tests/RUnit/RUnit_tests_08_h2oQuasiBinomGLM.R

In stremr: Streamlined Estimation of Survival for Static, Dynamic and Stochastic Treatment and Monitoring Regimes

# notest.h2oQuasiBinomGLM.Ensemble <- function() {
#     reqh2o <- requireNamespace("h2o", quietly = TRUE)
#     if (reqh2o) {
#         options(stremr.verbose = FALSE)
#         `%+%` <- function(a, b) paste0(a, b)
#         require("h2o")
#         h2o::h2o.init(nthreads = 1)
#         # h2o::h2o.init(nthreads = -1)
#         require("data.table")
#         # require("stremr")
#         data(OdatDT_10K)
#         Odat_DT <- OdatDT_10K

#         # ---------------------------------------------------------------------------
#         # Define some summaries (lags C[t-1], A[t-1], N[t-1])
#         # ---------------------------------------------------------------------------
#         ID <- "ID"; t <- "t"; TRT <- "TI"; I <- "highA1c"; outcome <- "Y.tplus1";
#         lagnodes <- c("C", "TI", "N")
#         newVarnames <- lagnodes %+% ".tminus1"
#         Odat_DT[, (newVarnames) := shift(.SD, n=1L, fill=0L, type="lag"), by=ID, .SDcols=(lagnodes)]
#         # indicator that the person has never been on treatment up to current t
#         Odat_DT[, ("barTIm1eq0") := as.integer(c(0, cumsum(get(TRT))[-.N]) %in% 0), by = eval(ID)]
#         Odat_DT[, ("lastNat1.factor") := as.factor(lastNat1)]


#         OData <- importData(Odat_DT, ID = "ID", t = "t", covars = c("highA1c", "lastNat1", "lastNat1.factor"), CENS = "C", TRT = "TI", MONITOR = "N", OUTCOME = outcome)
#         # OData$define_CVfolds(nfolds = 10, seed = 23)

#         # ---------------------------------------------------------------------------------------------------------
#         # VALIDATING QUASIBINOMIAL (cont Y) LOGISTIC REG in H2O glm WITH TMLE
#         # ---------------------------------------------------------------------------------------------------------
#         t.surv <- c(10)
#         Qforms <- rep.int("Q.kplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(t.surv)+1))
#         # h2o::h2o.init(nthreads = -1, startH2O = FALSE)
#         # h2o::h2o.shutdown()
#         # options(stremr.verbose = TRUE)

#         # speedglm:
#         params = list(fit.package = "speedglm", fit.algorithm = "glm")
#         gcomp_est <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
#         gcomp_est$estimates[]
#         #    est_name  t      risk      surv ALLsuccessTMLE nFailedUpdates rule.name
#         # 1:    GCOMP 10 0.2723941 0.7276059          FALSE             11 gTI.dhigh

#         # H2O glm w/ L_BFGS:
#         params = list(fit.package = "h2o", fit.algorithm = "glm", solver = "L_BFGS")
#         gcomp_est <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
#         gcomp_est$estimates[]
#         #    est_name  t      risk      surv ALLsuccessTMLE nFailedUpdates rule.name
#         # 1:    GCOMP 10 0.2723858 0.7276142          FALSE             11 gTI.dhigh

#         # H2O glm w/ IRLSM:
#         params = list(fit.package = "h2o", fit.algorithm = "glm", solver = "IRLSM")
#         gcomp_est <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", Qforms = Qforms, params_Q = params, stratifyQ_by_rule = FALSE)
#         gcomp_est$estimates[]
#         #    est_name  t      risk      surv ALLsuccessTMLE nFailedUpdates rule.name
#         # 1:    GCOMP 10 0.2723941 0.7276059          FALSE             11 gTI.dhigh

#         # H2O SL:
#         h2o.glm.1 <- function(..., alpha = 0.0) h2o.glm.wrapper(..., alpha = alpha)
#         h2o.glm.2 <- function(..., x = "highA1c", alpha = 0.0) h2o.glm.wrapper(..., x = x, alpha = alpha)
#         glm_hyper_params <- list(search_criteria = list(strategy = "RandomDiscrete", max_models = 2),
#                                  alpha = c(0,1,seq(0.1,0.9,0.1)), lambda = c(0,1e-7,1e-5,1e-3,1e-1))
#         SLparams <- list( fit.package = "h2o",
#                          fit.algorithm = "SuperLearner",
#                          grid.algorithm = c("glm"),
#                          learner = c("h2o.glm.2"),
#                          nfolds = 5,
#                          seed = 23,
#                          glm = glm_hyper_params)

#         gcomp_est <- fitSeqGcomp(OData, t_periods = t.surv, intervened_TRT = "gTI.dhigh", Qforms = Qforms, params_Q = SLparams, stratifyQ_by_rule = FALSE)
#         gcomp_est$estimates[]
#         #    est_name  t      risk      surv ALLsuccessTMLE nFailedUpdates   type rule.name
#         # 1:    GCOMP 10 0.2495067 0.7504933          FALSE             11 pooled gTI.dhigh

#         h2o::h2o.shutdown(prompt = FALSE)
#     }
# }