Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "../man/figures/README-"
)
library(dplyr)
load("../data/star.rda")
# specifying the outcome
outcomes <- "g3tlangss"
# specifying the treatment
treatment <- "treatment"
# specifying the data (remove other outcomes)
star_data <- star %>% dplyr::select(-c(g3treadss,g3tmathss))
# specifying the formula
user_formula <- as.formula(
"g3tlangss ~ treatment + gender + race + birthmonth +
birthyear + SCHLURBN + GRDRANGE + GKENRMNT + GKFRLNCH +
GKBUSED + GKWHITE ")
## ----multiple, message=TRUE, warning=TRUE-------------------------------------
library(evalITR)
# specify the trainControl method
fitControl <- caret::trainControl(
method = "repeatedcv",
number = 2,
repeats = 2)
# estimate ITR
set.seed(2021)
fit_cv <- estimate_itr(
treatment = "treatment",
form = user_formula,
data = star_data,
trControl = fitControl,
algorithms = c(
"causal_forest",
# "bartc",
# "rlasso", # from rlearner
# "ulasso", # from rlearner
"lasso" # from caret package
# "rf" # from caret package
), # from caret package
budget = 0.2,
n_folds = 2)
# evaluate ITR
est_cv <- evaluate_itr(fit_cv)
# summarize estimates
summary(est_cv)
## ----multiple_plot, fig.width=8, fig.height=6,fig.align = "center"------------
# plot the AUPEC with different ML algorithms
plot(est_cv)
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