make code changes

R_code/01-preface.R

@@ -0,0 +1 @@
+

R_code/02-roadmap.R

@@ -18,3 +18,4 @@ tidy_dag <- tidy_dagitty(dag)
 # visualize DAG
 ggdag(tidy_dag) +
   theme_dag()
+

R_code/03-tlverse.R

@@ -1,3 +1,4 @@
 ## ----installation, eval=FALSE-------------------------------------------------
 ## install.packages("devtools")
 ## devtools::install_github("tlverse/tlverse")
+

R_code/04-data.R

@@ -60,3 +60,4 @@ if (knitr::is_latex_output()) {
 } else {
   skim(nhefs_data)
 }
+

R_code/05-origami.R

@@ -16,7 +16,7 @@ washb_data <- fread(
 washb_data <- washb_data[1:30, ]
 head(washb_data) %>%
   kable() %>%
-  kableExtra::kable_styling(fixed_thead = TRUE) %>%
+  kable_styling(fixed_thead = TRUE) %>%
   scroll_box(width = "100%", height = "300px")
 
 
@@ -122,7 +122,7 @@ covars <- colnames(washb_data)[-which(names(washb_data) == outcome)]
 
 head(washb_data) %>%
   kable() %>%
-  kableExtra::kable_styling(fixed_thead = TRUE) %>%
+  kable_styling(fixed_thead = TRUE) %>%
   scroll_box(width = "100%", height = "300px")
 
 
@@ -285,3 +285,4 @@ mses <- cross_validate(
 )
 mses$mse
 colMeans(mses$mse[, c("arima", "arima2")])
+

R_code/06-sl3.R

@@ -23,7 +23,7 @@ washb_data <- fread(
 )
 head(washb_data) %>%
   kable() %>%
-  kableExtra::kable_styling(fixed_thead = T) %>%
+  kable_styling(fixed_thead = TRUE) %>%
   scroll_box(width = "100%", height = "300px")
 
 
@@ -236,13 +236,13 @@ head(sl_preds)
 
 
 ## ---- plot-predvobs-woohoo, eval=FALSE----------------------------------------
-##
+## 
 ## # df_plot <- data.frame(Observed = washb_data[["whz"]], Predicted = sl_preds,
 ## #                        count = seq(1:nrow(washb_data))
-##
+## 
 ## # df_plot_melted <- melt(df_plot, id.vars = "count",
 ## #                         measure.vars = c("Observed", "Predicted"))
-##
+## 
 ## # ggplot(df_plot_melted, aes(value, count, color = variable)) + geom_point()
 
 
@@ -262,15 +262,15 @@ CVsl <- CV_lrnr_sl(
 )
 CVsl %>%
   kable(digits = 4) %>%
-  kableExtra::kable_styling(fixed_thead = TRUE) %>%
+  kable_styling(fixed_thead = TRUE) %>%
   scroll_box(width = "100%", height = "300px")
 
 
 ## ----varimp-------------------------------------------------------------------
 washb_varimp <- importance(sl_fit, loss = loss_squared_error, type = "permute")
 washb_varimp %>%
   kable(digits = 4) %>%
-  kableExtra::kable_styling(fixed_thead = TRUE) %>%
+  kable_styling(fixed_thead = TRUE) %>%
   scroll_box(width = "100%", height = "300px")
 
 
@@ -295,7 +295,7 @@ chspred <- read_csv(file = db_data, col_names = TRUE)
 # take a quick peek
 head(chspred) %>%
   kable(digits = 4) %>%
-  kableExtra::kable_styling(fixed_thead = TRUE) %>%
+  kable_styling(fixed_thead = TRUE) %>%
   scroll_box(width = "100%", height = "300px")
 
 
@@ -324,14 +324,14 @@ ist_task_CVsl <- make_sl3_Task(
 ##   "https://raw.githubusercontent.com/benkeser/sllecture/master/chspred.csv"
 ## )
 ## chspred <- read_csv(file = db_data, col_names = TRUE)
-##
+## 
 ## # make task
 ## chspred_task <- make_sl3_Task(
 ##   data = chspred,
 ##   covariates = head(colnames(chspred), -1),
 ##   outcome = "mi"
 ## )
-##
+## 
 ## # make learners
 ## glm_learner <- Lrnr_glm$new()
 ## lasso_learner <- Lrnr_glmnet$new(alpha = 1)
@@ -344,11 +344,11 @@ ist_task_CVsl <- make_sl3_Task(
 ## ranger_learner <- Lrnr_ranger$new()
 ## svm_learner <- Lrnr_svm$new()
 ## xgb_learner <- Lrnr_xgboost$new()
-##
+## 
 ## # screening
 ## screen_cor <- make_learner(Lrnr_screener_correlation)
 ## glm_pipeline <- make_learner(Pipeline, screen_cor, glm_learner)
-##
+## 
 ## # stack learners together
 ## stack <- make_learner(
 ##   Stack,
@@ -357,17 +357,17 @@ ist_task_CVsl <- make_sl3_Task(
 ##   curated_glm_learner, mean_learner, glm_fast_learner,
 ##   ranger_learner, svm_learner, xgb_learner
 ## )
-##
+## 
 ## # make and train SL
 ## sl <- Lrnr_sl$new(
 ##   learners = stack
 ## )
 ## sl_fit <- sl$train(chspred_task)
 ## sl_fit$print()
-##
+## 
 ## CVsl <- CV_lrnr_sl(sl_fit, chspred_task, loss_loglik_binomial)
 ## CVsl
-##
+## 
 ## varimp <- importance(sl_fit, type = "permute")
 ## varimp %>%
 ##   importance_plot(
@@ -377,20 +377,20 @@ ist_task_CVsl <- make_sl3_Task(
 
