amp (version 1.0.0)

ic.data.examp: Function for calculating the influence function used for the real data example.

Description

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

Usage

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

Arguments

obs_data

the observed data. The first column should be the outcome.

what

the desired return value. Should be one of "ic" (influence curve), "est" (estimate), or "both".

control

any other control parameters to be passed to the estimator.

Value

If what is

- "est", then return the estimated parameter.

- "ic", then return the estimated IC of the parameter estimate.

- "both", then return both the parameter estimate and corresponding estimated IC.

Examples

# NOT RUN {
expit <- function(x) exp(x) / (1 + exp(x))
ws <- matrix(rnorm(3000), 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)
cats <- rep(1:10, 100)
obs_dat <- cbind(y, "cat" = cats, "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])
}

# }