Running ‘test-all.R’ [161s/177s]
Running the tests in ‘tests/test-all.R’ failed.
Complete output:
  > ## unit tests will not be done if RUnit is not available
  > # setwd("..")
  > # getwd()
  > # library(RUnit)
  > if(require("RUnit", quietly=TRUE)) {
  +     ## --- Setup ---
  + 
  +     pkg <- "stremr" # <-- Tested package name
  + 
  +     if(Sys.getenv("RCMDCHECK") == "FALSE") {
  +     ## Path to unit tests for standalone running under Makefile (not R CMD check)
  +     ## PKG/tests/../inst/unitTests
  +     # path <- file.path(getwd(), "..", "inst", "unitTests")
  +     } else {
  +     ## Path to unit tests for R CMD check
  +     ## PKG.Rcheck/tests/../PKG/unitTests
  +     # path <- system.file(package=pkg, "RUnit")
  + 
  +     # REPLACED WITH:
  +     path <- file.path(getwd(), "RUnit")
  +     }
  + 
  +     cat("\nRunning unit tests\n")
  +     print(list(pkg=pkg, getwd=getwd(), pathToUnitTests=path))
  + 
  +     library(package=pkg, character.only=TRUE)
  + 
  +     ## If desired, load the name space to allow testing of private functions
  +     ## if (is.element(pkg, loadedNamespaces()))
  +     ##     attach(loadNamespace(pkg), name=paste("namespace", pkg, sep=":"), pos=3)
  +     ##
  +     ## or simply call PKG:::myPrivateFunction() in tests
  + 
  +     ## --- Testing ---
  + 
  +     ## Define tests
  +     test.suite <- defineTestSuite(name=paste(pkg, "unit testing"),
  +                                         # dirs="./RUnit",
  +                                         dirs=path,
  +                                         testFileRegexp = "^RUnit_tests_+",
  +                                         testFuncRegexp = "^test.+",
  +                                         rngKind = "Marsaglia-Multicarry",
  +                                         rngNormalKind = "Kinderman-Ramage")
  +     ## Run
  +     tests <- runTestSuite(test.suite)
  + 
  +     ## Default report name
  +     pathReport <- file.path(path, "report")
  + 
  +     ## Report to stdout and text files
  +     cat("------------------- UNIT TEST SUMMARY ---------------------\n\n")
  +     printTextProtocol(tests, showDetails=FALSE)
  +     printTextProtocol(tests, showDetails=FALSE,
  +                         fileName=paste0(pathReport, "Summary.txt"))
  +     printTextProtocol(tests, showDetails=TRUE,
  +                         fileName=paste0(pathReport, ".txt"))
  +     ## Report to HTML file
  +     printHTMLProtocol(tests, fileName=paste0(pathReport, ".html"))
  + 
  +     ## Return stop() to cause R CMD check stop in case of
  +     ##  - failures i.e. FALSE to unit tests or
  +     ##  - errors i.e. R errors
  +     tmp <- getErrors(tests)
  +     if(tmp$nFail > 0 | tmp$nErr > 0) {
  +         stop(paste("\n\nunit testing failed (#test failures: ", tmp$nFail,
  +                ", #R errors: ",  tmp$nErr, ")\n\n", sep=""))
  +     }
  +     } else {
  +         warning("cannot run unit tests -- package RUnit is not available")
  + }
  
  Running unit tests
  $pkg
  [1] "stremr"
  
  $getwd
  [1] "/data/gannet/ripley/R/packages/tests-clang/stremr.Rcheck/tests"
  
  $pathToUnitTests
  [1] "/data/gannet/ripley/R/packages/tests-clang/stremr.Rcheck/tests/RUnit"
  
  
  
  Executing test function test.GenericModelPredict  ... Loading required package: data.table
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "speedglm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI, N"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: TI"
  [1] "StratifiedModel expressions: (t == 0L,t > 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI, TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t > 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01  9.238692e-12  2.688942e-12 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept           C     highA1c    lastNat1 
   0.87385792          NA -0.18986136 -0.06706656 
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
     Intercept            C           TI      highA1c     lastNat1 
  -0.025688525           NA           NA  0.001591313 -0.012014026 
  [1] "performing fitting for outcome based on stratified model for outcome: TI"
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept       CVD   highA1c N.tminus1 
  -2.156898  2.117370  2.269857        NA 
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t > 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
   0.6829138  1.4512950 -0.4353432  0.5852046 
  fit for TI var succeeded...
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept 
  -0.01358231 
  [1] "performing prediction for outcome based on stratified model: TI"
  [1] "performing prediction for outcome based on stratified model: TI"
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "speedglm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI, N"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "catA"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: catA"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: catA"
  [1] "StratifiedModel expressions: (t == 0L,t > 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: catA, catA"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "CategorModel outcome: catA"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: catA_B.1, catA_B.2, catA_B.3"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(catA_B.1|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(catA_B.2|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(catA_B.3|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "CategorModel outcome: catA"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: catA_B.1, catA_B.2, catA_B.3"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(catA_B.1|CVD, highA1c, N.tminus1);\\ Stratify: t > 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(catA_B.2|CVD, highA1c, N.tminus1);\\ Stratify: t > 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(catA_B.3|CVD, highA1c, N.tminus1);\\ Stratify: t > 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01  9.238692e-12  2.688942e-12 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept           C     highA1c    lastNat1 
   0.87385792          NA -0.18986136 -0.06706656 
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
     Intercept            C           TI      highA1c     lastNat1 
  -0.025688525           NA           NA  0.001591313 -0.012014026 
  [1] "performing fitting for outcome based on stratified model for outcome: catA"
  making matrix for dummies for cat. censoring, reference category is 0
  [1] "performing fitting for categorical outcome: catA"
  [1] "freq counts by bin for categorical outcome: "
  
     1    2    3 
  4747 4623 4619 
  [1] "binned dataset: "
       sA catA_B.1 catA_B.2 catA_B.3
  [1,]  1       NA        1       NA
  [2,]  2       NA        1       NA
  [3,]  2       NA        1       NA
  [4,]  3       NA        1       NA
  [5,]  1       NA        1       NA
  [1] "fitting the model: P(catA_B.1|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept       CVD   highA1c N.tminus1 
         NA        NA        NA        NA 
  [1] "fitting the model: P(catA_B.2|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept           CVD       highA1c     N.tminus1 
   2.656606e+01 -9.762950e-08  2.155173e-06            NA 
  [1] "fitting the model: P(catA_B.3|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept       CVD   highA1c N.tminus1 
         NA        NA        NA        NA 
  fit for catA var succeeded...
  making matrix for dummies for cat. censoring, reference category is 0
  [1] "performing fitting for categorical outcome: catA"
  [1] "freq counts by bin for categorical outcome: "
  
     1    2    3 
  4747 4623 4619 
  [1] "binned dataset: "
       sA catA_B.1 catA_B.2 catA_B.3
  [1,]  1       NA        1       NA
  [2,]  2       NA        1       NA
  [3,]  2       NA        1       NA
  [4,]  3       NA        1       NA
  [5,]  1       NA        1       NA
  [1] "fitting the model: P(catA_B.1|CVD, highA1c, N.tminus1);\\ Stratify: t > 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept       CVD   highA1c N.tminus1 
         NA        NA        NA        NA 
  [1] "fitting the model: P(catA_B.2|CVD, highA1c, N.tminus1);\\ Stratify: t > 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept           CVD       highA1c     N.tminus1 
   2.656606e+01 -3.738646e-08 -2.346217e-07 -2.013073e-07 
  [1] "fitting the model: P(catA_B.3|CVD, highA1c, N.tminus1);\\ Stratify: t > 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept       CVD   highA1c N.tminus1 
         NA        NA        NA        NA 
  fit for catA var succeeded...
  fit for catA var succeeded...
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept 
  -0.01358231 
  [1] "performing prediction for outcome based on stratified model: catA"
  [1] "performing prediction for categorical outcome: catA"
  [1] "performing prediction for categorical outcome: catA"
  [1] "performing prediction for outcome based on stratified model: catA"
  [1] "performing prediction for categorical outcome: catA"
  making matrix for dummies for cat. censoring, reference category is 0
  [1] "performing prediction for categorical outcome: catA"
  making matrix for dummies for cat. censoring, reference category is 0
   done successfully.
  
  
  
  Executing test function test.Nsamp.n.test  ...  done successfully.
  
