# ci.adaptive_iptw: Confidence intervals for adaptive_iptw objects In drtmle: Doubly-Robust Nonparametric Estimation and Inference

## Confidence intervals for adaptive_iptw objects

### Description

Estimate confidence intervals for objects of class `"adaptive_iptw"`

### Usage

```## S3 method for class 'adaptive_iptw'
ci(object, est = c("iptw_tmle"), level = 0.95, contrast = NULL, ...)
```

### Arguments

 `object` An object of class `"adaptive_iptw"` `est` A vector indicating for which estimators to return a confidence interval. Possible estimators include the TMLE IPTW (`"iptw_tmle"`, recommended), the one-step IPTW (`"iptw_os"`, not recommended), the standard IPTW (`"iptw"`, recommended only for comparison to the other two estimators). `level` The nominal coverage probability of the desired confidence interval (should be between 0 and 1). Default computes 95\ intervals. `contrast` Specifies the parameter for which to return confidence intervals. If `contrast=NULL`, then confidence intervals for the marginal means are computed. If instead, `contrast` is a numeric vector of ones, negative ones, and zeros to define linear combinations of the various means (e.g., to estimate an average treatment effect, see example). Finally, `contrast` can be a list with named functions `f`, `f_inv`, `h`, and `fh_grad`. The first two functions should take as input argument `eff`. Respectively, these specify which transformation of the effect measure to compute the confidence interval for and the inverse transformation to put the confidence interval back on the original scale. The function `h` defines the contrast to be estimated and should take as input `est`, a vector of the same length as `object\$a_0`, and output the desired contrast. The function `fh_grad` is the gradient of the function `h`. See examples and vignette for more information. `...` Other options (not currently used).

### Value

An object of class `"ci.adaptive_iptw"` with point estimates and confidence intervals of the specified level.

### Examples

```# load super learner
library(SuperLearner)
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))

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 confidence intervals for each mean
ci_mean <- ci(fit1)

# get confidence intervals for ATE
ci_ATE <- ci(fit1, contrast = c(1, -1))

# get confidence intervals for risk ratio
# by inputting own contrast function
# this computes CI on log scale and back transforms
myContrast <- list(
f = function(eff) {
log(eff)
},
f_inv = function(eff) {
exp(eff)
},
h = function(est) {
est[1] / est[2]
},