wald_test.adaptive_iptw | R Documentation |
Wald tests for adaptive_iptw objects
## S3 method for class 'adaptive_iptw' wald_test(object, est = c("iptw_tmle"), 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 IPTW ( |
null |
The null hypothesis value(s). |
contrast |
This option specifies what parameter to return confidence
intervals for. If |
... |
Other options (not currently used). |
An object of class "ci.adaptive_iptw"
with point estimates and
confidence intervals of the specified level.
# load super learner library(SuperLearner) # fit adaptive_iptw set.seed(123456) n <- 200 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)) fit1 <- adaptive_iptw( W = W, A = A, Y = Y, a_0 = c(1, 0), SL_g = c("SL.glm", "SL.mean", "SL.step"), SL_Qr = "SL.glm" ) # get test that each mean = 0.5 test_mean <- wald_test(fit1, null = 0.5) # get test that the ATE = 0 ci_ATE <- ci(fit1, contrast = c(1, -1), null = 0) # get test for risk ratio = 1 on log scale myContrast <- list( f = function(eff) { log(eff) }, f_inv = function(eff) { exp(eff) }, # not necessary h = function(est) { est[1] / est[2] }, fh_grad = function(est) { c(1 / est[1], -1 / est[2]) } ) ci_RR <- ci(fit1, contrast = myContrast, null = 1) #
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