 ## ----ex2-key, eval=FALSE------------------------------------------------------
 ## library(ROCR) # for AUC calculation
-##
+## 
 ## ist_data <- paste0(
 ##   "https://raw.githubusercontent.com/tlverse/",
 ##   "tlverse-handbook/master/data/ist_sample.csv"
 ## ) %>% fread()
-##
+## 
 ## # stack
 ## ist_task <- make_sl3_Task(
 ##   data = ist_data,
 ##   outcome = "DRSISC",
 ##   covariates = colnames(ist_data)[-which(names(ist_data) == "DRSISC")],
 ##   drop_missing_outcome = TRUE
 ## )
-##
+## 
 ## # learner library
 ## lrn_glm <- Lrnr_glm$new()
 ## lrn_lasso <- Lrnr_glmnet$new(alpha = 1)
@@ -415,17 +415,17 @@ ist_task_CVsl <- make_sl3_Task(
 ## ),
 ## recursive = TRUE
 ## )
-##
+## 
 ## # SL
 ## sl <- Lrnr_sl$new(learners)
 ## sl_fit <- sl$train(ist_task)
-##
+## 
 ## # AUC
 ## preds <- sl_fit$predict()
 ## obs <- c(na.omit(ist_data$DRSISC))
 ## AUC <- performance(prediction(sl_preds, obs), measure = "auc")@y.values[[1]]
 ## plot(performance(prediction(sl_preds, obs), "tpr", "fpr"))
-##
+## 
 ## # CVsl
 ## ist_task_CVsl <- make_sl3_Task(
 ##   data = ist_data,
@@ -440,7 +440,7 @@ ist_task_CVsl <- make_sl3_Task(
 ## )
 ## CVsl <- CV_lrnr_sl(sl_fit, ist_task_CVsl, loss_loglik_binomial)
 ## CVsl
-##
+## 
 ## # sl3 variable importance plot
 ## ist_varimp <- importance(sl_fit, type = "permute")
 ## ist_varimp %>%
@@ -451,3 +451,4 @@ ist_task_CVsl <- make_sl3_Task(
 
 ## ----ex3-key, eval=FALSE------------------------------------------------------
 ## # TODO
+

R_code/07-tmle3.R

@@ -183,3 +183,4 @@ ist_data <- fread(
     "master/data/ist_sample.csv"
   )
 )
+

R_code/08-tmle3mopttx.R

@@ -297,14 +297,15 @@ b_learner <- create_mv_learners(learners = learners)
 learner_list <- list(Y = Q_learner, A = g_learner, B = b_learner)
 
 
-## ----spec_init_WASH-----------------------------------------------------------
-# initialize a tmle specification
-tmle_spec <- tmle3_mopttx_blip_revere(
-  V = c("momedu", "floor", "asset_refrig"), type = "blip2",
-  learners = learner_list, maximize = TRUE, complex = TRUE,
-  realistic = FALSE
-)
+## ----spec_init_WASH, eval=FALSE-----------------------------------------------
+## # initialize a tmle specification
+## tmle_spec <- tmle3_mopttx_blip_revere(
+##   V = c("momedu", "floor", "asset_refrig"), type = "blip2",
+##   learners = learner_list, maximize = TRUE, complex = TRUE,
+##   realistic = FALSE
+## )
+## 
+## # fit the TML estimator
+## fit <- tmle3(tmle_spec, data = washb_data, node_list, learner_list)
+## fit
 
-# fit the TML estimator
-fit <- tmle3(tmle_spec, data = washb_data, node_list, learner_list)
-fit

R_code/09-tmle3shift.R

@@ -169,3 +169,4 @@ learner_list <- list(Y = sl_reg_lrnr, A = cv_hose_hal_lrnr)
 ## ----fit_tmle_wrapper_washb_shift, message=FALSE, warning=FALSE, eval=FALSE----
 ## washb_tmle_fit <- tmle3(washb_vim_spec, washb_data, node_list, learner_list)
 ## washb_tmle_fit
+

R_code/10-tmle3mediate.R

@@ -0,0 +1 @@
+

R_code/11-tmle3survival.R

@@ -77,3 +77,4 @@ tmle_fit_manual <- fit_tmle3(
 
 ## -----------------------------------------------------------------------------
 print(tmle_fit_manual)
+

R_code/99-primer_R6.R

@@ -0,0 +1 @@
+