Nothing
library(SuperLearner)
n <- 1000
set.seed(10)
DATA2 <- data.frame(A=rbinom(n, size=1, prob=0.5),
y=rbinom(n, size=1, prob=0.2),
x1=rnorm(n),
x2=rnorm(n),
x3=as.factor(rbinom(n, size=1, prob=0.5)),
z1=rbinom(n, size=1, prob=0.5),
z2=rbinom(n, size=1, prob=0.5))
DATA2[, "y"] <- NA
As <- DATA2$A == 1
DATA2[DATA2$A == 1, "y"] <- rbinom(sum(As), size=1, prob=exp(DATA2[As,]$x1)/(1+exp(DATA2[As,]$x1)))
DATA2[DATA2$A == 0, "y"] <- rbinom(n-sum(As), size=1, prob=exp(1 + 5*DATA2[!As,]$x1 + DATA2[!As,]$x2)/(1+exp(1 + 5*DATA2[!As,]$x1 + DATA2[!As,]$x2)))
DATA2$A <- as.factor(DATA2$A)
test_that("simple super learner with random forest.", {
sl.mod <- robincar_SL(
df=DATA2,
response_col="y",
treat_col="A",
car_strata_cols=c("z1"),
covariate_cols=c("x1"),
SL_libraries=c("SL.ranger"),
car_scheme="permuted-block",
covariate_to_include_strata=TRUE
)
})
test_that("simple super learner with median adjustment, random forest.", {
sl.mod <- robincar_SL_median(
seed=1,
n_times=3,
df=DATA2,
response_col="y",
treat_col="A",
car_strata_cols=c("z1"),
covariate_cols=c("x1"),
SL_libraries=c("SL.ranger"),
car_scheme="permuted-block",
covariate_to_include_strata=TRUE
)
})
test_that("super learner with custom learner", {
skip("This works, but not in the testthat environment.")
create_rf <- create.Learner("SL.ranger",
tune = list(num.trees=c(25, 50),
max.depth=c(0.1, 0.5)))
sl.mod <- robincar_SL(
df=DATA2,
response_col="y",
treat_col="A",
car_strata_cols=c("z1"),
covariate_cols=c("x1"),
SL_learners=list(create_rf),
car_scheme="permuted-block",
covariate_to_include_strata=TRUE
)
})
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