  
  
  Executing test function test.options  ... [1] "Possible `stremr` options:"
  [1] "fit.package"        "fit.algorithm"      "bin.method"        
  [4] "nbins"              "maxncats"           "maxNperBin"        
  [7] "lower_bound_zero_Q" "skip_update_zero_Q"
  Error in stremrOptions("blahblah") : 
    blahblah: this options does not exist
  Error in stremrOptions("blahblah", 5) : Invalid option name: blahblah
  Error in if (!(fit.algorithm %in% allowed.fit.algorithm)) stop("fit.algorithm must be one of: " %+%  : 
    argument is of length zero
  Error in set_all_stremr_options(fit.package = "speedglm", fit.algorithm = "blahblah",  : 
    fit.algorithm must be one of: glm, gbm, randomForest, deeplearning
  Error in set_all_stremr_options(fit.package = "blahblah", fit.algorithm = "glm",  : 
    fit.package must be one of: speedglm, glm, h2o
  Error in set_all_stremr_options(fit.package = "speedglm", fit.algorithm = "glm",  : 
    bin.method must be one of: equal.mass, equal.len, dhist
  $fit.package
  [1] "speedglm"
  
  $fit.algorithm
  [1] "glm"
  
  $bin.method
  [1] "equal.mass"
  
  $nbins
  [1] 10
  
  $maxncats
  [1] 20
  
  $maxNperBin
  [1] 500
  
  $lower_bound_zero_Q
  [1] TRUE
  
  $skip_update_zero_Q
  [1] TRUE
  
   done successfully.
  
  
  
  Executing test function test.regressionCases  ...  done successfully.
  
  
  
  Executing test function test.t.error  ...  done successfully.
  
  
  
  Executing test function test.DataStorageClass  ... [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "speedglm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  Error in OData$get.outvar(var = "blah") : 
    requested variable blah does not exist in DataStorageClass!
   done successfully.
  
  
  
  Executing test function test.error.fits.stratify  ... Error in create_subset_expr(outvars = res$outvars, stratify.EXPRS = stratify.EXPRS) : 
    Could not locate the appropriate regression variable(s) within the supplied stratification list stratify_CENS, stratify_TRT or stratify_MONITOR.
  The regression outcome variable(s) specified in gform_CENS, gform_TRT or gform_MONITOR were: ( 'C,TI' )
  However, the item names in the matching stratification list were: ( 'wrongC,TI,N' )
   done successfully.
  
  
  
  Executing test function test.helperfuns  ...  done successfully.
  
  
  
  Executing test function test.model.fits.stratify  ...  done successfully.
  
  
  
  Executing test function test.model.fits.categorCENSOR  ... [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "speedglm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI, N"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept     highA1c    lastNat1 
  -2.52652066  0.05456581  0.10858685 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept           C     highA1c    lastNat1 
   0.19067146          NA  0.43343872 -0.01789425 
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept           C          TI     highA1c    lastNat1 
  -0.01340964          NA          NA -0.03245807 -0.01185101 
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
  0.03161985 1.41249320 0.44869874 0.04224776 
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept 
  -0.02436859 
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "speedglm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI, N"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: C"
  [1] "StratifiedModel expressions: (t == 0L,t > 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, C"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t > 0"
  [1] "StratifiedModel outcome: TI"
  [1] "StratifiedModel expressions: (t == 0L & C == 0,t > 0 & C == 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI, TI"
  [1] "Predictors: C, highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0"
  [1] "StratifiedModel outcome: N"
  [1] "StratifiedModel expressions: (t == 0L & C == 0 & TI == 0,t > 0 & C == 0 & TI == 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N, N"
  [1] "Predictors: C, TI, highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0 & TI == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "performing fitting for outcome based on stratified model for outcome: C"
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t == 0L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01  8.599825e-13            NA 
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t > 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept     highA1c    lastNat1 
  -2.29183433 -0.07998923  0.03081559 
  fit for C var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: TI"
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept         C   highA1c  lastNat1 
  -1.816513        NA  2.835083        NA 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept           C     highA1c    lastNat1 
   0.64534186          NA  0.09366241 -0.16923766 
  fit for TI var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: N"
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0 & TI == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept           C          TI     highA1c    lastNat1 
  -0.01467019          NA          NA -0.45533330          NA 
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0 & TI == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept           C          TI     highA1c    lastNat1 
  -0.01135495          NA          NA -0.02269938 -0.01321850 
  fit for N var succeeded...
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
  0.03161985 1.41249320 0.44869874 0.04224776 
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept 
  -0.02436859 
  [1] "performing prediction for outcome based on stratified model: C"
  [1] "performing prediction for outcome based on stratified model: TI"
  [1] "performing prediction for outcome based on stratified model: N"
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "speedglm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: C"
  [1] "StratifiedModel expressions: (t == 0L,t > 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, C"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t > 0"
  [1] "StratifiedModel outcome: TI"
  [1] "StratifiedModel expressions: (t == 0L & C == 1,t > 0 & C == 1)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI, TI"
  [1] "Predictors: C, highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 1"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 1"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: highA1c, lastNat1, C, TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: N"
  [1] "StratifiedModel expressions: (t == 0L & C == 1 & TI == 1,t > 0 & C == 1 & TI == 1)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N, N"
  [1] "Predictors: highA1c, lastNat1, C, TI"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|highA1c, lastNat1, C, TI);\\ Stratify: t == 0L & C == 1 & TI == 1"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|highA1c, lastNat1, C, TI);\\ Stratify: t > 0 & C == 1 & TI == 1"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "performing fitting for outcome based on stratified model for outcome: C"
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t == 0L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01  8.599825e-13            NA 
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t > 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept     highA1c    lastNat1 
  -2.29183433 -0.07998923  0.03081559 
  fit for C var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: TI"
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 1"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept         C   highA1c  lastNat1 
         NA        NA        NA        NA 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 1"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept          C    highA1c   lastNat1 
          NA  0.4924641  0.1270091 -0.1911502 
  fit for TI var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: N"
  [1] "fitting the model: P(N|highA1c, lastNat1, C, TI);\\ Stratify: t == 0L & C == 1 & TI == 1"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept   highA1c  lastNat1         C        TI 
         NA        NA        NA        NA        NA 
  [1] "fitting the model: P(N|highA1c, lastNat1, C, TI);\\ Stratify: t > 0 & C == 1 & TI == 1"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept   highA1c  lastNat1         C        TI 
         NA        NA        NA        NA        NA 
  fit for N var succeeded...
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
  0.03161985 1.41249320 0.44869874 0.04224776 
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept 
  -0.02436859 
  [1] "performing prediction for outcome based on stratified model: C"
  [1] "performing prediction for outcome based on stratified model: TI"
  [1] "performing prediction for outcome based on stratified model: N"
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "speedglm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "StratifiedModel outcome: TI"
  [1] "StratifiedModel expressions: (t == 0L & C == 0,t > 0 & C == 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI, TI"
  [1] "Predictors: C, highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: highA1c, lastNat1, C, TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: N"
  [1] "StratifiedModel expressions: (t == 0L & C == 0 & TI == 0,t > 0 & C == 0 & TI == 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N, N"
  [1] "Predictors: highA1c, lastNat1, C, TI"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|highA1c, lastNat1, C, TI);\\ Stratify: t == 0L & C == 0 & TI == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|highA1c, lastNat1, C, TI);\\ Stratify: t > 0 & C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: TI"
  [1] "StratifiedModel expressions: (t == 0L,t > 0L & TI.tminus1 == 0L,t > 0L & TI.tminus1 == 1L)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI, TI, TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t > 0L & TI.tminus1 == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t > 0L & TI.tminus1 == 1L"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept     highA1c    lastNat1 
  -2.52652066  0.05456581  0.10858685 
  [1] "performing fitting for outcome based on stratified model for outcome: TI"
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept         C   highA1c  lastNat1 
  -1.816513        NA  2.835083        NA 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept           C     highA1c    lastNat1 
   0.64534186          NA  0.09366241 -0.16923766 
  fit for TI var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: N"
  [1] "fitting the model: P(N|highA1c, lastNat1, C, TI);\\ Stratify: t == 0L & C == 0 & TI == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept     highA1c    lastNat1           C          TI 
  -0.01467019 -0.45533330          NA          NA          NA 
  [1] "fitting the model: P(N|highA1c, lastNat1, C, TI);\\ Stratify: t > 0 & C == 0 & TI == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept     highA1c    lastNat1           C          TI 
  -0.01135495 -0.02269938 -0.01321850          NA          NA 
  fit for N var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: TI"
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept       CVD   highA1c N.tminus1 
  -2.156898  2.117370  2.269857        NA 
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t > 0L & TI.tminus1 == 0L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -21.2548627   0.9100488   2.1714524  19.1407169 
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t > 0L & TI.tminus1 == 1L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
     Intercept          CVD      highA1c    N.tminus1 
  2.656606e+01 1.092843e-07 1.158713e-07 1.320318e-07 
  fit for TI var succeeded...
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept 
  -0.02436859 
  [1] "performing prediction for outcome based on stratified model: TI"
  [1] "performing prediction for outcome based on stratified model: N"
  [1] "performing prediction for outcome based on stratified model: TI"
   done successfully.
  
