Running ‘testthat.R’ [9s/12s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(ck37r)
  > 
  > test_check("ck37r")
  Running SL via snow
  Found 0 text files in "inst/extdata" to import.
  Found 0 text files in "inst/extdata../" to import.
  
  
  These packages need to be installed: ck37_blah123 
  install.packages(c("ck37_blah123")) 
  Auto-installing from repository: @CRAN@ 
  Error in install.packages(pkgs[!result], ...) : 
    unable to install packages
  Removing 3 indicators that are constant.
  Local physical cores detected: 16 
  Our BLAS is setup for 1 threads and OMP is 1 threads.
  doPar backend registered: doSEQ 
  Workers enabled: 1 
  Local physical cores detected: 16 
  Our BLAS is setup for 1 threads and OMP is 1 threads.
  doPar backend registered: doSEQ 
  Workers enabled: 1 
  ── 1. Error: (unknown) (@test-sl_plot_roc.R#13)  ───────────────────────────────
  VECTOR_ELT() can only be applied to a 'list', not a 'double'
  1: sl_plot_roc(sl, Y = Boston$chas) at testthat/test-sl_plot_roc.R:13
  2: print(ggplot2::qplot(1 - methods::slot(perf1, "x.values")[[1]], methods::slot(perf1, 
         "y.values")[[1]], xlab = "1 - Specificity (false positives)", ylab = "Sensitivity (true positives)", 
         geom = "line", main = title) + ggplot2::theme_bw() + ggplot2::annotate("text", 
         x = 0.63, y = 0.15, label = txt, size = 6) + ggplot2::annotate("segment", x = 0, 
         xend = 1, y = 0, yend = 1))
  3: print.ggplot(ggplot2::qplot(1 - methods::slot(perf1, "x.values")[[1]], methods::slot(perf1, 
         "y.values")[[1]], xlab = "1 - Specificity (false positives)", ylab = "Sensitivity (true positives)", 
         geom = "line", main = title) + ggplot2::theme_bw() + ggplot2::annotate("text", 
         x = 0.63, y = 0.15, label = txt, size = 6) + ggplot2::annotate("segment", x = 0, 
         xend = 1, y = 0, yend = 1))
  4: grid.draw(gtable)
  5: grid.draw.gTree(gtable)
  6: recordGraphics(drawGTree(x), list(x = x), getNamespace("grid"))
  7: drawGTree(x)
  8: preDraw(x)
  9: preDraw.gTree(x)
  10: pushvpgp(x)
  11: pushgrobvp(x$vp)
  12: pushgrobvp.viewport(x$vp)
  13: pushViewport(vp, recording = FALSE)
  14: lapply(vps, push.vp, recording)
  15: FUN(X[[i]], ...)
  16: push.vp.viewport(X[[i]], ...)
  17: grid.Call.graphics(C_setviewport, vp, TRUE)
  
  'data.frame':	5000 obs. of  6 variables:
   $ W1: int  1 1 0 1 1 0 0 1 0 0 ...
   $ W2: int  1 1 1 1 1 1 1 0 0 1 ...
   $ W3: num  0.7887 0.9758 0.951 0.6037 0.0303 ...
   $ W4: int  3 2 1 3 2 1 2 1 2 0 ...
   $ A : int  0 0 0 1 0 0 0 1 1 1 ...
   $ Y : int  1 1 1 1 1 0 1 1 0 0 ...
  Local physical cores detected: 16 
  Restricting usage to 2 cores.
  Our BLAS is setup for 1 threads and OMP is 1 threads.
  doPar backend registered: doSEQ 
  Workers enabled: 1 
  X dataframe object size:  0.1  MB
  Stacked df dimensions: 10,000 5 
  Stacked dataframe object size:  0.3  MB
  Estimating Q using custom SuperLearner.
  Q init fit:
  
  
  Call:  
  sl_fn(Y = Y, X = X, family = family, SL.library = Q.SL.library, id = id,  
      verbose = verbose, cvControl = list(V = V)) 
  
  
                   Risk        Coef
  SL.mean_All 0.2386825 0.008542372
  SL.glm_All  0.1761028 0.991457628
  
  Q init times:
  $everything
     user  system elapsed 
    0.132   0.000   0.182 
  
  $train
     user  system elapsed 
    0.099   0.000   0.117 
  
  $predict
     user  system elapsed 
    0.029   0.000   0.061 
  
  
  Q object size: 4.3 Mb
  Estimating g using custom SuperLearner.
  g fit:
  
