* using log directory 'd:/Rcompile/CRANpkg/local/4.6/SuperLearner.Rcheck' * using R version 4.6.1 (2026-06-24 ucrt) * using platform: x86_64-w64-mingw32 * R was compiled by gcc.exe (GCC) 14.3.0 GNU Fortran (GCC) 14.3.0 * running under: Windows Server 2022 x64 (build 20348) * using session charset: UTF-8 * current time: 2026-07-28 07:24:43 UTC * checking for file 'SuperLearner/DESCRIPTION' ... OK * checking extension type ... Package * this is package 'SuperLearner' version '2.0-40' * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'SuperLearner' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... [0s] OK * checking whether the package can be loaded with stated dependencies ... [0s] OK * checking whether the package can be unloaded cleanly ... [0s] OK * checking whether the namespace can be loaded with stated dependencies ... [0s] OK * checking whether the namespace can be unloaded cleanly ... [0s] OK * checking loading without being on the library search path ... [1s] OK * checking whether startup messages can be suppressed ... [0s] OK * checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [9s] OK * checking Rd files ... [1s] OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... [5s] ERROR Running examples in 'SuperLearner-Ex.R' failed The error most likely occurred in: > ### Name: SL.glmnet > ### Title: Elastic net regression, including lasso and ridge > ### Aliases: SL.glmnet > > ### ** Examples > > > # Load a test dataset. > data(PimaIndiansDiabetes2, package = "mlbench") Warning in data(PimaIndiansDiabetes2, package = "mlbench") : data set 'PimaIndiansDiabetes2' not found > data = PimaIndiansDiabetes2 Error: object 'PimaIndiansDiabetes2' not found Execution halted * checking for unstated dependencies in 'tests' ... OK * checking tests ... [56s] ERROR Running 'testthat.R' [56s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(SuperLearner) Loading required package: nnls Loading required package: gam Loading required package: splines Loading required package: foreach Loaded gam 1.22-7 Super Learner Version: 2.0-40 Package created on 2025-12-14 > > test_check("SuperLearner") Call: SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library, xgb_grid$names), cvControl = list(V = 2)) Risk Coef SL.mean_All 1.060266 0.00000000 SL.xgboost_All 1.248342 0.17377091 SL.xgb.1_All 1.046647 0.00000000 SL.xgb.2_All 1.039310 0.48619144 SL.xgb.3_All 1.033972 0.27648732 SL.xgb.4_All 1.046058 0.00000000 SL.xgb.5_All 1.057237 0.00000000 SL.xgb.6_All 1.053070 0.00000000 SL.xgb.7_All 1.054268 0.00000000 SL.xgb.8_All 1.049384 0.06355032 SL.xgb.9_All 1.059925 0.00000000 SL.xgb.10_All 1.059592 0.00000000 SL.xgb.11_All 1.059665 0.00000000 SL.xgb.12_All 1.058975 0.00000000 Error in .jnew("bartMachine.bartMachineRegressionMultThread") : java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0 Error in .jnew("bartMachine.bartMachineRegressionMultThread") : java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0 Error in .jnew("bartMachine.bartMachineRegressionMultThread") : java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0 Error in .jnew("bartMachine.bartMachineRegressionMultThread") : java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0 Error in .jnew("bartMachine.bartMachineRegressionMultThread") : java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0 Error in .jnew("bartMachine.bartMachineRegressionMultThread") : java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0 lasso-penalized linear regression with n=506, p=13 At minimum cross-validation error (lambda=0.0222): ------------------------------------------------- Nonzero coefficients: 11 Cross-validation error (deviance): 23.29 R-squared: 0.72 Signal-to-noise ratio: 2.63 Scale estimate (sigma): 4.826 lasso-penalized logistic regression with n=506, p=13 At minimum cross-validation error (lambda=0.0026): ------------------------------------------------- Nonzero coefficients: 12 Cross-validation error (deviance): 0.66 R-squared: 0.48 Signal-to-noise ratio: 0.94 Prediction error: 0.123 lasso-penalized linear regression with