# Introduction

The enrichwith package provides the enrich method to enrich list-like R objects with new, relevant components. The resulting objects preserve their class, so all methods associated with them still apply.

This vignette is a short case study demonstrating how enriched glm objects can be used to implement a quasi Fisher scoring procedure for computing reduced-bias estimates in generalized linear models (GLMs). @kosmidis:10 describe a parallel quasi Newton-Raphson procedure.

# Endometrial cancer data

@heinze:02 used a logistic regression model to analyse data from a study on endometrial cancer. @agresti:15 [Section 5.7] provide details on the data set. Below, we fit a probit regression model with the same linear predictor as the logistic regression model in @heinze:02.

# Get the data from the online supplmementary material of Agresti (2015)
data("endometrial", package = "enrichwith")
modML <- glm(HG ~ NV + PI + EH, family = binomial("probit"), data = endometrial)
theta_mle <- coef(modML)
summary(modML)


As is the case for the logistic regression in @heinze:02, the maximum likelihood (ML) estimate of the parameter for NV is actually infinite. The reported, apparently finite value is merely due to false convergence of the iterative estimation procedure. The same is true for the estimated standard error, and, hence the value r round(coef(summary(modML))["NV", "z value"], 3) for the $z$-statistic cannot be trusted for inference on the size of the effect for NV.

In categorical-response models like the above, the bias reduction method in @firth:93 has been found to result in finite estimates even when the ML ones are infinite [see, @heinze:02, for logistic regressions; @kosmidis:11, for multinomial regressions; @kosmidis:14a, for cumulative link models].

One of the variants of that bias reduction method is implemented in the brglm R package, which estimates binomial-response GLMs using iterative ML fits on binomial pseudo-data [see, @kosmidis:07, Chapter 5, for details]. The reduced-bias estimates for the probit regression on the endometrial data can be computed as follows.  {r, echo = TRUE, eval = TRUE, messages = FALSE} library("brglm") modBR <- brglm(HG ~ NV + PI + EH, family = binomial("probit"), data = endometrial) theta_brglm <- coef(modBR) summary(modBR)

The $z$-statistic for NV has value r round(coef(summary(modBR))["NV", "z value"], 3) when based on the reduced-bias estimates, providing some evidence for the existence of an effect.

In the following, we use **enrichwith** to implement two variants of the bias reduction method via a unifying quasi Fisher scoring estimation procedure.

------

# Quasi Fisher scoring for bias reduction

Consider a parametric [statistical
model](https://en.wikipedia.org/wiki/Statistical_model)
$\mathcal{P}_\theta$ with unknown parameter $\theta \in \Re^p$ and the
iteration $$\theta^{(k + 1)} := \theta^{(k)} + \left\{i(\theta^{(k)})\right\}^{-1} s(\theta^{(k)}) - c(\theta^{(k)}) b(\theta^{(k)})$$ where $\theta^{(k)}$ is the value of $\theta$ at the
$k$th iteration, $s(\theta)$ is the gradient of the
log-[likelihood](https://en.wikipedia.org/wiki/Likelihood_function)
for $\mathcal{P}_\theta$, $i(\theta)$ is the [expected
information](https://en.wikipedia.org/wiki/Fisher_information) matrix,
and $b(\theta)$ is the $O(n^{-1})$ term in the expansion of the
[bias](https://en.wikipedia.org/wiki/Bias_of_an_estimator) of the
[ML
estimator](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation)
of $\theta$ [see, for example, @cox:68].

The above iteration defines a *quasi* Fisher scoring estimation
procedure in general, and reduces to exact Fisher scoring for ML
estimation when $c(\theta^{(k)}) = 0_p$, where $0_p$ is a vector of
$p$ zeros.

For either $c(\theta) = I_p$ or $c(\theta) = \left\{i(\theta)\right\}^{-1} j(\theta)$, where $I_p$ is the $p \times p$ identity matrix and $j(\theta)$ is the [observed
information](https://en.wikipedia.org/wiki/Observed_information),
$\theta^{(\infty)}$ (if it exists) is a reduced-bias estimate, in the
sense that it corresponds an estimator with bias of smaller asymptotic
order than that of the ML estimator [see, @firth:93;
@kosmidis:10]. The **brglm** estimates correspond to $c(\theta) = I_p$.

The asymptotic distribution of the reduced-bias estimators is the same
to that of the ML estimator [see, @firth:93 for
details]. So, the reduced-bias estimates can be readily used to
calculate $z$-statistics.

------

# Implementation using **enrichwith**

For implementing the iteration for bias reduction, we need functions that can compute the gradient of the log-likelihood, the observed and expected information matrix, and $b(\theta)$ at arbitrary values of $\theta$.