  
  
  Executing test function test.model.fits.categorCENSOR2  ... Loading required package: magrittr
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "speedglm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "CatC"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: CatC"
  [1] "Predictors: highA1c"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: CatC"
  [1] "StratifiedModel expressions: (t < 16,t == 16)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: CatC, CatC"
  [1] "Predictors: highA1c"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  ...fitting a model for categorical censoring...
  [1] "CategorModel outcome: CatC"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: CatC_B.1, CatC_B.2, CatC_B.3"
  [1] "Predictors: highA1c"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(CatC_B.1|highA1c);\\ Stratify: t < 16"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(CatC_B.2|highA1c);\\ Stratify: t < 16"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(CatC_B.3|highA1c);\\ Stratify: t < 16"
  ...fitting a model for categorical censoring...
  [1] "CategorModel outcome: CatC"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: CatC_B.1, CatC_B.2, CatC_B.3"
  [1] "Predictors: highA1c"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(CatC_B.1|highA1c);\\ Stratify: t == 16"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(CatC_B.2|highA1c);\\ Stratify: t == 16"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(CatC_B.3|highA1c);\\ Stratify: t == 16"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: TI"
  [1] "StratifiedModel expressions: (t == 0L,(t > 0L) & (N.tminus1 == 1L) & (barTIm1eq0 == 1L),(t > 0L) & (N.tminus1 == 0L) & (barTIm1eq0 == 1L),(t > 0L) & (barTIm1eq0 == 1L))"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI, TI, TI, TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 4"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: (t > 0L) & (N.tminus1 == 1L) & (barTIm1eq0 == 1L)"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: (t > 0L) & (N.tminus1 == 0L) & (barTIm1eq0 == 1L)"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: (t > 0L) & (barTIm1eq0 == 1L)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "performing fitting for outcome based on stratified model for outcome: CatC"
  making matrix for dummies for cat. censoring, reference category is 0
  [1] "performing fitting for categorical outcome: CatC"
  [1] "freq counts by bin for categorical outcome: "
  
     0    1    2 
  7469  347  314 
  [1] "binned dataset: "
       sA CatC_B.1 CatC_B.2 CatC_B.3
  [1,]  0        0        0       NA
  [2,]  0        0        0       NA
  [3,]  1        1       NA       NA
  [4,]  0        0        0       NA
  [5,]  0        0        0       NA
  [1] "fitting the model: P(CatC_B.1|highA1c);\\ Stratify: t < 16"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept    highA1c 
  -3.1487960  0.1616531 
  [1] "fitting the model: P(CatC_B.2|highA1c);\\ Stratify: t < 16"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept     highA1c 
  -3.13719779 -0.08098814 
  [1] "fitting the model: P(CatC_B.3|highA1c);\\ Stratify: t < 16"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept   highA1c 
         NA        NA 
  fit for CatC var succeeded...
  making matrix for dummies for cat. censoring, reference category is 0
  [1] "performing fitting for categorical outcome: CatC"
  [1] "freq counts by bin for categorical outcome: "
  
     0    1    2 
  7469  347  314 
  [1] "binned dataset: "
       sA CatC_B.1 CatC_B.2 CatC_B.3
  [1,]  0        0        0       NA
  [2,]  0        0        0       NA
  [3,]  1        1       NA       NA
  [4,]  0        0        0       NA
  [5,]  0        0        0       NA
  [1] "fitting the model: P(CatC_B.1|highA1c);\\ Stratify: t == 16"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept    highA1c 
  -2.9593646  0.5614693 
  [1] "fitting the model: P(CatC_B.2|highA1c);\\ Stratify: t == 16"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept   highA1c 
   -4.89784 -15.66823 
  [1] "fitting the model: P(CatC_B.3|highA1c);\\ Stratify: t == 16"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept   highA1c 
         NA        NA 
  fit for CatC var succeeded...
  fit for CatC var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: TI"
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: t == 0L"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept       CVD   highA1c N.tminus1 
  -2.156898  2.117370  2.269857        NA 
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: (t > 0L) & (N.tminus1 == 1L) & (barTIm1eq0 == 1L)"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
  -2.1141458  0.9100488  2.1714524         NA 
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: (t > 0L) & (N.tminus1 == 0L) & (barTIm1eq0 == 1L)"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept           CVD       highA1c     N.tminus1 
  -2.656607e+01  2.444839e-12  2.082888e-12            NA 
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: (t > 0L) & (barTIm1eq0 == 1L)"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -21.2548627   0.9100488   2.1714524  19.1407169 
  fit for TI var succeeded...
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept 
  -0.02436859 
  [1] "performing prediction for outcome based on stratified model: CatC"
  [1] "performing prediction for categorical outcome: CatC"
  [1] "performing prediction for categorical outcome: CatC"
  [1] "performing prediction for outcome based on stratified model: TI"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "N"
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
   done successfully.
  
  
  
