---
title: The Art of Influence
subtitle: A Practical Guide to Working with Influence Functions 
author: Klaus Kähler Holst
Rdate: "`r Sys.Date()`"
output:
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  rmarkdown::html_vignette:
    # toc_float: true
    fig_caption: true
    toc: true
    toc_depth: 2
    mathjax: "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS_CHTML.js"
vignette: >
  %\VignetteIndexEntry{The Art of Influence}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
bibliography: ref.bib
---

```{r  setup,include=FALSE }
library("lava")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
  )
has_mets <- lava:::versioncheck('mets', 1)
has_geepack <- lava:::versioncheck('geepack', 1)
```

\newcommand{\arctanh}{\operatorname{arctanh}}
\newcommand{\indep}{\!\perp\!\!\!\!\perp\!} 
\newcommand{\pr}{\mathbb{P}}
\newcommand{\E}{\mathbb{E}}
\newcommand{\var}{\mathbb{V}\!\text{ar}}
\newcommand{\cov}{\mathbb{C}\!\text{ov}}
\newcommand{\cor}{\mathbb{C}\!\text{or}}
\newcommand{\IC}{\operatorname{IC}}
\newcommand{\one}{\mathbf{1}}
<!-- \newcommand{\Dto}{\rightsquigarrow} -->
\newcommand{\Dto}{\overset{\mathcal{D}}{\longrightarrow}}
\newcommand{\bm}[1]{\mathbf{#1}}
\newcommand{\Pz}{P_{0}}
\newcommand{\op}{o_{P}}

# Influence functions

Estimators that have parametric convergence rates can often be fully
characterized by their *influence function* (IF), also referred to as an influence
curve or canonical gradient [@bickel_effic_adapt_estim_semip_model; @vaart_1998_asymp]. The IF allows for the direct estimation of properties of the estimator, including its asymptotic variance. Moreover,
estimates of the IF enable the simple combination and transformation of
estimators into new ones. 
This vignette describes how to estimate and manipulate IFs using the R-package
`lava` [@holst_budtzjorgensen_2013].


Formally, let \(Z_1,\ldots,Z_n\) be iid \(k\)-dimensional stochastic variables, \(Z_i=(Y_{i},A_{i},W_{i})\sim \Pz\), and \(\widehat{\theta}\) a
consistent estimator for the parameter \(\theta\in\mathbb{R}^p\). When
\(\widehat{\theta}\) is a *regular and asymptotic linear* (RAL) estimator, it has
a unique iid decomposition 
\begin{align*} \sqrt{n}(\widehat{\theta}-\theta) =
\frac{1}{\sqrt{n}}\sum_{i=1}^n \IC(Z_i; \Pz) + \op(1), 
\end{align*} 
where the
function \(\IC\) is the unique *Influence Function* s.t. \(\mathbb{E}\{\IC(Z_{i};
\Pz)\}=0\) and \(\var\{\IC(Z_{i}; \Pz)^{2}\}<\infty\) [@tsiatis2006semiparametric;
@vaart_1998_asymp]. The influence
function thus fully characterizes the asymptotic behaviour of the estimator and by the central limit
theorem it follows that the estimator converges weakly to a Gaussian distribution
\[
\sqrt{n}(\widehat{\theta}-\theta) \Dto
\mathcal{N}(0, \var\{\IC(Z; \Pz)\}),
\] where the empirical variance of the plugin estimator, \(\pr_{n}\IC(Z;
\widehat{P})^{\otimes 2} = \frac{1}{n}\sum_{i=1}^n \IC(Z_{i};
\widehat{P})\IC(Z_{i};
\widehat{P})^{\top}\) can be used to obtain a consistent estimate of the
asymptotic variance. Note, in practice the estimate \(\widehat{P}\) used in
the plugin-estimate, needs only to capture the parts of the distribution of
\(Z\) that is necessary to evaluate the IF. In some cases this nuisance
parameter can be estimated using flexible machine learning components and in
other (parametric) cases be derived directly from \(\widehat{\theta}\).

The IFs are easily derived for the parameters of many parametric statistical
models as illustrated in the [next example sections](#example-generalized-linear-model). More generally, the IF can
also be derived for a smooth target parameter \(\Psi:
\mathcal{P}\to\mathbb{R}\) where \(\mathcal{P}\) is a family of probability
distributions forming the statistical model, which often can be left completely
non-parametric. 
Formally, the parameter must be *pathwise differentiable* see [@vaart_1998_asymp]
in the sense that there exists linear bounded function  \(\dot\Psi\colon
L_{2}(P_{0})\to\mathbb{R}\) such that 
\(
[\Psi(P_{t}) - \Psi(P_{0}))]t^{-1} \to \dot\Psi(P_{0})(g) 
\)
as \(t\to 0\) for any parametric submodel \(P_t\) 
with score model \(g(z)= \partial/(\partial t) \log (p_t)(z)|_{t=0}\).  Riesz's
representation theorem then tells us that the directional derivative has a 
unique representer, \(\phi_{P_{0}}\) lying in the closure of the submodel score
space (the *tangent space*), s.t.
\begin{align*}
    \dot\Psi(P_0)(g) = \langle\phi_{P_0}, g\rangle =
    \int \phi_{P_0}(Z)g(X)\,dP_0
\end{align*}
The unique representer is exactly the IF which can be found by solving the above
integral equation.
For more details on how to derive influence
functions, we refer to [@targetedlearning_2011; @hines2022].

