Running ‘testthat.R’ [158s/340s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(SuperLearner)
  Loading required package: nnls
  Super Learner
  Version: 2.0-24
  Package created on 2018-08-10
  
  > 
  > 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 0.61898464
  SL.xgboost_All 1.217085 0.20113361
  SL.xgb.1_All   1.168038 0.00000000
  SL.xgb.2_All   1.088324 0.13391580
  SL.xgb.3_All   1.157705 0.00000000
  SL.xgb.4_All   1.084352 0.04596594
  SL.xgb.5_All   1.345353 0.00000000
  SL.xgb.6_All   1.317258 0.00000000
  SL.xgb.7_All   1.343700 0.00000000
  SL.xgb.8_All   1.315246 0.00000000
  SL.xgb.9_All   1.372713 0.00000000
  SL.xgb.10_All  1.369567 0.00000000
  SL.xgb.11_All  1.372352 0.00000000
  SL.xgb.12_All  1.368851 0.00000000
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0xc0c0ce8>, jclass = "java/lang/Throwable")
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0x4a1ad58>, jclass = "java/lang/Throwable")
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0xc0c0e38>, jclass = "java/lang/Throwable")
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0xb1300a0>, jclass = "java/lang/Throwable")
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0x85ef8a0>, jclass = "java/lang/Throwable")
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0xa1b3d48>, jclass = "java/lang/Throwable")
  ── 1. Error: (unknown) (@test-bartMachine.R#29)  ───────────────────────────────
  All metalearner coefficients are zero, cannot compute prediction.
  1: SuperLearner(Y = Y_reg, X = X, SL.library = SL.library, cvControl = list(V = 2), 
         family = gaussian()) at testthat/test-bartMachine.R:29
  2: method$computePred(predY = predY, coef = coef, control = control)
  3: stop("All metalearner coefficients are zero, cannot compute prediction.")
  
  lasso-penalized linear regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0073):
  -------------------------------------------------
    Nonzero coefficients: 12
    Cross-validation error (deviance): 24.60
    R-squared: 0.71
    Signal-to-noise ratio: 2.43
    Scale estimate (sigma): 4.960
  lasso-penalized logistic regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0017):
  -------------------------------------------------
    Nonzero coefficients: 13
    Cross-validation error (deviance): 0.64
    R-squared: 0.50
    Signal-to-noise ratio: 0.98
    Prediction error: 0.125
  lasso-penalized linear regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0068):
  -------------------------------------------------
    Nonzero coefficients: 12
    Cross-validation error (deviance): 23.94
    R-squared: 0.72
    Signal-to-noise ratio: 2.53
    Scale estimate (sigma): 4.893
  lasso-penalized logistic regression with n=506, p=13
  At minimum cross-validation error (lambda=0.0018):
  -------------------------------------------------
    Nonzero coefficients: 13
    Cross-validation error (deviance): 0.64
    R-squared: 0.50
    Signal-to-noise ratio: 0.99
    Prediction error: 0.123
  
  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     84.42119 0.04698365
  SL.biglasso_All 25.47217 0.95301635
  
  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     0.23918511    0
  SL.biglasso_All 0.09457758    1
  Y
   0  1 
  53 47 
  $grid
  NULL
  
  $names
  [1] "SL.randomForest_1"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  $params$ntree
  [1] 100
  
  
  [1] "SL.randomForest_1" "X"                 "Y"                
  [4] "create_rf"         "data"             
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2)) 
  
  
                            Risk Coef
  SL.randomForest_1_All 0.047901    1
  $grid
    mtry
  1    1
  2    4
  3   20
  
  $names
  [1] "SL.randomForest_1" "SL.randomForest_2" "SL.randomForest_3"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  list()
  
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2)) 
  
  
                              Risk      Coef
  SL.randomForest_1_All 0.06229353 0.1023683
  SL.randomForest_2_All 0.05076692 0.8976317
  SL.randomForest_3_All 0.05654268 0.0000000
  $grid
    alpha
  1  0.00
  2  0.25
  3  0.50
  4  0.75
  5  1.00
  
  $names
  [1] "SL.glmnet_0"    "SL.glmnet_0.25" "SL.glmnet_0.5"  "SL.glmnet_0.75"
  [5] "SL.glmnet_1"   
  
  $base_learner
  [1] "SL.glmnet"
  
  $params
  list()
  
  [1] "SL.glmnet_0"    "SL.glmnet_0.25" "SL.glmnet_0.5"  "SL.glmnet_0.75"
  [5] "SL.glmnet_1"   
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = ls(learners),  
      cvControl = list(V = 2), env = learners) 
  
