wald_test.drtmle | R Documentation |
Wald tests for drtmle objects
## S3 method for class 'drtmle' wald_test(object, est = c("drtmle"), null = 0, contrast = NULL, ...)
object |
An object of class |
est |
A vector indicating for which estimators to return a
confidence interval. Possible estimators include the TMLE with doubly robust
inference ( |
null |
The null hypothesis value. |
contrast |
This option specifies what parameter to return confidence
intervals for. If |
... |
Other options (not currently used). |
An object of class "ci.drtmle"
with point estimates and
confidence intervals of the specified level.
# load super learner library(SuperLearner) # simulate data set.seed(123456) n <- 100 W <- data.frame(W1 = runif(n), W2 = rnorm(n)) A <- rbinom(n, 1, plogis(W$W1 - W$W2)) Y <- rbinom(n, 1, plogis(W$W1 * W$W2 * A)) # fit drtmle with maxIter = 1 so runs fast fit1 <- drtmle( W = W, A = A, Y = Y, a_0 = c(1, 0), family = binomial(), stratify = FALSE, SL_Q = c("SL.glm", "SL.mean", "SL.glm.interaction"), SL_g = c("SL.glm", "SL.mean", "SL.glm.interaction"), SL_Qr = "SL.glm", SL_gr = "SL.glm", maxIter = 1 ) # get hypothesis test that each mean = 0.5 test_mean <- wald_test(fit1, null = 0.5) # get test that ATE = 0 test_ATE <- wald_test(fit1, null = 0, contrast = c(1, -1)) # get test that risk ratio = 1, computing test on log scale myContrast <- list( f = function(eff) { log(eff) }, f_inv = function(eff) { exp(eff) }, h = function(est) { est[1] / est[2] }, fh_grad = function(est) { c(1 / est[1], -1 / est[2]) } ) test_RR <- wald_test(fit1, contrast = myContrast, null = 1) #
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