As an example we might be interested in the target parameter \(\Psi(P) =
\E_P(Z)\) which can be shown to have the unique (and thereby efficient)
influence function \(Z\mapsto Z-\E_P(Z)\) under the non-parametric model. 
Another target parameter could be \(\Psi_{a}(P) = \E_{P}[\E_{P}(Y\mid A=a, W)]\) which is often
a key interest in causal inference and which has the IF 
\begin{align*}
\IC(Y,A,W; P) = \frac{\one(A=a)}{\pr(A=a\mid W)}(Y-\E_{P}[Y\mid A=a,W]) + \E_{P}[Y\mid
A=a,W] - \Psi_{a}(P)
    \end{align*}
See section on [average treatment effects](#average-treatment-effects).


# Examples
    
To illustrate the methods we consider data arising from the model \(Y_{ij} \sim
Bernoulli\{\operatorname{expit}(X_{ij} + A_{i} + W_{i})\}, A_{i} \sim
Bernoulli\{\operatorname{expit}(W_{i})\}\) 
with independent covariates
\(X_{ij}\sim\mathcal{N}(0,1), W_{i}\sim\mathcal{N}(0,1)\).
```{r sim_model}
m <- lvm() |>
  regression(y1 ~ x1 + a + w) |>
  regression(y2 ~ x2 + a + w) |>
  regression(y3 ~ x3 + a + w) |>
  regression(y4 ~ x4 + a + w) |>
  regression(a ~ w) |>
  distribution(~ y1 + y2 + y3 + y4 + a, value = binomial.lvm()) |>
  distribution(~id, value = Sequence.lvm(integer = TRUE))
```

We simulate from the model where \(Y_3\) is only observed for half of the subjects
```{r simulate}
n <- 4e2
dw <- sim(m, n, seed = 1) |>
  transform(y3 = y3 * ifelse(id > n / 2, NA, 1))
Print(dw)
## Data in long format
dl <- reshape(dw,
        varying = list(paste0("y",1:4),
                       paste0("x",1:4)),
        v.names=c("y", "x"), direction="long") |>
  na.omit()
dl <- dl[order(dl$id), ]
## dl <- mets::fast.reshape(dw, varying = c("y", "x")) |> na.omit()
Print(dl)
```

## Example: population mean

The main functions for working with influence functions are 

- `estimate` which prepares a model object and estimates the IF and
  corresponding robust standard errors. Can also be used to transform model
  parameters by application of the Delta Theorem.
- `merge`, `+` method for combining estimates via their estimated IFs
- `IC` method to extract the estimated IF

The `estimate` function is the primary tool for obtaining parameter estimates
and related information. It returns an object of the class type `estimate`,
which is a general container for holding information about estimated parameters.
The estimate function takes as input either a model object (the first argument
`x`), or a parameter vector and corresponding influence function (IF) matrix
specified using the `coef` and `IF` arguments. If the primary goal is to apply the
delta method or test linear hypotheses, it is also possible to provide the
asymptotic variance estimate via the `vcov` argument, without specifying the IF
matrix.

```{r estimate.syntax, eval=FALSE}
estimate(x=, ...)
estimate(coef=, IF=, ...)
estimate(coef=, vcov=, ...)
```
 
Here we first consider the problem of estimating the IF of the mean. For a
general transformation \(f: \mathbb{R}^k\to\mathbb{R}^p\) we have that
\[
\sqrt{n}\{\mathbb{P}_{n}f(X) - \E[f(X)]\} = \frac{1}{\sqrt{n}}\sum_{i=1}^{n} 
f(X_{i}) - \E[f(X)]
\]
<!-- \[
<!-- \sqrt{n}\{\mathbb{P}_{n}f(X) - \pr(Y_{1}=1)\} = \frac{1}{\sqrt{n}}\sum_{i=1}^{n} -->
<!-- \one(Y_{1i}=1) - \pr(Y_{1}=1) -->
<!-- \]  -->
and hence for the problem of estimating the proportion of the binary outcome
\(Y_1\), the IF is given by \(\one(Y_{1}=1) - \pr(Y_{1}=1)\). 

To estimate this parameter and its IF we will use the `estimate` function
```{r inp1}
inp <- as.matrix(dw[, c("y1", "y2")])
e <- estimate(inp[, 1, drop = FALSE], type="mean") 
class(e)
e
```
The reported standard errors from the `estimate` method
are the robust standard errors obtained from the IF.
The variance estimate and the parameters can be extracted with the `vcov`
and `coef` methods.
The IF itself can be extracted with the `IC` (or  `influence`) method:
```{r ic1}
IC(e) |> Print()
```

It is also possible to simultaneously estimate the proportions of each of the two
binary outcomes 
```{r inp2}
estimate(inp)
```
or alternatively the input can be a model object, here a `mlm` object:
```{r mlm}
e <- lm(cbind(y1, y2) ~ 1, data = dw) |>
  estimate()
IC(e) |> head()
```