  
                           Risk Coef
  SL.glmnet_0_All    0.06578497    0
  SL.glmnet_0.25_All 0.06393436    1
  SL.glmnet_0.5_All  0.06552848    0
  SL.glmnet_0.75_All 0.06939863    0
  SL.glmnet_1_All    0.08319900    0
  
  Call:  
  SuperLearner(Y = Y, X = X_clean, family = binomial(), SL.library = c("SL.mean",  
      svm$names), cvControl = list(V = 3)) 
  
  
                              Risk      Coef
  SL.mean_All           0.25993856 0.0000000
  SL.svm_polynomial_All 0.07494856 0.4917546
  SL.svm_radial_All     0.07714246 0.0000000
  SL.svm_sigmoid_All    0.07428697 0.5082454
                  Length Class  Mode   
  call               24  -none- call   
  first.sigma       100  -none- numeric
  sigma            1000  -none- numeric
  sigest              1  -none- numeric
  yhat.train      30000  -none- numeric
  yhat.train.mean    30  -none- numeric
  yhat.test       30000  -none- numeric
  yhat.test.mean     30  -none- numeric
  varcount        13000  -none- numeric
  y                  30  -none- numeric
               Length Class  Mode   
  call            24  -none- call   
  yhat.train   30000  -none- numeric
  yhat.test    30000  -none- numeric
  varcount     13000  -none- numeric
  binaryOffset    30  -none- numeric
                  Length Class  Mode   
  call               24  -none- call   
  first.sigma       100  -none- numeric
  sigma            1000  -none- numeric
  sigest              1  -none- numeric
  yhat.train      30000  -none- numeric
  yhat.train.mean    30  -none- numeric
  yhat.test       30000  -none- numeric
  yhat.test.mean     30  -none- numeric
  varcount        13000  -none- numeric
  y                  30  -none- numeric
               Length Class  Mode   
  call            24  -none- call   
  yhat.train   30000  -none- numeric
  yhat.test    30000  -none- numeric
  varcount     13000  -none- numeric
  binaryOffset    30  -none- numeric
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0xb1300b0>, jclass = "java/lang/Throwable")
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0xc0c0cf0>, jclass = "java/lang/Throwable")
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0xd340940>, jclass = "java/lang/Throwable")
  
  Call:  
  SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",  
      "SL.dbarts", "SL.bartMachine"), cvControl = list(V = 2)) 
  
  
                         Risk Coef
  SL.mean_All        85.79987    0
  SL.dbarts_All      37.97945    1
  SL.bartMachine_All       NA    0
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0xcf251f0>, jclass = "java/lang/Throwable")
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0xf26beb0>, jclass = "java/lang/Throwable")
  Error in .jcheck(silent = FALSE) : 
    Java Exception <no description because toString() failed>.jcall(o, "Ljava/lang/Class;", "getClass")new("jobjRef", jobj = <pointer: 0x7ffd900>, jclass = "java/lang/Throwable")
  
  Call:  
  SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
      "SL.dbarts", "SL.bartMachine"), cvControl = list(V = 2)) 
  
  
                          Risk Coef
  SL.mean_All        0.1955556    0
  SL.dbarts_All      0.1524522    1
  SL.bartMachine_All        NA    0
  ExtraTrees:
   - # of trees: 500
   - node size:  5
   - # of dim:   13
   - # of tries: 4
   - type:       numeric (regression)
   - multi-task: no
  ExtraTrees:
   - # of trees: 500
   - node size:  1
   - # of dim:   13
   - # of tries: 3
   - type:       factor (classification)
   - multi-task: no
  
  Call:  
  SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = sl_lib,  
      cvControl = list(V = 2)) 
  
  
                          Risk     Coef
  SL.extraTrees_All 0.09014668 0.378871
  SL.ranger_All     0.08937403 0.621129
  SL.mean_All       0.26220000 0.000000
  $grid
  NULL
  
  $names
  [1] "SL.extraTrees_1"
  
  $base_learner
  [1] "SL.extraTrees"
  
  $params
  list()
  
  
  Call:  
  SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
      lib$names), cvControl = list(V = 2)) 
  
  
                           Risk       Coef
  SL.mean_All         0.2406000 0.01438319
  SL.extraTrees_1_All 0.1329644 0.98561681
  SL.extraTrees_1 <- function(...) SL.extraTrees(...) 
  $grid
  NULL
  
  $names
  [1] "SL.extraTrees_1"
  
  $base_learner
  [1] "SL.extraTrees"
  
  $params
  list()
  