  
  Call:  
  sl_fn(Y = A, X = W, family = "binomial", SL.library = g.SL.library, id = id,  
      verbose = verbose, cvControl = list(V = V)) 
  
  
                   Risk         Coef
  SL.mean_All 0.2336885 0.0004017322
  SL.glm_All  0.1268937 0.9995982678
  
  g times:
  $everything
     user  system elapsed 
    0.115   0.000   0.243 
  
  $train
     user  system elapsed 
    0.084   0.000   0.164 
  
  $predict
     user  system elapsed 
    0.027   0.000   0.059 
  
  
  g object size: 4.2 Mb
  Passing results to tmle.
  	Estimating missingness mechanism 
  X dataframe object size:  0.1  MB
  Stacked df dimensions: 10,000 5 
  Stacked dataframe object size:  0.3  MB
  Estimating Q using custom SuperLearner.
  Q init fit:
  
  
  Call:  
  sl_fn(Y = Y, X = X, family = family, SL.library = Q.SL.library, id = id,  
      verbose = verbose, cvControl = list(V = V)) 
  
  
                   Risk        Coef
  SL.mean_All 0.2386825 0.008542372
  SL.glm_All  0.1761028 0.991457628
  
  Q init times:
  $everything
     user  system elapsed 
    0.124   0.000   0.128 
  
  $train
     user  system elapsed 
    0.098   0.000   0.099 
  
  $predict
     user  system elapsed 
    0.021   0.000   0.024 
  
  
  Q object size: 4.3 Mb
  Estimating g using custom SuperLearner.
  g fit:
  
  
  Call:  
  sl_fn(Y = A, X = W, family = "binomial", SL.library = g.SL.library, id = id,  
      verbose = verbose, cvControl = list(V = V)) 
  
  
                   Risk         Coef
  SL.mean_All 0.2336885 0.0004017322
  SL.glm_All  0.1268937 0.9995982678
  
  g times:
  $everything
     user  system elapsed 
    0.110   0.000   0.149 
  
  $train
     user  system elapsed 
    0.085   0.000   0.093 
  
  $predict
     user  system elapsed 
    0.019   0.000   0.032 
  
  
  g object size: 4.2 Mb
  Passing results to tmle.
  	Estimating missingness mechanism 
  1.4 Mb
  0.5 Mb
  9.1 Mb
  ── 2. Error: (unknown) (@test_cvsl_plot_roc.R#16)  ─────────────────────────────
  VECTOR_ELT() can only be applied to a 'list', not a 'double'
  1: cvsl_plot_roc(cvsl) at testthat/test_cvsl_plot_roc.R:16
  2: print(ggplot2::qplot(1 - methods::slot(perf1, "x.values")[[1]], methods::slot(perf1, 
         "y.values")[[1]], xlab = "1 - Specificity (false positives)", ylab = "Sensitivity (true positives)", 
         geom = "line", main = title) + ggplot2::theme_bw() + ggplot2::annotate("text", 
         x = 0.63, y = 0.15, label = txt, size = 6) + ggplot2::annotate("segment", x = 0, 
         xend = 1, y = 0, yend = 1))
  3: print.ggplot(ggplot2::qplot(1 - methods::slot(perf1, "x.values")[[1]], methods::slot(perf1, 
         "y.values")[[1]], xlab = "1 - Specificity (false positives)", ylab = "Sensitivity (true positives)", 
         geom = "line", main = title) + ggplot2::theme_bw() + ggplot2::annotate("text", 
         x = 0.63, y = 0.15, label = txt, size = 6) + ggplot2::annotate("segment", x = 0, 
         xend = 1, y = 0, yend = 1))
  4: grid.draw(gtable)
  5: grid.draw.gTree(gtable)
  6: recordGraphics(drawGTree(x), list(x = x), getNamespace("grid"))
  7: drawGTree(x)
  8: preDraw(x)
  9: preDraw.gTree(x)
  10: pushvpgp(x)
  11: pushgrobvp(x$vp)
  12: pushgrobvp.viewport(x$vp)
  13: pushViewport(vp, recording = FALSE)
  14: lapply(vps, push.vp, recording)
  15: FUN(X[[i]], ...)
  16: push.vp.viewport(X[[i]], ...)
  17: grid.Call.graphics(C_setviewport, vp, TRUE)
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 5 SKIPPED: 0 FAILED: 2
  1. Error: (unknown) (@test-sl_plot_roc.R#13) 
  2. Error: (unknown) (@test_cvsl_plot_roc.R#16) 
  
  Error: testthat unit tests failed
  Execution halted