Different methods are available for inspecting an `estimate`  object
```{r estimatemethods}
summary(e)
## extract parameter coefficients
coef(e)
## ## Asymptotic (robust) variance estimate
vcov(e)
## Matrix with estimates and confidence limits
estimate(e, level = 0.99) |> parameter()
## Influence curve
IC(e) |> head()
## Join estimates
e + e # Same as merge(e,e)
```

<!-- The IF for both the empirical mean and variance can also be estimated directly with the `IC` method: -->
<!-- ```{r} -->
<!-- with(dw, IC(y1)) |> head() -->
<!-- ## point estimates stores in the attributes -->
<!-- with(dw, IC(y1)) |> attributes() -->
<!-- ``` -->


## Example: generalized linear model

For a \(Z\)-estimator defined by the score equation \(E[U(Z; \theta)] = 0\), the
IF is given by \begin{align*} IC(Z; \theta) =
\E\Big\{\frac{\partial}{\partial\theta^\top}U(\theta; Z)\Big\}^{-1}U(Z; \theta)
\end{align*}
In particular, for a maximum likelihood estimator the score, \(U\), is given by the partial derivative of
the log-likelihood function.

As an example, we can obtain the estimates with robust standard errors for a
logistic regression model:
```{r glm}
g <- glm(y1 ~ a + x1, data = dw, family = binomial)
estimate(g)
```
We can compare that to the usual (non-robust) standard errors:
```{r glm.std}
estimate(g, robust = FALSE)
```

The IF can be extracted from the `estimate` object or directly from the 
model object
```{r ifglm}
IC(g) |> head()
```


The same estimates can be obtained with a 
*cumulative link regression* model which also generalizes to 
ordinal outcomes. Here we consider the proportional odds model given by 
\begin{align*}
\log\left(\frac{\pr(Y\leq j\mid x)}{1-\pr(Y\leq j\mid x)}\right) = \alpha_{j} + \beta^{t}x, \quad j=1,\ldots,J
\end{align*}
```{r ordreg}
ordreg(y1 ~ a + x1, dw, family=binomial(logit)) |> estimate()
```

Note that the `sandwich::estfun` function from the `sandwich` library [@r_sandwich] can also estimate the IF
for different parametric models, but does not provide the tools for combining
and transforming these.

## Example: right-censored outcomess

To illustrate the methods on survival data we will use the Mayo Clinic Primary
Biliary Cholangitis Data [@therneau00surv]

```{r mets}
library("survival")
data(pbc, package="survival")
```

The Cox proportional hazards model can be fitted with the `mets::phreg` method
which can estimate the IF for both the partial likelihood parameters and the
baseline hazard. Here we fit a survival model with right-censored event times
```{r phreg, eval=has_mets }
fit.phreg <- mets::phreg(Surv(time, status > 0) ~ age + sex, data = pbc)
fit.phreg
IC(fit.phreg) |> head()
```

The IF for the baseline cumulative hazard at a specific time point
\begin{align*}
\Lambda_0(t) = \int_0^t \lambda_0(u)\,du, 
\end{align*}
where \(\lambda_0(t)\) is the baseline hazard, can be estimated in similar way:
```{r phreg-baseline, eval=has_mets }
baseline <- function(object, time, ...) {
  ic <- mets::IC(object, baseline = TRUE, time = time, ...)
  est <- mets::predictCumhaz(object$cumhaz, new.time = time)[1, 2]
  estimate(NULL, coef = est, IC = ic, labels = paste0("chaz:", time))
}
tt <- 2000
baseline(fit.phreg, tt)
```

The `estimate` and `IF` methods are also available for parametric survival
models via `survival::survreg`, here a Weibull model:
```{r survreg, eval=has_mets }
survival::survreg(Surv(time, status > 0) ~ age + sex, data = pbc, dist="weibull") |>
  estimate()
```

<!-- glm poisson -->
<!-- ```{r lifetable} -->
<!-- pbc2 <- mets::lifetable(Surv(time, status > 0) ~ sex, data = pbc, breaks = seq(0, 5000, length.out = 10)) -->
<!-- head(pbc2) -->
<!-- glm(events ~ offset(log(atrisk)) + sex + factor(int.end), data = pbc2, family=poisson(log)) -->
<!-- ``` -->

  
## Example: random effects model / structural equation model

General structural equation models (SEMs) can be estimated with `lava::lvm`. 
Here we fit a random effects probit model
\[
\pr(Y_{ij} = 1 \mid U_{i}, W_{ij})=\Phi(\mu_{j} + \beta_{j} W_{ij} + U_{i}), \quad U_{i}\sim\mathcal{N}(0,\sigma_{u}^{2}),\quad j=1,2
\]
to the simulated dataset
```{r semfit, eval=has_mets }
sem <- lvm(y1 + y2 ~ 1 * u + w) |>
  latent(~ u) |>
  ordinal(K=2, ~ y1 + y2)
semfit <- estimate(sem, data = dw)

## Robust standard errors
estimate(semfit)
```

## Example: quantile

Let \(\beta\) denote the \(\tau\)th quantile of \(X\), with IF
\begin{align*}
\IC(x; \Pz) = \tau - \one(x\leq \beta)f_{0}(\beta)^{-1} 
\end{align*}

where \(f_{0}\) is the density function of \(X\).

