context("Stratification - estimate TSM in strata")
library(sl3)
library(uuid)
library(assertthat)
library(data.table)
library(future)
# data
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
### discrete ###
node_list <- list(
W = c("whz"),
V = "sexn",
A = "parity01",
Y = "haz01"
)
# leaners
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)
# estimators
tmle_spec <- tmle_MSM()
# 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)
# disable cvtmle for this test to compare with tmle package
updater <- tmle3_Update$new(cvtmle = FALSE, convergence_type = "sample_size")
targeted_likelihood <- Targeted_Likelihood$new(initial_likelihood, updater)
# define parameter
msm <- tmle_spec$make_params(tmle_task, targeted_likelihood)
updater$tmle_params <- msm
# fit
tmle_fit <- fit_tmle3(tmle_task, targeted_likelihood, msm, updater)
# extract results
tmle3_psi <- tmle_fit$summary$tmle_est
tmle3_se <- tmle_fit$summary$se
#################################################
# compare with the tmle package
library(tmle)
# construct likelihood estimates
cf_task1 <- msm$cf_likelihoods[["A_1"]]$cf_tasks[[1]]
cf_task0 <- msm$cf_likelihoods[["A_0"]]$cf_tasks[[1]]
# get Q
EY1 <- initial_likelihood$get_likelihoods(cf_task1, "Y")
EY0 <- initial_likelihood$get_likelihoods(cf_task0, "Y")
Q <- cbind(EY0, EY1)
# get g
pA1 <- initial_likelihood$get_likelihoods(cf_task1, "A")
pA0 <- initial_likelihood$get_likelihoods(cf_task0, "A")
h <- cbind(pA0, pA1)
tmle_classic_fit <- tmleMSM(
Y = tmle_task$get_tmle_node("Y"),
A = tmle_task$get_tmle_node("A"),
W = setnames(tmle_task$get_data(NULL, node_list[["W"]]), "W"),
V = setnames(tmle_task$get_data(NULL, node_list[["V"]]), "V"),
MSM = "A + V",
Q = Q,
hAV = h,
g1W = pA1,
family = "binomial"
)
# extract estimates
classic_psi <- tmle_classic_fit$psi
classic_se <- tmle_classic_fit$se
# only approximately equal (although it's O(1/n))
names(tmle3_psi) <- c("A_0", "A_1", "V")
classic_psi["A"] <- classic_psi["A"] + classic_psi["(Intercept)"]
names(classic_psi) <- c("A_0", "A_1", "V")
test_that("psi matches result from classic package", {
expect_equal(tmle3_psi, classic_psi, tol = 1e-3)
})
# only approximately equal (although it's O(1/n))
tmle3_se <- tmle3_se[c(1, 3)]
names(tmle3_se) <- c("A_0", "V")
classic_se <- classic_se[c(1, 3)]
names(classic_se) <- c("A_0", "V")
test_that("se matches result from classic package", {
expect_equal(tmle3_se, classic_se, tol = 1e-3)
})
### continuous ###
node_list <- list(
W = c(
"apgar1", "apgar5", "gagebrth", "mage",
"meducyrs", "sexn"
),
V = "agedays",
A = "whz",
Y = "haz"
)
processed <- process_missing(data[1:500, ], node_list)
data <- processed$data
node_list <- processed$node_list
# leaners
lrnr_mean <- make_learner(Lrnr_mean)
lrnr_xgboost <- make_learner(Lrnr_xgboost)
ls_metalearner <- make_learner(Lrnr_nnls)
mn_metalearner <- make_learner(
Lrnr_solnp, metalearner_linear_multinomial,
loss_loglik_multinomial
)
sl_Y <- Lrnr_sl$new(
learners = list(lrnr_mean, lrnr_xgboost),
metalearner = ls_metalearner
)
sl_A <- make_learner(Lrnr_density_semiparametric)
learner_list <- list(A = sl_A, Y = sl_Y)
# estimators
tmle_spec <- tmle_MSM()
# define data
tmle_task <- tmle_spec$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()
targeted_likelihood <- Targeted_Likelihood$new(initial_likelihood, updater)
# define parameter
msm <- tmle_spec$make_params(tmle_task, targeted_likelihood)
updater$tmle_params <- msm
# fit
tmle_fit <- fit_tmle3(tmle_task, targeted_likelihood, msm, updater)
# extract results
tmle_ests <- tmle_fit$summary$tmle_est
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