context("Use best estimate (early stopping)")
library(sl3)
library(tmle3)
library(uuid)
library(assertthat)
library(data.table)
library(future)
# setup data for test
data(cpp)
data <- as.data.table(cpp)
data$parity01 <- as.numeric(data$parity > 0)
data$parity01_fac <- factor(data$parity01)
data$haz01 <- as.numeric(data$haz > 0)
data[is.na(data)] <- 0
node_list <- list(
W = c(
"apgar1", "apgar5", "gagebrth", "mage",
"meducyrs", "sexn"
),
A = "parity01",
Y = "haz01"
)
qlib <- make_learner_stack(
"Lrnr_mean",
"Lrnr_glm_fast"
)
glib <- make_learner_stack(
"Lrnr_mean",
"Lrnr_glm_fast"
)
logit_metalearner <- make_learner(
Lrnr_solnp, metalearner_logistic_binomial,
loss_loglik_binomial
)
Q_learner <- make_learner(Lrnr_sl, qlib, logit_metalearner)
g_learner <- make_learner(Lrnr_sl, glib, logit_metalearner)
learner_list <- list(Y = Q_learner, A = g_learner)
tmle_spec <- tmle_TSM_all()
# define data
tmle_task <- tmle_spec$make_tmle_task(data, node_list)
# define likelihood
initial_likelihood <- tmle_spec$make_initial_likelihood(tmle_task, learner_list)
# define update method (submodel + loss function)
updater <- tmle3_Update$new(
one_dimensional = TRUE, constrain_step = TRUE,
maxit = 10000, cvtmle = TRUE,
convergence_type = "sample_size",
use_best = TRUE
)
# updater <- tmle3_Update$new()
targeted_likelihood <- Targeted_Likelihood$new(initial_likelihood, updater)
intervention <- define_lf(LF_static, "A", value = 1)
# params <- tmle_spec$make_params(tmle_task, targeted_likelihood)
tsm <- define_param(Param_TSM, targeted_likelihood, intervention)
updater$tmle_params <- tsm
# debugonce(updater$check_convergence)
tmle_fit <- fit_tmle3(tmle_task, targeted_likelihood, tsm, updater)
# extract results
tmle3_psi <- tmle_fit$summary$tmle_est
tmle3_se <- tmle_fit$summary$se
tmle3_epsilon <- updater$epsilons[[1]]$Y
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