To calculate the variance estimate, an estimate of the density is thus needed which can be
obtained by a kernel estimate. Alternatively, 
the resampling method of [@zenglin2008] can be applied.
Here we use a kernel smoother (additional arguments to the `estimate` function
are parsed on to `stats::density.default`) to estimate the quantiles and IF for 
the 25%, 50%, and 75% quantiles of \(W\) and \(X_1\)
```{r quantiles}
eq <- estimate(dw[, c("w", "x1")], type = "quantile", probs = c(0.25, 0.5, 0.75))
eq
IC(eq) |> head()
```


# Combining influence functions

A key benefit of working with the IFs of estimators is that this allows
for transforming or combining different estimates while easily deriving
the resulting IF and thereby asymptotic distribution of the new estimator. 

Let \(\widehat{\theta}_{1}, \ldots, \widehat{\theta}_{M}\) be \(M\)
different estimators with decompositions
\begin{align*}
\sqrt{n}(\widehat{\theta}_{m}-\theta_{m}) = \frac{1}{\sqrt{n}}\sum_{i=1}^{n}
\IC_m(Z_i; \Pz) + \op(1)
\end{align*}
based on iid data \(Z_1,\ldots,Z_n\). It then follows immediately
[@vaart_1998_asymp Theorem 18.10[vi]] that the
joint distribution of \(\widehat{\theta} - {\theta}=
(\widehat{\theta}_{1}^{\top},\ldots,\widehat{\theta}_{M}^{\top})^\top-
({\theta}_{1}^{\top},\ldots,{\theta}_{M}^{\top})^\top
\) is given by
\begin{align*}
\sqrt{n}(\widehat{\theta}-\theta) &= \frac{1}{\sqrt{n}}\sum_{i=1}^{n}
\underbrace{[\IC_{1}(Z_i; \Pz)^\top,\ldots,\IC_{M}(Z_i; \Pz)^\top]^{\top}}_{\overline{\IC}(Z_i; \Pz)} + \op(1) \\
&\Dto \mathcal{N}(0,\Sigma)
\end{align*}
by the CLT, and under regulatory conditions
\(\mathbb{P}_{n}\overline{\IC}(Z_i; \widehat{P})^{\otimes 2} \overset{P}{\longrightarrow}\Sigma\)
as \(n\to\infty\).

To illustrate this we consider two marginal logistic regression models fitted
separately for \(Y_1\) and \(Y_2\) and combine the estimates and IFs using the
`merge` method
```{r glmmarg}
g1 <- glm(y1 ~ a, family=binomial, data=dw)
g2 <- glm(y2 ~ a, family=binomial, data=dw)
e <- merge(g1, g2)
summary(e)
```
As we have access to the joint asymptotic distribution we can for example test
for whether the odds-ratio is the same for the two responses: 

```{r hypo1}
estimate(e, cbind(0,1,0,-1), null=0)
``` 
More details an be found in the Section on [hypothesis testing](#linear-contrasts-and-hypothesis-testing).

## Imbalanced data

Let \(O_{1} = (Z_{1}R_{1}, R_{1}), \ldots, O_{N}=(Z_{N}R_{N}, R_{N})\) be iid with
\(R_{i}\indep Z_i\) and let the full-data IF for some estimator of a parameter
\(\theta\in\mathbb{R}^p\) be \(IC(\cdot; \Pz)\). For convenience let the data be ordered
\(R_{i}=\one(i\leq n)\) where \(n\) is the number of observed data points, then the complete-case estimator is consistent and based
on same IF
\begin{align*}
\sqrt{n}(\widehat{\theta}-\theta) = \frac{1}{\sqrt{n}}\sum_{i=1}^n IC(Z_i; \Pz) + \op(1).
\end{align*}
This estimator can also be decomposed in terms of the observed data
\(O_1,\ldots,O_N\) noting that
\begin{align*}
\sqrt{N}(\widehat{\theta}-\theta) = \frac{1}{\sqrt{N}}\sum_{i=1}^N IC(Z_i;
P)\frac{R_i N}{n} + \op(1).
\end{align*}
where the term \(\frac{R_i N}{n}\) corresponds to an inverse probability
weighting with the empirical plugin estimate of the proportion of observed data \(R=1\).
Under a missing completely at random assumption we can therefore combine estimators that are estimated
on different datasets. Let the observed data be
\((Z_{11}R_{11}, R_{11}, Z_{21}R_{21}, R_{21}), \ldots, (Z_{1N}R_{1N}, R_{1N},
Z_{2N}R_{2N}, R_{2N}))\) with complete-case estimators \(\widehat{\theta}_1\) and
\(\widehat{\theta}_2\) for parameters \(\theta_1\) and
\(\theta_2\) based on \((Z_{11}R_{11}, \ldots, Z_{1N}R_{1N})\) and
\((Z_{21}R_{21}, \ldots, Z_{2N}R_{2N})\), respectively, and let the
corresponding IFs be
\(IC_{1}(\cdot; \Pz)\) and \(IC_{2}(\cdot;\ P)\). It then follows that
\begin{align*}
\sqrt{N}\left\{
\begin{pmatrix}
\widehat{\theta}_1 \\
\widehat{\theta}_2 
\end{pmatrix}
- 
\begin{pmatrix}
\vphantom{\widehat{\theta}_1}\theta_1 \\
\vphantom{\widehat{\theta}_1}\theta_2 
\end{pmatrix}
\right\}
= 
\frac{1}{\sqrt{N}}\sum_{i=1}^N 
\begin{pmatrix}
IC_1(Z_{1i}; \Pz)\frac{R_{1i}N}{R_{1\bullet}} \\
IC_2(Z_{2i}; \Pz)\frac{R_{2i}N}{R_{2\bullet}}
\end{pmatrix} 
+ \op(1)
\end{align*}
with \(R_{k\bullet} = \sum_{i=1}^{N}R_{ki}.\) Returning to the example, we can
combine the marginal estimates of two model objects that have been estimated
from different datasets (as the outcome \(Y_3\) is only available in half of the
data). Here we will use the overloaded `+` operator 

```{r glmmargmis}
g2 <- glm(y2 ~ 1, family = binomial, data = dw)
summary(g2)
dwc <- na.omit(dw) 
g3 <- glm(y3 ~ 1, family = binomial, data = dwc)
summary(g3)

e2 <- estimate(g2, id = dw$id)
e3 <- estimate(g3, id = "id", data=dwc)

merge(e2,e3) |> IC() |> Print()
vcov(e2 + e3)
## Same marginals as
list(vcov(e2), vcov(e3))
```