  [1] "SL.extraTrees_1"
  [1] 1
  
  Call:  
  SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
      lib$names), cvControl = list(V = 2), env = sl_env) 
  
  
                           Risk Coef
  SL.mean_All         0.2622000    0
  SL.extraTrees_1_All 0.1064021    1
  $grid
    mtry
  1    1
  2    2
  
  $names
  [1] "SL.extraTrees_1" "SL.extraTrees_2"
  
  $base_learner
  [1] "SL.extraTrees"
  
  $params
  list()
  
  [1] "SL.extraTrees_1" "SL.extraTrees_2"
  
  Call:  
  SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
      lib$names), cvControl = list(V = 2), env = sl_env) 
  
  
                           Risk Coef
  SL.mean_All         0.2382000    0
  SL.extraTrees_1_All 0.1120817    0
  SL.extraTrees_2_All 0.1063530    1
  $grid
    mtry
  1    1
  2    2
  
  $names
  [1] "SL.extraTrees_1" "SL.extraTrees_2"
  
  $base_learner
  [1] "SL.extraTrees"
  
  $params
  list()
  
  [1] "SL.extraTrees_1" "SL.extraTrees_2"
  
  Call:  
  SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
      lib$names), cvControl = list(V = 2), env = sl_env) 
  
  
                           Risk Coef
  SL.mean_All         0.2454000    0
  SL.extraTrees_1_All 0.1334782    0
  SL.extraTrees_2_All 0.1229371    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)
  
  Deviance 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
  
  (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)
  
  Deviance Residuals: 
      Min       1Q   Median       3Q      Max  
  -2.9747  -0.4250  -0.0911   0.2720   3.5929  
  
  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)
  
  Deviance 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
  
  (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)
  
  Deviance Residuals: 
      Min       1Q   Median       3Q      Max  
  -2.9747  -0.4250  -0.0911   0.2720   3.5929  
  
  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 84.6696 0.02186479
  SL.glm_All  24.3340 0.97813521
         V1        
   Min.   :-3.695  
   1st Qu.:17.557  
   Median :22.128  
   Mean   :22.533  
   3rd Qu.:27.303  
   Max.   :44.189  
  
  Call:  
  SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
      "SL.glm")) 
  
  
                    Risk       Coef
  SL.mean_All 0.23493657 0.01637138
  SL.glm_All  0.09601729 0.98362862
         V1          
   Min.   :0.006149  
   1st Qu.:0.036531  
   Median :0.196806  
   Mean   :0.375494  
   3rd Qu.:0.780364  
   Max.   :0.989308  
  Got an error, as expected.
  <simpleError in cbind2(1, newx) %*% nbeta: Cholmod error 'X and/or Y have wrong dimensions' at file ../MatrixOps/cholmod_sdmult.c, line 90>
  Got an error, as expected.
  <simpleError in cbind2(1, newx) %*% nbeta: Cholmod error 'X and/or Y have wrong dimensions' at file ../MatrixOps/cholmod_sdmult.c, line 90>
  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 84.6696 0.02186479
  SL.lm_All   24.3340 0.97813521
         V1        
   Min.   :-3.695  
   1st Qu.:17.557  
   Median :22.128  
   Mean   :22.533  
   3rd Qu.:27.303  
   Max.   :44.189  
  
  Call:  
  SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",  
      "SL.lm")) 
  
  
                   Risk Coef
  SL.mean_All 0.2349366    0
  SL.lm_All   0.1125027    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  0.1986827 0.31226655
  SL.glmnet_All 0.1803963 0.66105261
  SL.mean_All   0.2534500 0.02668084
  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         0.1921176 0.08939677
  SL.glmnet_All        0.1635548 0.91060323
  SL.mean_All          0.2504500 0.00000000
  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  0.2279346 0.05397859
  SL.glmnet_All 0.1670620 0.94602141
  SL.mean_All   0.2504500 0.00000000
  
  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  0.5804469 0.1760951
  SL.glmnet_All 0.5010294 0.8239049
  SL.mean_All   0.6964542 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 0.1338597
  SL.glmnet_All        0.5027498 0.8661403
  SL.mean_All          0.7000679 0.0000000
  SL.bad_algorithm_All        NA 0.0000000
  
  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  0.2033781 0.16438434
  SL.glmnet_All 0.1740498 0.82391928
  SL.mean_All   0.2516500 0.01169638
  
  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  295.8455 0.1014591
  SL.glmnet_All 205.3289 0.7867610
  SL.mean_All   277.1389 0.1117798
  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         212.5569 0.2707202
  SL.glmnet_All        193.9384 0.7292798
  SL.mean_All          277.1389 0.0000000
  SL.bad_algorithm_All       NA 0.0000000
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,  
      method = "method.AUC", verbose = F, cvControl = list(V = 2)) 
  
