable importance measures > vimp <- covregrf.obj$importance > > > > > > cleanEx() > nameEx("data") > ### * data > > flush(stderr()); flush(stdout()) > > ### Name: data > ### Title: Generated example data > ### Aliases: data > ### Keywords: datasets > > ### ** Examples > > ## load generated example data > data(data, package = "CovRegRF") > > > > > cleanEx() > nameEx("plot.vimp.covregrf") > ### * plot.vimp.covregrf > > flush(stderr()); flush(stdout()) > > ### Name: plot.vimp.covregrf > ### Title: Plot variable importance measures for covregrf objects > ### Aliases: plot.vimp.covregrf plot.vimp > > ### ** Examples > > options(rf.cores=2, mc.cores=2) > > ## load generated example data > data(data, package = "CovRegRF") > xvar.names <- colnames(data$X) > yvar.names <- colnames(data$Y) > data1 <- data.frame(data$X, data$Y) > > ## define train/test split > set.seed(2345) > smp <- sample(1:nrow(data1), size = round(nrow(data1)*0.6), replace = FALSE) > traindata <- data1[smp,,drop=FALSE] > testdata <- data1[-smp, xvar.names, drop=FALSE] > > ## formula object > formula <- as.formula(paste(paste(yvar.names, collapse="+"), ".", sep=" ~ ")) > > ## train covregrf > covregrf.obj <- covregrf(formula, traindata, params.rfsrc = list(ntree = 50), + importance = TRUE) > > ## plot vimp > plot.vimp(covregrf.obj) > > > > > > cleanEx() > nameEx("predict.covregrf") > ### * predict.covregrf > > flush(stderr()); flush(stdout()) > > ### Name: predict.covregrf > ### Title: Predict method for covregrf objects > ### Aliases: predict.covregrf > > ### ** Examples > > options(rf.cores=2, mc.cores=2) > > ## load generated example data > data(data, package = "CovRegRF") > xvar.names <- colnames(data$X) > yvar.names <- colnames(data$Y) > data1 <- data.frame(data$X, data$Y) > > ## define train/test split > set.seed(2345) > smp <- sample(1:nrow(data1), size = round(nrow(data1)*0.6), replace = FALSE) > traindata <- data1[smp,,drop=FALSE] > testdata <- data1[-smp, xvar.names, drop=FALSE] > > ## formula object > formula <- as.formula(paste(paste(yvar.names, collapse="+"), ".", sep=" ~ ")) > > ## train covregrf > covregrf.obj <- covregrf(formula, traindata, params.rfsrc = list(ntree = 50)) > > ## predict without new data (OOB predictions will be returned) > pred.obj <- predict(covregrf.obj) > pred.oob <- pred.obj$predicted > > ## predict with new test data > pred.obj2 <- predict(covregrf.obj, newdata = testdata) > pred <- pred.obj2$predicted > > > > > > cleanEx() > nameEx("print.covregrf") > ### * print.covregrf > > flush(stderr()); flush(stdout()) > > ### Name: print.covregrf > ### Title: Print summary output of a CovRegRF analysis > ### Aliases: print.covregrf > > ### ** Examples > > options(rf.cores=2, mc.cores=2) > > ## load generated example data > data(data, package = "CovRegRF") > xvar.names <- colnames(data$X) > yvar.names <- colnames(data$Y) > data1 <- data.frame(data$X, data$Y) > > ## define train/test split > set.seed(2345) > smp <- sample(1:nrow(data1), size = round(nrow(data1)*0.6), replace = FALSE) > traindata <- data1[smp,,drop=FALSE] > testdata <- data1[-smp, xvar.names, drop=FALSE] > > ## formula object > formula <- as.formula(paste(paste(yvar.names, collapse="+"), ".", sep=" ~ ")) > > ## train covregrf > covregrf.obj <- covregrf(formula, traindata, params.rfsrc = list(ntree = 50)) > > ## print the grow object > print(covregrf.obj) Sample size: 120 Number of X variables: 3 Number of Y variables: 3 Best terminal node size: 10 > > ## predict with new test data > pred.obj <- predict(covregrf.obj, newdata = testdata) > > ## print the predict object > print(pred.obj) Sample size of test data: 80 Number of X variables: 3 > > > > > cleanEx() > nameEx("vimp.covregrf") > ### * vimp.covregrf > > flush(stderr()); flush(stdout()) > > ### Name: vimp.covregrf > ### Title: Variable importance for covregrf objects > ### Aliases: vimp.covregrf vimp > > ### ** Examples > > options(rf.cores=2, mc.cores=2) > > ## load generated example data > data(data, package = "CovRegRF") > xvar.names <- colnames(data$X) > yvar.names <- colnames(data$Y) > data1 <- data.frame(data$X, data$Y) > > ## define train/test split > set.seed(2345) > smp <- sample(1:nrow(data1), size = round(nrow(data1)*0.6), replace = FALSE) > traindata <- data1[smp,,drop=FALSE] > testdata <- data1[-smp, xvar.names, drop=FALSE] > > ## formula object > formula <- as.formula(paste(paste(yvar.names, collapse="+"), ".", sep=" ~ ")) > > ## train covregrf > covregrf.obj <- covregrf(formula, traindata, params.rfsrc = list(ntree = 50), + importance = TRUE) > > ## get the variable importance measures > vimp <- covregrf.obj$importance > vimp2 <- vimp(covregrf.obj)$importance > > > > > > ### *