Note, it is also possible to directly specify the id-variables in the `merge` call:
```{r merge}
merge(e2, e3, id = list(dw$id, dwc$id))
```

In the above example the `id` argument defines the identifier that makes it
possible to link the rows in the different IFs that should be glued together. 
If omitted then the `id` will automatically be extracted from the model-specific
`IC` method (deriving it from the original data.frame used for estimating the
model). This automatically works with all models and `IC` methods described in this document.
```{r estimatenoid}
estimate(g2) |>
  IC() |> head()
vcov(estimate(g2) + estimate(g3))
(estimate(g2) + estimate(g3)) |>
  (rownames %++% head %++% IC)()
```

To force that the id variables are not overlapping between the merged model
objects, i.e., assuming that there is complete independence between the
estimates, the argument `id=NULL` can be used
```{r merge_idnull}
merge(g1, g2, id = NULL) |> (Print %++% IC)()
merge(g1, g2, id = NULL) |> vcov()
```

## Renaming and subsetting parameters

To only keep a subset of the parameters the `keep` argument can be used.  
```{r mergekeep}
merge(g1, g2, keep = c("(Intercept)", "(Intercept).1"))
```
The argument can be given either as character vector or a vector of indices:
```{r merge2}
merge(g1,g2, keep=c(1, 3))
```
or as a vector of perl-style regular expressions 
```{r merge3}
merge(g1, g2, keep = "cept", regex = TRUE)
merge(g1, g2, keep = c("\\)$", "^a$"), regex = TRUE, ignore.case = TRUE)
```

When merging estimates unique parameter names are created. It is also possible
to rename the parameters with the `labels` argument 
```{r merge4}
merge(g1, g2, labels = c("a", "b", "c")) |> estimate(keep = c("a", "c"))
merge(g1, g2,
      labels = c("a", "b", "c"),
      keep = c("a", "c")
)
estimate(g1, labels=c("a", "b"))
```

Finally, the `subset` argument can be used to subset the parameters and IFs
before the actual merging is being done 
```{r merge5}
merge(g1, g2, subset="(Intercept)")
```

## Clustered data (non-iid case)

Let \(Z_i = (Z_{i1},\ldots,Z_{iN_{i}})\) and assume that
\((Z_{i}, N_{i}) \sim P\), \(i=1,\ldots,n\) are iid and \(N_i\indep
Z_{ij}\). The variables \(Z_{i1},\ldots,Z_{iN_{i}}\) we assume are exchangeable
but not necessarily independent. Define \(N = \sum_{i=1}^{n} N_i\), and
assume that a parameter estimate, \(\widehat{\theta}\in\mathbb{R}^p\) has the decomposition 
\[
\sqrt{N}(\widehat{\theta}-\theta) =
\frac{1}{\sqrt{N}}
\sum_{i=1}^{n} \sum_{k=1}^{N_{i}} IC(Z_{ik}; \Pz) + \op(1).
\]
It then follows that 
\[
\sqrt{n}(\widehat{\theta}-\theta) = 
\frac{1}{\sqrt{n}}
\sum_{i=1}^{n} \widetilde{\IC}(Z_{i}; \Pz) + \op(1)
\]
with \(\widetilde{\IC}(Z_{i}; \Pz)
= \sum_{k=1}^{N_{i}} \frac{n}{N}IC(Z_{ik}; \Pz)\), \(i=1,\ldots,n\) which are iid
an therefore admits the usual CLT to derive the asymptotic variance of \(\widehat{\theta}\). 
Turning back to the example data, we can estimate the marginal model
```{r cluster1}
g0 <- glm(y ~ a + w + x, data = dl, family = binomial())
```
The asymptotic variance estimate ignoring that the observations are not
independent is not consistent. Instead we can calculate the cluster robust
standard errors from the above iid decomposition
```{r cluster2}
estimate(g0, id=dl$id)
```
We can confirm that this situation is equivalent to the variance estimates we 
obtain from a GEE marginal model with working independence correlation structure [@r_geepack]
```{r geepack, eval=has_geepack }
gee0 <- geepack::geeglm(y ~ a + w + x, data = dl, id = dl$id, family=binomial)
summary(gee0)
```