  
                     Risk      Coef
  SL.rpart_All  0.2533780 0.3333333
  SL.glmnet_All 0.1869683 0.3333333
  SL.mean_All   0.5550495 0.3333333
  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 = T, cvControl = list(V = 2)) 
  
  
  
                            Risk      Coef
  SL.rpart_All         0.2467721 0.2982123
  SL.glmnet_All        0.1705535 0.3508938
  SL.mean_All          0.5150135 0.3508938
  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
  Y
   0  1 
  62 38 
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = sl_lib, cvControl = list(V = 2)) 
  
  
  
                            Risk Coef
  SL.randomForest_All 0.04141665    1
  SL.mean_All         0.23560000    0
  $grid
  NULL
  
  $names
  [1] "SL.randomForest_1"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  list()
  
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2)) 
  
  
                              Risk Coef
  SL.randomForest_1_All 0.06761492    1
  SL.randomForest_1 <- function(...) SL.randomForest(...) 
  $grid
  NULL
  
  $names
  [1] "SL.randomForest_1"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  list()
  
  [1] "SL.randomForest_1"
  [1] 1
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2), env = sl_env) 
  
  
                            Risk Coef
  SL.randomForest_1_All 0.056687    1
  $grid
    mtry
  1    1
  2    2
  
  $names
  [1] "SL.randomForest_1" "SL.randomForest_2"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  list()
  
  [1] "SL.randomForest_1" "SL.randomForest_2"
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2), env = sl_env) 
  
  
                              Risk      Coef
  SL.randomForest_1_All 0.05775282 0.2130143
  SL.randomForest_2_All 0.05021309 0.7869857
  $grid
    mtry
  1    1
  2    2
  
  $names
  [1] "SL.randomForest_1" "SL.randomForest_2"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  list()
  
  [1] "SL.randomForest_1" "SL.randomForest_2"
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2), env = sl_env) 
  
  
                              Risk      Coef
  SL.randomForest_1_All 0.04492449 0.4071852
  SL.randomForest_2_All 0.03961866 0.5928148
  $grid
    mtry nodesize maxnodes
  1    1     NULL     NULL
  2    2     NULL     NULL
  
  $names
  [1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  list()
  
  [1] "SL.randomForest_1_NULL_NULL" "SL.randomForest_2_NULL_NULL"
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2), env = sl_env) 
  
  
                                        Risk      Coef
  SL.randomForest_1_NULL_NULL_All 0.04645335 0.2596168
  SL.randomForest_2_NULL_NULL_All 0.04111806 0.7403832
  $grid
    mtry maxnodes
  1    1        5
  2    2        5
  3    1       10
  4    2       10
  5    1     NULL
  6    2     NULL
  
  $names
  [1] "SL.randomForest_1_5"    "SL.randomForest_2_5"    "SL.randomForest_1_10"  
  [4] "SL.randomForest_2_10"   "SL.randomForest_1_NULL" "SL.randomForest_2_NULL"
  
  $base_learner
  [1] "SL.randomForest"
  
  $params
  list()
  
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2), env = sl_env) 
  
  
                                   Risk      Coef
  SL.randomForest_1_5_All    0.05181234 0.0000000
  SL.randomForest_2_5_All    0.04334241 0.0000000
  SL.randomForest_1_10_All   0.04782853 0.3745053
  SL.randomForest_2_10_All   0.04335836 0.6254947
  SL.randomForest_1_NULL_All 0.04870680 0.0000000
  SL.randomForest_2_NULL_All 0.04383429 0.0000000
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2)) 
  
  
                                   Risk Coef
  SL.randomForest_1_5_All    0.05171393    0
  SL.randomForest_2_5_All    0.04183487    0
  SL.randomForest_1_10_All   0.04885838    0
  SL.randomForest_2_10_All   0.03917066    1
  SL.randomForest_1_NULL_All 0.04730564    0
  SL.randomForest_2_NULL_All 0.04117284    0
  
  Call:  
  SuperLearner(Y = Y, X = X, family = binomial(), SL.library = create_rf$names,  
      cvControl = list(V = 2)) 
  