  Executing test function test.buildingblocks  ... [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "glm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  Error in getIPWeights(OData = OData, intervened_TRT = "TI.set1", rule_name = "TI1") : 
    ...cannot locate propensity scores in 'OData' object - must run fitPropensity(...) prior to calling this function
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: C"
  [1] "StratifiedModel expressions: (t < 16,t == 16)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, C"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t < 16"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t == 16"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "performing fitting for outcome based on stratified model for outcome: C"
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t < 16"
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01 -1.198768e-13 -5.125570e-14 
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t == 16"
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01 -6.143963e-14 -2.839781e-14 
  fit for C var succeeded...
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c  N.tminus1 
   0.6250400  1.3852271 -0.1432429  0.1288901 
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept 
  -0.01358231 
  [1] "performing prediction for outcome based on stratified model: C"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "performing estimation for the following TRT/MONITOR rules found in column 'rule.name': TI0,TI1"
  [1] "periods"
   [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
  [1] "defined t.dummy: Periods.0to0"
  [1] "defined t.dummy: Periods.1to1"
  [1] "defined t.dummy: Periods.2to2"
  [1] "defined t.dummy: Periods.3to3"
  [1] "defined t.dummy: Periods.4to4"
  [1] "defined t.dummy: Periods.5to5"
  [1] "defined t.dummy: Periods.6to6"
  [1] "defined t.dummy: Periods.7to7"
  [1] "defined t.dummy: Periods.8to11"
  [1] "defined t.dummy: Periods.12to15"
  [1] "Periods.0to0_TI0"
  [1] "Periods.1to1_TI0"
  [1] "Periods.2to2_TI0"
  [1] "Periods.3to3_TI0"
  [1] "Periods.4to4_TI0"
  [1] "Periods.5to5_TI0"
  [1] "Periods.6to6_TI0"
  [1] "Periods.7to7_TI0"
  [1] "Periods.8to11_TI0"
  [1] "Periods.12to15_TI0"
  [1] "Periods.0to0_TI1"
  [1] "Periods.1to1_TI1"
  [1] "Periods.2to2_TI1"
  [1] "Periods.3to3_TI1"
  [1] "Periods.4to4_TI1"
  [1] "Periods.5to5_TI1"
  [1] "Periods.6to6_TI1"
  [1] "Periods.7to7_TI1"
  [1] "Periods.8to11_TI1"
  [1] "Periods.12to15_TI1"
  ...fitting hazard MSM with speedglm::speedglm.wfit...
  [1] "MSM fits"
    Periods.0to0_TI0   Periods.1to1_TI0   Periods.2to2_TI0   Periods.3to3_TI0 
       -5.465039e+00      -4.324378e+00      -4.012934e+00      -1.166619e+15 
    Periods.4to4_TI0   Periods.5to5_TI0   Periods.6to6_TI0   Periods.7to7_TI0 
       -6.871355e+14      -4.884654e+14      -6.732368e+00      -6.414551e+00 
   Periods.8to11_TI0 Periods.12to15_TI0   Periods.0to0_TI1   Periods.1to1_TI1 
       -2.490327e+15      -3.057464e+15      -3.089228e+00      -4.070768e+00 
    Periods.2to2_TI1   Periods.3to3_TI1   Periods.4to4_TI1   Periods.5to5_TI1 
       -4.097356e+00      -1.444049e+01      -3.626218e+00      -4.172158e+00 
    Periods.6to6_TI1   Periods.7to7_TI1  Periods.8to11_TI1 Periods.12to15_TI1 
       -5.928687e+00      -3.921940e+00      -4.645602e+00      -3.410729e+00 
  ...evaluating MSM-based survival curves...
  ...evaluating SEs based on MSM hazard fit and the estimated IC...
  [1] "Should all be very small: "
    Periods.0to0_TI0   Periods.1to1_TI0   Periods.2to2_TI0   Periods.3to3_TI0 
        9.379699e-17       2.900859e-16       7.383396e-15       7.293270e+00 
    Periods.4to4_TI0   Periods.5to5_TI0   Periods.6to6_TI0   Periods.7to7_TI0 
        6.561300e+01       4.445403e+02       3.220105e-14       2.800474e-14 
   Periods.8to11_TI0 Periods.12to15_TI0   Periods.0to0_TI1   Periods.1to1_TI1 
        2.030033e+02       7.899748e+02       7.913397e-18       2.865011e-18 
    Periods.2to2_TI1   Periods.3to3_TI1   Periods.4to4_TI1   Periods.5to5_TI1 
        7.009244e-18       4.879654e-08       1.399031e-19       7.292022e-18 
    Periods.6to6_TI1   Periods.7to7_TI1  Periods.8to11_TI1 Periods.12to15_TI1 
        2.532557e-18       1.471364e-17       3.615109e-16       1.717254e-15 
  [1] "Should all be very small: "
    Periods.0to0_TI0   Periods.1to1_TI0   Periods.2to2_TI0   Periods.3to3_TI0 
        9.340525e-15       3.126553e-15       1.800440e-14       3.336980e+14 
    Periods.4to4_TI0   Periods.5to5_TI0   Periods.6to6_TI0   Periods.7to7_TI0 
        7.563694e+14       1.183848e+15       5.560005e-15       5.426432e-15 
   Periods.8to11_TI0 Periods.12to15_TI0   Periods.0to0_TI1   Periods.1to1_TI1 
        1.830248e+14       5.408850e+14       4.968619e-15       3.569094e-15 
    Periods.2to2_TI1   Periods.3to3_TI1   Periods.4to4_TI1   Periods.5to5_TI1 
        6.622362e-15       1.000001e+00       8.253617e-17       2.682887e-15 
    Periods.6to6_TI1   Periods.7to7_TI1  Periods.8to11_TI1 Periods.12to15_TI1 
        3.708932e-15       2.085976e-15       9.255499e-15       3.032843e-15 
  [1] "Should all be very small: "
   [1] 3.953818e-17 7.916801e-17 2.288376e-16 2.288376e-16 2.288376e-16
   [6] 2.288376e-16 2.227361e-16 2.339417e-16 2.339417e-16 2.339417e-16
  [11] 2.339417e-16 2.339417e-16 2.339417e-16 2.339417e-16 2.339417e-16
  [16] 2.339417e-16
  [1] "Should all be very small: "
   [1] 2.087237e-16 2.567391e-16 1.552191e-16 4.951408e-07 4.823035e-07
   [6] 4.749798e-07 4.737188e-07 4.645200e-07 4.601013e-07 4.557247e-07
  [11] 4.513896e-07 4.470959e-07 4.328058e-07 4.189726e-07 4.055814e-07
  [16] 3.926183e-07
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "glm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI, N"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01 -2.240428e-13 -9.163027e-14 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept           C     highA1c    lastNat1 
   0.87385792          NA -0.18986136 -0.06706656 
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
     Intercept            C           TI      highA1c     lastNat1 
  -0.025688525           NA           NA  0.001591313 -0.012014026 
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c  N.tminus1 
   0.6250400  1.3852271 -0.1432429  0.1288901 
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept 
  -0.01358231 
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "performing estimation for the following TRT/MONITOR rules found in column 'rule.name': TI0,TI1"
  [1] "periods"
   [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
  [1] "defined t.dummy: Periods.0to0"
  [1] "defined t.dummy: Periods.1to1"
  [1] "defined t.dummy: Periods.2to2"
  [1] "defined t.dummy: Periods.3to3"
  [1] "defined t.dummy: Periods.4to4"
  [1] "defined t.dummy: Periods.5to5"
  [1] "defined t.dummy: Periods.6to6"
  [1] "defined t.dummy: Periods.7to7"
  [1] "defined t.dummy: Periods.8to11"
  [1] "defined t.dummy: Periods.12to15"
  [1] "Periods.0to0_TI0"
  [1] "Periods.1to1_TI0"
  [1] "Periods.2to2_TI0"
  [1] "Periods.3to3_TI0"
  [1] "Periods.4to4_TI0"
  [1] "Periods.5to5_TI0"
  [1] "Periods.6to6_TI0"
  [1] "Periods.7to7_TI0"
  [1] "Periods.8to11_TI0"
  [1] "Periods.12to15_TI0"
  [1] "Periods.0to0_TI1"
  [1] "Periods.1to1_TI1"
  [1] "Periods.2to2_TI1"
  [1] "Periods.3to3_TI1"
  [1] "Periods.4to4_TI1"
  [1] "Periods.5to5_TI1"
  [1] "Periods.6to6_TI1"
  [1] "Periods.7to7_TI1"
  [1] "Periods.8to11_TI1"
  [1] "Periods.12to15_TI1"
  ...fitting hazard MSM with speedglm::speedglm.wfit...
  [1] "MSM fits"
    Periods.0to0_TI0   Periods.1to1_TI0   Periods.2to2_TI0   Periods.3to3_TI0 
       -5.479625e+00      -4.224521e+15      -4.040682e+15      -3.195295e+15 
    Periods.4to4_TI0   Periods.5to5_TI0   Periods.6to6_TI0   Periods.7to7_TI0 
       -3.082898e+15      -2.778817e+15      -3.052026e+15      -3.796228e+15 
   Periods.8to11_TI0 Periods.12to15_TI0   Periods.0to0_TI1   Periods.1to1_TI1 
       -2.715406e+15      -1.534193e+15      -3.048470e+00      -4.020852e+00 
    Periods.2to2_TI1   Periods.3to3_TI1   Periods.4to4_TI1   Periods.5to5_TI1 
       -4.106498e+00      -1.007712e+01      -3.450746e+00      -4.160778e+00 
    Periods.6to6_TI1   Periods.7to7_TI1  Periods.8to11_TI1 Periods.12to15_TI1 
       -5.909203e+00      -4.008647e+00      -4.781623e+00      -3.422484e+15 
  ...evaluating MSM-based survival curves...
  ...evaluating SEs based on MSM hazard fit and the estimated IC...
  [1] "Should all be very small: "
    Periods.0to0_TI0   Periods.1to1_TI0   Periods.2to2_TI0   Periods.3to3_TI0 
        1.363995e-15       3.892340e+00       1.153066e+02       3.014492e+03 
    Periods.4to4_TI0   Periods.5to5_TI0   Periods.6to6_TI0   Periods.7to7_TI0 
        2.096739e+04       2.100000e+04       1.300000e+04       5.000000e+03 
   Periods.8to11_TI0 Periods.12to15_TI0   Periods.0to0_TI1   Periods.1to1_TI1 
        2.700000e+04       1.000000e+04       2.218218e-17       1.992734e-17 
    Periods.2to2_TI1   Periods.3to3_TI1   Periods.4to4_TI1   Periods.5to5_TI1 
        1.087903e-17       1.670762e-05       2.380815e-17       8.984546e-17 
    Periods.6to6_TI1   Periods.7to7_TI1  Periods.8to11_TI1 Periods.12to15_TI1 
        4.050727e-18       8.676045e-17       3.446043e-04       2.159757e+02 
  [1] "Should all be very small: "
    Periods.0to0_TI0   Periods.1to1_TI0   Periods.2to2_TI0   Periods.3to3_TI0 
        2.012238e-14       5.581563e+13       7.715289e+13       2.180508e+14 
    Periods.4to4_TI0   Periods.5to5_TI0   Periods.6to6_TI0   Periods.7to7_TI0 
        2.367837e+14       2.874638e+14       2.419289e+14       1.178953e+14 
   Periods.8to11_TI0 Periods.12to15_TI0   Periods.0to0_TI1   Periods.1to1_TI1 
        2.980323e+14       4.949011e+14       9.314984e-15       1.161900e-14 
    Periods.2to2_TI1   Periods.3to3_TI1   Periods.4to4_TI1   Periods.5to5_TI1 
        3.404072e-15       1.000042e+00       9.042079e-16       3.580885e-15 
    Periods.6to6_TI1   Periods.7to7_TI1  Periods.8to11_TI1 Periods.12to15_TI1 
        4.712621e-16       6.766642e-16       1.571325e-04       1.801860e+14 
  [1] "Should all be very small: "
   [1] 8.330934e-17 8.330934e-17 8.330934e-17 8.330934e-17 8.330934e-17
   [6] 8.330934e-17 8.330934e-17 8.330934e-17 8.330934e-17 8.330934e-17
  [11] 8.330934e-17 8.330934e-17 8.330934e-17 8.330934e-17 8.330934e-17
  [16] 8.330934e-17
  [1] "Should all be very small: "
   [1] 3.999902e-16 2.024836e-16 2.525296e-16 3.877971e-05 3.758736e-05
   [6] 3.701017e-05 3.690999e-05 3.625173e-05 3.706763e-05 3.786745e-05
  [11] 3.865142e-05 3.941974e-05 3.941974e-05 3.941974e-05 3.941974e-05
  [16] 3.941974e-05
  Error in process_opt_wts(wts_data_used, weights, nodes) : 
    input 'weights' must be a data.table with 3 columns, two of which must be named as: 'ID' and 't'.
  In addition: Warning messages:
  1: glm.fit: algorithm did not converge 
  2: glm.fit: algorithm did not converge 
  3: glm.fit: algorithm did not converge 
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -5.8682574  0.2154351  3.3021509  0.4033691         NA  0.7501467         NA 