## Computational aspects 

Working with large and potentially multiple different IFs can be memory-intensive. A
remedy is to use the idea of aggregating the IFs by introducing a random
coarser grouping variable. Following the same arguments as in the previous
section, the aggregated IF will still be iid and allows us to estimate the
asymptotic variance. Obviously, the same grouping must be used across estimates
when combining IFs.   
```{r aggregate}
set.seed(1)
y <- cbind(rnorm(1e5))
N <- 2e2 ## Number of aggregated groups, the number of observations in the new IF
id <- foldr(nrow(y), N, list=FALSE)
Print(cbind(table(id)))

## Aggregated IF
e <- estimate(cbind(y), id = id) 
object.size(e)
e
```

# IF building blocks: transformations and the delta theorem

Let \(\phi\colon \mathbb{R}^p\to\mathbb{R}^m\) be differentiable at \(\theta\)
and assume that \(\widehat{\theta}_n\) is RAL estimator with IF given by
\(\IC(\cdot; \Pz)\) such that 
\begin{align*} 
\sqrt{n}(\widehat{\theta}_n - \theta) =
\frac{1}{\sqrt{n}}\sum_{i=1}^n \IC(Z_i; \Pz) + \op(1), 
\end{align*} 
then by the delta method [@vaart_1998_asymp Theorem 3.1]
\begin{align*} 
\sqrt{n}\{\phi(\widehat{\theta}_n) - \phi(\theta)\}  =
\frac{1}{\sqrt{n}}\sum_{i=1}^n \nabla\phi(\theta)\IC(Z_i; \Pz) + \op(1), 
\end{align*} 

where \(\phi\colon \theta\mapsto
(\phi_{1}(\theta),\ldots,\phi_{m}(\theta))^\top\) and \(\nabla\) is the partial
derivative operator
\begin{align*}
\nabla\phi(\theta) = 
\begin{pmatrix}
\tfrac{\partial}{\partial\theta_1}\phi_1(\theta) & \cdots &
\tfrac{\partial}{\partial\theta_p}\phi_1(\theta) \\
\vdots & \ddots & \vdots \\
\tfrac{\partial}{\partial\theta_1}\phi_m(\theta) & \cdots &
\tfrac{\partial}{\partial\theta_p}\phi_m(\theta) \\
\end{pmatrix}.
\end{align*}

Together with the ability to derive the joint IF from marginal IFs, this
provides us with a powerful tool for constructing new estimates using the IFs as
the fundamental building blocks.

To apply the delta method the transformation of the parameters function must be
supplied to the `estimate` method (argument `f`)
```{r delta1}
estimate(g1, sum)
estimate(g1, function(p) list(a = sum(p))) # named list
## Multiple parameters
estimate(g1, function(x) c(x, x[1] + exp(x[2]), inv = 1 / x[2]))
estimate(g1, exp)         
```
The gradient can be provided as the attribute `grad` and otherwise numerical differentiation is applied. 
  
## Example: Pearson correlation
As a simple toy example consider the problem of estimating the covariance of two
variables \(X_1\) and \(X_2\)
\begin{align*}
\widehat{\cov}(X_1,X_2) = \mathbb{P}_n(X_1-\mathbb{P}_n X_1)(Y_1-\mathbb{P}_n Y_1).
\end{align*}
It is easily verified that the IF of the sample estimate of \((\E X_{1}, \E
X_{2}, \E\{X_{1}X_{2}\})^\top\) given by
is \(\IC(X1,X2; \Pz) = (X_{1}-\E X_{1}, X_{2}-\E X_{2},
X_{1}X_{2}-\E\{X_{1}X_{2}\})^\top\). By the delta theorem with \(\phi(x,y,z) =
z-xy\) we have  \(\nabla\phi(x,y,z) = (-y -x 1)\) and thus the IF for the
sample covariance estimate becomes
\begin{align*}
\IC_{x_1, x_2}(X_1, X_2; \Pz) = (X_1 - \E X_1)(X_2 - \E X_2) - \cov(X_1,X_2)
\end{align*}

We can implement this directly using the `estimate` function via the `IC`
argument which allows us to provide a user-specificed IF and with the point
estimate given by the `coef` argument
```{r cov}
Cov <- function(x, y, ...) {
  est <- mean(x * y)-mean(x)*mean(y)
    estimate(
      coef = est,
      IC = (x - mean(x)) * (y - mean(y)) - est,
      ...
    )
}
with(dw, Cov(x1, x2))
```