n=506, p=13 At minimum cross-validation error (lambda=0.0362): ------------------------------------------------- Nonzero coefficients: 11 Cross-validation error (deviance): 23.30 R-squared: 0.72 Signal-to-noise ratio: 2.62 Scale estimate (sigma): 4.827 lasso-penalized logistic regression with n=506, p=13 At minimum cross-validation error (lambda=0.0016): ------------------------------------------------- Nonzero coefficients: 13 Cross-validation error (deviance): 0.63 R-squared: 0.50 Signal-to-noise ratio: 0.99 Prediction error: 0.132 Call: SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean", "SL.biglasso"), cvControl = list(V = 2)) Risk Coef SL.mean_All 84.62063 0.02136708 SL.biglasso_All 26.01864 0.97863292 Call: SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean", "SL.biglasso"), cvControl = list(V = 2)) Risk Coef SL.mean_All 0.2346857 0 SL.biglasso_All 0.1039122 1 Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, model = model) Coefficients: (Intercept) crim zn indus chas nox 3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01 rm age dis rad tax ptratio 3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01 black lstat 9.312e-03 -5.248e-01 Degrees of Freedom: 505 Total (i.e. Null); 492 Residual Null Deviance: 42720 Residual Deviance: 11080 AIC: 3028 Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, model = model) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 *** crim -1.080e-01 3.286e-02 -3.287 0.001087 ** zn 4.642e-02 1.373e-02 3.382 0.000778 *** indus 2.056e-02 6.150e-02 0.334 0.738288 chas 2.687e+00 8.616e-01 3.118 0.001925 ** nox -1.777e+01 3.820e+00 -4.651 4.25e-06 *** rm 3.810e+00 4.179e-01 9.116 < 2e-16 *** age 6.922e-04 1.321e-02 0.052 0.958229 dis -1.476e+00 1.995e-01 -7.398 6.01e-13 *** rad 3.060e-01 6.635e-02 4.613 5.07e-06 *** tax -1.233e-02 3.760e-03 -3.280 0.001112 ** ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 *** black 9.312e-03 2.686e-03 3.467 0.000573 *** lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for gaussian family taken to be 22.51785) Null deviance: 42716 on 505 degrees of freedom Residual deviance: 11079 on 492 degrees of freedom AIC: 3027.6 Number of Fisher Scoring iterations: 2 Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, model = model) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 10.682635 3.921395 2.724 0.006446 ** crim -0.040649 0.049796 -0.816 0.414321 zn 0.012134 0.010678 1.136 0.255786 indus -0.040715 0.045615 -0.893 0.372078 chas 0.248209 0.653283 0.380 0.703989 nox -3.601085 2.924365 -1.231 0.218170 rm 1.155157 0.374843 3.082 0.002058 ** age -0.018660 0.009319 -2.002 0.045252 * dis -0.518934 0.146286 -3.547 0.000389 *** rad 0.255522 0.061391 4.162 3.15e-05 *** tax -0.009500 0.003107 -3.057 0.002233 ** ptratio -0.409317 0.103191 -3.967 7.29e-05 *** black -0.001451 0.002558 -0.567 0.570418 lstat -0.318436 0.054735 -5.818 5.96e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 669.76 on 505 degrees of freedom Residual deviance: 296.39 on 492 degrees of freedom AIC: 324.39 Number of Fisher Scoring iterations: 7 [1] "coefficients" "residuals" "fitted.values" [4] "effects" "R" "rank" [7] "qr" "family" "linear.predictors" [10] "deviance" "aic" "null.deviance" [13] "iter" "weights" "prior.weights" [16] "df.residual" "df.null" "y" [19] "converged" "boundary" "call" [22] "formula" "terms" "data" [25] "offset" "control" "method" [28] "contrasts" "xlevels" Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, model = model) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 *** crim -1.080e-01 3.286e-02 -3.287 0.001087 ** zn 4.642e-02 1.373e-02 3.382 0.000778 *** indus 2.056e-02 6.150e-02 0.334 0.738288 chas 2.687e+00 8.616e-01 3.118 0.001925 ** nox -1.777e+01 3.820e+00 -4.651 4.25e-06 *** rm 3.810e+00 4.179e-01 9.116 < 2e-16 *** age 6.922e-04 1.321e-02 0.052 0.958229 dis -1.476e+00 1.995e-01 -7.398 6.01e-13 *** rad 3.060e-01 6.635e-02 4.613 5.07e-06 *** tax -1.233e-02 3.760e-03 -3.280 0.001112 ** ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 *** black 9.312e-03 2.686e-03 3.467 0.000573 *** lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for gaussian family taken to be 22.51785) Null deviance: 42716 on 505 degrees of freedom Residual deviance: 11079 on 492 degrees of freedom AIC: 3027.6 Number of Fisher Scoring iterations: 2 Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights, model = model) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 10.682635 3.921395 2.724 0.006446 ** crim -0.040649 0.049796 -0.816 0.414321 zn 0.012134 0.010678 1.136 0.255786 indus -0.040715 0.045615 -0.893 0.372078 chas 0.248209 0.653283 0.380 0.703989 nox -3.601085 2.924365 -1.231 0.218170 rm 1.155157 0.374843 3.082 0.002058 ** age -0.018660 0.009319 -2.002 0.045252 * dis -0.518934 0.146286 -3.547 0.000389 *** rad 0.255522 0.061391 4.162 3.15e-05 *** tax -0.009500 0.003107 -3.057 0.002233 ** ptratio -0.409317 0.103191 -3.967 7.29e-05 *** black -0.001451 0.002558 -0.567 0.570418 lstat -0.318436 0.054735 -5.818 5.96e-09 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 669.76 on 505 degrees of freedom Residual deviance: 296.39 on 492 degrees of freedom AIC: 324.39 Number of Fisher Scoring iterations: 7 Call: SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean", "SL.glm")) Risk Coef SL.mean_All 84.74142 0.0134192 SL.glm_All 23.62549 0.9865808 V1 Min. :-3.921 1st Qu.:17.514 Median :22.124 Mean :22.533 3rd Qu.:27.345 Max. :44.376 Call: SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean", "SL.glm")) Risk Coef SL.mean_All 0.23580362 0.01315872 SL.glm_All 0.09519266 0.98684128 V1 Min. :0.004942 1st Qu.:0.035424 Median :0.196222 Mean :0.375494 3rd Qu.:0.781687 Max. :0.991313 Saving _problems/test-glmnet-11.R Saving _problems/test-kernelKnn-10.R Saving _problems/test-knn-10.R Call: lda(X, grouping = Y, prior = prior, method = method, tol = tol, CV = CV, nu = nu) Prior probabilities of groups: 0 1 0.6245059 0.3754941 Group means: crim zn indus chas nox rm age dis 0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307 1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371 rad tax ptratio black lstat 0 11.588608 459.9209 19.19968 340.6392 16.042468 1 6.157895 322.2789 17.21789 383.3425 7.015947 Coefficients of linear discriminants: LD1 crim 0.0012515925 zn 0.0095179029 indus -0.0166376334 chas 0.1399207112 nox -2.9934367740 rm 0.5612713068 age -0.0128420045 dis -0.3095403096 rad 0.0695027989 tax -0.0027771271 ptratio -0.2059853828 black 0.0006058031 lstat -0.0816668897 Call: lda(X, grouping = Y, prior = prior, method = method, tol = tol, CV = CV, nu = nu) Prior probabilities of groups: 0 1 0.6245059 0.3754941 Group means: crim zn indus chas nox rm age dis 0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307 1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371 rad tax ptratio black lstat 0 11.588608 459.9209 19.19968 340.6392 16.042468 1 6.157895 322.2789 17.21789 383.3425 7.015947 Coefficients of linear discriminants: LD1 crim 0.0012515925 zn 0.0095179029 indus -0.0166376334 chas 0.1399207112 nox -2.9934367740 rm 0.5612713068 age -0.0128420045 dis -0.3095403096 rad 0.0695027989 tax -0.0027771271 ptratio -0.2059853828 black 0.0006058031 lstat -0.0816668897 Call: stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model) Coefficients: (Intercept) crim zn indus chas nox 3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01 rm age dis rad tax ptratio 3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01 black lstat 9.312e-03 -5.248e-01 Call: stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model) Residuals: Min 1Q Median 3Q Max -15.595 -2.730 -0.518 1.777 26.199 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 *** crim -1.080e-01 3.286e-02 -3.287 0.001087 ** zn 4.642e-02 1.373e-02 3.382 0.000778 *** indus 2.056e-02 6.150e-02 0.334 0.738288 chas 2.687e+00 8.616e-01 3.118 0.001925 ** nox -1.777e+01 3.820e+00 -4.651 4.25e-06 *** rm 3.810e+00 4.179e-01 9.116 < 2e-16 *** age 6.922e-04 1.321e-02 0.052 0.958229 dis -1.476e+00 1.995e-01 -7.398 6.01e-13 *** rad 3.060e-01 6.635e-02 4.613 5.07e-06 *** tax -1.233e-02 3.760e-03 -3.280 0.001112 ** ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 *** black 9.312e-03 2.686e-03 3.467 0.000573 *** lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.745 on 492 degrees of freedom Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338 F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16 Call: stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model) Residuals: Min 1Q Median 3Q Max -0.80469 -0.23612 -0.03105 0.23080 1.05224 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6675402 0.3662392 4.553 6.67e-06 *** crim 0.0003028 0.0023585 0.128 0.897888 zn 0.0023028 0.0009851 2.338 0.019808 * indus -0.0040254 0.0044131 -0.912 0.362135 chas 0.0338534 0.0618295 0.548 0.584264 nox -0.7242540 0.2741160 -2.642 0.008501 ** rm 0.1357981 0.0299915 4.528 7.48e-06 *** age -0.0031071 0.0009480 -3.278 0.001121 ** dis -0.0748924 0.0143135 -5.232 2.48e-07 *** rad 0.0168160 0.0047612 3.532 0.000451 *** tax -0.0006719 0.0002699 -2.490 0.013110 * ptratio -0.0498376 0.0093885 -5.308 1.68e-07 *** black 0.0001466 0.0001928 0.760 0.447370 lstat -0.0197591 0.0036395 -5.429 8.91e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3405 on 492 degrees of freedom Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065 F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16 [1] "coefficients" "residuals" "fitted.values" "effects" [5] "weights" "rank" "assign" "qr" [9] "df.residual" "xlevels" "call" "terms" Call: stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model) Residuals: Min 1Q Median 3Q Max -15.595 -2.730 -0.518 1.777 26.199 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 *** crim -1.080e-01 3.286e-02 -3.287 0.001087 ** zn 4.642e-02 1.373e-02 3.382 0.000778 *** indus 2.056e-02 6.150e-02 0.334 0.738288 chas 2.687e+00 8.616e-01 3.118 0.001925 ** nox -1.777e+01 3.820e+00 -4.651 4.25e-06 *** rm 3.810e+00 4.179e-01 9.116 < 2e-16 *** age 6.922e-04 1.321e-02 0.052 0.958229 dis -1.476e+00 1.995e-01 -7.398 6.01e-13 *** rad 3.060e-01 6.635e-02 4.613 5.07e-06 *** tax -1.233e-02 3.760e-03 -3.280 0.001112 ** ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 *** black 9.312e-03 2.686e-03 3.467 0.000573 *** lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 4.745 on 492 degrees of freedom Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338 F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16 Call: stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model) Residuals: Min 1Q Median 3Q Max -0.80469 -0.23612 -0.03105 0.23080 1.05224 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.6675402 0.3662392 4.553 6.67e-06 *** crim 0.0003028 0.0023585 0.128 0.897888 zn 0.0023028 0.0009851 2.338 0.019808 * indus -0.0040254 0.0044131 -0.912 0.362135 chas 0.0338534 0.0618295 0.548 0.584264 nox -0.7242540 0.2741160 -2.642 0.008501 ** rm 0.1357981 0.0299915 4.528 7.48e-06 *** age -0.0031071 0.0009480 -3.278 0.001121 ** dis -0.0748924 0.0143135 -5.232 2.48e-07 *** rad 0.0168160 0.0047612 3.532 0.000451 *** tax -0.0006719 0.0002699 -2.490 0.013110 * ptratio -0.0498376 0.0093885 -5.308 1.68e-07 *** black 0.0001466 0.0001928 0.760 0.447370 lstat -0.0197591 0.0036395 -5.429 8.91e-08 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3405 on 492 degrees of freedom Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065 F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16 Call: SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean", "SL.lm")) Risk Coef SL.mean_All 84.74142 0.0134192 SL.lm_All 23.62549 0.9865808 V1 Min. :-3.921 1st Qu.:17.514 Median :22.124 Mean :22.533 3rd Qu.:27.345 Max. :44.376 Call: SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean", "SL.lm")) Risk Coef SL.mean_All 0.2358036 0 SL.lm_All 0.1115499 1 V1 Min. :0.0000 1st Qu.:0.1281 Median :0.3530 Mean :0.3899 3rd Qu.:0.6091 Max. :1.0000 Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library, method = "method.NNLS", verbose = F, cvControl = list(V = 2)) Risk Coef SL.rpart_All 0.2219962 0 SL.glmnet_All 0.1675829 1 SL.mean_All 0.2491000 0 Error in (function (Y, X, newX, ...) : bad algorithm Error in (function (Y, X, newX, ...) : bad