  [1] "Surv est: 0.983758322675102"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=0: 0.01624"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
  -4.67474313  0.19724908  2.21184261 -0.09839186  0.76483183  0.36869687 
   TI.tminus1 
  -0.98759997 
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
  -3.48082464  0.25283340  2.46508499  0.04786835          NA -0.43706261 
   TI.tminus1 
           NA 
  [1] "Surv est: 0.969902221464484"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=1: 0.0301"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.8073588 -1.1071567  1.3574257 -0.4540453  0.6083749  0.3767599 -1.4726757 
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.7570081 -0.3346276  1.4801253 -0.3532999  0.5055449 -0.5127511 -0.6589153 
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
  -2.90325161 -0.07708853  2.01607750 -0.02829320          NA -0.32644563 
   TI.tminus1 
           NA 
  [1] "Surv est: 0.953782240282527"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=2: 0.04622"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 3" "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 4"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 3"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 4
   $ t_period          : int 3
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 3"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -4.2729961  0.6045142  2.3185601 -1.4506484  0.4525435 -1.0902410 -1.7027505 
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
  -3.45925760 -0.49121038  1.40054082 -0.35287095  0.50934192 -0.02283871 
   TI.tminus1 
  -1.28687470 
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.8442520 -0.1010111  1.5056775 -0.2372471  0.4546915 -0.4782690 -0.6244729 
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
  -2.94053966  0.04857403  1.99765895  0.01058704          NA -0.32554792 
   TI.tminus1 
           NA 
  [1] "Surv est: 0.953681572539541"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=3: 0.04632"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 4" "t == 3" "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 5"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 4"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 5
   $ t_period          : int 4
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 4"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 3"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 4
   $ t_period          : int 3
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 3"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi FALSE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi FALSE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.0863662  1.4415952  1.1242617 -0.4941329 -0.1391926 -0.8298997 -0.6156850 
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.1883548  1.2010569  1.3144619 -0.3947993  0.1333006 -0.9913184 -0.5170650 
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
  -3.11978976  0.36019767  1.14780785 -0.23837798  0.37263297 -0.05869163 
   TI.tminus1 
  -0.85675524 
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.6602088  0.3624027  1.3288288 -0.1996436  0.3619692 -0.4185487 -0.4804042 
  [1] "calling glm.fit..."
  [1] "glm fits:"
     Intercept          CVD      highA1c            N     lastNat1           TI 
  -2.757594710  0.346812991  1.758549008  0.002438552           NA -0.302239540 
    TI.tminus1 
            NA 
  [1] "Surv est: 0.943575229684677"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=4: 0.05642"
  ...evaluating IPWeights for TMLE...
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
   -4.694637   0.368584   3.290803  -0.666631         NA         NA         NA 
  [1] "tmle update: -0.00149135496809919"
  [1] "Surv est: 0.984107401301778"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=0: 0.01589"
  [1] "...empirical mean of the estimated EIC: -3.09079900454493e-05"
  [1] "...estimated TMLE variance: 2.41999050417123e-05"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -4.8004930  0.9282348  0.6879804 -0.5514791  0.7438655         NA         NA 
  [1] "tmle update: 0.0727160966327068"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.6000572  0.5722892  2.2618565 -0.7563574         NA         NA         NA 
  [1] "tmle update: -0.0022357049164653"
  [1] "Surv est: 0.969834495012909"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=1: 0.03017"
  [1] "...empirical mean of the estimated EIC: -6.07101133764977e-05"
  [1] "...estimated TMLE variance: 6.61356670225727e-05"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
   -5.1764346 -17.9817186 -17.2807447  -0.6408232   1.6792590          NA 
   TI.tminus1 
           NA 
  [1] "tmle update: -0.00489595614761723"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.5420862 -0.2635159  0.0856417 -1.1860831  1.1179562         NA         NA 
  [1] "tmle update: 0.0333715771607933"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.8806788  0.1306788  1.8884045 -0.7910252         NA         NA         NA 
  [1] "tmle update: -0.00334470300657529"
  [1] "Surv est: 0.951909613102674"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=2: 0.04809"
  [1] "...empirical mean of the estimated EIC: -0.00010436571628225"
  [1] "...estimated TMLE variance: 0.000251078927354862"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 3" "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 4"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 3"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 4
   $ t_period          : int 3
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 3"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept           CVD       highA1c             N      lastNat1 
  -2.656607e+01 -2.363684e-14 -1.874001e-14 -2.747293e-14  9.552227e-15 
             TI    TI.tminus1 
             NA            NA 
  [1] "tmle update: 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
   -5.1764346 -17.9785757 -17.2777471  -0.6408232   1.6792590          NA 
   TI.tminus1 
           NA 
  [1] "tmle update: -0.00489595625433471"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.5420862 -0.2635159  0.0856417 -1.1860831  1.1179562         NA         NA 
  [1] "tmle update: 0.0333715771369513"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.8806788  0.1306788  1.8884045 -0.7910252         NA         NA         NA 
  [1] "tmle update: -0.00334470300648418"
  [1] "Surv est: 0.95190961310293"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=3: 0.04809"
  [1] "...empirical mean of the estimated EIC: -0.000104365720571848"
  [1] "...estimated TMLE variance: 0.000251078927359577"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 4" "t == 3" "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 5"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 4"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 5
   $ t_period          : int 4
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 4"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 3"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 4
   $ t_period          : int 3
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 3"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.6781561  0.8302792  2.3040444 -1.0845762 -0.9213487         NA         NA 
  [1] "tmle update: 0.209019147885717"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -4.8958776  0.4984205  0.7750137  1.4400473  0.1142727         NA         NA 
  [1] "tmle update: 0.0520880449204392"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.1879874 -0.3427790  0.1064457 -0.4879009  0.5164562         NA         NA 
  [1] "tmle update: -0.00448980897354264"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.7827815  0.1202478  0.3514419 -0.6841186  0.4586810         NA         NA 
  [1] "tmle update: 0.0158905661689546"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.4482301  0.1976754  1.4967452 -0.5174974         NA         NA         NA 
  [1] "tmle update: -0.00227377143477276"
  [1] "Surv est: 0.925542187707399"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=4: 0.07446"
  [1] "...empirical mean of the estimated EIC: -0.000520790968265705"
  [1] "...estimated TMLE variance: 0.00803708770988044"
  ...evaluating IPWeights for TMLE...
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 3" "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 4"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 3"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 4
   $ t_period          : int 3
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 3"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept           CVD       highA1c             N      lastNat1 
  -2.656607e+01 -2.363684e-14 -1.874001e-14 -2.747293e-14  9.552227e-15 
             TI    TI.tminus1 
             NA            NA 
  [1] "tmle update: 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
   -5.1764346 -17.9785757 -17.2777471  -0.6408232   1.6792590          NA 
   TI.tminus1 
           NA 
  [1] "tmle update: -0.0048959544125669"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.5420862 -0.2635159  0.0856417 -1.1860831  1.1179562         NA         NA 
  [1] "tmle update: 0.0333715758304584"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.8806788  0.1306788  1.8884045 -0.7910252         NA         NA         NA 
  [1] "tmle update: -0.00334470301977723"
  [1] "Surv est: 0.951909613112289"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=3: 0.04809"
  [1] "...empirical mean of the estimated EIC: -1.65538533979754e-05"
  [1] "...estimated TMLE variance: 8.3806787779791e-06"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 4" "t == 3" "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 5"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 4"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 5
   $ t_period          : int 4
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 4"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 3"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 4
   $ t_period          : int 3
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 3"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.6781561  0.8302792  2.3040444 -1.0845762 -0.9213487         NA         NA 
  [1] "tmle update: 0.209019147869959"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -4.8958776  0.4984205  0.7750137  1.4400473  0.1142727         NA         NA 
  [1] "tmle update: 0.0520880449202836"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.1879874 -0.3427790  0.1064457 -0.4879009  0.5164562         NA         NA 
  [1] "tmle update: -0.00448980955099783"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.7827815  0.1202478  0.3514419 -0.6841186  0.4586810         NA         NA 
  [1] "tmle update: 0.0158905661764066"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.4482301  0.1976754  1.4967452 -0.5174974         NA         NA         NA 
  [1] "tmle update: -0.00227377139578826"
  [1] "Surv est: 0.925542187729508"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=4: 0.07446"
  [1] "...empirical mean of the estimated EIC: -8.12433860103523e-05"