As an illustration we could also derive this estimate from simpler building
blocks of \(\E X_{1}\), \(\E X_{2}\), and \(\E(X_{1}X_{2})\).
```{r cov2}
e1 <- lm(cbind(x1, x2, x1 * x2) ~ 1, data = dw) |>
  estimate(labels = c("Ex1", "Ex2", "Ex1x2"))
e1
estimate(e1, function(x) c(x, cov=with(as.list(x), Ex1x2 - Ex2* Ex1)))
```
The variance estimates can be estimated in the same way and the combined
estimates be used to estimate the correlation
```{r rho}
e2 <- with(dw, Cov(x1, x2, labels = "c", id = id) +
               Cov(x1, x1, labels = "v1", id = id) +
               Cov(x2, x2, labels = "v2", id = id))
rho <- estimate(e2, function(x) list(rho = x[1] / (x[2] * x[3])^.5))
rho
```

by using a variance stabilizing transformation, Fishers
\(z\)-transform [@lehmann2023_testing],
\(z = \arctanh(\widehat{\rho}) =
\frac{1}{2}\log\left(\frac{1+\widehat{\rho}}{1-\widehat{\rho}}\right)\),
confidence limits with general better coverage can be obtained
```{r tanh}
estimate(rho, atanh, back.transform = tanh)
```
The confidence limits are calculated on the \(\arctanh\)-scale and transformed
back to the original correlation scale via the `back.transform` argument.
In this case, where the estimates are far away from the boundary of the parameter space, the variance
stabilizing transform does almost not have any impact, and the confidence limits
agrees with the original symmetric confidence limits.

## TODO Example: survival 

## Linear contrasts and hypothesis testing

An important special case of parameter transformations are linear
transformations. A particular interest may be formulated around testing
null-hypotheses of the form
\begin{align*}
H_0\colon\quad \bm{B}\theta = \bm{b}_0
\end{align*}

where \(\bm{B}\in\mathbb{R}^{m\times p}\) is a matrix of estimable contrasts and
\(\bm{b}_0\in\mathbb{R}^{m}\). 

As an example consider marginal models for the binary response variables
  \(Y_1, Y_2, Y_3, Y_4\) 
```{r}
g <- lapply(
  list(y1 ~ a, y2 ~ a, y3 ~ a), #, y4 ~ a+x4),
  function(f) glm(f, family = binomial, data = dw)
)
gg <- Reduce(merge, g)
gg
```

A linear transformation can be specified via the `f` as a matrix argument
instead of function object
```{r contrast1}
B <- cbind(0,1, 0,-1, 0,0)
estimate(gg, B)
```

The \(\bm{b}_0\) vector (default assumed to be zero) can be specified via the `null` argument
```{r estcontrast1}
estimate(gg, B, null=1)
```

For testing multiple hypotheses we use that 
\((\bm{B}\widehat{\theta}-\bm{b}_0)^{\top}
(\bm{B}\widehat{\Sigma}\bm{B}^{\top})^{-1}
(\bm{B}\widehat{\theta}-\bm{b}_0) \sim
 \chi^2_{\operatorname{rank}(B)}\) under the null hypothesis where
 \(\widehat{\Sigma}\) is the estimated variance of \(\theta\) (i.e., 
`vcov(gg)`) 
```{r estcontrast2}
B <- rbind(cbind(0,1, 0,-1, 0,0),
           cbind(0,1, 0,0, 0,-1))
estimate(gg, B)
```

Such linear statistics can also be specified directly as expressions of the parameter names
```{r}
estimate(gg, a + a.1, 2*a - a.2, a, null=c(2,1,1))
```

We refer to the function `lava::contr`  and `lava::parsedesigns` for defining
contrast matrices. 
```{r contr}
contr(list(1, c(1, 2), c(1, 4)), n = 5)
```
A particular useful contrast is the following for considering all pairwise
comparisons of different exposure estimates:

```{r pairwise.diff}
pairwise.diff(3)
estimate(gg, pairwise.diff(3), null=c(1,1,1), use=c(2,4,6))
```

When conducting multiple tests each at a nominal-level of \(\alpha\) the overall
type I error is not controlled at \(\alpha\)-level.  
The influence function also allows for adjusting for multiple comparisons.
Let \(Z_{1},\ldots,Z_{p}\) denote \(Z\)-statistics from \(p\) distinct two-sided
hypothesis tests which we will assume is asymptotically distributed under the
null hypothesis as a zero-mean Gaussian distribution with correlation matrix
\(R.\) Let \( Z_{max} = \max_{i=1,\ldots,p} |Z_i|\) then the family-wise error
rate (FWER) under the null can be approximated by \[ P(Z_{max} > z) =
1-\int_{-z}^{z} \cdots \int_{-z}^{z} \phi_{R}(x_{1},\ldots,x_{p})
\,dx_{1}\cdots\,dx_{p} \] where \(\phi_{R}\) is the multivariate normal density
function with mean 0 and variance given by the correlation matrix \(R\). The
adjusted \(p\)-values can then be calculated as \[P(Z_{max} >
\Phi^{-1}(1-p/2))\] where \(\Phi\) is the standard Gaussian CDF. As described in 
in [@RitzPipper_multcomp] the joint distribution of \(Z_{1},\ldots,Z_{p}\)
can be estimated from the IFs. This is implemented in the `p.correct` method

```{r pcorrect, eval=has_mets }
gg0 <- estimate(gg, use="^a", regex=TRUE, null=rep(.8, 3))
p.correct(gg0)
```

While this always yields a more powerful test compared to Bonferroni
adjustments, a more powerful closed-testing procedure [@marcus1976], can be generally
obtained by considering all intersection hypotheses. 

