==3036== Memcheck, a memory error detector ==3036== Copyright (C) 2002-2017, and GNU GPL'd, by Julian Seward et al. ==3036== Using Valgrind-3.15.0 and LibVEX; rerun with -h for copyright info ==3036== Command: /data/blackswan/ripley/R/R-devel-vg/bin/exec/R --vanilla --encoding=UTF-8 ==3036== R Under development (unstable) (2020-03-14 r77968) -- "Unsuffered Consequences" Copyright (C) 2020 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > pkgname <- "owl" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('owl') > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("coef.Owl") > ### * coef.Owl > > flush(stderr()); flush(stdout()) > > ### Name: coef.Owl > ### Title: Obtain coefficients > ### Aliases: coef.Owl > > ### ** Examples > > fit <- owl(mtcars$mpg, mtcars$vs, n_sigma = 1) > coef(fit) (Intercept) V1 -0.67805384 0.05552609 > > > > cleanEx() > nameEx("deviance.Owl") > ### * deviance.Owl > > flush(stderr()); flush(stdout()) > > ### Name: deviance.Owl > ### Title: Model deviance > ### Aliases: deviance.Owl > > ### ** Examples > > fit <- owl(heart$x, heart$y, family = "binomial") > deviance(fit) [1] 370.9593 352.9908 333.8938 317.7167 303.8756 291.8126 281.0661 270.5324 [9] 260.9091 252.4471 244.9932 238.4299 232.6748 227.6071 222.8242 217.4378 [17] 212.5163 207.8711 203.7203 199.6878 196.2031 192.9188 189.9912 187.4567 [25] 185.2710 183.2485 181.5044 179.9265 178.5430 177.3053 176.1985 175.2377 [33] 174.4021 173.6924 173.0818 172.5474 172.0739 171.6674 171.2815 170.9316 [41] 170.6394 170.3815 170.1597 169.9700 169.8087 169.6730 169.5569 169.4587 [49] 169.3712 169.2973 169.2350 169.1812 169.1352 169.0959 169.0633 169.0359 [57] 169.0128 168.9936 168.9775 168.9640 168.9527 168.9433 168.9354 168.9289 [65] 168.9234 168.9189 168.9150 168.9119 168.9092 168.9070 168.9052 168.9037 > > > > cleanEx() > nameEx("owl") > ### * owl > > flush(stderr()); flush(stdout()) > > ### Name: owl > ### Title: Generalized linear models regularized with the SLOPE (OWL) norm > ### Aliases: owl > > ### ** Examples > > > # Gaussian response, default lambda sequence > > fit <- owl(bodyfat$x, bodyfat$y) > > # Binomial response, BH-type lambda sequence > > fit <- owl(heart$x, heart$y, family = "binomial", lambda = "bh") > > # Poisson response, OSCAR-type lambda sequence > > fit <- owl(abalone$x, + abalone$y, + family = "poisson", + lambda = "oscar", + q = 0.4) > > # Multinomial response, custom sigma and lambda > > m <- length(unique(wine$y)) - 1 > p <- ncol(wine$x) > > sigma <- 0.005 > lambda <- exp(seq(log(2), log(1.8), length.out = p*m)) > > fit <- owl(wine$x, + wine$y, + family = "multinomial", + lambda = lambda, + sigma = sigma) > > > > > cleanEx() > nameEx("plot.Owl") > ### * plot.Owl > > flush(stderr()); flush(stdout()) > > ### Name: plot.Owl > ### Title: Plot coefficients > ### Aliases: plot.Owl > > ### ** Examples > > fit <- owl(heart$x, heart$y) > plot(fit) > > > > cleanEx() > nameEx("plot.TrainedOwl") > ### * plot.TrainedOwl > > flush(stderr()); flush(stdout()) > > ### Name: plot.TrainedOwl > ### Title: Plot results from cross-validation > ### Aliases: plot.TrainedOwl > > ### ** Examples > > # Cross-validation for a SLOPE binomial model > set.seed(123) > tune <- trainOwl(subset(mtcars, select = c("mpg", "drat", "wt")), + mtcars$hp, + q = c(0.1, 0.2), + number = 10) ==3036== Conditional jump or move depends on uninitialised value(s) ==3036== at 0x1D740DB1: Rcpp::Vector<19, Rcpp::PreserveStorage> owlCpp >(arma::Mat&, arma::Mat&, Rcpp::Vector<19, Rcpp::PreserveStorage>) (packages/tests-vg/owl/src/owl.cpp:302) ==3036== by 0x1D716493: owlDense(arma::Mat, arma::Mat, Rcpp::Vector<19, Rcpp::PreserveStorage>) (packages/tests-vg/owl/src/owl.cpp:374) ==3036== by 0x1D6FC401: _owl_owlDense (packages/tests-vg/owl/src/RcppExports.cpp:31) ==3036== by 0x49C15F: R_doDotCall (svn/R-devel/src/main/dotcode.c:604) ==3036== by 0x4D8906: bcEval (svn/R-devel/src/main/eval.c:7610) ==3036== by 0x4E8827: Rf_eval (svn/R-devel/src/main/eval.c:688) ==3036== by 0x4EA3E6: R_execClosure (svn/R-devel/src/main/eval.c:1853) ==3036== by 0x4EB1C3: Rf_applyClosure (svn/R-devel/src/main/eval.c:1779) ==3036== by 0x4DBCDD: bcEval (svn/R-devel/src/main/eval.c:7022) ==3036== by 0x4E8827: Rf_eval (svn/R-devel/src/main/eval.c:688) ==3036== by 0x4EA3E6: R_execClosure (svn/R-devel/src/main/eval.c:1853) ==3036== by 0x4EB1C3: Rf_applyClosure (svn/R-devel/src/main/eval.c:1779) ==3036== Uninitialised value was created by a stack allocation ==3036== at 0x1D73F105: Rcpp::Vector<19, Rcpp::PreserveStorage> owlCpp >(arma::Mat&, arma::Mat&, Rcpp::Vector<19, Rcpp::PreserveStorage>) (packages/tests-vg/owl/src/owl.cpp:15) ==3036== > plot(tune, ci_col = "salmon", col = "black") > > > > cleanEx() > nameEx("plotDiagnostics") > ### * plotDiagnostics > > flush(stderr()); flush(stdout()) > > ### Name: plotDiagnostics > ### Title: Plot results from diagnostics collected during model fitting > ### Aliases: plotDiagnostics > > ### ** Examples > > x <- owl(abalone$x, abalone$y, sigma = 2, diagnostics = TRUE) > plotDiagnostics(x) > > > > cleanEx() > nameEx("predict.Owl") > ### * predict.Owl > > flush(stderr()); flush(stdout()) > > ### Name: predict.Owl > ### Title: Generate predictions from owl models > ### Aliases: predict.Owl predict.OwlGaussian predict.OwlBinomial > ### predict.OwlPoisson predict.OwlMultinomial > > ### ** Examples > > fit <- with(mtcars, owl(cbind(mpg, hp), vs, family = "binomial")) > predict(fit, with(mtcars, cbind(mpg, hp)), type = "class") [1] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [19] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [37] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" [55] "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "1" [73] "0" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "0" "1" [91] "0" "1" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "0" "1" "0" "0" "0" "0" [109] "0" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "0" "1" "1" "1" "0" "0" [127] "0" "0" "0" "0" "1" "0" "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" [145] "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "0" "0" [163] "1" "0" "0" "0" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" [181] "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" [199] "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" [217] "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "0" "0" "1" "1" "0" [235] "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" [253] "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" [271] "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" [289] "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" [307] "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" [325] "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" [343] "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" [361] "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" [379] "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" [397] "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" [415] "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" [433] "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" [451] "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" [469] "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" [487] "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" [505] "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" [523] "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" [541] "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "0" "0" "0" "0" [559] "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" [577] "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "0" "0" "0" "0" "0" "0" "0" "1" [595] "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" [613] "0" "1" "0" "1" "1" "1" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" [631] "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" [649] "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" [667] "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" [685] "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" [703] "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" [721] "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" [739] "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" [757] "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" [775] "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" [793] "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" [811] "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" [829] "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" [847] "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" [865] "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" [883] "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" [901] "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" [919] "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" [937] "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" [955] "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" [973] "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" [991] "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" [1009] "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" [1027] "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" [1045] "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" [1063] "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" [1081] "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" [1099] "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" [1117] "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" [1135] "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" [1153] "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" [1171] "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" [1189] "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" [1207] "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" [1225] "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" [1243] "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" [1261] "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" [1279] "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" [1297] "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" [1315] "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" [1333] "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" [1351] "0" "1" "1" "1" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" [1369] "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" [1387] "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" [1405] "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "1" "1" "0" "0" "0" [1423] "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" [1441] "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" [1459] "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" [1477] "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" [1495] "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" [1513] "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" [1531] "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" [1549] "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" [1567] "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" [1585] "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" [1603] "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" [1621] "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" [1639] "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" [1657] "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" [1675] "