algorithm Error in (function (Y, X, newX, ...) : bad algorithm Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library, "SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2)) Risk Coef SL.rpart_All 0.2062976 0.00000000 SL.glmnet_All 0.1656934 0.97040848 SL.mean_All 0.2464000 0.02959152 SL.bad_algorithm_All NA 0.00000000 Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library, method = "method.NNLS2", verbose = F, cvControl = list(V = 2)) Risk Coef SL.rpart_All 0.2071145 0.000000000 SL.glmnet_All 0.1644480 0.996278512 SL.mean_All 0.2491000 0.003721488 Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library, method = "method.NNloglik", verbose = F, cvControl = list(V = 2)) Risk Coef SL.rpart_All 0.5646032 0.1435759 SL.glmnet_All 0.4860594 0.8564241 SL.mean_All 0.6956962 0.0000000 Error in (function (Y, X, newX, ...) : bad algorithm Error in (function (Y, X, newX, ...) : bad algorithm Error in (function (Y, X, newX, ...) : bad algorithm Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library, "SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2)) Risk Coef SL.rpart_All Inf 0 SL.glmnet_All 0.4866903 1 SL.mean_All 0.7160070 0 SL.bad_algorithm_All NA 0 Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library, method = "method.CC_LS", verbose = F, cvControl = list(V = 2)) Risk Coef SL.rpart_All 0.2058305 0 SL.glmnet_All 0.1542369 1 SL.mean_All 0.2464000 0 Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library, method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2)) Risk Coef SL.rpart_All 230.4838 0.1247876 SL.glmnet_All 192.7564 0.8752124 SL.mean_All 274.6155 0.0000000 Error in (function (Y, X, newX, ...) : bad algorithm Error in (function (Y, X, newX, ...) : bad algorithm Error in (function (Y, X, newX, ...) : bad algorithm Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library, "SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2)) Risk Coef SL.rpart_All 226.0518 0.173761529 SL.glmnet_All 188.4293 0.818098488 SL.mean_All 274.6155 0.000000000 SL.bad_algorithm_All NA 0.008139983 Saving _problems/test-methods-218.R Saving _problems/test-methods-220.R Saving _problems/test-methods-227.R Saving _problems/test-methods-229.R Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library, method = "method.AUC", verbose = FALSE, cvControl = list(V = 2)) Risk Coef SL.rpart_All 0.2287439 0.5103394 SL.glmnet_All 0.1693385 0.4896606 SL.mean_All 0.6014610 0.0000000 Error in (function (Y, X, newX, ...) : bad algorithm Error in (function (Y, X, newX, ...) : bad algorithm Removing failed learners: SL.bad_algorithm_All Error in (function (Y, X, newX, ...) : bad algorithm Call: SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library, "SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2)) Risk Coef SL.rpart_All 0.2298600 0.3333333 SL.glmnet_All 0.1532062 0.3333333 SL.mean_All 0.5000000 0.3333333 SL.bad_algorithm_All NA 0.0000000 Call: qda(X, grouping = Y, prior = prior, method = method, tol = tol, CV = CV, nu = nu) Prior probabilities of groups: 0 1 0.6245059 0.3754941 Group means: crim zn indus chas nox rm age dis 0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307 1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371 rad tax ptratio black lstat 0 11.588608 459.9209 19.19968 340.6392 16.042468 1 6.157895 322.2789 17.21789 383.3425 7.015947 Call: qda(X, grouping = Y, prior = prior, method = method, tol = tol, CV = CV, nu = nu) Prior probabilities of groups: 0 1 0.6245059 0.3754941 Group means: crim zn indus chas nox rm age dis 0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307 1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371 rad tax ptratio black lstat 0 11.588608 459.9209 19.19968 340.6392 16.042468 1 6.157895 322.2789 17.21789 383.3425 7.015947 Ranger result Call: ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose) Type: Regression Number of trees: 500 Sample size: 506 Number of independent variables: 13 Mtry: 3 Target node size: 5 Variable importance mode: none Splitrule: variance OOB prediction error (MSE): 10.60169 R squared (OOB): 0.8746648 Ranger result Call: ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose) Type: Probability estimation Number of trees: 500 Sample size: 506 Number of independent variables: 13 Mtry: 3 Target node size: 1 Variable importance mode: none Splitrule: gini OOB prediction error (Brier s.): 0.08149979 Ranger result Call: ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose) Type: Regression Number of trees: 500 Sample size: 506 Number of independent variables: 13 Mtry: 3 Target node size: 5 Variable importance mode: none Splitrule: variance OOB prediction error (MSE): 10.39997 R squared (OOB): 0.8770496 Ranger result Call: ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose) Type: Probability estimation Number of trees: 500 Sample size: 506 Number of independent variables: 13 Mtry: 3 Target node size: 1 Variable importance mode: none Splitrule: gini OOB prediction error (Brier s.): 0.08457002 Generalized Linear Model of class 'speedglm': Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k) Coefficients: (Intercept) crim zn indus chas nox 3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01 rm age dis rad tax ptratio 3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01 black lstat 9.312e-03 -5.248e-01 Generalized Linear Model of class 'speedglm': Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k) Coefficients: ------------------------------------------------------------------ Estimate Std. Error t value Pr(>|t|) (Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 *** crim -1.080e-01 0.032865 -3.2865 1.087e-03 ** zn 4.642e-02 0.013727 3.3816 7.781e-04 *** indus 2.056e-02 0.061496 0.3343 7.383e-01 chas 2.687e+00 0.861580 3.1184 1.925e-03 ** nox -1.777e+01 3.819744 -4.6513 4.246e-06 *** rm 3.810e+00 0.417925 9.1161 1.979e-18 *** age 6.922e-04 0.013210 0.0524 9.582e-01 dis -1.476e+00 0.199455 -7.3980 6.013e-13 *** rad 3.060e-01 0.066346 4.6129 5.071e-06 *** tax -1.233e-02 0.003761 -3.2800 1.112e-03 ** ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 *** black 9.312e-03 0.002686 3.4668 5.729e-04 *** lstat -5.248e-01 0.050715 -10.3471 7.777e-23 *** ------------------------------------------------------------------- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 --- null df: 505; null deviance: 42716.3; residuals df: 492; residuals deviance: 11078.78; # obs.: 506; # non-zero weighted obs.: 506; AIC: 3027.609; log Likelihood: -1498.804; RSS: 11078.8; dispersion: 22.51785; iterations: 1; rank: 14; max tolerance: 1e+00; convergence: FALSE. Generalized Linear Model of class 'speedglm': Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k) Coefficients: ------------------------------------------------------------------ Estimate Std. Error z value Pr(>|z|) (Intercept) 10.682635 3.921395 2.7242 6.446e-03 ** crim -0.040649 0.049796 -0.8163 4.143e-01 zn 0.012134 0.010678 1.1364 2.558e-01 indus -0.040715 0.045615 -0.8926 3.721e-01 chas 0.248209 0.653283 0.3799 7.040e-01 nox -3.601085 2.924365 -1.2314 2.182e-01 rm 1.155157 0.374843 3.0817 2.058e-03 ** age -0.018660 0.009319 -2.0023 4.525e-02 * dis -0.518934 0.146286 -3.5474 3.891e-04 *** rad 0.255522 0.061391 4.1622 3.152e-05 *** tax -0.009500 0.003107 -3.0574 2.233e-03 ** ptratio -0.409317 0.103191 -3.9666 7.291e-05 *** black -0.001451 0.002558 -0.5674 5.704e-01 lstat -0.318436 0.054735 -5.8178 5.964e-09 *** ------------------------------------------------------------------- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 --- null df: 505; null deviance: 669.76; residuals df: 492; residuals deviance: 296.39; # obs.: 506; # non-zero weighted obs.: 506; AIC: 324.3944; log Likelihood: -148.1972; RSS: 1107.5; dispersion: 1; iterations: 7; rank: 14; max tolerance: 7.55e-12; convergence: TRUE. Generalized Linear Model of class 'speedglm': Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k) Coefficients: ------------------------------------------------------------------ Estimate Std. Error t value Pr(>|t|) (Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 *** crim -1.080e-01 0.032865 -3.2865 1.087e-03 ** zn 