![*Figure: closed testing via Wald tests of all intersections hypotheses.*](figs/closedtesting.pdf)

To reject the \(H_{1}\) at
an correct \(\alpha\)-level we should test all intersection hypotheses involving
\(H_{1}\) and check if there all are rejected at the \(\alpha\)-level. The
adjusted \(p\)-values can here be obtained as the maximum \(p\)-value across all
the composite hypothesis tests. Unfortunately, this only works for relatively
few comparisons as the number of tests grows exponentially.

```{r closedtesting, eval=has_mets}
closed.testing(gg0)
```


## Averaging

Some parameters of interest are expressed as averages over functions of the
observed data and estimated parameters of a model. The asymptotic distribution
can in some of these cases also be derived from the influence function.
Let \(Z_{1},\ldots,Z_{n}\) be iid observations, \(Z_{1}\sim \Pz\) and let  \(X_{i}\subset Z_{i}\).

Assume that \(\widehat{\theta}\) is RAL estimator of \(\theta\in\Omega\subset \mathbb{R}^{p}\)
\[\sqrt{n}(\widehat{\theta}-\theta) = \frac{1}{\sqrt{n}}\sum_{i=1}^{n}\phi(Z_{i}; \Pz) + \op(1).
\]
Let \(f:\mathcal{X}\times\Omega\to\mathbb{R}\) be continuous differentiable in \(\theta\)
\[\sqrt{n}\{f(X; \widehat{\theta})-f(X; \theta)\} =
\frac{1}{\sqrt{n}}\nabla_{\theta}f(X;\theta)\sum_{i=1}^{n}\phi(Z_{i}; P) + \op(1).
\]

Let \(\Psi = \Pz f(X;\theta)\) and \(\widehat{\Psi} = P_{n} f(X;\widehat{\theta})\). \(\Pz\) and \(P_{n}\) are here everywhere the integrals wrt. \(X\). It is easily verified that
\begin{align*}
\widehat{\Psi}-\Psi &= (P_{n}-\Pz)(f(X; \theta)-\Psi) + P[f(X;\widehat{\theta})-f(X;\theta)] \\
&\quad + (P_{n}-\Pz)[f(X;\widehat{\theta})-f(X;\theta)]
\end{align*}

From Lemma 19.24 [@vaart_1998_asymp] it follows that for the last term
\begin{align*}
\sqrt{n}(P_{n}-\Pz)[f(X;\widehat{\theta})-f(X;\theta)] = \op(1)
\end{align*}
when \(f\) for example is Lipschitz and more generally when \(f(X;\theta)\) forms a \(\Pz\)-Donsker class.

It therefore follows that
\begin{align*}
\sqrt{n}(\widehat{\Psi}-\Psi) &= \sqrt{n}P_{n}(f(X; \theta)-\Psi) +
\frac{1}{\sqrt{n}}P\nabla_{\theta}f(X;\theta)\sum_{i=1}^{n}\phi(Z_{i}; \Pz) + \op(1) \\
&=
\frac{1}{\sqrt{n}}\sum_{i=1}^{n}\{f(X;\theta)-\Psi\} +
\frac{1}{\sqrt{n}}P\nabla_{\theta}f(X;\theta)\sum_{i=1}^{n}\phi(Z_{i}, \Pz) + \op(1)
\end{align*}
Hence the IF for \(\widehat{\Psi}\) becomes
\[
IC(Z; \Pz) = f(X;\theta)-\Psi +
[\Pz\nabla_{\theta}f(X;\theta)]\phi(Z).
\]


Turning back to the example we can estimate the logistic regression model
\(\operatorname{logit}(E\{Y_1 | A,X_1,W\}) = \beta_0 + \beta_a A + \beta_{x_1}
X_1 + \beta_w W\), and from this we want to estimate the target parameter 
\[
\theta(P) = \E_{P}[E(Y\mid A=1, X_{1}, W)].
\]
To do this we need first to estimate the model and then define a function that
gives the predicted probability \(\pr(Y=1\mid,A=a,X_{1},W)\) for any observed
values of \(X_1,W\) but with the treatment variable \(A\) kept fixed at the
value \(1\)
```{r estpred}
g <- glm(y1 ~ a + x1 + w, data=dw, family=binomial)
pr <- function(p, data, ...)
  with(data, expit(p[1] + p["a"] + p["x1"]*x1 + p["w"]*w))
pr(coef(g), dw) |> head()
```
The target parameter can now be estimated with the syntax
```{r average}
id <- foldr(NROW(dw), 100, list=FALSE)
ea <- estimate(g, pr, average=TRUE, id=id)
ea
IC(ea) |> head()
```

<!-- ## Example: Average Treatment Effects -->

<!-- ```{r targeted, cache=TRUE, eval=has_targeted } -->
<!-- a1 <- targeted::cate(a ~ 1, -->



<!--                      data = dw, -->
<!--                      response_model = y1 ~ x1+w+a, -->
<!--                      propensity_model = a ~ x1*w -->
<!--                      ) -->
<!-- a1 -->
<!-- IC(a1) |> head() -->
<!-- ``` -->

# SessionInfo

```{r sessionInfo}
sessionInfo()
```

# Bibliography

