ic.data.examp: Function for calculating the influence function used for the...
In adam-s-elder/amar: Statistical Test for the Multivariate Point Null Hypotheses

Function for calculating the influence function used for the real data example.

1	ic.data.examp(obs_data, what = "both", control = NULL)

`obs_data`	the observed data. The first column should be the outcome.
`what`	the desired return value. Should be one of '"ic"' (infludence curve), '"est"' (estimate), or '"both"'.
`control`	any other control parameters to be passed to the estimator.

expit <- function(x) exp(x) / (1 + exp(x))
ws <- matrix(rnorm(30000), ncol = 3)
probs <- expit(ws  %*% c(-1, 0, 2))
y <- rbinom(n = nrow(probs), size = 1, prob = probs[, 1])
wts <-   abs(rnorm(length(y))) + 1
wts <- length(wts) * wts / sum(wts)
obs_dat <- cbind(y, "wt" = wts, ws)
est_ic <- ic.data.examp(obs_dat, what = "both")
my_est <- est_ic$est
my_ic <- est_ic$ic / nrow(ws)
var_mat <- t(my_ic) %*% my_ic
sqrt(diag(var_mat))
for(cov_idx in 1:ncol(ws)){
 print(summary(stats::glm(y ~ ws[, cov_idx], weights = obs_dat[, "wt"],
                    family = binomial))$coefficients[2, 1:2])
}