4.642e-02 0.013727 3.3816 7.781e-04 *** indus 2.056e-02 0.061496 0.3343 7.383e-01 chas 2.687e+00 0.861580 3.1184 1.925e-03 ** nox -1.777e+01 3.819744 -4.6513 4.246e-06 *** rm 3.810e+00 0.417925 9.1161 1.979e-18 *** age 6.922e-04 0.013210 0.0524 9.582e-01 dis -1.476e+00 0.199455 -7.3980 6.013e-13 *** rad 3.060e-01 0.066346 4.6129 5.071e-06 *** tax -1.233e-02 0.003761 -3.2800 1.112e-03 ** ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 *** black 9.312e-03 0.002686 3.4668 5.729e-04 *** lstat -5.248e-01 0.050715 -10.3471 7.777e-23 *** ------------------------------------------------------------------- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 --- null df: 505; null deviance: 42716.3; residuals df: 492; residuals deviance: 11078.78; # obs.: 506; # non-zero weighted obs.: 506; AIC: 3027.609; log Likelihood: -1498.804; RSS: 11078.8; dispersion: 22.51785; iterations: 1; rank: 14; max tolerance: 1e+00; convergence: FALSE. Generalized Linear Model of class 'speedglm': Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k) Coefficients: ------------------------------------------------------------------ Estimate Std. Error z value Pr(>|z|) (Intercept) 10.682635 3.921395 2.7242 6.446e-03 ** crim -0.040649 0.049796 -0.8163 4.143e-01 zn 0.012134 0.010678 1.1364 2.558e-01 indus -0.040715 0.045615 -0.8926 3.721e-01 chas 0.248209 0.653283 0.3799 7.040e-01 nox -3.601085 2.924365 -1.2314 2.182e-01 rm 1.155157 0.374843 3.0817 2.058e-03 ** age -0.018660 0.009319 -2.0023 4.525e-02 * dis -0.518934 0.146286 -3.5474 3.891e-04 *** rad 0.255522 0.061391 4.1622 3.152e-05 *** tax -0.009500 0.003107 -3.0574 2.233e-03 ** ptratio -0.409317 0.103191 -3.9666 7.291e-05 *** black -0.001451 0.002558 -0.5674 5.704e-01 lstat -0.318436 0.054735 -5.8178 5.964e-09 *** ------------------------------------------------------------------- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 --- null df: 505; null deviance: 669.76; residuals df: 492; residuals deviance: 296.39; # obs.: 506; # non-zero weighted obs.: 506; AIC: 324.3944; log Likelihood: -148.1972; RSS: 1107.5; dispersion: 1; iterations: 7; rank: 14; max tolerance: 7.55e-12; convergence: TRUE. Linear Regression Model of class 'speedlm': Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) Coefficients: (Intercept) crim zn indus chas nox 3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01 rm age dis rad tax ptratio 3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01 black lstat 9.312e-03 -5.248e-01 Linear Regression Model of class 'speedlm': Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) Coefficients: ------------------------------------------------------------------ coef se t p.value (Intercept) 36.459488 5.103459 7.144 3.283e-12 *** crim -0.108011 0.032865 -3.287 1.087e-03 ** zn 0.046420 0.013727 3.382 7.781e-04 *** indus 0.020559 0.061496 0.334 7.383e-01 chas 2.686734 0.861580 3.118 1.925e-03 ** nox -17.766611 3.819744 -4.651 4.246e-06 *** rm 3.809865 0.417925 9.116 1.979e-18 *** age 0.000692 0.013210 0.052 9.582e-01 dis -1.475567 0.199455 -7.398 6.013e-13 *** rad 0.306049 0.066346 4.613 5.071e-06 *** tax -0.012335 0.003761 -3.280 1.112e-03 ** ptratio -0.952747 0.130827 -7.283 1.309e-12 *** black 0.009312 0.002686 3.467 5.729e-04 *** lstat -0.524758 0.050715 -10.347 7.777e-23 *** ------------------------------------------------------------------- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 --- Residual standard error: 4.745298 on 492 degrees of freedom; observations: 506; R^2: 0.741; adjusted R^2: 0.734; F-statistic: 108.1 on 13 and 492 df; p-value: 0. Linear Regression Model of class 'speedlm': Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) Coefficients: ------------------------------------------------------------------ coef se t p.value (Intercept) 1.667540 0.366239 4.553 6.670e-06 *** crim 0.000303 0.002358 0.128 8.979e-01 zn 0.002303 0.000985 2.338 1.981e-02 * indus -0.004025 0.004413 -0.912 3.621e-01 chas 0.033853 0.061829 0.548 5.843e-01 nox -0.724254 0.274116 -2.642 8.501e-03 ** rm 0.135798 0.029992 4.528 7.483e-06 *** age -0.003107 0.000948 -3.278 1.121e-03 ** dis -0.074892 0.014313 -5.232 2.482e-07 *** rad 0.016816 0.004761 3.532 4.515e-04 *** tax -0.000672 0.000270 -2.490 1.311e-02 * ptratio -0.049838 0.009389 -5.308 1.677e-07 *** black 0.000147 0.000193 0.760 4.474e-01 lstat -0.019759 0.003639 -5.429 8.912e-08 *** ------------------------------------------------------------------- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 --- Residual standard error: 0.340537 on 492 degrees of freedom; observations: 506; R^2: 0.519; adjusted R^2: 0.506; F-statistic: 40.86 on 13 and 492 df; p-value: 0. Linear Regression Model of class 'speedlm': Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) Coefficients: ------------------------------------------------------------------ coef se t p.value (Intercept) 36.459488 5.103459 7.144 3.283e-12 *** crim -0.108011 0.032865 -3.287 1.087e-03 ** zn 0.046420 0.013727 3.382 7.781e-04 *** indus 0.020559 0.061496 0.334 7.383e-01 chas 2.686734 0.861580 3.118 1.925e-03 ** nox -17.766611 3.819744 -4.651 4.246e-06 *** rm 3.809865 0.417925 9.116 1.979e-18 *** age 0.000692 0.013210 0.052 9.582e-01 dis -1.475567 0.199455 -7.398 6.013e-13 *** rad 0.306049 0.066346 4.613 5.071e-06 *** tax -0.012335 0.003761 -3.280 1.112e-03 ** ptratio -0.952747 0.130827 -7.283 1.309e-12 *** black 0.009312 0.002686 3.467 5.729e-04 *** lstat -0.524758 0.050715 -10.347 7.777e-23 *** ------------------------------------------------------------------- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 --- Residual standard error: 4.745298 on 492 degrees of freedom; observations: 506; R^2: 0.741; adjusted R^2: 0.734; F-statistic: 108.1 on 13 and 492 df; p-value: 0. Linear Regression Model of class 'speedlm': Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights) Coefficients: ------------------------------------------------------------------ coef se t p.value (Intercept) 1.667540 0.366239 4.553 6.670e-06 *** crim 0.000303 0.002358 0.128 8.979e-01 zn 0.002303 0.000985 2.338 1.981e-02 * indus -0.004025 0.004413 -0.912 3.621e-01 chas 0.033853 0.061829 0.548 5.843e-01 nox -0.724254 0.274116 -2.642 8.501e-03 ** rm 0.135798 0.029992 4.528 7.483e-06 *** age -0.003107 0.000948 -3.278 1.121e-03 ** dis -0.074892 0.014313 -5.232 2.482e-07 *** rad 0.016816 0.004761 3.532 4.515e-04 *** tax -0.000672 0.000270 -2.490 1.311e-02 * ptratio -0.049838 0.009389 -5.308 1.677e-07 *** black 0.000147 0.000193 0.760 4.474e-01 lstat -0.019759 0.003639 -5.429 8.912e-08 *** ------------------------------------------------------------------- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 --- Residual standard error: 0.340537 on 492 degrees of freedom; observations: 506; R^2: 0.519; adjusted R^2: 0.506; F-statistic: 40.86 on 13 and 492 df; p-value: 0. [ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ] ══ Skipped tests (4) ═══════════════════════════════════════════════════════════ • empty test (4): , , , ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ─────────── Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found ── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ──────── Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found ── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ────────────── Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found ── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ─────── Expected `min(sl_bad$SL.predict)` >= 0. Actual comparison: NA < 0.0 ── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ─────── Expected `max(sl_bad$SL.predict)` <= 1. Actual comparison: NA > 1.0 ── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ─────── Expected `min(pred$pred)` >= 0. Actual comparison: NA < 0.0 ── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ─────── Expected `max(pred$pred)` <= 1. Actual comparison: NA > 1.0 [ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ] Error: ! Test failures. Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... [136s] OK * checking PDF version of manual ... [21s] OK * checking HTML version of manual ... [13s] OK * DONE Status: 2 ERRORs