  [1] "...estimated TMLE variance: 0.00019629047768188"
  ...evaluating IPWeights for TMLE...
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 3" "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 4"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 3"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 4
   $ t_period          : int 3
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 3"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept           CVD       highA1c             N      lastNat1 
  -2.656607e+01 -2.363684e-14 -1.874001e-14 -2.747293e-14  9.552227e-15 
             TI    TI.tminus1 
             NA            NA 
  [1] "tmle update: 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
   -5.1764346 -17.9785757 -17.2777471  -0.6408232   1.6792590          NA 
   TI.tminus1 
           NA 
  [1] "tmle update: 0.00455038396959821"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.5351943 -0.2698513  0.0819634 -1.1897532  1.1194722         NA         NA 
  [1] "tmle update: 0.00148791277372786"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.9037005  0.1316045  1.9048099 -0.7888752         NA         NA         NA 
  [1] "tmle update: 0.0038737545211174"
  [1] "Surv est: 0.952423126954576"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=3: 0.04758"
  [1] "...empirical mean of the estimated EIC: -3.11352727229658e-05"
  [1] "...estimated TMLE variance: 4.05438882728483e-05"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "t == 4" "t == 3" "t == 2" "t == 1" "t == 0"
  [1] "Outcomes: "
  [1] "Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1,Q.kplus1"
  [1] "No. of regressions: 5"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 4"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 5
   $ t_period          : int 4
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 4"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 3"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 4
   $ t_period          : int 3
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 3"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 2"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 3
   $ t_period          : int 2
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 2"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 1"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 2
   $ t_period          : int 1
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 1"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: Q.kplus1"
  [1] "Predictors: CVD, highA1c, N, lastNat1, TI, TI.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of QlearnModel :"
  [1] "P(Q.kplus1|CVD, highA1c, N, lastNat1, TI, TI.tminus1);\\ Stratify: t == 0"
  [1] "initialized Q class"
  List of 15
   $ Qreg_counter      : int 1
   $ t_period          : int 0
   $ TMLE              : logi TRUE
   $ outvar            : chr "Q.kplus1"
   $ predvars          : chr [1:6] "CVD" "highA1c" "N" "lastNat1" ...
   $ outvar.class      : chr "Qlearn"
   $ subset_vars       : chr "Q.kplus1"
   $ subset_exprs      : chr "t == 0"
   $ subset_censored   : NULL
   $ stratifyQ_by_rule : logi TRUE
   $ lower_bound_zero_Q: logi TRUE
   $ regimen_names     : logi NA
   $ pool_regimes      : logi FALSE
   $ model_contrl      :List of 1
    ..$ distribution: chr "gaussian"
   $ censoring         : logi FALSE
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -3.6781561  0.8302792  2.3040444 -1.0845762 -0.9213487         NA         NA 
  [1] "tmle update: 0.0043556607594549"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -5.1065413  0.4996280  0.7537557  1.4595663  0.1268353         NA         NA 
  [1] "tmle update: -6.23198919478809e-11"
  [1] "calling glm.fit..."
  [1] "glm fits:"
     Intercept          CVD      highA1c            N     lastNat1           TI 
  -3.365723662 -0.465839994  0.005840225 -0.509288785  0.596038204           NA 
    TI.tminus1 
            NA 
  [1] "tmle update: -4.44506521637441e-10"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept         CVD     highA1c           N    lastNat1          TI 
  -2.88176714  0.08310961  0.32781298 -0.75064933  0.51647978          NA 
   TI.tminus1 
           NA 
  [1] "tmle update: 5.99566016142493e-11"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c          N   lastNat1         TI TI.tminus1 
  -2.5397115  0.1898817  1.5664979 -0.5505100         NA         NA         NA 
  [1] "tmle update: 0.0027933549293886"
  [1] "Surv est: 0.931389922902869"
  [1] "No. of obs for last prediction of Q: 1000"
  [1] "EY^* estimate at t=4: 0.06861"
  [1] "...empirical mean of the estimated EIC: -3.04000131367507e-05"
  [1] "...estimated TMLE variance: 5.41527268962014e-05"
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "glm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI, N"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01 -2.240428e-13 -9.163027e-14 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept           C     highA1c    lastNat1 
   0.87385792          NA -0.18986136 -0.06706656 
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
     Intercept            C           TI      highA1c     lastNat1 
  -0.025688525           NA           NA  0.001591313 -0.012014026 
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c  N.tminus1 
   0.6250400  1.3852271 -0.1432429  0.1288901 
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept 
  -0.01358231 
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "TI"
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "glm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI, N"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: C"
  [1] "StratifiedModel expressions: (t == 0L,t > 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, C"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t > 0"
  [1] "StratifiedModel outcome: TI"
  [1] "StratifiedModel expressions: (t == 0L & C == 0,t > 0 & C == 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI, TI"
  [1] "Predictors: C, highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0"
  [1] "StratifiedModel outcome: N"
  [1] "StratifiedModel expressions: (t == 0L & C == 0 & TI == 0,t > 0 & C == 0 & TI == 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N, N"
  [1] "Predictors: C, TI, highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0 & TI == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "performing fitting for outcome based on stratified model for outcome: C"
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t == 0L"
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01 -4.661343e-14            NA 
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t > 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01 -1.830025e-13 -7.964050e-14 
  fit for C var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: TI"
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
  Intercept         C   highA1c  lastNat1 
  -1.816513        NA  2.835083        NA 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept          C    highA1c   lastNat1 
   1.2490429         NA -0.4673254 -0.1775312 
  fit for TI var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: N"
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0 & TI == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept           C          TI     highA1c    lastNat1 
  -0.01467019          NA          NA -0.45533330          NA 
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0 & TI == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept           C          TI     highA1c    lastNat1 
  -0.03015004          NA          NA  0.01124218 -0.01120127 
  fit for N var succeeded...
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c  N.tminus1 
   0.6250400  1.3852271 -0.1432429  0.1288901 
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept 
  -0.01358231 
  [1] "performing prediction for outcome based on stratified model: C"
  [1] "performing prediction for outcome based on stratified model: TI"
  [1] "performing prediction for outcome based on stratified model: N"
  [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "glm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI, N"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "StratifiedModel outcome: C"
  [1] "StratifiedModel expressions: (t == 0L,t > 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, C"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t == 0L"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: t > 0"
  [1] "StratifiedModel outcome: TI"
  [1] "StratifiedModel expressions: (t == 0L & C == 0,t > 0 & C == 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI, TI"
  [1] "Predictors: C, highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0"
  [1] "StratifiedModel outcome: N"
  [1] "StratifiedModel expressions: (t == 0L & C == 0 & TI == 0,t > 0 & C == 0 & TI == 0)"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N, N"
  [1] "Predictors: C, TI, highA1c, lastNat1"
  [1] "No. of regressions: 2"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0 & TI == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "TI"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: TI"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "performing fitting for outcome based on stratified model for outcome: C"
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t == 0L"
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01 -4.661343e-14            NA 
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: t > 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01 -1.830025e-13 -7.964050e-14 
  fit for C var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: TI"
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
  Intercept         C   highA1c  lastNat1 
  -1.816513        NA  2.835083        NA 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept          C    highA1c   lastNat1 
   1.2490429         NA -0.4673254 -0.1775312 
  fit for TI var succeeded...
  [1] "performing fitting for outcome based on stratified model for outcome: N"
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: t == 0L & C == 0 & TI == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept           C          TI     highA1c    lastNat1 
  -0.01467019          NA          NA -0.45533330          NA 
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: t > 0 & C == 0 & TI == 0"
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept           C          TI     highA1c    lastNat1 
  -0.03015004          NA          NA  0.01124218 -0.01120127 
  fit for N var succeeded...
  [1] "fitting the model: P(TI|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
   Intercept        CVD    highA1c  N.tminus1 
   0.6250400  1.3852271 -0.1432429  0.1288901 
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling glm.fit..."
  [1] "glm fits:"
    Intercept 
  -0.01358231 
  [1] "performing prediction for outcome based on stratified model: C"
  [1] "performing prediction for outcome based on stratified model: TI"
  [1] "performing prediction for outcome based on stratified model: N"
   done successfully.
  
