alean: Assumption Lean inference for generalized linear model...

In targeted: Targeted Inference

Assumption Lean inference for generalized linear model parameters

Description

Assumption lean inference via cross-fitting (Double ML). See <doi:10.1111/rssb.12504

Usage

alean(
  response_model,
  exposure_model,
  data,
  link = "identity",
  g_model,
  nfolds = 1,
  silent = FALSE,
  mc.cores,
  ...
)

Arguments

response_model	formula or ml_model object (formula => glm)
exposure_model	model for the exposure
data	data.frame
link	Link function (g)
g_model	Model for E[g(Y|A,W)|W]
nfolds	Number of folds
silent	supress all messages and progressbars
mc.cores	mc.cores Optional number of cores. parallel::mcmapply used instead of future
...	additional arguments to future.apply::future_mapply

Details

Let Y be the response variable, A the exposure and W covariates. The target parameter is:

\Psi(P) = \frac{E(Cov[A, g\{E(Y|A,W)\}\mid W])} {E\{Var(A\mid W)\}}

The response_model is the model for E(Y|A,W), and exposure_model is the model for E(A|W). link specifies g.

Value

alean.targeted object

Author(s)

Klaus Kähler Holst

Examples

sim1 <- function(n, family=gaussian(), ...) {
   m <- lvm() |>
     distribution(~ y, binomial.lvm()) |>
     regression('a', value=function(l) l) |>
     regression('y', value=function(a,l) a + l)
     if (family$family=="binomial")
        distribution(m, ~a) <- binomial.lvm()
   sim(m, n)
}

library(splines)
f <- binomial()
d <- sim1(1e4, family=f)
e <- alean(response_model=ML(y ~ a + bs(l, df=3), family=binomial),
           exposure_model=ML(a ~ bs(l, df=3), family=f),
           data=d,
           link = "logit", mc.cores=1, nfolds=1)
e

e <- alean(response_model=ML(y ~ a + l, family=binomial),
           exposure_model=ML(a ~ l),
           data=d,
           link = "logit", mc.cores=1, nfolds=1)
e

targeted documentation built on May 29, 2024, 2:08 a.m.