  
                                   Risk       Coef
  SL.randomForest_1_5_All    0.05147493 0.00000000
  SL.randomForest_2_5_All    0.04114267 0.00000000
  SL.randomForest_1_10_All   0.04763217 0.00000000
  SL.randomForest_2_10_All   0.03809721 0.07433538
  SL.randomForest_1_NULL_All 0.04806088 0.00000000
  SL.randomForest_2_NULL_All 0.03820266 0.92566462
  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):       10.61927 
  R squared (OOB):                  0.874457 
  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.):  0.08262419 
  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):       10.48759 
  R squared (OOB):                  0.8760137 
  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.):  0.08395011 
  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 3.28e-12 ***
  crim        -1.080e-01   0.032865  -3.2865 1.09e-03 ** 
  zn           4.642e-02   0.013728   3.3816 7.78e-04 ***
  indus        2.056e-02   0.061496   0.3343 7.38e-01    
  chas         2.687e+00   0.861580   3.1184 1.93e-03 ** 
  nox         -1.777e+01   3.819744  -4.6513 4.25e-06 ***
  rm           3.810e+00   0.417925   9.1161 1.98e-18 ***
  age          6.922e-04   0.013210   0.0524 9.58e-01    
  dis         -1.476e+00   0.199455  -7.3980 6.01e-13 ***
  rad          3.060e-01   0.066346   4.6129 5.07e-06 ***
  tax         -1.233e-02   0.003760  -3.2800 1.11e-03 ** 
  ptratio     -9.527e-01   0.130827  -7.2825 1.31e-12 ***
  black        9.312e-03   0.002686   3.4668 5.73e-04 ***
  lstat       -5.248e-01   0.050715 -10.3471 7.78e-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 6.45e-03 ** 
  crim        -0.040649   0.049796 -0.8163 4.14e-01    
  zn           0.012134   0.010678  1.1364 2.56e-01    
  indus       -0.040715   0.045615 -0.8926 3.72e-01    
  chas         0.248209   0.653283  0.3799 7.04e-01    
  nox         -3.601085   2.924365 -1.2314 2.18e-01    
  rm           1.155156   0.374843  3.0817 2.06e-03 ** 
  age         -0.018661   0.009319 -2.0023 4.53e-02 *  
  dis         -0.518934   0.146286 -3.5474 3.89e-04 ***
  rad          0.255522   0.061391  4.1622 3.15e-05 ***
  tax         -0.009500   0.003107 -3.0574 2.23e-03 ** 
  ptratio     -0.409317   0.103191 -3.9666 7.29e-05 ***
  black       -0.001452   0.002558 -0.5674 5.70e-01    
  lstat       -0.318436   0.054735 -5.8178 5.96e-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 3.28e-12 ***
  crim        -1.080e-01   0.032865  -3.2865 1.09e-03 ** 
  zn           4.642e-02   0.013728   3.3816 7.78e-04 ***
  indus        2.056e-02   0.061496   0.3343 7.38e-01    
  chas         2.687e+00   0.861580   3.1184 1.93e-03 ** 
  nox         -1.777e+01   3.819744  -4.6513 4.25e-06 ***
  rm           3.810e+00   0.417925   9.1161 1.98e-18 ***
  age          6.922e-04   0.013210   0.0524 9.58e-01    
  dis         -1.476e+00   0.199455  -7.3980 6.01e-13 ***
  rad          3.060e-01   0.066346   4.6129 5.07e-06 ***
  tax         -1.233e-02   0.003760  -3.2800 1.11e-03 ** 
  ptratio     -9.527e-01   0.130827  -7.2825 1.31e-12 ***
  black        9.312e-03   0.002686   3.4668 5.73e-04 ***
  lstat       -5.248e-01   0.050715 -10.3471 7.78e-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 6.45e-03 ** 
  crim        -0.040649   0.049796 -0.8163 4.14e-01    
  zn           0.012134   0.010678  1.1364 2.56e-01    
  indus       -0.040715   0.045615 -0.8926 3.72e-01    
  chas         0.248209   0.653283  0.3799 7.04e-01    
  nox         -3.601085   2.924365 -1.2314 2.18e-01    
  rm           1.155156   0.374843  3.0817 2.06e-03 ** 
  age         -0.018661   0.009319 -2.0023 4.53e-02 *  
  dis         -0.518934   0.146286 -3.5474 3.89e-04 ***
  rad          0.255522   0.061391  4.1622 3.15e-05 ***
  tax         -0.009500   0.003107 -3.0574 2.23e-03 ** 
  ptratio     -0.409317   0.103191 -3.9666 7.29e-05 ***
  black       -0.001452   0.002558 -0.5674 5.70e-01    
  lstat       -0.318436   0.054735 -5.8178 5.96e-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.
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 68 SKIPPED: 0 WARNINGS: 39 FAILED: 1
  1. Error: (unknown) (@test-bartMachine.R#29) 
  
  Error: testthat unit tests failed
  Execution halted