  
  
  Executing test function test.speedglm.allestimators10Kdata  ... [1] "defined t.dummy: Periods.0to0"
  [1] "defined t.dummy: Periods.1to1"
  [1] "defined t.dummy: Periods.2to2"
  [1] "defined t.dummy: Periods.3to3"
  [1] "defined t.dummy: Periods.4to4"
  [1] "defined t.dummy: Periods.5to5"
  [1] "defined t.dummy: Periods.6to6"
  [1] "defined t.dummy: Periods.7to7"
  [1] "defined t.dummy: Periods.8to11"
  [1] "defined t.dummy: Periods.12to15"
  writing report to directory: /tmp/RtmpZB7f3p
  writing related figures to report subdirectory: figure.sim.data.example.fup2
  
  
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  Output created: sim.data.example.fup2.html
  writing report to directory: /tmp/RtmpZB7f3p
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  /usr/local/bin/pandoc +RTS -K512m -RTS /tmp/RtmpZB7f3p/report-script-rmd.utf8.md --to html4 --from markdown+autolink_bare_uris+ascii_identifiers+tex_math_single_backslash+escaped_line_breaks --output /tmp/RtmpZB7f3p/sim.data.example.fup.html --smart --email-obfuscation none --self-contained --standalone --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /data/gannet/ripley/R/test-clang/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --number-sections --variable 'theme:bootstrap' --include-in-header /tmp/RtmpZB7f3p/rmarkdown-str5dc976e703c6.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' 
  
  Output created: sim.data.example.fup.html
  writing report to directory: /tmp/RtmpZB7f3p
  writing related figures to report subdirectory: figure.sim.data.example.fup
  
  
  processing file: /tmp/RtmpZB7f3p/report-script-rmd.spin.Rmd
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  output file: /tmp/RtmpZB7f3p/report-script-rmd.knit.md
  
  /usr/local/bin/pandoc +RTS -K512m -RTS /tmp/RtmpZB7f3p/report-script-rmd.utf8.md --to html4 --from markdown+autolink_bare_uris+ascii_identifiers+tex_math_single_backslash+escaped_line_breaks --output /tmp/RtmpZB7f3p/sim.data.example.fup.html --smart --email-obfuscation none --self-contained --standalone --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /data/gannet/ripley/R/test-clang/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --number-sections --variable 'theme:bootstrap' --include-in-header /tmp/RtmpZB7f3p/rmarkdown-str5dc9244a590.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' 
  
  Output created: sim.data.example.fup.html
  writing report to directory: /tmp/RtmpZB7f3p
  writing related figures to report subdirectory: figure.sim.data.example.fup
  
  
  processing file: /tmp/RtmpZB7f3p/report-script-rmd.spin.Rmd
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  output file: /tmp/RtmpZB7f3p/report-script-rmd.knit.md
  
  /usr/local/bin/pandoc +RTS -K512m -RTS /tmp/RtmpZB7f3p/report-script-rmd.utf8.md --to html4 --from markdown+autolink_bare_uris+ascii_identifiers+tex_math_single_backslash+escaped_line_breaks --output /tmp/RtmpZB7f3p/sim.data.example.fup.html --smart --email-obfuscation none --self-contained --standalone --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /data/gannet/ripley/R/test-clang/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --number-sections --variable 'theme:bootstrap' --include-in-header /tmp/RtmpZB7f3p/rmarkdown-str5dc92f238270.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' 
  
  Output created: sim.data.example.fup.html
  writing report to directory: /tmp/RtmpZB7f3p
  writing related figures to report subdirectory: figure.sim.data.example.fup
  
  
  processing file: /tmp/RtmpZB7f3p/report-script-rmd.spin.Rmd
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  output file: /tmp/RtmpZB7f3p/report-script-rmd.knit.md
  
  /usr/local/bin/pandoc +RTS -K512m -RTS /tmp/RtmpZB7f3p/report-script-rmd.utf8.md --to html4 --from markdown+autolink_bare_uris+ascii_identifiers+tex_math_single_backslash+escaped_line_breaks --output /tmp/RtmpZB7f3p/sim.data.example.fup.html --smart --email-obfuscation none --self-contained --standalone --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /data/gannet/ripley/R/test-clang/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --number-sections --variable 'theme:bootstrap' --include-in-header /tmp/RtmpZB7f3p/rmarkdown-str5dc950e468c1.html --mathjax --variable 'mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' 
  
  Output created: sim.data.example.fup.html
  
  
  processing file: /tmp/RtmpZB7f3p/report-script-rmd.spin.Rmd
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  output file: /tmp/RtmpZB7f3p/report-script-rmd.knit.md
  
  /usr/local/bin/pandoc +RTS -K512m -RTS /tmp/RtmpZB7f3p/report-script-rmd.utf8.md --to latex --from markdown+autolink_bare_uris+ascii_identifiers+tex_math_single_backslash+escaped_line_breaks --output /tmp/RtmpZB7f3p/sim.data.example.fup.tex --table-of-contents --toc-depth 2 --template /data/gannet/ripley/R/test-clang/rmarkdown/rmd/latex/default-1.17.0.2.tex --number-sections --highlight-style tango --latex-engine pdflatex --variable graphics=yes --variable 'geometry:margin=1in' 
  You are recommended to install the tinytex package to build PDF.FALSE
  Latexmk: This is Latexmk, John Collins, 17 Jan. 2018, version: 4.55.
  
  Latexmk: No file name specified, and I couldn't find any
  Use
     latexmk -help
  to get usage information
  
  Output created: sim.data.example.fup.pdf
  Timing stopped at: 50.88 3.09 59.34
  Error in make_report_rmd(OData, NPMSM = list(surv1, surv2), MSM = MSM.IPAW,  : 
    file '/tmp/RtmpZB7f3p/sim.data.example.fup.pdf' does not exist
   done successfully.
  
  
  
  Executing test function test.speedglm.stochastic.TMLE.NDE.1Kdata  ...  done successfully.
  
  
  
  Executing test function test.GCOMP.TMLE.10Kdata  ...  done successfully.
  
  
  
  Executing test function test.directRegressionDefn.10Kdata  ...  done successfully.
  
  
  
  Executing test function test.iterTMLE.10Kdata  ...  done successfully.
  
  
  
  Executing test function test.ContinModel  ... [1] "Using the following stremr options/settings: "
  