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" [1693] "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" [1711] "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" [1729] "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" [1747] "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" [1765] "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" [1783] "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" [1801] "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" [1819] "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" [1837] "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" [1855] "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" [1873] "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" [1891] "1" "1" "0" "1" "0" "1" "1" "0" "0" "0" "0" "0" "0" "0" "0" "1" "1" "1" [1909] "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" [1927] "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" [1945] "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" [1963] "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" [1981] "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" [1999] "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" [2017] "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" [2035] "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" [2053] "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" [2071] "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" [2089] "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" [2107] "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" [2125] "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" [2143] "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" [2161] "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" [2179] "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" [2197] "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" [2215] "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" [2233] "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" [2251] "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" [2269] "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" [2287] "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" [2305] "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" [2323] "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" [2341] "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" [2359] "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" [2377] "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" [2395] "1" "1" "0" "0" "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" [2413] "0" "0" "0" "0" "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" [2431] "0" "1" "1" "1" "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" [2449] "0" "1" "1" "1" "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" "1" "1" [2467] "1" "1" "0" "1" "0" "1" "1" "0" "1" "0" "0" "0" "0" "0" "0" "1" "1" "1" [2485] "1" "0" "0" "0" "0" "1" "1" "1" "0" "0" "0" "1" > > > > > cleanEx() > nameEx("print.Owl") > ### * print.Owl > > flush(stderr()); flush(stdout()) > > ### Name: print.Owl > ### Title: Print results from owl fit > ### Aliases: print.Owl print.TrainedOwl > > ### ** Examples > > fit <- owl(wine$x, wine$y, family = "multinomial") > print(fit, digits = 1) Call: owl(x = wine$x, y = wine$y, family = "multinomial") Path summary: sigma deviance_ratio n_nonzero 1 1.797 0.005 1 2 1.637 0.069 3 3 1.492 0.137 4 4 1.359 0.206 4 5 1.239 0.267 4 6 1.128 0.320 4 7 1.028 0.368 4 8 0.937 0.410 4 9 0.854 0.450 5 10 0.778 0.487 5 11 0.709 0.520 6 12 0.646 0.554 7 13 0.588 0.586 8 14 0.536 0.615 8 15 0.488 0.643 9 16 0.445 0.670 10 17 0.406 0.696 11 18 0.370 0.720 11 19 0.337 0.742 12 20 0.307 0.762 12 21 0.280 0.780 12 22 0.255 0.797 12 23 0.232 0.813 12 24 0.211 0.826 13 25 0.193 0.839 13 26 0.176 0.851 13 27 0.160 0.862 13 28 0.146 0.872 13 29 0.133 0.881 13 30 0.121 0.889 13 31 0.110 0.897 13 32 0.100 0.904 13 33 0.092 0.910 13 34 0.083 0.917 13 35 0.076 0.922 14 36 0.069 0.927 14 37 0.063 0.932 14 38 0.057 0.937 15 39 0.052 0.941 15 40 0.048 0.945 15 41 0.043 0.948 15 42 0.040 0.952 17 43 0.036 0.955 17 44 0.033 0.959 17 45 0.030 0.962 17 46 0.027 0.964 17 47 0.025 0.967 18 48 0.023 0.969 18 49 0.021 0.972 18 50 0.019 0.974 18 51 0.017 0.976 18 52 0.016 0.977 18 53 0.014 0.979 18 54 0.013 0.981 18 55 0.012 0.982 18 56 0.011 0.984 18 57 0.010 0.985 18 58 0.009 0.986 18 59 0.008 0.987 18 60 0.007 0.988 18 61 0.007 0.989 18 62 0.006 0.990 18 63 0.006 0.991 17 64 0.005 0.991 17 65 0.005 0.992 17 66 0.004 0.993 17 67 0.004 0.993 17 68 0.004 0.994 17 69 0.003 0.994 17 70 0.003 0.995 17 71 0.003 0.995 17 Lambda sequence: [1] 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 [16] 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 > > > > > cleanEx() > nameEx("score") > ### * score > > flush(stderr()); flush(stdout()) > > ### Name: score > ### Title: Compute one of several loss metrics on a new data set > ### Aliases: score score.OwlGaussian score.OwlBinomial score.OwlMultinomial > ### score.OwlPoisson > > ### ** Examples > > x <- subset(infert, select = c("induced", "age", "pooled.stratum")) > y <- infert$case > > fit <- owl(x, y, family = "binomial") > score(fit, x, y, measure = "auc") p1 p2 p3 p4 p5 p6 p7 p8 0.4520628 0.5083607 0.5137641 0.4974808 0.4893027 0.4968237 0.5014239 0.4920774 p9 p10 0.4971888 0.5162468 > > > > cleanEx() > nameEx("trainOwl") > ### * trainOwl > > flush(stderr()); flush(stdout()) > > ### Name: trainOwl > ### Title: Train a owl model > ### Aliases: trainOwl > > ### ** Examples > > # 8-fold cross-validation repeated 5 times > tune <- trainOwl(subset(mtcars, select = c("mpg", "drat", "wt")), + mtcars$hp, + q = c(0.1, 0.2), + number = 8, + repeats = 5) > > > > ### *