The **enrichwith** package can produce those functions for any glm object through the auxiliary_functions enrichment option (to see all available enrichment options for glm objects run get_enrichment_options(modML).
 {r, echo = TRUE, eval = TRUE}
library("enrichwith")
enriched_modML <- enrich(modML, with = "auxiliary functions")


Let's extract the functions from the enriched_modML object.  {r, echo = TRUE, eval = TRUE}

# Extract the ingredients for the quasi Fisher scoring iteration from the enriched glm object

gradient <- enriched_modML$auxiliary_functions$score # gradient of the log-likelihood information <- enriched_modML$auxiliary_functions$information # information matrix bias <- enriched_modML$auxiliary_functions$bias # first-order bias

For the more technically minded, note here that the above functions are specific to modML in the sense that they look into a special environment for necessary objects like the model matrix, the model weights, the response vector, and so on.

This stems from the way **enrichwith** has been implemented. In particular, if create_enrichwith_skeleton is used, the user/developer can directly implement enrichment options to enrich objects with functions that directly depend on other components in the object to be enriched.

The following code chunk uses enriched_modML to implement the quasi Fisher scoring procedure for the analysis of the endometrial cancer data. For p <- length(theta_mle) r p <- length(theta_mle), the starting value for the parameter vector is set to theta_current <- rep(0, p) r theta_current <- rep(0, p), and the maximum number of iterations to maxit <- 100 r maxit <- 100. As stopping criterion, we use the absolute change in each parameter value with tolerance epsilon <- 1e-06 r epsilon <- 1e-06
 {r, echo = TRUE, eval = TRUE}
# The quasi Fisher scoring iteration using c(theta) = identity
for (k in seq.int(maxit)) {
i_matrix <- information(theta_current, type = "expected")
b_vector <- bias(theta_current)
step <- solve(i_matrix) %*% s_vector - b_vector
theta_current <- theta_current + step
# A stopping criterion
if (all(abs(step) < epsilon)) {
break
}
}
(theta_e <- drop(theta_current))


The estimation procedure took r k iterations to converge, and, as expected, the estimates are numerically the same to the ones that brglm returned.  {r, echo = TRUE, eval = TRUE} all.equal(theta_e, theta_brglm, check.attributes = FALSE, tolerance = epsilon)

A set of alternative reduced-bias estimates can be obtained using $c(\theta) = \left\{i(\theta)\right\}^{-1} j(\theta)$. Starting again at theta_current <- rep(0, p) r theta_current <- rep(0, p)
 {r, echo = TRUE, eval = TRUE}
# The quasi Fisher scoring iteration using c(theta) = solve(i(theta)) %*% j(theta)
for (k in seq.int(maxit)) {
i_matrix <- information(theta_current, type = "expected")
j_matrix <- information(theta_current, type = "observed")
b_vector <- bias(theta_current)
step <- solve(i_matrix) %*% (s_vector - j_matrix %*% b_vector)
theta_current <- theta_current + step
# A stopping criterion
if (all(abs(step) < epsilon)) {
break
}
}
(theta_o <- drop(theta_current))


The estimation procedure took r k iterations to converge.

The ML estimates and the estimates from the two variants of the bias reduction method are  {r, echo = TRUE, eval = TRUE} round(data.frame(theta_mle, theta_e, theta_o), 3)

Note that the reduced-bias estimates have shrunk towards zero. This is typical for reduced-bias estimation in binomial-response GLMs [see, for example, @cordeiro:91, Section 8; @kosmidis:07, Section 5.2; @kosmidis:14a for shrinkage in cumulative link models].

The corresponding $z$-statistics are
 {r, echo = TRUE, eval = TRUE}
se_theta_mle <- sqrt(diag(solve(information(theta_mle, type = "expected"))))
se_theta_e <- sqrt(diag(solve(information(theta_e, type = "expected"))))
se_theta_o <- sqrt(diag(solve(information(theta_o, type = "expected"))))
round(data.frame(z_mle = theta_mle/se_theta_mle,
z_br_e = theta_e/se_theta_e,
z_br_o = theta_o/se_theta_o), 3)


The two variants for bias reduction result in slightly different reduced-bias estimates and $z$-statistics, though the $z$-statistics from both variants provide some evidence for the existence of an effect for NV.

# Notes

A general family of bias reduction methods is described in @kosmidis:09.

The quasi Fisher scoring iteration that has been described here is at the core of the brglm2 R package, which provides various bias reduction methods for GLMs.

# References

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enrichwith documentation built on Jan. 11, 2020, 9:21 a.m.