  List of 8
   $ fit.package       : chr "speedglm"
   $ fit.algorithm     : chr "glm"
   $ bin.method        : chr "equal.mass"
   $ nbins             : num 10
   $ maxncats          : num 20
   $ maxNperBin        : num 500
   $ lower_bound_zero_Q: logi TRUE
   $ skip_update_zero_Q: logi TRUE 
  [1] "...detecting the type of each input column..."
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "gC" "gA" "gN"
  [1] "Outcomes: "
  [1] ""
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "C,TI,N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: C, TI, N"
  [1] "Predictors: highA1c, lastNat1"
  [1] "No. of regressions: 3"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "continA"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: continA"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "ContinModel outcome: continA"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: continA_B.1, continA_B.2, continA_B.3, continA_B.4, continA_B.5, continA_B.6, continA_B.7, continA_B.8, continA_B.9, continA_B.10, continA_B.11, continA_B.12, continA_B.13, continA_B.14, continA_B.15, continA_B.16, continA_B.17, continA_B.18, continA_B.19, continA_B.20, continA_B.21, continA_B.22, continA_B.23, continA_B.24, continA_B.25, continA_B.26, continA_B.27, continA_B.28, continA_B.29"
  [1] "Predictors: CVD, highA1c, N.tminus1"
  [1] "No. of regressions: 29"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.1|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.2|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.3|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.4|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.5|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.6|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.7|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.8|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.9|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.10|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.11|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.12|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.13|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.14|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.15|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.16|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.17|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.18|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.19|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.20|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.21|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.22|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.23|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.24|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.25|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.26|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.27|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.28|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(continA_B.29|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "...ListOfRegressionForms..."
  [1] "Outcomes: "
  [1] "N"
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? FALSE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of GenericModel:"
  [1] "#----------------------------------------------------------------------------------"
  [1] "Outcomes: N"
  [1] "Predictors: "
  [1] "No. of regressions: 1"
  [1] "All outcomes binary? TRUE"
  [1] "#----------------------------------------------------------------------------------"
  [1] "New instance of BinaryOutcomeModel :"
  [1] "P(N|);\\ Stratify: "
  [1] "fitting the model: P(C|highA1c, lastNat1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept       highA1c      lastNat1 
  -2.656607e+01  9.238692e-12  2.688942e-12 
  [1] "fitting the model: P(TI|C, highA1c, lastNat1);\\ Stratify: C == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept           C     highA1c    lastNat1 
   0.87385792          NA -0.18986136 -0.06706656 
  [1] "fitting the model: P(N|C, TI, highA1c, lastNat1);\\ Stratify: C == 0 & TI == 0"
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
     Intercept            C           TI      highA1c     lastNat1 
  -0.025688525           NA           NA  0.001591313 -0.012014026 
  [1] "performing fitting for continuous outcome: continA"
  [1] "freq counts by bin for continuous outcome: "
  
    2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26 
  519 518 518 518 518 518 518 518 518 518 518 518 518 519 518 518 518 518 518 518 518 518 518 518 518 
   27  28 
  518 519 
  [1] "binned dataset: "
          continA_B.1 continA_B.2 continA_B.3 continA_B.4 continA_B.5 continA_B.6 continA_B.7
  [1,] 24           0           0           0           0           0           0           0
  [2,]  9           0           0           0           0           0           0           0
  [3,]  5           0           0           0           0           1          NA          NA
  [4,] 18           0           0           0           0           0           0           0
  [5,] 28           0           0           0           0           0           0           0
       continA_B.8 continA_B.9 continA_B.10 continA_B.11 continA_B.12 continA_B.13 continA_B.14
  [1,]           0           0            0            0            0            0            0
  [2,]           0           1           NA           NA           NA           NA           NA
  [3,]          NA          NA           NA           NA           NA           NA           NA
  [4,]           0           0            0            0            0            0            0
  [5,]           0           0            0            0            0            0            0
       continA_B.15 continA_B.16 continA_B.17 continA_B.18 continA_B.19 continA_B.20 continA_B.21
  [1,]            0            0            0            0            0            0            0
  [2,]           NA           NA           NA           NA           NA           NA           NA
  [3,]           NA           NA           NA           NA           NA           NA           NA
  [4,]            0            0            0            1           NA           NA           NA
  [5,]            0            0            0            0            0            0            0
       continA_B.22 continA_B.23 continA_B.24 continA_B.25 continA_B.26 continA_B.27 continA_B.28
  [1,]            0            0            1           NA           NA           NA           NA
  [2,]           NA           NA           NA           NA           NA           NA           NA
  [3,]           NA           NA           NA           NA           NA           NA           NA
  [4,]           NA           NA           NA           NA           NA           NA           NA
  [5,]            0            0            0            0            0            0            1
       continA_B.29
  [1,]           NA
  [2,]           NA
  [3,]           NA
  [4,]           NA
  [5,]           NA
  [1] "fitting the model: P(continA_B.1|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept           CVD       highA1c     N.tminus1 
  -2.656607e+01 -1.172459e-11 -1.310221e-11 -2.568646e-11 
  [1] "fitting the model: P(continA_B.2|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -3.25569434 -0.02064393 -0.06922219  0.02808151 
  [1] "fitting the model: P(continA_B.3|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -3.17151013  0.01182405  0.04859668 -0.11287426 
  [1] "fitting the model: P(continA_B.4|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -3.15484559 -0.13606106  0.09084122 -0.05321791 
  [1] "fitting the model: P(continA_B.5|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -3.10607338  0.20492244 -0.08873951 -0.07063684 
  [1] "fitting the model: P(continA_B.6|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -3.16596541 -0.13345206  0.08718581  0.12823726 
  [1] "fitting the model: P(continA_B.7|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -3.02411769 -0.03504767  0.23023186 -0.12800230 
  [1] "fitting the model: P(continA_B.8|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept           CVD       highA1c     N.tminus1 
  -2.9797461098  0.1278918571 -0.1665653695 -0.0005284063 
  [1] "fitting the model: P(continA_B.9|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
  -2.9843095 -0.1791423 -0.1506455  0.1562250 
  [1] "fitting the model: P(continA_B.10|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -2.84584999  0.07244430  0.07948422 -0.13335530 
  [1] "fitting the model: P(continA_B.11|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
  -2.6914692 -0.1378704 -0.1908098 -0.1790428 
  [1] "fitting the model: P(continA_B.12|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -2.72176041  0.06782506 -0.16446537 -0.05229677 
  [1] "fitting the model: P(continA_B.13|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -2.76293865  0.04183247 -0.02825520  0.10121856 
  [1] "fitting the model: P(continA_B.14|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -2.66808832  0.04668767 -0.01682537  0.04966896 
  [1] "fitting the model: P(continA_B.15|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -2.56907935  0.24920018  0.04169701 -0.06121848 
  [1] "fitting the model: P(continA_B.16|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -2.45866133  0.07037545 -0.05523806 -0.04366296 
  [1] "fitting the model: P(continA_B.17|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -2.46155938 -0.04440029  0.09059188  0.09003156 
  [1] "fitting the model: P(continA_B.18|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
  -2.2919153 -0.2950521 -0.1660820  0.0906833 
  [1] "fitting the model: P(continA_B.19|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -2.17862710 -0.19097423 -0.14938683  0.05523841 
  [1] "fitting the model: P(continA_B.20|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -2.03531060  0.00610739 -0.08797900 -0.05204959 
  [1] "fitting the model: P(continA_B.21|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -1.92165109 -0.02872073  0.02153659 -0.04888803 
  [1] "fitting the model: P(continA_B.22|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -1.81920359 -0.09223465 -0.09967640  0.10484349 
  [1] "fitting the model: P(continA_B.23|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -1.60761458  0.05711750 -0.09840099  0.02107149 
  [1] "fitting the model: P(continA_B.24|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
  -1.2719793 -0.1752018 -0.1228110 -0.1361714 
  [1] "fitting the model: P(continA_B.25|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -1.08534038  0.07139360  0.01019766 -0.04564447 
  [1] "fitting the model: P(continA_B.26|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept         CVD     highA1c   N.tminus1 
  -0.76438456  0.07360828  0.01950537  0.10593805 
  [1] "fitting the model: P(continA_B.27|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
   Intercept        CVD    highA1c  N.tminus1 
   0.1301801  0.2439900 -0.1011878 -0.2581866 
  [1] "fitting the model: P(continA_B.28|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
      Intercept           CVD       highA1c     N.tminus1 
   2.656607e+01 -3.933463e-06 -8.548236e-08 -3.378729e-07 
  [1] "fitting the model: P(continA_B.29|CVD, highA1c, N.tminus1);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
  Intercept       CVD   highA1c N.tminus1 
         NA        NA        NA        NA 
  fit for outcome continA succeeded...
  [1] "fitting the model: P(N|);\\ Stratify: "
  [1] "calling speedglm.wfit..."
  [1] "speedglm fits:"
    Intercept 
  -0.01358231 
  [1] "performing prediction for continuous outcome: continA"
  [1] "performing prediction for continuous outcome: continA"
   done successfully.
  
  ------------------- UNIT TEST SUMMARY ---------------------
  
  RUNIT TEST PROTOCOL -- Wed Mar  7 18:03:22 2018 
  *********************************************** 
  Number of test functions: 18 
  Number of errors: 1 
  Number of failures: 0 
  
   
  1 Test Suite : 
  stremr unit testing - 18 test functions, 1 error, 0 failures
  ERROR in test.speedglm.allestimators10Kdata: Error in make_report_rmd(OData, NPMSM = list(surv1, surv2), MSM = MSM.IPAW,  : 
    file '/tmp/RtmpZB7f3p/sim.data.example.fup.pdf' does not exist
  Error: 
  
  unit testing failed (#test failures: 0, #R errors: 1)
  
  Execution halted